Sage Journals: Discover world-class research

Abstract

Background:

Depressive disorders are significantly more common in the medically ill compared to the general population. Depression is associated with worsening of physical symptoms, greater healthcare utilization and poorer treatment adherence. The present paper provides a critical review on the assessment and management of depression in the medically ill.

Methods:

Relevant articles pertaining to depression in the medically ill were identified, reviewed and synthesized qualitatively. A systematic review was not performed due to the large breadth of this topic, making a meaningful summary of all published and unpublished studies not feasible. Notable studies were reviewed and synthesized by a diverse set of experts to provide a balanced summary.

Results:

Depression is frequently under-recognized in medical settings. Differential diagnoses include delirium, personality disorders and depressive disorders secondary to substances, medications or another medical condition. Depressive symptoms in the context of an adjustment disorder should be initially managed by supportive psychological approaches. Once a mild to moderate major depressive episode is identified, a stepped care approach should be implemented, starting with general psychoeducation, psychosocial interventions and ongoing monitoring. For moderate to severe symptoms, or mild symptoms that are not responding to low-intensity interventions, the use of antidepressants or higher intensity psychotherapeutic interventions should be considered. Psychotherapeutic interventions have demonstrated benefits with small to moderate effect sizes. Antidepressant medications have also demonstrated benefits with moderate effect sizes; however, special caution is needed in evaluating side effects, drug–drug interactions as well as dose adjustments due to impairment in hepatic metabolism and/or renal clearance. Novel interventions for the treatment of depression and other illness-related psychological symptoms (e.g. death anxiety, loss of dignity) are under investigation.

Limitations:

Non-systematic review of the literature.

Conclusion:

Replicated evidence has demonstrated a bidirectional interaction between depression and medical illness. Screening and stepped care using pharmacological and non-pharmacological interventions is merited.

Keywords

Major depressive disorder cancer antidepressant screening adjustment disorder psychotherapy psychosomatics organic illness

Introduction

Major depressive disorder (MDD) is a common and disabling illness, with an annual prevalence of 2–4% in the general population (Global Burden of Disease (GBD) DALYs and Hale Collaborators et al., 2015). The prevalence of MDD is approximately doubled in the medically ill with an estimated annual prevalence of 5–10% (Read et al., 2017). The interaction is likely bidirectional as MDD is also independently associated with an increased prevalence of chronic medical disorders. Comorbid medical illness and MDD are associated with reciprocally poorer psychiatric and medical outcomes (Barth et al., 2004; Darwish et al., 2018; Penninx, 2017; Rutledge et al., 2006). Depression is associated with greater physical symptom burden, poorer treatment adherence and quality of life, increased health care utilization and increased all-cause mortality in individuals with chronic medical illnesses (Barber et al., 2016; Barefoot et al., 1996; Barth et al., 2004; Bhattarai et al., 2013; Fitzgerald et al., 2013; Gold et al., 2016; Mausbach and Irwin, 2017). Furthermore, Gallo et al. (2013) demonstrated a reduction in all-cause mortality in primary care in elderly patients given evidence-based treatments for MDD, as compared with treatment as usual. Notably, however, a recent umbrella review strongly suggests that the association between depression and all-cause mortality is not necessarily causal (Machado et al., 2018).

The assessment and management of MDD in the medically ill has several important differences and special considerations compared to MDD in the general population (Ramasubbu et al., 2012). Thus, the aim of the present review is to provide an up-to-date critical appraisal of available evidence on this issue. The following topics will be discussed: (1) epidemiology and pathogenesis, (2) evidence-based screening practices, (3) the accurate diagnosis of MDD in the medically ill and (4) prevention and management strategies. Unanswered questions and high-priority areas for future research will also be discussed. Of note, a systematic review was not performed due to the large breadth of this topic, making a meaningful summary of all published and unpublished studies not feasible. Notable studies were reviewed and synthesized by a diverse set of experts to provide a balanced summary.

Epidemiology and pathogenesis

Several observational and epidemiological studies have demonstrated an increased incidence of both MDD and depressive symptoms in people with chronic medical illnesses. However, the accuracy of prevalence estimates of MDD in the medically ill is often questioned, given significant limitations in study design, such as inaccurate case identification (Thombs et al., 2018). Most studies have relied on self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001). Validated structured diagnostic interviews are considered to be the ‘gold standard’ for the diagnosis of MDD in research (Levis et al., 2017). However, even with a structured interview, the diagnosis of MDD in medically ill populations is complex with no agreed upon ‘gold standard’ for confirming the diagnosis, leading to high inter-rater variability (Galeazzi et al., 2004; Koenig et al., 1995). In addition, sampling methods are often biased using convenient samples from clinics or academic tertiary care centers (Bornstein et al., 2013; Matte et al., 2016; Mikocka-Walus et al., 2016; Wells et al., 1988). More recently, the use of healthcare administrative databases has facilitated acquisition of more generalizable samples to decrease the inherent bias of convenient sampling (i.e. Berkson’s bias), although such databases may have limited reliability as the diagnosis is typically clinical and not standardized (Lo et al., 2013; Ratnasingham et al., 2013; Videbech and Deleuran, 2016).

While acknowledging these limitations, estimates of MDD in chronic medical illness have been consistently reported to be greater than twofold higher than the general population, in addition to the high prevalence of clinically relevant subthreshold depressive symptoms in this population (Li et al., 2019). Non-communicable medical and common psychiatric disorders may share many individual, social and biological pathways (O’Neil et al., 2015). While the magnitude of the association may be questioned, the presence of a statistically and clinically significant association between medical illness and depression is now rarely debated, given the consistent replication of this finding (Li et al., 2019). Furthermore, the presence of multiple medical conditions (e.g. ‘multimorbidity’) appears to be associated with even higher rates of depression and poorer medical and psychiatric outcomes (Gallo et al., 2016; Read et al., 2017).

Neurological disorders are strongly linked with increased rates of MDD, with an estimated lifetime prevalence of 20–40% across stroke, epilepsy, multiple sclerosis (MS), traumatic brain injury (TBI), Alzheimer’s disease (AD) and Parkinson’s disease (PD) (Bhattarai et al., 2013; Diniz et al., 2013; Elger et al., 2017; Rickards, 2005). Depression is associated with poorer medical outcomes and increased suicidality in neurological disorders (Bahraini et al., 2013; Bartoli et al., 2017; Christensen et al., 2007; Go et al., 2013; Pan et al., 2011; Pompili et al., 2012, 2006; Rickards, 2005; Robinson and Jorge, 2016). Of note, the diagnosis of comorbid MDD in AD and PD is controversial, with some investigators and clinicians advocating that depressive symptoms are part of the core features of these neurological disorders, rather than a comorbid syndrome on its own right, even when meeting full criteria for MDD (Caraci et al., 2010; Dauer and Przedborski, 2003).

Non-neurological disorders are also associated with significantly elevated rates of MDD, including cancer (lifetime prevalence of MDD of 10–20%), diabetes (6–20%), cardiovascular diseases (CVD; 15–25%), HIV (25–40%), chronic obstructive pulmonary disease (COPD; 20–30%) and autoimmune disorders (10–30%), such as irritable bowel disease (IBD), lupus, psoriasis and rheumatoid arthritis (RA) (Ali et al., 2006; Ciechanowski et al., 2000; Eshun-Wilson et al., 2018; Geng et al., 2018; Han et al., 2011; Palagini et al., 2013; Penninx, 2017; Pérez-Stable et al., 1990; Thom et al., 2019; Wang et al., 2016). In these chronic medical illnesses, MDD is also associated with poorer medical outcomes, increased health care utilization, decreased treatment adherence and poorer health-related quality of life. Increased all-cause mortality and disease progression has been associated with comorbid MDD specifically in cancer, HIV, CVD and diabetes; however, a causal relationship has never been clearly established (Cuijpers et al., 2014; Machado et al., 2018).

The bidirectional interaction between depression and medical illness is mediated by common biological, iatrogenic, psychological, social and lifestyle risk factors, as shown in Table 1. Common lifestyle risks include physical inactivity, smoking and poor diet (Berk and Jacka, 2019; Pasco et al., 2008; Strawbridge et al., 2002). Psychosocial factors associated with depression that are common to many chronic medical illnesses include the impact of physical limitations, disability, role transitions and loss of independence/autonomy (Thom et al., 2019). Many illnesses, such as HIV, continue to have significant stigma, often leading to social isolation (Green et al., 2012; Van Luenen et al., 2018). Biological disease-specific factors may also increase the incidence of depressive symptoms (Table 1). For example, immune activation and increased oxidative and nitrosative stress pathways in peripheral blood may explain the comorbidity between depression and peripheral autoimmune and immune disorders, including CVD, IBD and lupus erythematosus, as well as increased morbidity and mortality due to depression in these peripheral immune disorders (Berk et al., 2013b; Maes et al., 2011). Increased neuroinflammation coupled with oxidative stress may explain the comorbidity between depression and neurodegenerative/neuroinflammatory disorders including MS, PD, stroke and AD (Berk et al., 2013b; Maes et al., 2011). These factors may also mediate the accelerated cellular aging associated with depression, as shown by shortened leukocyte telomere length (LTL), which may also increase the risk of several medical illnesses, such as cancer, CVD and dementia (Muneer and Minhas, 2019).

Table 1.

Formulation of depression in the medically ill using the biopsychosocial model.

	Biological	Psychological	Social
Predisposing	Previous MDD Family history of MDD Baseline endocrine and inflammatory abnormalities Substance use Sedentary lifestyle Sensitive temperament	Insecure attachment style Neuroticism Low self-esteem	Adverse childhood experiences (trauma, neglect, major changes) Allostatic overload of negative life events
Precipitating/perpetuating	Physical pain Inflammation Treatment effects Endocrine changes (thyroid, insulin, glucose, cortisol) Anemia Illness-related fatigue Sleep pattern Iatrogenic effects	Loss of control activating insecure schemas Change in identity Thanatophobia Uncertainty about the future Unhealthy coping strategies Demoralization Guilt and self-blame	Physical limitations Disability Role transitions Financial stress Change in physical appearance Altered life trajectory Loss of dignity Social isolation Burdening caregivers
Protective	Effective pharmacological treatment of medical and psychiatric symptoms	Psychological flexibility Healthy coping strategies Cognitive and behavioral strategies	Supportive family/friends Supportive care team Religious/spiritual/community supports

MDD: major depressive disorder.

Inflammation associated with autoimmune disorders, chronic infections, CVD, stroke and many cancers often causes anergic depressive symptoms (e.g. ‘sickness behavior’) mediated by pro-inflammatory cytokines causing downstream effects, including decreased monoamine levels, microglial over-activity and impaired neuroplasticity (Dantzer et al., 2008; Rosenblat et al., 2014). Similarly, the hypodopaminergic effects of PD, neurotoxic effects of insulin resistance, sleep apnea-related hypoxemia of the brain, and the direct effects of hypothyroidism on energy, mood and cognitive function represent other disease-specific biological pathways mediating the high rates of comorbidity between medical illness and depressive symptoms (Banks et al., 2012; Dauer and Przedborski, 2003; Irwin et al., 2016; Loas et al., 2012; Ramasubbu et al., 2012). The iatrogenic depressant effects of medications used to treat chronic medical illness (e.g. barbiturates, vigabatrin, topiramate, flunarizine, corticosteroids, mefloquine, beta blockers [association more recently questioned], efavirenz, interferon-α) may also have a relevant role (Agustini and Berk, 2018; Celano et al., 2011; Machado et al., 2017).

Screening for depression

Effective identification of depression in the medically ill is of great importance, given its high prevalence and the deleterious effects on quality of life, treatment adherence, healthcare utilization and medical outcomes (Ciechanowski et al., 2000). Depression is often underdiagnosed in the medically ill (Hermanns et al., 2013; Pérez-Stable et al., 1990). The propensity to ‘miss’ the diagnosis of MDD is multifactorial and partly relating to time pressures, competing clinical demands and patient and clinician discomfort with discussing psychological distress (Chen et al., 2016; Saver et al., 2007). In addition, diagnostic overshadowing may cause attribution of physical symptoms (e.g. insomnia, fatigue, decreased appetite) exclusively to the underlying medical condition. Notably, diagnostic overshadowing with MDD commonly also has the opposite effects, with clinicians at times misattributing some physical symptoms as ‘psychosomatic’ when a diagnosis of MDD is known, thus delaying investigations of potential reversible underlying medical causes (Byrne, 2018).

Paradoxically, although depressive disorders are often undetected in many medical settings, they may also be over-diagnosed, at times resulting in inappropriate treatment with antidepressants. The factors that contribute to these symptoms should be identified to ensure appropriate intervention, which include attention to pain or other physical distress, social or psychological factors (Grover et al., 2017; Iaboni et al., 2015; Kose et al., 2018). To prevent the over-diagnosis of MDD in the medically ill, an understanding of the spectrum of sadness is required. Indeed, upon receiving and living with a diagnosis of a chronic medical illness, an expression of sadness may be a normal and expected adaptive reaction. However, symptoms that exceed the normative, expected reactions and are causing significant distress and/or functional impairment may represent a comorbid depressive or adjustment disorder (APA, 2013). A careful assessment is required to determine where along the spectrum these depressive symptoms lie. Furthermore, psychological and physical symptom burden may change over the course of the illness, meriting regular reassessment, rather than one-time screening. Systematic depression screening practices may therefore be of value in the medically ill (Caruso et al., 2017b; Li et al., 2019).

