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Abstract

Background: Early and integrated palliative care is recommended for patients with idiopathic pulmonary fibrosis. Unfortunately, palliative care delivery remains poor due to various barriers in practice. This study describes various palliative care delivery models in a real-world cohort of patients with idiopathic pulmonary fibrosis, examines the predictors of survival in this cohort of patients, and explores the impact of palliative care on survival. Design: Charts were reviewed retrospectively and analyzed. The primary outcome was survival during a 4-year follow-up period. Two multivariable models were created to examine the impact of therapeutic strategies including palliative intervention on survival. Results: 298 patients with idiopathic pulmonary fibrosis were enrolled from 3 interstitial lung disease clinics with different palliative care models in Edmonton, Canada; Bristol, UK; and Kingston, Canada. 200 (67%) patients received palliative care and 119 (40%) died during follow up. Primary palliative care models (Edmonton and Bristol) delivered palliative care to 96% and 100% respectively compared 21% in the referral model (Queens). Palliative care [adjusted hazard ratio (aHR) .28 (.12-.65)] along with the use of antifibrotics [aHR .56 (.37-.84)], and body mass index >30 [aHR .47 (.37-.85)] reduced the risk of death in our idiopathic pulmonary fibrosis cohort. Opioid use was associated with worse survival [aHR 2.11 (1.30-23.43)]. Conclusions: Both palliative care and antifibrotic use were associated with survival benefit in this cohort of patients with idiopathic pulmonary fibrosis after adjusting for covariates. The benefit was seen despite differences in disease severity and different palliative care delivery models.

Keywords

pulmonary fibrosis palliative care survival mortality symptom assessment antifibrotic agents

Introduction

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrosing lung disease with high morbidity and mortality. Declining lung function and high symptom burden lead to poor quality of life and premature death.¹ Median survival is estimated to be 2-5 years from diagnosis.² Health-related quality of life is significantly impaired in patients with IPF, with marked deterioration as death approaches.³ A joint policy statement by American Thoracic Society, American Academy of Hospice and Palliative Medicine, Hospice and Palliative Nurses Association, and Social Work Hospice and Palliative Care Network recommends incorporation of palliative care early in the care continuum for patients with serious respiratory illnesses including IPF.⁴ Unfortunately, this is not frequently implemented in practice due to a variety of barriers, including lack of practice guidelines for providers as well as lack of prioritization of the palliative care needs of these patients.⁵ There are limited descriptions of palliative care delivery and its impact in IPF, and even fewer studies on palliative care delivery models.

Oncology research has demonstrated numerous benefits of early integration of palliative care, including improved survival. Temel et al. showed that early referral to palliative care improved median survival in patients with metastatic non-small-cell lung cancer.⁶ Several uncontrolled studies on palliative care in IPF have shown similar benefits, but its impact on survival has not been explored.^7-10 In this paper, we describe 3 models of palliative care delivery and examine the impact of palliative care on survival in IPF. We hypothesize that delivery of palliative care will improve survival in IPF patients.

Methods

Study Design

This is a retrospective multicenter international study of 3 cohorts of patients with IPF. Adult patients (age >18 years) who were diagnosed with IPF based on American Thoracic Society (ATS) 2011 guidelines and seen between 2012-2018 in three interstitial lung disease clinics were included. Patients with missing data (patient moved or data not recorded) were censored at the time of leaving the region or loss to follow-up. We did not have any recorded data after patients moved away. Patients with a diagnosis of non-idiopathic pulmonary fibrosis were excluded.

The models for palliative care delivery at the 3 sites are as follows (also described in Table 1):

(1) Edmonton Multidisciplinary Collaborative Interstitial Lung Disease Clinic, Edmonton, Canada (EC) utilized a primary palliative care model. A multidisciplinary interstitial lung disease care team delivers all palliative care in clinic and referral to specialist palliative care occurs as needed for hospice placement only.

(2) Bristol Interstitial Lung Disease Service, Bristol, UK (BC) utilized a primary palliative care model. Palliative care is provided by the interstitial lung disease care team. Patients with higher needs are reviewed by the multidisciplinary palliative-interstitial lung disease-psychology (PIP) team which includes a palliative care physician, psychologist, and pharmacist.¹¹ If indicated, patients are referred to specialist palliative service.

