Physical health algorithms for mental health care

Introduction

It has been known for some time that many people with severe mental illness experience comorbid physical health problems (Lawrence et al., 2001). Indeed, the gap in life expectancy has widened between people with a mental illness and the general population for preventable illness, with most excess deaths (around 80%) associated with physical health conditions such as cardiovascular disease, cancer, and respiratory disease (Lawrence et al., 2013). Mortality rates coupled with high prevalence rates for other health conditions such as obesity and type 2 diabetes mellitus highlight the need for a pre-treatment baseline physical health assessment and ongoing monitoring (Bradshaw and Mairs, 2014; Stanley and Laugharne, 2011, 2014; Stanley et al., 2013). There is, therefore, an increasing requirement for an emphasis on preventative measures rather than reactive measures.

Many mental health services around Australia are now implementing physical health screening protocols for mental health patients, with some states well ahead of others (New South Wales Department of Health, 2009). In Western Australia, the South Metropolitan Health Service (SMHS) Mental Health Physical Health Care Strategy Working Group acknowledged a need for the additional training of mental health staff. In order to assist with this process, we have developed a number of algorithms to aid clinical staff (doctors/nurses) in the management of patients’ physical health and we present these algorithms here. These algorithms complement our Clinical Guidelines package (Stanley and Laugharne, 2011), which takes an holistic approach to physical health assessment and ongoing monitoring in the areas of lifestyle, medication effects, alcohol and drug problems, pre-existing or developing physical disorders and allergies, and social supports.

The metabolic syndrome (MetS) algorithm (Figure 1) assesses blood pressure, waist circumference, fasting lipids and fasting blood glucose. Additional algorithms have been included in this paper covering clinical decision pathways for a number of key investigations – full blood count, liver function tests, urea and electrolytes (Figure 2), thyroid function tests, electrocardiogram (ECG) and serum prolactin (Figure 3).

Figure 1.

Clinical algorithm for monitoring metabolic syndrome in mental health patients (data adapted from Waterreus and Laugharne, 2009). HDL = high density lipoproteins.

Figure 2.

Blood, liver and kidney algorithms for monitoring physical health in mental health patients. MPV = mean platelet volume, MCV = mean cell volume, MCH = mean cell haemoglobin, MCHC = mean cell haemoglobin concentration, RDW = red cell distribution width, WCC = white cell count, GGT = gamma-glutamyl transferase, ALT = alanine transaminase, LFT = liver function test, eGFR = estimated glomerular filtration rate.

Figure 3.

Thyroid, ECG and prolactin algorithms for monitoring physical health in mental health patients. ECG = electrocardiogram.

All algorithms were reviewed by a number of health professionals, including general practitioners, psychiatrists, and nursing staff. They were then endorsed for use in local mental health clinics by the Stokes Mental Health Review Implementation Steering Committee, a taskforce formed to ensure the consistent implementation of effective services, policies and practices within Western Australia mental health.

Metabolic syndrome

The first of the algorithms relates to MetS, a cluster of risk factors which contributes to increased risk for conditions such as type 2 diabetes mellitus and cardiovascular disease (Barnes et al., 2008; International Diabetes Federation (IDF), 2006; Waterreus and Laugharne, 2009). The core components of MetS are central obesity, hypertension, hyperglycaemia and dyslipidaemia. As this condition is more prevalent in people with a mental illness when compared to the general population (Ganguli and Strassnig, 2011; John et al., 2009), screening is essential and must be conducted regularly (i.e. every 3–6 months), dependent upon the general health of the patient and the medication he/she is prescribed.

The MetS algorithm details IDF (2006) worldwide consensus reference ranges for waist circumference, blood pressure, fasting lipids and fasting blood glucose (Waterreus and Laugharne, 2009). To be defined as having the MetS according to the IDF (2006) definition, an individual must have central obesity plus two of the remaining four factors – reduced high-density lipoprotein cholesterol, or elevated triglycerides, blood pressure or fasting blood glucose – or be receiving treatment for at least two previously diagnosed conditions such as dyslipidaemia, hypertension or type 2 diabetes mellitus.

Further investigations

To obtain a complete physical health baseline, a physical examination is recommended for all patients regardless of mental health diagnosis or medications prescribed. A full blood count, urea and electrolytes, liver function tests, thyroid function tests, and ECG are also recommended at baseline (Taylor et al., 2014) to ensure an adequate evaluation of the patient’s current physical health. This will also assist clinicians when prescribing medications, particularly as psychotropic medications can have a range of adverse medical effects. If, for example, it is found that the patient has an increased QTc interval on ECG examination, this will need to be taken into account when determining treatment options.

Reference ranges for the clinical monitoring algorithms were obtained from a number of sources representing best practice service provision (Cadogan and Nickson, 2014; Healthscope Functional Pathology, 2011; PathWest 2013; PathWest Fremantle Hospital, 2012; Taylor et al., 2014). Taylor et al. (2014) give recommendations on appropriate actions to take when considering abnormal test results. This direction is vital when considering the needs of clinicians working in busy services and the potential problems that may emerge during the course of treatment.

Once baseline levels have been obtained, regular monitoring is advised to keep track of changes occurring over time (Stanley and Laugharne, 2011). This is necessary for many reasons including lifestyle and environmental changes, familial factors/genetic history, and potential toxicity from psychotropic medications. For example, blood dyscrasias have been reported for most classes of psychotropic agents (Flanagan and Dunk, 2008), and practitioners need to be mindful of disorders such as neutropaenia, leucopaenia, agranulocytosis, and thrombocytosis.

Kidney, liver, heart and thyroid function, and serum prolactin levels can also be affected by many psychotropic drugs (Ajmal et al., 2014; Atasoy et al., 2007; Liamis et al., 2008). For instance, drug-induced hyponatraemia has been linked to antidepressants such as selective serotonin reuptake inhibitors (SSRIs), tricyclics and monoamine oxidase inhibitors (MAOIs), and antiepileptic agents such as carbamazepine and sodium valproate (Liamis et al., 2008). A study examining liver function tests and three different atypical antipsychotics revealed that after 6 months of treatment, increased liver enzymes were observed in 18.8–31% of patients (Atasoy et al., 2007). While liver damage during treatment is relatively rare, the study shows that the potential for damage is elevated. Antipsychotics can also elevate prolactin levels in up to 70% of patients (Inder and Castle, 2011), and Ajmal et al. (2014) suggest that raised levels of prolactin are also found in people prescribed antidepressants.

Conclusion

The debilitating effects of mental illness are often compounded by a concordant relationship with physical health issues. The main aim of this paper is to assist clinicians in their decision making when examining the physical health test results of mental health patients. This will ease the burden placed upon practitioners dealing with multiple issues for each patient, and will also assist patients by reminding practitioners to monitor patients’ physical health.

Effective service response to the poor physical health of mental health patients requires a complex and multi-faceted approach. All three algorithms represent evidence-based, standardised responses for best practice service provision within mental health and primary care clinics.