International guidelines for most chronic medical conditions recommend routine screening for depression, as part of the standard of care (Butow et al., 2015; McIntyre et al., 2012; Reusch and Manson, 2017; Siu et al., 2016). Optimal screening methods should have the following key attributes: (1) high sensitivity and specificity, (2) ease of use by providers and patients, (3) low cost and (4) use of available and actionable approaches to confirm the diagnosis and implement evidence-based treatments (McAlpine and Wilson, 2004; Siu et al., 2016). Use of screening tools should always be part of a two-stage assessment process, with positive screens followed by further clinical assessment. A positive screen should lead to a clinical assessment to determine whether further psychological, social or biological interventions are required. Screening tools are not diagnostic for a depressive disorder and should never replace clinical assessment. Items 1–3 are related to the psychometric properties of screening tools and cut-off values selected, whereas item 4 relates to the algorithm implemented with a positive screen. Systematic reviews have demonstrated that screening for depression on its own does not improve outcomes (Thombs et al., 2014); however, systematically combining screening with actionable interventions and monitoring has demonstrated benefits for clinical outcomes (Gilbody et al., 2006, 2008; Meijer et al., 2011; Thombs et al., 2013).

Numerous depression screening tools have been assessed in people with chronic medical conditions, with variable clinical utility, given differences in specificity, sensitivity and patient/provider burden (e.g. time required to complete scale). Low sensitivity may lead to under-diagnosis, whereas low specificity may lead to inappropriately high rates of positive screens with unsustainable over-referral to specialized psychiatric/psychological services (Chen et al., 2016). Depression screening tools can be broadly categorized into very brief screening tools (e.g. single-item or two-item questionnaires) or full-length screening tools, typically with 10–20 self-report items. Very brief screening tools have a main advantage of low patient burden, whereas some longer screening tools have the advantage of increased sensitivity and specificity.

The most commonly used single-item tool is the Distress Thermometer, a 10-point Likert-type scale measuring level of distress in the past week. While easy to use, the clinical utility is limited as it has low sensitivity and specificity with poor positive predictive value, regardless of time-frame or cut-off scores selected (Grassi et al., 2009; Pantilat et al., 2012; Zwahlen et al., 2008). Other single-item screening practices use single questions with ‘yes’ or ‘no’ response options, such as ‘Are you depressed?’ or ‘Do you often feel sad or depressed?’ These single-question screening tools have been evaluated in medical populations with improved sensitivity compared to the Distress Thermometer, but they still have poor specificity, yielding high false positive rates (Frazier et al., 2014; Siu et al., 2016). The two-item questionnaire (Patient Health Questionnaire—2-item depression module [PHQ-2], asking about low mood and anhedonia) has demonstrated good sensitivity but poor specificity (Arroll et al., 2010). Therefore, the PHQ-2 is sometimes used as a pre-screener to trigger completion of a PHQ-9, which has shown good sensitivity and specificity in medical populations (80–90%) (Manea et al., 2012; Van Steenbergen-Weijenburg et al., 2010). Notably, however, cut-off scores used for a ‘positive screen’ are often higher than those in the general population (PHQ-9 > 10), since baseline somatic symptoms in medical illness elevate scores on most screening tools (Van Steenbergen-Weijenburg et al., 2010; Wasteson et al., 2009).

The four most commonly used full-length depression screening tools validated in chronic medical illness include the PHQ-9, the Center for Epidemiologic Studies Depression Scale (CES-D) (Cosco et al., 2019), the Beck Depression Inventory-II (BDI-II) (Steer et al., 1999) and the Hospital Anxiety and Depression Scale (HADS) (Cosco et al., 2012). All four scales have been translated and validated in numerous languages and in a variety of chronic medical illnesses (Siu et al., 2016). The CES-D and PHQ-9 are freely available, whereas the HADS and BDI-II are under copyright and must be purchased. The PHQ-9 and BDI-II are more specific to depressive symptoms, whereas the HADS and CES-D also capture non-specific psychological distress, with poorer specificity for MDD (Siu et al., 2016). Given that the PHQ-9 broadly maps onto the Diagnostic and Statistical Manual of Mental Disorders (5th edition; DSM-5) criteria, has been widely validated, is freely available and is relatively brief, its effective use as a depression screening tool continues to grow worldwide, with validated screening cut-offs in specific sub-populations (Manea et al., 2012). The properties of these commonly used screening tools are further summarized in Table 2.

Table 2.

Commonly used screening tools validated in the medically ill.

Screening tool	Items	Time to complete (minutes)	Copyright	Clinical utility for screening depression in the medically ill
Distress Thermometer	1	<1	No	Poor sensitivity and specificity
‘Do you feel depressed’	1	<1	No	Moderate sensitivity and poor specificity
PHQ-2	2	1–2	No	Good sensitivity and poor specificity
PHQ-9	9	3–6	No	Good sensitivity and specificity
HADS	14	5–10	Yes	Captures non-specific psychological distress
BDI-II	21	10–15	Yes	Good sensitivity and specificity
CES-D	20	10–15	No	Good sensitivity and poor specificity (captures non-specific psychological distress)

PHQ: Patient Health Questionnaire; HADS: Hospital Anxiety and Depression Scale; BDI-II: Beck Depression Inventory-II; CES-D: Center for Epidemiologic Studies Depression Scale.

All listed scales have been tested in numerous languages and treatment settings (e.g. primary care, specialized clinics, community samples).

Notably, a brief version of the BDI, the BDI-6, has shown good psychometric properties and has been recommended as a second step in the some screening procedures, since it has been validated to capture the severity of depressed mood (Blom et al., 2012). In addition, screening tools for sub-domains of depression, such as cognitive dysfunction, are being validated in the medically ill, given the importance of specific symptoms domains (e.g. cognition, anhedonia, energy) which may differentially respond to treatment (McIntyre et al., 2019; Siu et al., 2016).

In summary, numerous screening tools are available, with variable sensitivity, specificity, cost and acceptability. The use of a systematic screening method in chronic medical illness may increase the detection of depression. In addition, the use of tools should be made under the concept of incremental validity (Derogatis, 1987). A critical component of the screening process is an appreciation that a positive screen is not diagnostic, but rather, should prompt the screening provider to conduct a clinical assessment to better understand the depressive symptoms, prior to planning or initiating treatment. Harm may occur when screening tests are used to substitute a diagnostic assessment, as false positive screens may result in unnecessary initiation of antidepressant medications with associated risks to the patient including the emergence of ‘iatrogenic comorbidities’ (Carvalho et al., 2016; Fava et al., 2016). The reporting of depressive symptoms on a screening measure should trigger a clinic-based conversation with the patient to determine the most appropriate intervention, rather than an automatic referral for specialized psychiatric care. While assessing distress, conversations with primary and secondary health care providers, treatment of poorly controlled physical symptoms and engagement of social work, spiritual care or other support services may all be of value in this circumstance to address factors that contribute to depressive symptoms as a final common pathway of distress (Lo et al., 2010; Thombs et al., 2018).

Assessing depressive symptoms: the spectrum from normal sadness to subthreshold depressive symptoms to MDD

In assessing depressive symptoms, a comprehensive differential diagnosis of potential psychological, social, medical and psychiatric causes should be considered (Box 1), along with an appreciation for the spectrum of depressive symptoms (Figure 1). Sadness is a normal adaptive reaction to a new diagnosis of a chronic medical illness, to a recurrence of a medical condition or to a chronic illness (Chochinov, 2001). With milder levels of depression, patients may still be able to maintain closeness with their family and friends and to still enjoy activities, even if these activities are now different given their new physical and functional limitations (Caruso et al., 2017a). Maintaining some hope for the future and motivation to engage with the treating team would also be expected with normal sadness. Depressive symptoms that progress and cause distress and functional impairment may represent threshold (i.e. MDD) or subthreshold depressive symptoms. At this point, patients may begin to withdraw, potentially losing hope and motivation to receive treatment, even when the prognosis is favorable (Caruso et al., 2017a; Tecuta et al., 2015). As depressive symptoms progress further, the patient may meet criteria for a depressive disorder, with characteristic neurovegetative symptoms and negative cognitions distorting their views of themselves, the people around them and what the future will hold (APA, 2013). More significant social withdrawal, further worsening of low mood, anhedonia and worsening of physical symptoms would also be expected with an underlying depressive disorder.

Box 1.

Key differential diagnoses for depressive symptoms in medically ill.

1. Normal sadness: no psychiatric diagnosis; symptoms are in keeping with a normal and expected reaction to being diagnosed and living with medical illness

2. Adjustment disorder: subthreshold symptoms of depression and/or anxiety beyond what is normally expected with significant distress and/or functional impairment

3. Major depressive disorder (MDD): primary depressive disorder, meeting full criteria for a current major depressive episode (MDE), not better explained by another cause

4. Persistent depressive disorder (PDD): continued depressive symptoms for over 2 years, never with >2 months without symptoms

5. Depressive disorder secondary to another medical condition: depressive symptoms are best explained by and almost exclusively secondary to direct physiologic effect of a medical condition (e.g. hypothyroidism, anemia)

6. Depressive disorder secondary to medication/substance use: depressive symptoms are best explained by and almost exclusively secondary to direct physiologic effect of a medication (e.g. barbiturates, vigabatrin, topiramate, flunarizine, corticosteroids, mefloquine, efavirenz, interferon-α) or substance (e.g. cannabis, alcohol)

7. Other specified depressive disorders: previously minor depression; less than five symptoms of depression or symptoms persisting less than 2 weeks

8. Delirium: cognitive impairment with inattention, acute onset and fluctuations secondary to an organic cause; inattention, cognitive dysfunction and sleep-wake disturbances may be misinterpreted as signs of depression

9. Major neurocognitive disorder: subjective and objective cognitive impairment with functional impairment and a progressive course

10. Bipolar disorder: current depressive symptoms with history of mania or hypomania

11. Personality disorders: symptoms are better explained by persistent, long-standing maladaptive characterological traits impacting emotionality, impulsivity, interpersonal style and cognitive biases

Figure 1.

Spectrum of depressive symptoms from normal sadness to major depression.

In more severe cases, profound hopelessness and suicidal ideation may emerge. At times, suicidal ideation and behaviors secondary to depression may be difficult to differentiate from desire for hastened death (DHD), relating to their medical illness (Bellido-Pérez et al., 2017; Wilson et al., 2016). Differentiating suicidal thoughts and behaviors from DHD may be particularly important in regions where assisted dying is legalized for terminal medical illness (Sheehan et al., 2017). Notably, several medical illnesses, including cancer (Calati et al., 2017), end-stage renal disease (ESRD) (Pompili et al., 2013), HIV (Green et al., 2012), epilepsy (Christensen et al., 2007; Pompili et al., 2006), MS (Pompili et al., 2012), stroke (Bartoli et al., 2017) and TBI (Bahraini et al., 2013), are associated with increased rates of suicidal ideation, suicide attempts and death by suicide, meriting a detailed risk assessment in the medically ill with depressive symptoms. Of note, earlier research suggested that diabetes was also associated with increased suicide risk, but more recent meta-analytic level evidence has suggested there is no increased relative risk compared to appropriately matched controls (Wang et al., 2016). Added caution is needed regarding prescription dispensing intervals with suicidal patients, given the frequent large number of potentially lethal medications being prescribed and increased risk of overdose in some medical illnesses, such as HIV (Green et al., 2012).

Understanding and appreciating where depressive symptoms lie on this spectrum of severity is essential for accurately determining the diagnosis of depression in the medically ill. An initial assessment of the number of symptoms, severity, distress level and degree of functional impairment is needed to determine if depressive symptoms are subthreshold (e.g. less than five depressive symptoms) or above the threshold for a major depressive episode (MDE) (e.g. five or more symptoms with functional impairment occurring for greater than 2 weeks) (APA, 2013). In the medically ill, if symptoms are subthreshold and not better explained by the direct effects of medications, substances or a medical condition, the most likely diagnosis is an adjustment disorder (Mitchell et al., 2011). Adjustment disorders are commonly diagnosed conditions in the medically ill (15–30% lifetime prevalence) when presenting with symptoms of depression or anxiety (Mitchell et al., 2011). The diagnosis of an adjustment disorder requires symptoms to be disproportionate to what would be expected under the circumstances (e.g. diagnosis of medical illness), be functional impairing and/or subjectively distressing, although such distinctions may be difficult or impossible to determine. Importantly, while many providers may feel the diagnosis of adjustment disorder is less stigmatizing and more palatable for medical patients, if the criteria for MDD are met, this diagnosis takes priority (APA, 2013). This distinction is more relevant with regard to more severe depressive disorders that require antidepressant medication.

In establishing the diagnosis of MDD, several other important potential causes for symptoms should be ruled out (Box 1). Clearly establishing the chronology of symptoms is often helpful in refining the differential diagnosis. Symptoms starting acutely with a new medical illness may often relate to: (1) direct physiologic effects of the illness, (2) direct effects of a new medication being prescribed or (3) psychological distress relating to receiving a new diagnosis, along with adjusting to new physical limitations and role changes. Individual symptoms, such as tearfulness or apathy, can be associated with specific neurological disorders and are not necessarily indicative of an underlying, evolving depressive episode. In patients with a history of MDD, a new medical diagnosis is a significant risk factor for relapse or recurrence of depression in someone who may have previously been fully remitted (Rabiee et al., 2016). As in the general population, a thorough history of previous mood episodes (depressive, manic, hypomanic or mixed) and personal history is of great significance for an accurate diagnosis and biopsychosocial formulation that will directly impact the management plan.