(3) Queens Interstitial Lung Disease Clinic, Kingston, Canada (QC) utilized a specialist palliative care referral model wherein patients with a Medical Research Council Dyspnea Scale score ≥4/5, requiring supplemental oxygen, and/or experiencing high symptom burden were referred to a palliative care specialist.¹⁰

Table 1.

Description of Palliative Care Models.

	Multidisciplinary Collaborative ILD Clinic Edmonton, Canada	Bristol ILD Service Bristol, UK	Queens University ILD Clinic Kingston, Canada
PC model	Primary palliative care model: Palliative care integrated with routine interstitial lung disease care and provided by interstitial lung disease team. No referral to specialist palliative care for symptom management.	Primary palliative care model: Palliative care integrated with routine interstitial lung disease care and provided by the interstitial lung disease team with input from palliative care specialists. Some patients referred to palliative care specialists.	Specialist palliative care referral: Patients referred to specialist palliative care clinic not integrated with the interstitial lung disease clinic
Care team	2 physicians (expertise in ILD and palliative approach with no formal PC training) 1 nurse	2 ILD physicians 1-2 nurses Palliative care team: palliative care specialist, psychologist, pharmacist	ILD clinic: 1 ILD physician and nurse Palliative care clinic: 1 palliative care specialist
PC screening tools in ILD clinic	Yes (EDI, needs review tool)²⁹	Yes (how are you?)	None
Symptom management	Pacing, energy conservation, education, action plans, early oxygen start with frequent titrations, cough therapies, opioids and benzodiazepines provided by ILD clinic	Pacing, energy conservation, education, action plans, fan, early oxygen start with frequent titrations, cough therapies, opioids and benzodiazepines provided by ILD clinic	ILD clinic: Oxygen only Palliative care clinic: Pacing, energy conservation, education, oxygen, cough therapies, opioids and benzodiazepines
Advance care planning	Interstitial lung disease team discusses and documents:	Interstitial lung disease team discusses and documents:	Palliative care specialist discusses and documents:
	1. Patient understanding of illness/life values,	1. Patient understanding of illness/life values,	1. Patient understanding of illness/life values,
	2. Patient preferences for location and type of care and location of death,	2. Patient preferences for location and type of care and location of death,	2. Patient preferences for location and type of care and location of death,
	3. Locally mandated goals of care forms (if any), and 4. End of life planning discussions (eg, financial and legal planning/funeral home/power of attorney/substitute decision maker/community supports)	3. Locally mandated goals of care forms (if any), and 4. End of life planning discussions (eg, financial and legal planning/funeral home/power of attorney/substitute decision maker/community supports)	3. Locally mandated goals of care forms (if any), and 4. End of life planning discussions (eg, financial and legal planning/funeral home/power of attorney/substitute decision maker/community supports)
End of life care	Provided by interstitial lung disease team in collaboration with community allied teams	Provided by interstitial lung disease team in collaboration with community allied teams	Interstitial lung disease team not involved; palliative care specialist refers patients to community teams or hospital-based end of life care units
Informal caregiver engagement	Consistent	Consistent	Sporadic
Case management approach	Yes	Yes	No

ILD = interstitial lung disease; PC = palliative care; EDI = Edmonton dyspnea inventory.

Data Collection

The principal investigators developed a REDCap database for data collection; this was trialed by 2 sites to ensure adequacy prior to implementation. EC and BC data were entered into REDCap by respective site personnel. QC data were provided in an Excel spreadsheet and entered into REDCap by the principal investigator team. Data sources included electronic charts (eClinician at EC), paper charts (BC and QC), and the Queen’s University-Kingston Health Sciences interstitial lung disease clinic database and patient care system (electronic document management system).

Variables of interest included demographics, body mass index (BMI), smoking, pulmonary function tests, 6-minute walk distance, antifibrotics, opioids, supplementary oxygen needs, non-pharmacologic symptom therapies, advance care planning documentation, specialist palliative care referral, and location and cause of death if known for decedents. Deaths were confirmed based on report to the clinics at BC and QC, and via an online data repository at EC.

For this study, we used 2 criteria to classify patients as having received palliative care at EC and BC: any patient who had (i) a comprehensive assessment and management of symptoms and needs (both sites used their own needs assessment tools in the absence of a standard tool; management included any of non-pharmacologic strategies, oxygen, or opiates) AND (ii) iterative advance care planning. For QC, any patient referred to and seen by a palliative care specialist was classified as having received palliative care.

Data Analysis

Data were summarized by medians and interquartile ranges, counts and percentages as appropriate. Survival data were visualized using Kaplan-Meier curves and compared using a log rank test. Median survival and 95% confidence intervals were calculated for each center. Cox Proportional Hazards models were examined to determine the multivariable relationships between survival and palliative care intervention, care model, age, gender, forced vital capacity (FVC), diffusing capacity of the lung for carbon monoxide (DLCO), BMI, smoking history, antifibrotics, opioids, oxygen, and Gender-Age-Physiology (GAP) stage. GAP stage is used to determine the average risk of mortality in IPF and higher stage is associated with greater mortality.¹² We did unadjusted and adjusted analyses for all baseline predictors as above. Adjusted analyses are presented as 2 multivariable models. Model 1 was adjusted for age, gender, FVC, DLCO, BMI, oxygen, smoking history, antifibrotics, opioids, palliative care intervention and care model. Model 2 was adjusted for the same variables but age, gender, FVC, and DLCO were replaced by GAP stage which includes all 4 variables. Model residuals were examined for goodness of fit.

Given the nature of the palliative care intervention, which was initiated early when patients were first assessed in the EC and BC models but could be delayed after being referred to palliative care in the QC model, we addressed the potential for immortal time bias as follows: We divided the EC and BC cohorts into 3 groups, those who received palliative care at their initial visit, those who received palliative care at a later visit, and those who never received palliative care, and compared their clinical features. In order to control for potential immortal time bias, we performed a sensitivity analysis examining the subgroup of patients who had palliative care initiated at their first visit in the EC and BC cohorts and compared it to those who did not receive palliative care. For the QC cohort, we considered the referral date to palliative care as time zero for all survival analyses, instead of the date the patient was actually seen by palliative care.

All analyses were conducted using R v4.1.0 (Vienna, Austria).

Ethics Approval

The Health Research Ethics Boards in Alberta (REB Pro00082981); Ontario (HSREB 6025437); and Bristol (IRAS 254044) approved this study with waiver of informed consent due to minimal risk posed and difficulty obtaining informed consent from all participants for a variety of reasons, including loss to follow-up and death.

Results

298 IPF patients were included in this study across the 3 sites (EC n = 95, BC n = 84, QC n = 119). Baseline demographics and clinical characteristics are summarized in Table 2.¹⁰ The median (IQR) age was 71 (67-78) years and Charlson comorbidity index was 4 (3-5). Median (IQR) FVC was 73% (61%-85%), and DLCO was 48% (39%-61%). GAP Stage II or higher was reported in 63% (175/277) of the cohort. More patients in EC (21.8%; 19/87) and BC (30.9%; 26/84) were GAP stage III compared to QC (9.4%; 10/106), indicating higher proportion of severe patients in these cohorts. 67% (200/298) of the cohort received palliative care over the course of the study; this included 96% EC, 100% BC, and only 21% of QC patients (Table 2). More patients in the primary care models (EC and BC) received palliative care and earlier in the disease trajectory (Table S3). Antifibrotics were used in 63% (187/297), 28% (84/298) were on opiates, and 56% (166/298) were on oxygen therapy. Median follow-up time in an interstitial lung disease clinic (first visit to death or end of study) was 16 (5-28) months in the overall cohort. 119 patients died during the study, giving a mortality rate of 40% (43 EC [45%], 52 BC [62%], and 24 QC [20%]; P < .001). Median time to death from the first visit was 14 (5-22) months in the overall cohort. Overall, 76% of decedents received palliative care (data not shown).¹⁰

Table 2.

Patient Characteristics (all Cohorts and by Site).