People with personality difficulties are at particular risk of adjustment difficulties in the face of stressors, such as medical illness, that characteristically manifest as depressive symptoms. While beyond the scope of this paper, other factors such as personality disorder can be a powerful mediator of the relationship between medical and psychiatric comorbidity. Personality disorder is a risk for medical multimorbidity and independently is associated with a greater risk for depression as well as poorer outcomes in both groups of disorder (Quirk et al., 2015, 2016, 2017).

In establishing an MDD diagnosis, special attention should be paid to assessing the underlying etiology of specific symptoms, to differentiate symptoms primarily due to a depressive disorder, to a medical condition, or to both, or are compounded by comorbid personality difficulties. If a symptom is clearly secondary to a medical condition (e.g. fatigue in untreated hypothyroidism), the symptom should not be ‘counted’ toward a diagnosis of MDD when determining if greater than or equal to five symptoms of depression are present. However, it is often difficult to determine if somatic symptoms are entirely driven by a medical condition, as medically ill patients with depression report higher somatic symptom burden (Fitzgerald et al., 2013). Nevertheless, DSM-5 suggests that ‘clinician judgment’ should determine if specific symptoms are ‘counted’ toward the criteria of MDD (APA, 2013). As such, the clinician’s judgment is the current gold standard for determining if a patient meets specific somatic symptom criteria as part of a current MDE (Fava et al., 2015).

Given the complexity of deciding which somatic symptoms ‘count’ toward the diagnosis of an MDE, some investigators and clinicians have advocated for either an ‘exclusive’ approach (e.g. removing somatic symptoms completely in the medically ill) or a ‘substitutive approach’ where somatic symptoms are replaced by other non-somatic symptoms of depression that are not directly affected by medical illness (Huey et al., 2018). For example, in a study of outpatients at a large cancer center (n = 611) participants completed a modified PHQ-9 with additional items covering the Endicott and Cavanaugh substitutive criteria. Depression prevalence varied significantly by diagnostic approach, with the inclusive approach identifying the largest proportion as depressed (9.3%), followed by the Endicott-substitutive (6.2%), exclusive (4.6%) and Cavanaugh-substitutive approach (1.8%) (Saracino et al., 2018). In screening, the inclusive approach (e.g. including somatic symptoms) is generally recommended, but there continues to be debate and minimal consensus on which approach is best when making the diagnosis of MDD in the medically ill (Mitchell et al., 2012). Further research is needed to establish the construct and face validity of different criteria for MDD in the subgroup of the medically ill (Huey et al., 2018). The HADS, designed for depression in medically ill people, eliminates vegetative symptoms for this reason although its validity has been questioned (Stern, 2014).

Delirium is an important differential diagnosis as well, especially in inpatient and nursing home settings where rates of delirium are >25% (Ahmed et al., 2014). A large proportion of referrals to psychiatry services for depression in inpatient medical settings are identified as delirium upon further assessment (Grover et al., 2017). The presentation of delirium includes acute onset of fluctuating cognitive function, inattention and sleep-wake disturbances secondary to an underlying organic cause (APA, 2013). These signs and symptoms may be mistaken as ‘depressive symptoms’ of poor concentration, insomnia, fatigue, anhedonia and social withdrawal. However, the time course, fluctuating nature, medical status and lack of negative cognitions or subjective low mood may help differentiate delirium from depressive symptoms. Delirium is more common in people with dementia and early mild cognitive impairment, which is also an important differential diagnosis. Ruling out delirium is of particular importance given the high rate of mortality associated with this neuropsychiatric condition (Witlox et al., 2010).

Collaborative care models for the treatment of depression in the medically ill

Extensive evidence indicates that collaborative care (CC) interventions are effective for the management of depression in the context of co-occurring medical conditions (Bruce et al., 2004; Huffman et al., 2014; Katon et al., 2010; Patel et al., 2010; Unützer et al., 2002). Furthermore, CC interventions for depression are both cost-effective and improve outcomes for both depression and comorbid medical illness (Reiss-Brennan et al., 2016; Van Eck van der Sluijs et al., 2018). Typically, CC interventions are team-based interventions that are based on the principles of measurement-based care. For example, in a CC intervention for depression in primary care, patients are routinely assessed with a validated screening tool (e.g. the PHQ-9) in regular clinical visits. Second, these models use a non-physician case manager, usually a nurse, who is located in the medical setting who can further assess patients with a positive-screen (e.g. two-stage screening). If a patient meets criteria for entry into a CC program, the care manager can continue assessment and monitoring of symptoms over time (e.g. with repeated PHQ-9s); coordinate pharmacotherapy recommendations to general practitioners (GPs); and provide support, education and evidence-based psychotherapy to patients as appropriate. Finally, CC programs include specialist-provided stepped care recommendations, often from a psychiatrist, who reviews the case with the case manager and provides specific recommendations prompted by results of longitudinal assessments. A stepped care approach tailors the intensity of treatment to the patient’s initial depression severity and subsequent treatment response (Figure 2) (Bockting et al., 2018; Guidi et al., 2016).

Figure 2.

Stepped Care model of depression in the medically ill based on recommendations from National Institute for Health and Care Excellence (NICE) guidelines for treatment of depression with chronic physical illness.

With a stepped care approach, wellness strategies (e.g. exercise, peer support) are recommended for any subthreshold depressive symptoms (step 1). With mild to moderate symptoms, or persistent subthreshold symptoms that did not improve with step 1 interventions, low-intensity psychosocial interventions should be considered (e.g. individual or group psychotherapy), using different approaches according to the stage of the illness (Guidi et al., 2017). Depending on the clinical formulation, patient preference and severity of symptoms, antidepressant medications may also be considered at step 2 (Macneil et al., 2012). However, psychosocial interventions are usually preferable at this stage, given the high burden of polypharmacy with potential for harmful drug–drug and drug–disorder interactions (Mausbach and Irwin, 2017; Scott et al., 2015) and the potential iatrogenic adverse effects of psychopharmacological treatments (Carvalho et al., 2016; Fava et al., 2013, 2014; Offidani et al., 2014). Individual conceptualization of illness and personal treatment preference also predicts treatment response and adherence.

With moderate to severe symptoms or mild to moderate symptoms unresponsive to steps 1 and 2 interventions, a trial of medication or high-intensity psychotherapy should be considered. Medications and high-intensity psychotherapy may be used sequentially, although, in special circumstances, a combination of both may be required to achieve remission of symptoms (Guidi et al., 2016). Using a collaborative care approach to optimize both psychiatric and medical outcomes may also be beneficial and an efficient use of limited healthcare resources (Gilbody et al., 2006; Li et al., 2016). Finally, for severe and treatment-resistant depression (TRD), more intensive treatments, such as inpatient admission and brain stimulation (e.g. repetitive transcranial magnetic stimulation [rTMS] or electroconvulsive therapy [ECT]) should be considered (NICE, n.d.; Perera et al., 2016).

Psychotherapy in the medically ill

Given the high prevalence of polypharmacy and propensity for drug–drug and drug–disease interactions, as well as the phenomenon of iatrogenic comorbidity, psychological treatments are often preferable, when feasible and available, in this population (Scott et al., 2015). In addition, in some medical illnesses, such as heart failure, psychotherapy might be more effective than antidepressant medications (Das et al., 2019). In the general population and in the medically ill, psychotherapy is the preferred treatment for patients with subthreshold depressive symptoms and mild MDD, whereas moderate to severe MDD often requires pharmacological interventions (Kennedy et al., 2016). Clinical formulation should also guide treatment; factors such as family history, childhood trauma, comorbid personality and other disorders and prior history of response are factors that predict differential treatment utility. Notably, however, in the medically ill, there is a very limited number of studies directly comparing pharmacological and non-pharmacological treatments in subgroups of interest. In the general population, level 1 evidence is available to support the efficacy of the following psychological interventions in the acute treatment of an MDE: cognitive behavioral therapy (CBT), interpersonal therapy (IPT) and behavioral activation (BA) (Parikh et al., 2016). Tailored psychotherapies have also been shown to relieve depressive symptoms in some medical populations (Rodin et al., 2018). For maintenance treatment (e.g. relapse prevention) in the general population, level 1 evidence is available for CBT and mindfulness-based cognitive therapy (MBCT). In addition, staging may allow to apply a psychotherapeutic intervention to specific phases of development of depression: well-being therapy and MBCT appear to be uniquely suited for addressing the residual symptoms of depression, whereas IPT has been mainly tested in the acute phase. CBT appears to be suitable for all phases, but with persistent depressive disorder (PDD) its modifications (e.g. cognitive behavioral analysis system of psychotherapy) may be required.

These modalities, especially CBT, have also been shown to be efficacious in several chronic disease populations, but with a lower level of evidence (levels 2–3), given the limited number of studies and smaller sample sizes (Parikh et al., 2016). Structured and semi-structured psychotherapeutic interventions (e.g. CBT, IPT) have demonstrated antidepressant effects with small to moderate effect sizes in patients with cancer (Chong Guan et al., 2016; Okuyama et al., 2017), diabetes (Baumeister et al., 2014), coronary heart disease (Richards et al., 2018), heart failure (Das et al., 2019), HIV (Eshun-Wilson et al., 2018; Van Luenen et al., 2018), epilepsy (Elger et al., 2017) and MS (Fiest et al., 2016; Sesel et al., 2018). CBT has not been shown to be efficacious in advanced disease, although meaning-based and supportive-expressive therapies have been shown to relieve depressive symptoms in this population (Li et al., 2019; Rodin et al., 2018). Limited and mixed evidence also supports the use of psychotherapeutic interventions for post-stroke depression and in ESRD, but further study is still needed to more definitely determine the potential benefits in these populations (Cohen et al., 2007; Hadidi et al., 2017; King-Wing Ma and Kam-Tao Li, 2016).

Growing evidence has also suggested a role for focused psychotherapy, such as problem-solving therapy (PST), for prevention of depression in the medically ill. In a randomized controlled trial (RCT) of escitalopram and PST for prevention of post-stroke depression, the use of escitalopram or PST resulted in a significantly lower incidence of depression over 12 months of treatment compared with placebo (Robinson et al., 2008). Similarly, in a large RCT comparing PST (n = 105) to treatment as usual (n = 100), PST prevented depressive disorders and loss of valued activities in patients with age-related macular degeneration (Rovner et al., 2007). More recently, in a large RCT with older adults with subsyndromal depressive symptoms (n = 181) at rural and urban primary care clinics in India, Dias et al. (2019) demonstrated that lay counselors providing PST, brief behavioral treatment for insomnia and education in self-care of common medical disorders were effective for preventing major depressive episodes in this population. Taken together, these studies suggest a potential role for psychotherapeutic interventions for prevention of depression in these high-risk, medically ill groups.

There is minimal evidence to indicate which sub-populations (e.g. based on demographics, illness stage) are more likely to benefit from psychological interventions. Across all medical illnesses, illness-related physical symptoms such as fatigue and pain, and cognitive dysfunction may limit the effectiveness of psychotherapeutic interventions by directly impairing the ability of the patient to effectively engage in the therapy process and complete necessary tasks (e.g. homework for CBT) (Kok and Reynolds, 2017). The potential to treat these symptoms, which are strong risk factors for depression, should be carefully considered. Psychotherapeutic approaches also need to be adjusted based on the ability of the patient to tolerate and adequately engage in psychotherapy. This assessment may be an ongoing process over the course of treatment, as the physical status, symptom burden and cognitive function may change with progression of disease. Notably, however, cognitive impairment is not a contraindication to psychotherapy, as some with such impairment may still benefit from modified psychological interventions (Gitlin and Aravena, 2017; Orgeta et al., 2015).

Given the association of depression with poorer medical outcomes, numerous studies have evaluated the effects of psychotherapy on medical outcomes. Psychological interventions have been shown to improve depressive symptoms in diabetes, but did not have an effect on glycemic control (Baumeister et al., 2014). For coronary heart disease, meta-analytic level data suggest that psychological interventions may reduce cardiovascular mortality, while having no effect on all-cause mortality, future myocardial infarction (MI) or revascularization (Barth et al., 2004; Richards et al., 2018). For cancer, a recent large-scale RCT found that a collaborative care intervention referred to as Depression Care for People with Cancer (DCPC) that had both psychosocial and pharmacological components significantly improved depression and quality of life in patients with cancer compared to treatment as usual with no significant effects on survival (Mulick et al., 2018). A tailored supportive-expressive therapy, referred to as Managing Cancer and Living Meaningfully (CALM), has been shown in a large RCT to reduce depressive symptoms and death anxiety in patients with advanced cancer (Rodin et al., 2018). There is only weak evidence with low effect sizes for psychological interventions for primary prevention of depression (Forsman et al., 2011).

Taken together, moderate evidence supports the use of psychological interventions in the medically ill; however, effect sizes are generally lower in the medically ill than in the general population, which may also be partly driven by decreased response in older adults (Dodd et al., 2014). While significant effects are observed in psychiatric outcomes, the benefits of psychological interventions for medical outcomes remain unclear with mixed results to date. Several additional psychological interventions are currently under investigation for treatment of depression and psychological distress in specific medical sub-populations. For example, in cancer and palliative care, several new modalities have shown promising results, such as meaning-centered psychotherapy (Breitbart et al., 2012; Dose and Rhudy, 2018; Martínez et al., 2017) and CALM therapy (Rodin et al., 2018). Specifically in migraine patients, well-being therapy has been proposed (Mansueto et al., 2018). Future research may build further on determining subgroups that may benefit from specific interventions and tailoring psychological interventions to the unique needs of the medically ill.

Pharmacological interventions in the medically ill

Similar to psychotherapeutic interventions, antidepressants with efficacy in the general population have demonstrated efficacy in the medically ill, but with fewer supporting studies, smaller samples sizes and lower antidepressant effect sizes (Kennedy et al., 2016; Ostuzzi et al., 2018). Meta-analytic level data has demonstrated small-to-moderate antidepressant effect sizes of antidepressants for MDD in cancer (Ostuzzi et al., 2018), diabetes (Baumeister et al., 2014; Darwish et al., 2018), coronary heart disease (Ostuzzi et al., 2019), post-stroke (Robinson and Jorge, 2016; Sun et al., 2017), HIV (Eshun-Wilson et al., 2018), epilepsy (Elger et al., 2017; Maguire et al., 2014) and MS (Fiest et al., 2016). The benefits of antidepressants in heart failure and major neurocognitive disorder are unclear with mixed results (Das et al., 2019; Dudas et al., 2018). Potential benefits of antidepressants for the primary prevention of depression have been demonstrated in post-stroke and post-MI populations, but due to study limitations and risk of polypharmacy, routine antidepressant prophylaxis is not recommended (Ha and Wong, 2011; Ostuzzi et al., 2019; Towfighi et al., 2017). Use of antidepressants has mostly failed to demonstrate improvements in all-cause mortality in the medically ill (Almeida et al., 2010; Barth et al., 2004).

Available evidence is limited regarding the comparative efficacy of specific antidepressants in specific medical populations. In the general population, comparative antidepressant effects of different antidepressant classes and agents have been largely equivalent (Cipriani et al., 2018). Clinically, such group equivalence is not easily translatable to the individual, as many people will respond to one antidepressant class or agent and not another. As such, the pragmatic selection of pharmacological treatments is based on prior and family history of response, variability in potential behavioral toxicity, symptom-domain specific effects and side effects. As summarized in Table 3, the propensity for specific behavioral toxicity and side effects of common antidepressants and augmenting agents vary widely, but they all reduce depressive symptom severity overall (Carvalho et al., 2016; Cipriani et al., 2018; Fava et al., 2016). Special attention to these effects in the medically ill is of great importance, as off-target effects may improve, or worsen, some physical symptoms of medical illness. For example, sedating antidepressants may be of benefit for patients with prominent insomnia, which may be secondary to both depression and many chronic medical illnesses. Conversely, added caution about side effects in the medically ill is warranted to prevent worsening of already problematic physical symptoms as well as the emergence of burdensome iatrogenic comorbidities (Fava et al., 2016). For example, in a patient with cancer-related fatigue (CRF) and hypersomnia, use of antidepressants with sedating effects should be avoided. Rather, antidepressants and augmenting agents with wake-promoting effects should be preferentially prescribed in this circumstance. Within a single patient, sedation may be desirable or deleterious at different phases of illness. Consideration of this ‘behavioral toxicity’ is important and may change depending on phase of medical illness and depression (Fava et al., 2016). Similarly, in cardiac patients with arrhythmias and a prolonged QT interval, citalopram, lithium, tricyclic antidepressants (TCAs) and antipsychotics should be avoided if possible, given the arrhythmogenic potential of these agents (Vandael et al., 2017).

Table 3.

Side effect and biobehavioral toxicity considerations of antidepressants and common augmenting agents.

Notable effects	Medications (antidepressants and augmenting agents) with specific effects
Sedating effects	Mirtazapine, agomelatine, trazodone, fluvoxamine, doxepin, amitriptyline, imipramine, clomipramine, quetiapine, olanzapine, asenapine
Wake-promoting effects	Bupropion, venlafaxine, desvenlafaxine, levomilnacipran, stimulants (methylphenidate, amphetamine), modafinil, T3
Anti-emetic effects	Mirtazapine, olanzapine, haloperidol
Pro-cognitive effects	Vortioxetine; possibly bupropion, stimulants and SNRIs
QTc prolongation	Citalopram, escitalopram, antipsychotics, lithium, TCAs
Appetite/weight gain effects	Increased: mirtazapine, antipsychotics (especially olanzapine, quetiapine), lithium Decreased: bupropion, stimulants
Analgesic effects	TCAs, duloxetine and possibly other SNRIs (venlafaxine, desvenlafaxine, levomilnacipran)
Bleeding risk	Increased: SSRIs, SNRIs Neutral: mirtazapine, bupropion, MAO-Is
Sexual side effects	Common: fluoxetine, paroxetine, fluvoxamine, sertraline, antipsychotics, citalopram, escitalopram, SNRIs, TCAs Uncommon: bupropion, mirtazapine, vortioxetine, vilazodone, reboxetine, moclobemide, selegiline, stimulants, modafinil, T3
Serotonin syndrome risk	High risk: irreversible MAO-Is Moderate risk: SSRIs, SNRIs, clomipramine, amitriptyline, nortriptyline Low risk: bupropion, agomelatine, mirtazapine, desipramine, reboxetine
Discontinuation symptoms	Common: paroxetine, citalopram, escitalopram, sertraline, fluvoxamine, SNRIs, TCAs Rare: fluoxetine, mirtazapine, bupropion, vortioxetine, trazodone

TCA: tricyclic antidepressant; SSRI: Selective serotonin reuptake inhibitor; SNRI: Serotonin–norepinephrine reuptake inhibitor; MAO-I: Monoamine oxidase inhibitor.

In addition to considering side effects, potential drug–drug interactions and drug–disease interactions must be critically evaluated. Given the rapidly growing number of medications used in chronic disorders (e.g. chemotherapeutics, biologics, anti-retrovirals), most published interaction guides are out-dated. The use of up-to-date drug interaction software packages is recommended when adding a psychotropic medication or making any dose adjustments in medically complex patients (e.g. Lexicomp). For medical disorders affecting liver and/or renal function, dose adjustments are often required, as summarized in Table 4, with some agents being contraindicated in hepatic and renal impairment. Given the propensity for both known and unknown interactions in this population, the general approach of ‘start low and go slow’ is advisable to minimize the risk of toxic effects. Most antidepressants have not been extensively tested in hepatic or renal impairment so product monographs often lack explicit, evidence-based direction for dose adjustment (Hedayati et al., 2012; Mauri et al., 2014). Therefore, added caution is needed, even in the absence of explicit dose adjustment recommendations from the product monograph.

Table 4.

Dose adjustment in renal and hepatic impairment based on product monographs.

Drug/classes	Hepatic impairment (HI) adjustments	Renal impairment (RI) adjustments
Antidepressants
SSRIs	Reduce dose by 50%	Mild RI: no adjustment needed Moderate RI: 50% dose reduction Severe RI: 75% dose reduction
Venlafaxine	Reduce dose by 50%	Mild to moderate RI: reduce by 25–50% Severe RI: reduce by 50–75%
Desvenlafaxine	No adjustment needed (max dose of 100 mg in moderate to severe HI)	Mild RI: no adjustment needed Moderate RI: max 50 mg/d Severe RI: max 25 mg/d or 50 mg every other day
Duloxetine	Contraindicated in HI	No adjustment needed for RI, but not recommended with ESRD or dialysis
Levomilnacipran	No adjustment needed	Do not exceed 80 mg/d for moderate RI or 40 mg/d for severe RI
Vortioxetine	No adjustment needed for mild to moderate HI. Use not recommended in severe HI	No adjustment needed
Mirtazapine	Cautious dosing; no explicit dosing guidelines for HI	Moderate RI: decrease by 30% Severe RI: decrease by 50%
Bupropion	Moderate to severe HI: do not exceed 75 mg/d for IR, 100 mg/d for SR or 150 mg every other day for XL	Consider reducing initial dose. Water-soluble metabolite may accumulate in ESRD
TCAs	Potentially serious hepatic effects; no direct dosing guidelines	Use with caution in RI, no explicit recommendations
MAO-Is	Contraindicated in HI	Reduced and cautious dose in mild RI. Contraindicated in severe RI
Trazodone	Cautious dosing; no explicit dosing guidelines for HI	Cautious dosing; no explicit dosing guidelines for RI
Nefazodone	Contraindicated in HI (may cause liver failure)	No dose adjustment with mild to moderate RI. Contraindicated in severe RI
Common augmenting agents
Risperidone	In severe HI, reduce starting dose and decrease rate of titration	Reduce starting dose by 50% and decrease rate of titration
Aripiprazole	No adjustment needed	No adjustment needed
Quetiapine	Used with great caution in patients with moderate or severe HI (limited data in HI)	Use with caution with reduced starting dose and decreased rate of titration (limited data in RI)
Olanzapine	Lower initial starting dose and slower titration to initial target dose	Lower initial starting dose and slower titration to initial target dose
Brexpiprazole	Moderate to severe HI: max dose of 2 mg/d	Moderate to severe RI: max dose of 2 mg/d
Paliperidone	Mild to moderate HI: no adjustment needed Severe HI: limited data	Mild RI: max 6 mg/d Moderate RI: max 3 mg/d Severe RI: contraindicated
Methylphenidate	Limited data, no recommendations	Limited data, no recommendations
Modafinil	Reduce by 50% in severe HI	Limited data, no recommendations
Lithium	No adjustment needed	Moderate RI: 50–75% dose reduction Dialysis: supplement 300 mg after each dialysis session (lithium is dialyzable)
Lamotrigine	Reduce initial, titration and maintenance dose by 50% in moderate HI and 75% in severe HI	Use with caution with reduced starting dose and decreased rate of titration (limited data in RI)

ESRD: end-stage renal disease; TCA: tricyclic antidepressant.

For agents with ‘no adjustment needed’, cautious dosing is still recommended with hepatic or renal impairment.

Several novel interventions primarily acting outside of the monoaminergic system are under investigation (Carvalho et al., 2014; Rosenblat et al., 2015). For example, the use of immune-regulatory, anti-inflammatory and anti-oxidant agents, with neuroprotective and mitochondria-protective properties, including curcumin, zinc, omega-3 polyunsaturated acids and N-acetylcysteine (NAC) have proven beneficial in the treatment of depression (Grosso et al., 2014, 2016; Li et al., 2017; Maes et al., 2012; Swardfager et al., 2013; Zheng et al., 2018). Results of RCTs are regarded as proof of concept supporting the neuro-immune and neuro-oxidative stress theory of depression (Leonard and Maes, 2012). These agents have also been investigated in the general population (Kohler et al., 2014). Notably, the potential role of anti-inflammatories in primary prevention of depression is also currently underway (Berk et al., 2013a, 2016).

In palliative care, growing focus has been placed on ‘rapid acting’ antidepressants, when life expectancy is limited, requiring a more immediate effect, to allow for improved quality of life in the final weeks to months of living (Irwin and Iglewicz, 2010; Ng et al., 2014). Stimulants, such as methylphenidate, initially were reported to demonstrate rapid reduction in depressive symptom, but further analysis revealed that the effects were primarily related to improved energy (Andrew et al., 2018; Hardy, 2009; Lavretsky et al., 2015; McIntyre et al., 2017; Ng et al., 2014). Several new and emerging medications are currently under investigation in the treatment of depression in the medically ill. Recent studies have demonstrated promising preliminary results for rapid antidepressant effects with psilocybin in patients receiving palliative care (Ross, 2018; Ross et al., 2016). Ketamine and esketamine have demonstrated rapid and robust antidepressant effects in the general population and are currently under investigation in patients with terminal cancer as well (Caddy et al., 2015; Irwin et al., 2013; Irwin and Iglewicz, 2010). Ketamine may have the added benefit of improvements in pain control, which is commonly problematic in palliative care (Bell et al., 2012; Fallon et al., 2018). However, the known risk of ketamine dependence will be a challenge to manage in clinical rollout, especially in non-terminal phases of the illness (Berk et al., 2018).

Brain stimulation and convulsive therapies in the medically ill

ECT is one of the oldest and most effective treatments for depression in the general population (Milev et al., 2016). The technique is based on the induction of controlled seizures in patients under general anesthesia via depolarization of a larger number of neurons using a stimulus of alternating polarity pulses with amplitude of 800–900 mA via two electrodes placed over one’s head (Milev et al., 2016). ECT is not widely disseminated as a first-line treatment due to issues such as costs, stigma, tolerability (e.g. memory deficits and untoward cognitive effects) and requirement for general anesthesia. Nevertheless, for acute and severe suicidality, treatment-resistance or severe depression, ECT should be considered (Milev et al., 2016). In the medically ill and the elderly, ECT is relatively safe and well-tolerated, with the added advantage of avoiding additional augmenting antidepressant medications when polypharmacy may already be high (Rasmussen et al., 2002). ECT has been tested in numerous populations including MS, PD, various endocrinological disorders and renal failure, demonstrating similar efficacy compared to the general population. It may be particularly beneficial for PD with comorbid MDD, as ECT is effective to treat depressive symptoms as well as the core symptoms and functional outcomes of PD (Borisovskaya et al., 2016; Williams et al., 2017). While there are no absolute contraindications for ECT, caution should be exercised with a history of stroke or MI in the past 6 months or with pre-morbid significant cognitive impairment. Cardiovascular risk factors might present contraindications for ECT, due to high blood pressure peaks immediately following the seizure. Magnetic seizure therapy (MST) has most recently been developed with encouraging preliminary results, potentially with less cognitive effects than ECT. However, MST still requires general anesthesia as a seizure is induced and MST has not been adequately studied in the medically ill (Cretaz et al., 2015).