	All Cohorts (n = 298)	EC (n = 95)	BC (n = 84)	QC (n = 119)
Age, years (median, IQR)	71, 67-78	71, 65-76	74, 68-78	74, 68-79
Gender	M 73.8%; F 26.2%	M 65.3%; F 34.7%	M 85.7%; F 14.3%	M 72.3%; F 27.7%
BMI (median, IQR)	28.3, 25.5-32.0	29.0, 26.0-33.3	26.4, 24.6-28.7	29.9, 26.5-32.2
CCI (median, IQR)	4, 3-5	3, 3-4	4, 3-5	4, 3-5
Ever smoker	78.1% (225/288)	82.0% (73/89)	77.1% (64/83)	75.9% (88/116)
Dyspnea	97.0% (289/298)	95.8% (91/95)	96.4% (81/84)	98.3% (117/119)
MRC score (median, IQR)	3, 2-4	3, 2-4	3, 2-4	2, 2-4
Cough	81.6% (239/293)	79.8% (75/94)	75.9% (63/83)	87.1% (101/116)
Fatigue	32.1% (86/268)	52.9% (37/70)	27.2% (22/81)	23.1% (27/117)
% Pred FVC (median, IQR)	73%, 61%-85%	73%, 63%-83%	69%, 57%-80%	81%, 70%-92%
FVC ≤50%	10.1% (28/278)	9.2% (8/87)	13.1% (11/84)	8.4% (9/107)
% Pred DLCO (median, IQR)	48%, 39%–61%	47%, 38%-59%	38%, 30%-46%	54%, 45%-67%
DLCO ≤ 35%	16.4% (40/243)	20.5% (15/73)	30.4% (21/69)	4.0% (4/101)
6MWD, m (median, IQR)	330, 240-424	328, 210-445	260, 200-340	384, 314-456
Nadir % SpO2 (median, IQR)	87%, 82%-91%	84%, 80%-88%	85%, 82%-89%	91%, 85%-95%
GAP stage I	36.8% (102/277)	36.8% (32/87)	23.8% (20/84)	47.2% (50/106)
GAP stage II	43.3% (120/277)	41.4% (36/87)	45.2% (38/84)	43.4% (46/106)
GAP stage III	19.9% (55/277)	21.8% (19/87)	30.9% (26/84)	9.43% (10/106)
Antifibrotics	60.6% (180/297)	48.9% (46/94)	72.6% (61/84)	61.3% (73/119)
Palliative care^a	67.1% (200/298)	95.8% (91/95)	100.0% (84/84)	21.0% (25/119)
Referral to PC specialist	19.5% (58/298)	3.2% (3/95)	11.9% (10/84)	21.0% (25/119)
Transplant	1 transplanted, 9 listed	0 transplanted, 6 listed	1 transplanted, 1 listed	0 transplanted, 2 listed
Died during followup	39.9% (119/298)	45.3% (43/95)	61.9% (52/84)	20.1% (24/119)

EC = Edmonton centre; BC = Bristol centre; QC = Queens centre; BMI = body mass index; CCI = Charlson Comorbidity Index; MRC = Medical Research Council dyspnea score; FVC = forced vital capacity; DLCO = diffusing capacity for carbon monoxide; 6MWD = 6 minute walk distance; GAP = gender, age, physiology; PC = palliative care.

^aAs per the study definition detailed in the Methods section.

The panels in Figure 1 depict survival based on individual prognostic factors (adjusted according to Model (2) over the 4-year follow-up period. A univariate analysis demonstrated that a lower GAP stage, BMI >30 kg/m², and antifibrotic use reduced the risk of death. Palliative care intervention did not show survival benefit in the unadjusted analysis, but in the adjusted analysis, it reduced risk of death even after accounting for antifibrotic use [model 2 aHR .28 (.12-.65)]. 66.0% of patients who received palliative care were also on antifibrotic therapy, compared to 49.0% of patients who did not receive palliative care (Table S3). Opioid use was associated with an increased risk of death in both models. Oxygen use was not a significant predictor of mortality in either model. BMI >30 kg/m² had a protective effect on survival. The concordance index for the model was .76 (se .03).

Figure 1.

Adjusted Kaplan-Meier curves for various prognostic factors: (A) GAP stage, (B) Smoking, (C) BMI >30, (D) Oxygen therapy, (E) Antifibrotic therapy, (F) Opiate therapy, (G) Palliative care, and (H) SIte. Survival curves are adjusted according to model 2. There are 298 at risk at t = 0, 152 (51%) at risk at 1 year, 70 (23%) at 2 years, 36 (12%) at 3 years, and 16 (5%) at 4 years of follow-up. The EC and BC curves in panel H are overlapping because their corresponding HR was 1.01. N = no; Y = yes; GAP = gender, age, physiology; BMI = body mass index; PC = palliative care; EC = Edmonton centre; BC = Bristol centre; QC = Queens centre.

As variability in intervention duration may lead to favorable bias in survival analysis, we performed a sensitivity analysis based on time of onset of palliative care. Among the 200 patients who received palliative care as per the study definition, 76% (152/200) were initiated on it at the first visit. Compared to patients who received palliative care at the first visit, more patients who received palliative care later (visit 2 onwards) were in GAP stage I, corresponding to a lower disease severity (Table S3). In order to minimize bias further we repeated the univariate analysis with patients who were initiated on palliative therapies at first visit only, in other words excluding those who received palliative care later. The signal of benefit for palliative care intervention is preserved but not statistically significant [aHR .55 (.17-1.72)] (Table S1). If further stratified by GAP stage, the signal of benefit is only preserved in the GAP stage I patients (Table S2).