In contrast, non-invasive brain stimulation (NIBS) therapies are relatively recent techniques that are generally less effective than ECT (Brunoni et al., 2019; Mutz et al., 2019). NIBS techniques include transcranial magnetic and direct current stimulation (TMS and tDCS, respectively), which use magnetic or electric fields to promote changes in cortical excitability (Brunoni et al., 2019). TMS is based on the principle of electromagnetic induction via an electric current passing through a coil, whereas for tDCS an electric current of low intensity (usually ⩽3 mA) is injected in the brain via electrodes placed over the scalp (Brunoni et al., 2019). These approaches do not require sedation or anesthesia and evidence indicates they have excellent safety and tolerability profiles (Bikson et al., 2016; Rossi et al., 2009). For tDCS, additional advantages include its affordability, ease of use, and portability and lack of serious adverse events, including the small risk of seizures that has been reported with TMS (Aparício et al., 2016). For depressed patients with medical conditions who are at risk for pharmacological interactions and toxicity, NIBS might present as interesting alternative modalities (Valiengo et al., 2013).

Several rTMS protocols are effective for the treatment of unipolar depression (Brunoni et al., 2017a; Mutz et al., 2018). In post-stroke depression, rTMS has replicated RCT level evidence of moderate quality demonstrating efficacy with comparable antidepressant effect sizes to medications (Deng et al., 2017; Shen et al., 2017). Conversely, in depression with PD, results have been mixed with rTMS (Zhou et al., 2018). Trials that investigated the efficacy of tDCS with or combined with antidepressant drugs have shown that tDCS is inferior to full-dose escitalopram (Brunoni et al., 2017b), although its effects are enhanced when combined with sertraline (Brunoni et al., 2013). tDCS is also effective in patients with post-stroke depression (Valiengo et al., 2017).

Limitations

The current review had several limitations, most notably, the approach to reviewing the current topic and the limits of the available evidence. The current review was not a systematic review or meta-analysis as the authors believed that an exhaustive review of the literature on this topic would be disadvantageous and elected to synthesize and summarize the evidence instead of reviewing all articles published on this large topic. This approach may lead to presenting the evidence in a biased fashion by potentially missing evidence that would have been otherwise reported in a systematic review. Moreover, as emphasized throughout the article, the evidence base on this topic is still limited, preventing conclusive answers on the topics discussed.

Concluding remarks and outlook

Replicated evidence has demonstrated a bidirectional interaction between depression and medical illness with high rates of comorbidity. MDD comorbid with medical disease is associated with poorer outcomes of both conditions. Epidemiological studies have demonstrated increased rates of subthreshold depressive symptoms and threshold MDD in almost all disorders, compared to healthy controls. However, prevalence estimates vary greatly, with heterogeneity in study designs, screening/diagnostic methods and populations of interest making it difficult to determine the absolute risk increase of MDD in the medically ill compared to medically well. Common disorders with particularly high risk of MDD include cancer, CVD, HIV, COPD and neurological disorders (MS, epilepsy, stroke) with estimated lifetime MDD prevalence rates of 20–40%. Risk of suicide is also increased in medically ill populations requiring careful assessment.

Preventive efforts for the management of MDD in the medically ill may be a relevant research direction. It was proposed that antidepressants may prevent the emergence of depression in patients with stroke or hepatitis C virus treated with interferon (Ehret and Sobieraj, 2014); however, evidence for such preventive effects in the medically ill remains limited. People with a medical illness and subthreshold depressive symptoms may also be candidates for targeted preventive strategies (Lee et al., 2019).

Accumulating evidence indicates that patients with depression and comorbid medical illness benefit from CC interventions with stepped care that may include both pharmacological and psychosocial treatments that have been proven effective in these populations. CC interventions may be challenging to deliver to remote and rural areas, although an increasing number of studies have attempted to assess the usefulness of technology-facilitated (e.g. digital) CC interventions for the management of depression in the medically ill with encouraging initial outcomes (Rojas et al., 2018; Wu et al., 2018). This research direction clearly deserves further investigation to improve access to the optimal care in rural communities.

Time-limited structured and semi-structured psychological interventions have also demonstrated antidepressant efficacy in these populations, with replicated studies using CBT, Mindfulness-based stress reduction therapy (MBSRT), IPT and supportive-expressive therapy. Antidepressant selection and dosing may need to be modified in the context of hepatic or renal impairment, or with drug-drug interactions, as polypharmacy is common in these populations. While evidence is limited, some studies have suggested symptom-targeted treatments to be of benefit. Brain stimulation is safe in most medical illnesses and is particularly beneficial in the elderly and some neurological disorders, but requires further study in the medically ill.

Key areas for future research include further evaluations of algorithms for screening and treatment of depression that are more customized and validated for specific chronic medical disorders (Table 5). New psychotherapy modalities being developed specifically for the medically ill hold hope for improved outcomes in both depressive symptoms and other psychological distress associated with medical illness. Novel modes of delivery (e.g. smartphone-based interventions; Firth et al., 2017) may improve the availability of evidence-based psychological treatments in remote and underserved areas where trained psychotherapists are not readily available, although few studies to date have been conducted in medically ill populations. Furthermore, novel pharmacological interventions are being investigated that target neurotransmission systems other than the conventionally targeted monoamine system. Understanding subgroups that are more likely to respond to specific interventions is also of great importance to allow for more personalized treatments rather than trial-and-error algorithms within the emerging framework of precision psychiatry (Fernandes et al., 2017; Insel, 2014).

Table 5.

Future direction of study for depression in the medically ill.

Area	Future direction of study and high-priority areas of interest
Etiology	Integration of biomarkers and neuroimaging to better characterize the specific pathophysiology of depression in specific chronic medical illnesses which likely have overlapping and different mechanisms involved in the onset and progression and depression
Epidemiology	Prioritizing studies that determine prevalence with gold-standard diagnostic interviews, rather than relying on depression screening tools
Screening	Further development of screening tools that are more sensitive and specific in medically ill populations, with refined algorithms for next steps with a positive screen
Diagnosis	Investigation of alternative diagnostic criteria for subgroup of MDD in the medically ill, carefully evaluating construct and face validity of new criteria
Treatment	Development and assessment of psychological interventions tailored to medically ill, considering somatic symptoms and other special needs Identifying subgroups more likely to respond to pharmacological versus non-pharmacological interventions to personalize treatment approaches Testing the efficacy and safety of novel pharmacological treatments acting outside of the monoaminergic system (e.g. ketamine, psilocybin, anti-inflammatory agents)
Prevention	Larger studies evaluating interventions for primary prevention of depression, with special considerations for cost-effectiveness and potential harms from widespread prophylactic treatments in the medically ill

MDD: major depressive disorder.

Footnotes

Author Contributions

J.D.R. and A.F.C. developed the outline of the paper. J.D.R. wrote the initial manuscript draft. P.K., F.C., M.L., G.R., A.R.B. and R.S.M. provided significant revisions with a substantial intellectual contribution. J.D.R. and A.F.C. integrated all revisions into the final draft. All authors approved the final submitted manuscript.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship and/or publication of this article: J.D.R., P.K., F.C., M.L., G.R. and A.F.C. have no conflicts of interest to declare. R.S.M. declares that he has been on advisory boards and/or received honoraria for educational activities and/or research grants from AstraZeneca, Bristol-Myers Squibb, Janssen-Ortho, Eli Lily, Forest, Lundbeck, Pfizer, Shire, Merck, Sepracor and Otsuk.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iDs

Joshua D Rosenblat

Fiammetta Cosci

Michael Berk

References

Agustini

Berk

(2018) Medications with depression as an adverse effect. JAMA 320: 1815–1816.

Ahmed

Leurent

Sampson

(2014) Risk factors for incident delirium among older people in acute hospital medical units: A systematic review and meta-analysis. Age and Ageing 43: 326–333.

Ali

Stone

Peters

, et al. (2006) The prevalence of co-morbid depression in adults with type 2 diabetes: A systematic review and meta-analysis. Diabetic Medicine : A Journal of the British Diabetic Association 23: 1165–1173.

Almeida

Alfonso

Hankey

, et al. (2010) Depression, antidepressant use and mortality in later life: The Health In Men Study. PLoS ONE 5: e11266.

Andrew

Guan

Jaafar

NRN

(2018) The use of methylphenidate for physical and psychological symptoms in cancer patients: A review. Current Drug Targets 19: 877–887.

APA (2013) Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. Washington, DC: APA.

Aparício

LVM

Guarienti

Razza

, et al. (2016) A systematic review on the acceptability and tolerability of transcranial direct current stimulation treatment in neuropsychiatry trials. Brain Stimulation 9: 671–681.

Arroll

Goodyear-Smith

Crengle

, et al. (2010) Validation of PHQ-2 and PHQ-9 to screen for major depression in the primary care population. Annals of Family Medicine 8: 348–353.

Bahraini

Simpson

Brenner

, et al. (2013) Suicidal ideation and behaviours after traumatic brain injury: A systematic review. Brain Impairment 14: 92–112.

10.

Banks

Owen

Erickson

(2012) Insulin in the brain: There and back again. Pharmacology & Therapeutics 136: 82–93.

11.

Barber

Dergousoff

Slater

, et al. (2016) Depression and survival in patients with head and neck cancer: A systematic review. JAMA Otolaryngology–Head & Neck Surgery 142: 284–288.

12.

Barefoot

Helms

Mark

, et al. (1996) Depression and long-term mortality risk in patients with coronary artery disease. The American Journal of Cardiology 78: 613–617.

13.

Barth

Schumacher

Herrmann-Lingen

(2004) Depression as a risk factor for mortality in patients with coronary heart disease: A meta-analysis. Psychosomatic Medicine 66: 802–813.

14.

Bartoli

Pompili

Lillia

, et al. (2017) Rates and correlates of suicidal ideation among stroke survivors: A meta-analysis. Journal of Neurology, Neurosurgery, and Psychiatry 88: 498–504.

15.

Baumeister

Hutter

Bengel

(2014) Psychological and pharmacological interventions for depression in patients with diabetes mellitus: An abridged Cochrane review. Diabetic Medicine: A Journal of the British Diabetic Association 31: 773–786.

16.

Bell

Eccleston

Kalso

(2012) Ketamine as an adjuvant to opioids for cancer pain. Cochrane Database of Systematic Reviews 11: CD003351.

17.

Bellido-Pérez

Monforte-Royo

Tomás-Sábado

, et al. (2017) Assessment of the wish to hasten death in patients with advanced disease: A systematic review of measurement instruments. Palliative Medicine 31: 510–525.

18.

Berk

Jacka

(2019) Diet and depression-from confirmation to implementation. JAMA 321: 842–843.

19.

Berk

Dean

Drexhage

, et al. (2013a) Aspirin: A review of its neurobiological properties and therapeutic potential for mental illness. BMC Medicine 11: 74.

20.

Berk

Loo

Davey

, et al. (2018) Ketamine and rapidly acting antidepressants: Breaking the speed of sound or light? Australian and New Zealand Journal of Psychiatry 52: 1026–1029.

21.

Berk

Williams

Jacka

, et al. (2013b) So depression is an inflammatory disease, but where does the inflammation come from? BMC Medicine 11: 200.

22.

Berk

Woods

Nelson

, et al. (2016) ASPREE-D: Aspirin for the prevention of depression in the elderly. International Psychogeriatrics 28: 1741–1748.

23.

Bhattarai

Charlton

Rudisill

, et al. (2013) Prevalence of depression and utilization of health care in single and multiple morbidity: A population-based cohort study. Psychological Medicine 43: 1423–1431.

24.

Bikson

Grossman

Thomas

, et al. (2016) Safety of transcranial direct current stimulation: Evidence based update 2016. Brain Stimulation 9: 641–661.

25.

Blom

Bech

Högberg

, et al. (2012) Screening for depressed mood in an adolescent psychiatric context by brief self-assessment scales—Testing psychometric validity of WHO-5 and BDI-6 indices by latent trait analyses. Health and Quality of Life Outcomes 10: 149.

26.

Bockting

CLH

Klein

Elgersma

, et al. (2018) Effectiveness of preventive cognitive therapy while tapering antidepressants versus maintenance antidepressant treatment versus their combination in prevention of depressive relapse or recurrence (DRD study): A three-group, multicentre, randomised controlled trial. The Lancet Psychiatry 5: 401–410.

27.

Borisovskaya

Bryson

Buchholz

, et al. (2016) Electroconvulsive therapy for depression in Parkinson’s disease: Systematic review of evidence and recommendations. Neurodegenerative Disease Management 6: 161–176.

28.

Bornstein

Jager

Putnick

(2013) Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review (DR) 33: 357–370.

29.

Breitbart

Poppito

Rosenfeld

, et al. (2012) Pilot randomized controlled trial of individual meaning-centered psychotherapy for patients with advanced cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 30: 1304–1309.

30.

Bruce

Ten Have

Reynolds

, et al. (2004) Reducing suicidal ideation and depressive symptoms in depressed older primary care patients: A randomized controlled trial. JAMA 291: 1081–1091.

31.

Brunoni

Chaimani

Moffa

, et al. (2017a) Repetitive transcranial magnetic stimulation for the acute treatment of major depressive episodes: A systematic review with network meta-analysis. JAMA Psychiatry 74: 143–152.