Discussion

This is the first study to describe various palliative care models in patients with IPF. In our international multicenter cohort, 67% received palliative care, with 76% being initiated at the first visit. This is in stark contrast to previous studies; Lindell and colleagues noted that only 14% of IPF decedents received palliative care, with a majority occurring in the last month of life.¹³ The early onset of palliative care in our cohort is consistent with the recommendations in the 2022 ATS IPF guidelines.¹ In addition, we described 2 clinic models that integrated primary palliative approach with routine care in line with the 2022 ATS policy paper on palliative care for chronic respiratory diseases.⁴ We are the first to report that a primary palliative approach can lead to more IPF patients receiving palliative care and earlier in the disease trajectory. Our hypothesis that palliative care can improve survival in IPF is also supported by our results, which showed that palliative care reduced the risk of death over the 4-year follow-up period. The pattern persisted after adjusting for known confounders including antifibrotic therapy and GAP stage. Our findings are further supported by a recent single center study of deceased fibrotic ILD patients demonstrating that patients who received palliative care had a significantly longer median survival at 23.9 months compared to 11.4 months in the usual care group.¹⁴ Regardless, given the retrospective nature of our study, our results should be formally validated in a controlled prospective study.

The improved survival in QC compared to the EC and BC models (Figure 1(h)) could be attributed to more patients seen in earlier disease stages rather than the palliative delivery model itself. 91% of QC patients were in GAP stage I and II, resulting in an incomplete control of the confounding effect of GAP stage in the multivariable Cox models. This is a reflection of local referral patterns in the absence of community respirology clinics in that area. Interestingly, of all the variables analyzed in our model, palliative care was the strongest predictor of survival. This suggests that the survival benefit of palliative care stands, regardless of the care model used. Subgroup analysis of those who received palliative care at the first visit (with more advanced disease) also demonstrated a similar benefit.

Immortal time bias, which can occur when participants do not live long enough to receive an intervention, is important to consider in survival studies when a therapeutic intervention is evaluated. It is unlikely that immortal time bias played a significant role in our results because in the EC and BC models palliative care was initiated in most patients at the first visit, and in the QC model we defined the date of referral to palliative care as time zero rather than the date patients were actually seen by palliative care. We also attempted to mitigate the impact of immortal time bias with a subgroup analysis of patients who received palliative care at the first visit compared to those who did not receive palliative care at all. Although the benefit to survival was no longer statistically significant due to the reduced sample size, the magnitude of the palliative care benefit persisted.

The mechanisms by which palliative care may influence survival are unclear. Educating patients regarding illness trajectory and assessing health beliefs and preferences to promote shared decision-making can lead to more realistic treatment goals and improved symptom management. Intentional and timely management of dyspnea and cough, independent predictors of survival in IPF, may improve survival.^15-18 Palliative care models in oncology highlight how integrated palliative care can achieve systematic symptom screening for timely care and consolidation of care teams for improved continuity and efficiency.¹⁹ Patients with IPF have similarly complex care needs and desire comprehensive care in 1 location. A palliative referral-based model does not address this need and may lead to inequitable access, as demonstrated by our study, and fragmented care.²⁰ On the other hand, clinics may not have all the necessary resources like allied health services to meet patient needs, and clinicians may not have the training to deliver primary palliative approach, and thus it is imperative to integrate community resources and other expertise to help bridge personnel limitations. This not only improves access, but also promotes disease self-management in the community.^21,22 Ideally, a majority of a patient’s care needs would be addressed within the framework of the interstitial lung disease clinic, but there is a role for specialist referral depending on the limitations of each individual program. Our proposed palliative care model is summarized in Figure 2.

Figure 2.

Model for integrated palliative care in IPF. ILD = interstitial lung disease, PC = palliative care, mMRC = Modified Medical Research Council Dyspnea Scale, EDI = Edmonton Dyspnea Inventory,²⁹ QOL = quality of life, 6MWT = 6-minute walk test, ACP = advance care planning, EOL = end of life. ¹In select cases only, eg, refractory symptoms, hospice referral, challenging advance care planning discussions, etc.