32.

Brunoni

Moffa

Sampaio-Junior

, et al. (2017b) Trial of electrical direct-current therapy versus escitalopram for depression. The New England Journal of Medicine 376: 2523–2533.

33.

Brunoni

Sampaio-Junior

Moffa

, et al. (2019) Noninvasive brain stimulation in psychiatric disorders: A primer. Revista Brasileira De Psiquiatria (Sao Paulo, Brazil 1999) 41: 70–81.

34.

Brunoni

Valiengo

Baccaro

, et al. (2013) The sertraline vs. electrical current therapy for treating depression clinical study: Results from a factorial, randomized, controlled trial. JAMA Psychiatry 70: 383–391.

35.

Butow

Price

Shaw

, et al. (2015) Clinical pathway for the screening, assessment and management of anxiety and depression in adult cancer patients: Australian guidelines. Psycho-Oncology 24: 987–1001.

36.

Byrne

(2018) Physical health in psychiatric patients. Medicine 46: 725–730.

37.

Caddy

Amit

McCloud

, et al. (2015) Ketamine and other glutamate receptor modulators for depression in adults. Cochrane Database of Systematic Reviews 2015: CD011612.

38.

Calati

Mattei

Courtet

(2017) Risk of suicide mortality among cancer patients: A meta-analysis of observational studies. European Psychiatry: The Journal of the Association of European Psychiatrists 41: S290–S291.

39.

Caraci

Copani

Nicoletti

, et al. (2010) Depression and Alzheimer’s disease: Neurobiological links and common pharmacological targets. European Journal of Pharmacology 626: 64–71.

40.

Caruso

Nanni

Riba

, et al. (2017a) Depressive spectrum disorders in cancer: Diagnostic issues and intervention. A critical review. Current Psychiatry Reports 19: 33.

41.

Caruso

Nanni

Riba

, et al. (2017b) Depressive spectrum disorders in cancer: Prevalence, risk factors and screening for depression: A critical review. Acta Oncologica (Stockholm, Sweden) 56: 146–155.

42.

Carvalho

Berk

Hyphantis

, et al. (2014) The integrative management of treatment-resistant depression: A comprehensive review and perspectives. Psychotherapy and Psychosomatics 83: 70–88.

43.

Carvalho

Sharma

Brunoni

, et al. (2016) The safety, tolerability and risks associated with the use of newer generation antidepressant drugs: A critical review of the literature. Psychotherapy and Psychosomatics 85: 270–288.

44.

Celano

Freudenreich

Fernandez-Robles

, et al. (2011) Depressogenic effects of medications: A review. Dialogues in Clinical Neuroscience 13: 109–125.

45.

Chen

Evans

Larkins

(2016) Why are hospital doctors not referring to consultation-liaison psychiatry?—A systemic review. BMC Psychiatry 16: 390.

46.

Chochinov

(2001) Depression in cancer patients. The Lancet Oncology 2: 499–505.

47.

Chong Guan

Mohamed

Kian Tiah

, et al. (2016) Psychotherapy for cancer patients. International Journal of Psychiatry in Medicine 51: 414–430.

48.

Christensen

Vestergaard

Mortensen

, et al. (2007) Epilepsy and risk of suicide: A population-based case–control study. The Lancet Neurology 6: 693–698.

49.

Ciechanowski

Katon

Russo

(2000) Depression and diabetes: Impact of depressive symptoms on adherence, function, and costs. Archives of Internal Medicine 160: 3278–3285.

50.

Cipriani

Furukawa

Salanti

, et al. (2018) Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: A systematic review and network meta-analysis. The Lancet (London, England) 391: 1357–1366.

51.

Cohen

Norris

Acquaviva

, et al. (2007) Screening, diagnosis, and treatment of depression in patients with end-stage renal disease. Clinical Journal of the American Society of Nephrology: CJASN 2: 1332–1342.

52.

Cosco

Doyle

Ward

, et al. (2012) Latent structure of the Hospital Anxiety and Depression Scale: A 10-year systematic review. Journal of Psychosomatic Research 72: 180–184.

53.

Cosco

Lachance

Blodgett

, et al. (2019) Latent structure of the Centre for Epidemiologic Studies Depression Scale (CES-D) in older adult populations: A systematic review. Aging & Mental Health. Epub ahead of print 19 January. DOI: 10.1080/13607863.2019.1566434.

54.

Cretaz

Brunoni

Lafer

(2015) Magnetic seizure therapy for unipolar and bipolar depression: A systematic review. Neural Plasticity 2015: 521398.

55.

Cuijpers

Vogelzangs

Twisk

, et al. (2014) Comprehensive meta-analysis of excess mortality in depression in the general community versus patients with specific illnesses. The American Journal of Psychiatry 171: 453–462.

56.

Dantzer

O’Connor

Freund

, et al. (2008) From inflammation to sickness and depression: When the immune system subjugates the brain. Nature Reviews Neuroscience 9: 46–56.

57.

Darwish

Beroncal

Sison

, et al. (2018) Depression in people with type 2 diabetes: Current perspectives. Diabetes, Metabolic Syndrome and Obesity : Targets and Therapy 11: 333–343.

58.

Das

Roy

Schwarzer

, et al. (2019) Comparison of treatment options for depression in heart failure: A network meta-analysis. Journal of Psychiatric Research 108: 7–23.

59.

Dauer

Przedborski

(2003) Parkinson’s disease: Mechanisms and models. Neuron 39: 889–909.

60.

Deng

Sun

Qiu

, et al. (2017) Interventions for management of post-stroke depression: A Bayesian network meta-analysis of 23 randomized controlled trials. Scientific Reports 7: 16466.

61.

Derogatis

(1987) The Derogatis Stress Profile (DSP): Quantification of psychological stress. Research Paradigms in Psychosomatic Medicine 17: 30–54.

62.

Dias

Azariah

Anderson

, et al. (2019) Effect of a lay counselor intervention on prevention of major depression in older adults living in low- and middle-income countries: A randomized clinical trial. JAMA Psychiatry 76: 13–20.

63.

Diniz

Butters

Albert

, et al. (2013) Late-life depression and risk of vascular dementia and Alzheimer’s disease: Systematic review and meta-analysis of community-based cohort studies. The British Journal of Psychiatry: The Journal of Mental Science 202: 329–335.

64.

Dodd

Berk

Kelin

, et al. (2014) Application of the Gradient Boosted method in randomised clinical trials: Participant variables that contribute to depression treatment efficacy of duloxetine, SSRIs or placebo. Journal of Affective Disorders 168: 284–293.

65.

Dose

Rhudy

(2018) Perspectives of newly diagnosed advanced cancer patients receiving dignity therapy during cancer treatment. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer 26: 187–195.

66.

Dudas

Malouf

McCleery

, et al. (2018) Antidepressants for treating depression in dementia. Cochrane Database of Systematic Reviews 4: CD003944.

67.

Ehret

Sobieraj

(2014) Prevention of interferon-alpha-associated depression with antidepressant medications in patients with hepatitis C virus: A systematic review and meta-analysis. International Journal of Clinical Practice 68: 255–261.

68.

Elger

Johnston

Hoppe

(2017) Diagnosing and treating depression in epilepsy. Seizure 44: 184–193.

69.

Eshun-Wilson

Siegfried

Akena

, et al. (2018) Antidepressants for depression in adults with HIV infection. Cochrane Database of Systematic Reviews 1: CD008525.

70.

Fallon

Wilcock

Kelly

, et al. (2018) Oral ketamine vs placebo in patients with cancer-related neuropathic pain: A randomized clinical trial. JAMA Oncology 4: 870–872.

71.

Fava

Cosci

Offidani

, et al. (2016) Behavioral toxicity revisited: Iatrogenic comorbidity in psychiatric evaluation and treatment. Journal of Clinical Psychopharmacology 36: 550–553.

72.

Fava

Guidi

Rafanelli

, et al. (2015) The clinical inadequacy of evidence-based medicine and the need for a conceptual framework based on clinical judgment. Psychotherapy and Psychosomatics 84: 1–3.

73.

Fava

Tomba

Tossani

(2013) Innovative trends in the design of therapeutic trials in psychopharmacology and psychotherapy. Progress in Neuro-Psychopharmacology & Biological Psychiatry 40: 306–311.

74.

Fava

Tossani

Bech

, et al. (2014) Emerging clinical trends and perspectives on comorbid patterns of mental disorders in research. International Journal of Methods in Psychiatric Research 23: 92–101.

75.

Fernandes

Williams

Steiner

, et al. (2017) The new field of ‘precision psychiatry’. BMC Medicine 15: 80.

76.

Fiest

Walker

Bernstein

, et al. (2016) Systematic review and meta-analysis of interventions for depression and anxiety in persons with multiple sclerosis. Multiple Sclerosis and Related Disorders 5: 12–26.

77.

Firth

Torous

Nicholas

, et al. (2017) The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry: Official Journal of the World Psychiatric Association (WPA) 16: 287–298.

78.

Fitzgerald

, et al. (2013) The relationship between depression and physical symptom burden in advanced cancer. BMJ Supportive & Palliative Care 5: 381–388.

79.

Forsman

Schierenbeck

Wahlbeck

(2011) Psychosocial interventions for the prevention of depression in older adults: Systematic review and meta-analysis. Journal of Aging and Health 23: 387–416.

80.

Frazier

Sanner

, et al. (2014) Using a single screening question for depressive symptoms in acute coronary syndrome patients. The Journal of Cardiovascular Nursing 29: 347–353.

81.

Galeazzi

Ferrari

Mackinnon

, et al. (2004) Interrater reliability, prevalence, and relation to ICD-10 diagnoses of the diagnostic criteria for psychosomatic research in consultation-liaison psychiatry patients. Psychosomatics 45: 386–393.

82.

Gallo

Hwang

Joo

, et al. (2016) Multimorbidity, depression, and mortality in primary care: Randomized clinical trial of an evidence-based depression care management program on mortality risk. Journal of General Internal Medicine 31: 380–386.

83.

Gallo

Morales

Bogner

, et al. (2013) Long term effect of depression care management on mortality in older adults: Follow-up of cluster randomized clinical trial in primary care. BMJ (Clinical Research Ed.) 346: f2570.

84.

Geng

Zhang

Q-E

Wang

, et al. (2018) Comparison of comorbid depression between irritable bowel syndrome and inflammatory bowel disease: A meta-analysis of comparative studies. Journal of Affective Disorders 237: 37–46.

85.

Gilbody

Bower

Fletcher

, et al. (2006) Collaborative care for depression: A cumulative meta-analysis and review of longer-term outcomes. Archives of Internal Medicine 166: 2314–2321.

86.

Gilbody

Sheldon

House

(2008) Screening and case-finding instruments for depression: A meta-analysis. CMAJ 178: 997–1003.

87.

Gitlin

Aravena

(2017) Nonpharmacologic treatments for depression in older adults. JAMA 318: 1283–1284.

88.

Global Burden of Disease (GBD) DALYs and Hale Collaborators, Murray

Barber

, et al. (2015) Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: Quantifying the epidemiological transition. The Lancet (London, England) 386: 2145–2191.

89.

Mozaffarian

Roger

, et al. (2013) Heart disease and stroke statistics–2013 update: A report from the American Heart Association. Circulation 127: e6–e245.

90.

Gold

Dunn

Phoenix

, et al. (2016) Co-occurrence of anxiety and depressive symptoms following breast cancer surgery and its impact on quality of life. European Journal of Oncology Nursing 20: 97–105.

91.

Grassi

Sabato

Rossi

, et al. (2009) Affective syndromes and their screening in cancer patients with early and stable disease: Italian ICD-10 data and performance of the Distress Thermometer from the Southern European Psycho-Oncology Study (SEPOS). Journal of Affective Disorders 114: 193–199.

92.

Green

McGowan

Yokell

, et al. (2012) HIV infection and risk of overdose: A systematic review and meta-analysis. AIDS (London, England) 26: 403–417.

93.

Grosso

Micek

Marventano

, et al. (2016) Dietary n-3 PUFA, fish consumption and depression: A systematic review and meta-analysis of observational studies. Journal of Affective Disorders 205: 269–281.

94.

Grosso

Pajak

Marventano

, et al. (2014) Role of omega-3 fatty acids in the treatment of depressive disorders: A comprehensive meta-analysis of randomized clinical trials. PLoS ONE 9: e96905.

95.

Grover

Sahoo

Aggarwal

, et al. (2017) Reasons for referral and diagnostic concordance between physicians/surgeons and the consultation-liaison psychiatry team: An exploratory study from a tertiary care hospital in India. Indian Journal of Psychiatry 59: 170–175.

96.

Guidi

Tomba

Fava

(2016) The sequential integration of pharmacotherapy and psychotherapy in the treatment of major depressive disorder: A meta-analysis of the sequential model and a critical review of the literature. The American Journal of Psychiatry 173: 128–137.

97.

Guidi

Tomba

Cosci

, et al. (2017) The role of staging in planning psychotherapeutic interventions in depression. Journal of Clinical Psychiatry 78: 456–463.

98.

J-H

Wong

C-K

(2011) Pharmacologic treatment of depression in patients with myocardial infarction. Journal of Geriatric Cardiology (JGC) 8: 121–126.

99.

Hadidi

Huna Wagner

Lindquist

(2017) Nonpharmacological treatments for post-stroke depression: An integrative review of the literature. Research in Gerontological Nursing 10: 182–195.

100.

Han

Lofland

Zhao

, et al. (2011) Increased prevalence of psychiatric disorders and health care-associated costs among patients with moderate-to-severe psoriasis. Journal of Drugs in Dermatology 10: 843–850.

101.

Hardy

(2009) Methylphenidate for the treatment of depressive symptoms, including fatigue and apathy, in medically ill older adults and terminally ill adults. The American Journal of Geriatric Pharmacotherapy 7: 34–59.

102.

Hedayati

Yalamanchili

Finkelstein

(2012) A practical approach to the treatment of depression in patients with chronic kidney disease and end-stage renal disease. Kidney International 81: 247–255.

103.

Hermanns

Caputo

Dzida

, et al. (2013) Screening, evaluation and management of depression in people with diabetes in primary care. Primary Care Diabetes 7: 1–10.

104.

Huey

Guan

Gill

, et al. (2018) Core symptoms of major depressive disorder among palliative care patients. International Journal of Environmental Research and Public Health 15: 1758.

105.

Huffman

Niazi

Rundell

, et al. (2014) Essential articles on collaborative care models for the treatment of psychiatric disorders in medical settings: A publication by the academy of psychosomatic medicine research and evidence-based practice committee. Psychosomatics 55: 109–122.

106.

Iaboni

Seitz

Fischer

, et al. (2015) Initiation of antidepressant medication after hip fracture in community-dwelling older adults. The American Journal of Geriatric Psychiatry 23: 1007–1015.

107.

Insel

(2014) The NIMH Research Domain Criteria (RDoC) project: Precision medicine for psychiatry. The American Journal of Psychiatry 171: 395–397.

108.

Irwin

Olmstead

Carroll

(2016) Sleep disturbance, sleep duration, and inflammation: A systematic review and meta-analysis of cohort studies and experimental sleep deprivation. Biological Psychiatry 80: 40–52.

109.

Irwin

Iglewicz

(2010) Oral ketamine for the rapid treatment of depression and anxiety in patients receiving hospice care. Journal of Palliative Medicine 13: 903–908.

110.

Irwin

Iglewicz

Nelesen

, et al. (2013) Daily oral ketamine for the treatment of depression and anxiety in patients receiving hospice care: A 28-day open-label proof-of-concept trial. Journal of Palliative Medicine 16: 958–965.

111.

Katon

Lin

EHB

Von Korff

, et al. (2010) Collaborative care for patients with depression and chronic illnesses. The New England Journal of Medicine 363: 2611–2620.

112.

Kennedy

Lam

McIntyre

, et al. (2016) Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 3. Pharmacological treatments. The Canadian Journal of Psychiatry 61: 540–560.

113.

King-Wing

Ma T

Kam-Tao Li

(2016) Depression in dialysis patients. Nephrology (Carlton, VIC) 21: 639–646.

114.

Koenig

Pappas

Holsinger

, et al. (1995) Assessing diagnostic approaches to depression in medically ill older adults: How reliably can mental health professionals make judgments about the cause of symptoms? Journal of the American Geriatrics Society 43: 472–478.

115.

Kohler

Benros

Nordentoft

, et al. (2014) Effect of anti-inflammatory treatment on depression, depressive symptoms, and adverse effects: A systematic review and meta-analysis of randomized clinical trials. JAMA Psychiatry 71: 1381–1391.

116.

Kok

Reynolds

(2017) Management of depression in older adults: A review. JAMA 317: 2114–2122.

117.

Kose

Hirai

Seki

, et al. (2018) Role of potentially inappropriate medication use in rehabilitation outcomes for geriatric patients after strokes. Geriatrics & Gerontology International 18: 321–328.

118.

Kroenke

Spitzer

Williams

(2001) The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine 16: 606–613.

119.

Lavretsky

Reinlieb

St Cyr

, et al. (2015) Citalopram, methylphenidate, or their combination in geriatric depression: A randomized, double-blind, placebo-controlled trial. The American Journal of Psychiatry 172: 561–569.

120.

Lee

Stockings

Harris

, et al. (2019) The risk of developing major depression among individuals with subthreshold depression: A systematic review and meta-analysis of longitudinal cohort studies. Psychological Medicine 49: 92–102.

121.

Leonard

Maes

(2012) Mechanistic explanations how cell-mediated immune activation, inflammation and oxidative and nitrosative stress pathways and their sequels and concomitants play a role in the pathophysiology of unipolar depression. Neuroscience & Biobehavioral Reviews 36: 764–785.

122.

Levis

Benedetti

Levis

, et al. (2017) Selective cutoff reporting in studies of diagnostic test accuracy: A comparison of conventional and individual-patient-data meta-analyses of the Patient Health Questionnaire-9 depression screening tool. The American Journal of Epidemiology 185: 954–964.

123.

Kennedy

Byrne

, et al. (2016) Systematic review and meta-analysis of collaborative care interventions for depression in patients with cancer. Psycho-Oncology 26: 573–587.

124.

Rosenblat

Rodin

(2019) Chapter 7: Depression. In: Levenson

(ed.) The American Psychiatric Association publishing textbook of psychosomatic medicine and consultation-liaison psychiatry, 3rd Edition. pp. 245–260. Philadelphia, PA: American Psychiatric Association.

125.

Song

, et al. (2017) Dietary zinc and iron intake and risk of depression: A meta-analysis. Psychiatry Research 251: 41–47.

126.

Calzavara

Kurdyak

, et al. (2013) Depression and use of health care services in patients with advanced cancer. Canadian Family Physician 59: e168–e174.

127.

Zimmermann

Rydall

, et al. (2010) Longitudinal study of depressive symptoms in patients with metastatic gastrointestinal and lung cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 28: 3084–3089.

128.

Loas

Krystkowiak

Godefroy

(2012) Anhedonia in Parkinson’s disease: An overview. Journal of Neuropsychiatry and Clinical Neurosciences 24: 444–451.

129.

McAlpine

Wilson

(2004) Screening for depression in primary care: What do we still need to know? Depression and Anxiety 19: 137–145.

130.

Machado

Oriolo

Bortolato

, et al. (2017) Biological mechanisms of depression following treatment with interferon for chronic hepatitis C: A critical systematic review. Journal of Affective Disorders 209: 235–245.

131.

Machado

Veronese

Sanches

, et al. (2018) The association of depression and all-cause and cause-specific mortality: An umbrella review of systematic reviews and meta-analyses. BMC Medicine 16: 112.

132.

McIntyre

Anderson

Baune

, et al. (2019) Expert consensus on screening and assessment of cognition in psychiatry. CNS Spectrums 24: 154–162.

133.

McIntyre

Lee

Zhou

, et al. (2017) The efficacy of psychostimulants in major depressive episodes: A systematic review and meta-analysis. Journal of Clinical Psychopharmacology 37: 412–418.

134.

McIntyre

Rosenbluth

Ramasubbu

, et al. (2012) Managing medical and psychiatric comorbidity in individuals with major depressive disorder and bipolar disorder. Annals of Clinical Psychiatry 24: 163–169.

135.

Macneil

Hasty

Conus

, et al. (2012) Is diagnosis enough to guide interventions in mental health? Using case formulation in clinical practice. BMC Medicine 10: 111.

136.

Maes

Fišar

Medina

, et al. (2012) New drug targets in depression: Inflammatory, cell-mediated immune, oxidative and nitrosative stress, mitochondrial, antioxidant, and neuroprogressive pathways. And new drug candidates—Nrf2 activators and GSK-3 inhibitors. Inflammopharmacology 20: 127–150.

137.

Maes

Kubera

Obuchowiczwa

, et al. (2011) Depression’s multiple comorbidities explained by (neuro)inflammatory and oxidative & nitrosative stress pathways. Neuro Endocrinology Letters 32: 7–24.

138.

Maguire

Weston

Singh

, et al. (2014) Antidepressants for people with epilepsy and depression. The Cochrane Database of Systematic Reviews 12: CD010682.

139.

Manea

Gilbody

McMillan

(2012) Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): A meta-analysis. CMAJ 184: E191–E196.

140.

Mansueto

De Cesaris

Geppetti

, et al. (2018) Protocol and methods for testing the efficacy of well-being therapy in chronic migraine patients: A randomized controlled trial. Trials 19: 561.

141.

Martínez

Arantzamendi

Belar

, et al. (2017) ‘Dignity therapy’, a promising intervention in palliative care: A comprehensive systematic literature review. Palliative Medicine 31: 492–509.

142.

Matte

Pizzichini

MMM

Hoepers

ATC

, et al. (2016) Prevalence of depression in COPD: A systematic review and meta-analysis of controlled studies. Respiratory Medicine 117: 154–161.

143.

Mauri

Fiorentini

Paletta

, et al. (2014) Pharmacokinetics of antidepressants in patients with hepatic impairment. Clinical Pharmacokinetics 53: 1069–1081.

144.

Mausbach

Irwin

(2017) Depression and healthcare service utilization in patients with cancer. Psycho-Oncology 26: 1133–1139.

145.

Meijer

Roseman

Milette

, et al. (2011) Depression screening and patient outcomes in cancer: A systematic review. PLoS ONE 6: e27181.

146.

Mikocka-Walus

Knowles

Keefer

, et al. (2016) Controversies revisited: A systematic review of the comorbidity of depression and anxiety with inflammatory bowel diseases. Inflammatory Bowel Diseases 22: 752–762.

147.

Milev

Giacobbe

Kennedy

, et al. (2016) Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 4. Neurostimulation treatments. The Canadian Journal of Psychiatry/Revue Canadienne de Psychiatrie 61: 561–575.

148.

Mitchell

Chan

Bhatti

, et al. (2011) Prevalence of depression, anxiety, and adjustment disorder in oncological, haematological, and palliative-care settings: A meta-analysis of 94 interview-based studies. The Lancet Oncology 12: 160–174.

149.

Mitchell

Lord

Symonds

(2012) Which symptoms are indicative of DSM IV depression in cancer settings? An analysis of the diagnostic significance of somatic and non-somatic symptoms. Journal of Affective Disorders 138: 137–148.

150.

Mulick

Walker

Puntis

, et al. (2018) Does depression treatment improve the survival of depressed patients with cancer? A long-term follow-up of participants in the SMaRT Oncology-2 and 3 trials. The Lancet Psychiatry 5: 321–326.

151.

Muneer

Minhas

(2019) Telomere biology in mood disorders: An updated, comprehensive review of the literature. Clinical Psychopharmacology and Neuroscience: The Official Scientific Journal of the Korean College of Neuropsychopharmacology 17: 343–363.

152.

Mutz

Edgcumbe

Brunoni

, et al. (2018) Efficacy and acceptability of non-invasive brain stimulation for the treatment of adult unipolar and bipolar depression: A systematic review and meta-analysis of randomised sham-controlled trials. Neuroscience & Biobehavioral Reviews 92: 291–303.

153.

Mutz

Vipulananthan

Carter

, et al. (2019) Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: Systematic review and network meta-analysis. BMJ (Clinical Research Ed) 364: l1079.

154.

Boks

Roes

, et al. (2014) Rapid response to methylphenidate as an add-on therapy to mirtazapine in the treatment of major depressive disorder in terminally ill cancer patients: A four-week, randomized, double-blinded, placebo-controlled study. European Neuropsychopharmacology 24: 491–498.

155.

NICE (n.d.) Overview: Depression in adults with a chronic physical health problem: Recognition management (Guidance). Available at: www.nice.org.uk/guidance/cg91 (accessed 15 March 2019).

156.

Offidani

Fava

Sonino

(2014) Iatrogenic comorbidity in childhood and adolescence: New insights from the use of antidepressant drugs. CNS Drugs 28: 769–774.

157.

Okuyama

Akechi

Mackenzie

, et al. (2017) Psychotherapy for depression among advanced, incurable cancer patients: A systematic review and meta-analysis. Cancer Treatment Reviews 56: 16–27.

158.

O’Neil

Jacka

Quirk

, et al. (2015) A shared framework for the common mental disorders and non-communicable disease: Key considerations for disease prevention and control. BMC Psychiatry 15: 15.

159.

Orgeta

Qazi

Spector

, et al. (2015) Psychological treatments for depression and anxiety in dementia and mild cognitive impairment: Systematic review and meta-analysis. The British Journal of Psychiatry: The Journal of Mental Science 207: 293–298.

160.

Ostuzzi

Matcham

Dauchy

, et al. (2018) Antidepressants for the treatment of depression in people with cancer. Cochrane Database of Systematic Reviews 4: CD011006.

161.

Ostuzzi

Turrini

Gastaldon

, et al. (2019) Efficacy and acceptability of antidepressants in patients with ischemic heart disease: Systematic review and meta-analysis. International Clinical Psychopharmacology 34: 65–75.

162.

Palagini

Mosca

Tani

, et al. (2013) Depression and systemic lupus erythematosus: A systematic review. Lupus 22: 409–416.

163.

Pan

Sun

Okereke

, et al. (2011) Depression and risk of stroke morbidity and mortality: A meta-analysis and systematic review. JAMA 306: 1241–1249.

164.

Pantilat

O’Riordan

Dibble

, et al. (2012) An assessment of the screening performance of a single-item measure of depression from the Edmonton Symptom Assessment Scale among chronically ill hospitalized patients. Journal of Pain and Symptom Management 43: 866–873.

165.

Parikh

Quilty

Ravitz

, et al. (2016) Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 2. Psychological treatments. The Canadian Journal of Psychiatry/Revue Canadienne de Psychiatrie 61: 524–539.

166.

Pasco

Williams

Jacka

, et al. (2008) Tobacco smoking as a risk factor for major depressive disorder: Population-based study. The British Journal of Psychiatry: The Journal of Mental Science 193: 322–326.

167.

Patel

Weiss

Chowdhary

, et al. (2010) Effectiveness of an intervention led by lay health counsellors for depressive and anxiety disorders in primary care in Goa, India (MANAS): A cluster randomised controlled trial. The Lancet (London, England) 376: 2086–2095.

168.

Penninx

(2017) Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms. Neuroscience & Biobehavioral Reviews 74: 277–286.

169.

Perera

George

Grammer

, et al. (2016) The clinical TMS society consensus review and treatment recommendations for TMS therapy for major depressive disorder. Brain Stimulation 9: 336–346.

170.

Pérez-Stable

Miranda

Muñoz

, et al. (1990) Depression in medical outpatients: Underrecognition and misdiagnosis. Archives of Internal Medicine 150: 1083–1088.

171.

Pompili

Forte

Palermo

, et al. (2012) Suicide risk in multiple sclerosis: A systematic review of current literature. Journal of Psychosomatic Research 73: 411–417.

172.

Pompili

Girardi

Tatarelli

(2006) Death from suicide versus mortality from epilepsy in the epilepsies: A meta-analysis. Epilepsy & Behavior 9: 641–648.

173.

Pompili

Venturini

Montebovi

, et al. (2013) Suicide risk in dialysis: Review of current literature. International Journal of Psychiatry in Medicine 46: 85–108.

174.

Quirk

Stuart

Berk

, et al. (2017) Personality disorder is an excess risk factor for physical multimorbidity among women with mental state disorders. Psychiatry Research 257: 546–549.

175.

Quirk

Stuart

Brennan-Olsen

, et al. (2016) Physical health comorbidities in women with personality disorder: Data from the Geelong Osteoporosis Study. European Psychiatry: The Journal of the Association of European Psychiatrists 34: 29–35.

176.

Quirk

Williams

Chanen

, et al. (2015) Personality disorder and population mental health. The Lancet Psychiatry 2: 201–202.

177.

Rabiee

Nikayin

Hashem

, et al. (2016) Depressive symptoms after critical illness: A systematic review and meta-analysis. Critical Care Medicine 44: 1744–1753.

178.

Ramasubbu

Beaulieu

Taylor

, et al. (2012) The CANMAT task force recommendations for the management of patients with mood disorders and comorbid medical conditions: Diagnostic, assessment, and treatment principles. Annals of Clinical Psychiatry 24: 82–90.

179.

Rasmussen

Rummans

Richardson

(2002) Electroconvulsive therapy in the medically ill. The Psychiatric Clinics of North America 25: 177–193.

180.

Ratnasingham

Cairney

Manson

, et al. (2013) The burden of mental illness and addiction in Ontario. The Canadian Journal of Psychiatry 58: 529–537.

181.

Read

Sharpe

Modini

, et al. (2017) Multimorbidity and depression: A systematic review and meta-analysis. Journal of Affective Disorders 221: 36–46.

182.

Reiss-Brennan

Brunisholz

Dredge

, et al. (2016) Association of integrated team-based care with health care quality, utilization, and cost. JAMA 316: 826–834.

183.

Reusch

JEB

Manson

(2017) Management of type 2 diabetes in 2017: Getting to goal. JAMA 317: 1015–1016.

184.

Richards

Anderson

Jenkinson

, et al. (2018) Psychological interventions for coronary heart disease: Cochrane systematic review and meta-analysis. European Journal of Preventive Cardiology 25: 247–259.

185.

Rickards

(2005) Depression in neurological disorders: Parkinson’s disease, multiple sclerosis, and stroke. Journal of Neurology, Neurosurgery, and Psychiatry 76: i48–i52.

186.

Robinson

Jorge

(2016) Post-stroke depression: A review. The American Journal of Psychiatry 173: 221–231.

187.

Robinson

Jorge

Moser

, et al. (2008) Escitalopram and problem-solving therapy for prevention of poststroke depression: A randomized controlled trial. JAMA 299: 2391–2400.

188.

Rodin

Rydall

, et al. (2018) Managing Cancer and Living Meaningfully (CALM): A randomized controlled trial of a psychological intervention for patients with advanced cancer. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology 36: 2422–2432.

189.

Rojas

Guajardo

Martínez

, et al. (2018) A remote collaborative care program for patients with depression living in rural areas: Open-label trial. Journal of Medical Internet Research 20: e158.

190.

Rosenblat

Cha

Mansur

, et al. (2014) Inflamed moods: A review of the interactions between inflammation and mood disorders. Progress in Neuro-Psychopharmacology & Biological Psychiatry 53: 23–34.

191.

Rosenblat

McIntyre

Alves

, et al. (2015) Beyond monoamines-novel targets for treatment-resistant depression: A comprehensive review. Current Neuropharmacology 13: 636–655.

192.

Ross

(2018) Therapeutic use of classic psychedelics to treat cancer-related psychiatric distress. International Review of Psychiatry (Abingdon, England) 30: 317–330.

193.

Ross

Bossis

Guss

, et al. (2016) Rapid and sustained symptom reduction following psilocybin treatment for anxiety and depression in patients with life-threatening cancer: A randomized controlled trial. Journal of Psychopharmacology (Oxford, England) 30: 1165–1180.

194.

Rossi

Hallett

Rossini

, et al. (2009) Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 120: 2008–2039.

195.

Rovner

Casten

Hegel

, et al. (2007) Preventing depression in age-related macular degeneration. Archives of General Psychiatry 64: 886–892.

196.

Rutledge

Reis

Linke

, et al. (2006) Depression in heart failure: A meta-analytic review of prevalence, intervention effects, and associations with clinical outcomes. Journal of the American College of Cardiology 48: 1527–1537.

197.

Saracino

Rosenfeld

Nelson

(2018) Performance of four diagnostic approaches to depression in adults with cancer. General Hospital Psychiatry 51: 90–95.

198.

Saver

Van-Nguyen

Keppel

, et al. (2007) A qualitative study of depression in primary care: Missed opportunities for diagnosis and education. Journal of the American Board of Family Medicine 20: 28–35.

199.

Scott

Hilmer

Reeve

, et al. (2015) Reducing inappropriate polypharmacy: The process of deprescribing. JAMA Internal Medicine 175: 827–834.

200.

Sesel

A-L

Sharpe

Naismith

(2018) Efficacy of psychosocial interventions for people with multiple sclerosis: A meta-analysis of specific treatment effects. Psychotherapy and Psychosomatics 87: 105–111.

201.

Sheehan

Gaind

Downar

(2017) Medical assistance in dying: Special issues for patients with mental illness. Current Opinion in Psychiatry 30: 26–30.

202.

Shen

Liu

Cheng

, et al. (2017) Repetitive transcranial magnetic stimulation for the treatment of post-stroke depression: A systematic review and meta-analysis of randomized controlled clinical trials. Journal of Affective Disorders 211: 65–74.

203.

Siu

Bibbins-Domingo

Grossman

, et al. (2016) Screening for depression in adults: US preventive services task force recommendation statement. JAMA 315: 380–387.

204.

Steer

Cavalieri

Leonard

, et al. (1999) Use of the Beck depression inventory for primary care to screen for major depression disorders. General Hospital Psychiatry 21: 106–111.

205.

Stern

(2014) The Hospital Anxiety and Depression Scale. Occupational Medicine (Oxford, England) 64: 393–394.

206.

Strawbridge

Deleger

Roberts

, et al. (2002) Physical activity reduces the risk of subsequent depression for older adults. The American Journal of Epidemiology 156: 328–334.

207.

Sun

Liang

Jiao

, et al. (2017) Comparative efficacy and acceptability of antidepressant treatment in poststroke depression: A multiple-treatments meta-analysis. BMJ Open 7: e016499.

208.

Swardfager

Herrmann

Mazereeuw

, et al. (2013) Zinc in depression: A meta-analysis. Biological Psychiatry 74: 872–878.

209.

Tecuta

Tomba

Grandi

, et al. (2015) Demoralization: A systematic review on its clinical characterization. Psychological Medicine 45: 673–691.

210.

Thom

Silbersweig

Boland

(2019) Major depressive disorder in medical illness: A review of assessment, prevalence, and treatment options. Psychosomatic Medicine 81: 246–255.

211.

Thombs

Kwakkenbos

Levis

, et al. (2018) Addressing overestimation of the prevalence of depression based on self-report screening questionnaires. CMAJ 190: E44–E49.

212.

Thombs

Roseman

Coyne

, et al. (2013) Does evidence support the American Heart Association’s recommendation to screen patients for depression in cardiovascular care? An updated systematic review. PLoS ONE 8: e52654.

213.

Thombs

Ziegelstein

Roseman

, et al. (2014) There are no randomized controlled trials that support the United States Preventive Services Task Force Guideline on screening for depression in primary care: A systematic review. BMC Medicine 12: 13.

214.

Towfighi

Ovbiagele

El Husseini

, et al. (2017) Poststroke depression: A scientific statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 48: e30–e43.

215.

Unützer

Katon

Callahan

, et al. (2002) Collaborative care management of late-life depression in the primary care setting: A randomized controlled trial. JAMA 288: 2836–2845.

216.

Valiengo

LCL

Benseñor

Lotufo

, et al. (2013) Transcranial direct current stimulation and repetitive transcranial magnetic stimulation in consultation-liaison psychiatry. Brazilian Journal of Medical and Biological Research = Revista Brasileira de Pesquisas Medicas e Biologicas 46: 815–823.

217.

Valiengo

LCL

Goulart

de Oliveira

, et al. (2017) Transcranial direct current stimulation for the treatment of post-stroke depression: Results from a randomised, sham-controlled, double-blinded trial. Journal of Neurology, Neurosurgery, and Psychiatry 88: 170–175.

218.

Van Eck van der Sluijs

Castelijns

Eijsbroek

, et al. (2018) Illness burden and physical outcomes associated with collaborative care in patients with comorbid depressive disorder in chronic medical conditions: A systematic review and meta-analysis. General Hospital Psychiatry 50: 1–14.

219.

Van Luenen

Garnefski

Spinhoven

, et al. (2018) The benefits of psychosocial interventions for mental health in people living with HIV: A systematic review and meta-analysis. AIDS and Behavior 22: 9–42.

220.

Van Steenbergen-Weijenburg

de Vroege

Ploeger

, et al. (2010) Validation of the PHQ-9 as a screening instrument for depression in diabetes patients in specialized outpatient clinics. BMC Health Services Research 10: 235.

221.

Vandael

Vandenberk

Vandenberghe

, et al. (2017) Risk factors for QTc-prolongation: Systematic review of the evidence. International Journal of Clinical Pharmacy 39: 16–25.

222.

Videbech

Deleuran

(2016) The Danish Depression Database. Clinical Epidemiology 8: 475–478.

223.

Wang

Tang

, et al. (2016) Association between diabetes and risk of suicide death: A meta-analysis of 3 million participants. Comprehensive Psychiatry 71: 11–16.

224.

Wasteson

Brenne

Higginson

, et al. (2009) Depression assessment and classification in palliative cancer patients: A systematic literature review. Palliative Medicine 23: 739–753.

225.

Wells

Golding

Burnam

(1988) Psychiatric disorder in a sample of the general population with and without chronic medical conditions. The American Journal of Psychiatry 145: 976–981.

226.

Williams

Bentzley

Sahlem

, et al. (2017) Unilateral ultra-brief pulse electroconvulsive therapy for depression in Parkinson’s disease. Acta Neurologica Scandinavica 135: 407–411.

227.

Wilson

Dalgleish

Chochinov

, et al. (2016) Mental disorders and the desire for death in patients receiving palliative care for cancer. BMJ Supportive & Palliative Care 6: 170–177.

228.

Witlox

Eurelings

LSM

Jonghe

JFM

, et al. (2010) Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: A meta-analysis. JAMA 304: 443–451.

229.

Ell

Jin

, et al. (2018) Comparative effectiveness of a technology-facilitated depression care management model in safety-net primary care patients with type 2 diabetes: 6-month outcomes of a large clinical trial. Journal of Medical Internet Research 20: e147.

230.

Zheng

Zhang

Q-E

Cai

D-B

, et al. (2018) N-acetylcysteine for major mental disorders: A systematic review and meta-analysis of randomized controlled trials. Acta Psychiatrica Scandinavica 137: 391–400.

231.

Zhou

Guo

Xing

, et al. (2018) Antidepressant effects of repetitive transcranial magnetic stimulation over prefrontal cortex of Parkinson’s disease patients with depression: A meta-analysis. Frontiers in Psychiatry 9: 769.

232.

Zwahlen

Hagenbuch

Carley

, et al. (2008) Screening cancer patients’ families with the distress thermometer (DT): A validation study. Psycho-Oncology 17: 959–966.

Depression in the medically ill

Abstract

Background:

Methods:

Results:

Limitations:

Conclusion:

Keywords

Introduction

Epidemiology and pathogenesis

Screening for depression

Assessing depressive symptoms: the spectrum from normal sadness to subthreshold depressive symptoms to MDD

Collaborative care models for the treatment of depression in the medically ill

Psychotherapy in the medically ill

Pharmacological interventions in the medically ill

Brain stimulation and convulsive therapies in the medically ill

Limitations

Concluding remarks and outlook

Footnotes

Author Contributions

Declaration of Conflicting Interests

Funding

ORCID iDs

References