Patients who received opioids had worse survival after adjusting for covariates (Figure 1(i)). This is not in keeping with previous work by Bajwah and colleagues showing no increase in mortality or hospitalizations in patients on opioids.²³ This discrepancy may in part be because their study included non-idiopathic pulmonary fibrosis patients, and many may have been on opioids for reasons other than dyspnea. All patients in our study were prescribed opioids for dyspnea relief and likely represent a subset of patients with higher mortality risk by virtue of having more dyspnea.¹⁵ While it is important to be cognizant of adverse effects, discomfort with opioid prescribing is a major barrier to adequate symptom management in IPF.

Other previously described prognostic factors such as GAP stage, BMI, and antifibrotic therapy continued to significantly impact survival (Figure 1(a), 1(c) and 1(e)) (Table 3).^15,24-26 Overall, our study population was similar to previously described cohorts, supporting a degree of generalizability to our results.

Table 3.

Multivariable Survival Analysis for all Study Subjects.

Variable	Unadjusted HR (95% CI)	Adjusted HR (95% CI) MODEL 1	Adjusted HR (95% CI) MODEL 2
Age	1.01 (.99-1.03)	1.01 (.99-1.03)
Male	1.49 (.91-2.42)	1.36 (.86-2.18)
FVC	.95 (.94-.97)	.96 (.95-.98)
DLCO	.96 (.95-.98)	.98 (.96-.99)
GAP 2 vs 1 (control)	2.59 (1.50-4.45)		2.09 (1.51-4.56)
GAP 3 vs 1 (control)	4.84 (2.51-8.00)		2.09 (1.27-3.45)
Smoking Y vs N (control)	.81 (.52-1.29)	.79 (.48-1.29)	.95 (.63-1.45)
BMI >30 vs < 30 (control)	.47 (.29-.74)	.44 (.26-.75)	.47 (.30-.84)
Oxygen Y vs N (control)	1.25 (.78-2.01)	1.09 (.46-2.57)	1.15 (.59-2.25)
Anti-fibrotic Y vs N (control)	.42 (.27-.65)	.59 (.37-.95)	.56 (.37-.84)
Opioid Y vs N (control)	2.47 (1.64-3.72)	1.83 (1.07-3.13)	2.11 (1.30-3.43)
PC intervention Y vs N (control)	.80 (.47-1.35)	.32 (.11-.87)	.28 (.12-.65)
PC model EC vs QC (control)	1.25 (.72-2.19)	1.27 (.73-2.28)	2.68 (1.31-5.51)
PC model BC vs QC (control)	1.58 (.94-2.65)	1.45 (.79-3.11)	2.31 (1.13-4.71)

HR = hazard ratio; CI = confidence interval; FVC = forced vital capacity; DLCO = diffusing capacity for carbon monoxide; GAP = gender, age, physiology; BMI = body mass index; PC = palliative care; EC = Edmonton center; BC = Bristol center; QC = Queens center.

The bolded are statistically significant hazard ratios where the CI does not include 1.

Strengths and Limitations

The biggest strength of our study is utilization of data from a real-world setting with varying resources, clinic structure, and palliative care models. We acknowledge and adjust for known confounders including antifibrotics, GAP stage, and immortal time bias using statistical methods, and still demonstrate survival benefit of palliative care. In terms of the limitations of our study, a retrospective design has inherent biases from data not being collected specifically to address our question. The intervention was not uniform across study groups, which limits the assessment of the best method to apply palliative care, but this is also a strength as it demonstrates that real-world clinics are likely to have an impact on survival as we showed, despite different palliative care models. Future prospective studies utilizing a standardized model will hopefully shed more light on the best palliative care models for IPF.

We were not able to randomize our patients due to the retrospective nature of our study, but it is also important to note that randomization by disease severity alone may not ensure that groups are equivalent. The palliative care needs of individual patients are idiosyncratic and impacted by varying degrees of physical, psychosocial, and spiritual suffering and thus do not necessarily correlate with easily measured indices of disease severity. In addition, heterogeneity in time of onset and total exposure time to palliative interventions is inherent as shown in our study. Thus, our work highlights the potential challenges with conducting a controlled palliative care study in this population.

Implications for Future Research and Clinical Practice

Patients with IPF suffer from a high burden of symptoms and have complex care needs which escalate as they approach end-of-life. IPF registry studies have consistently observed GAP stage to correlate strongly with mortality.^15,24-26 Many GAP stage II and III patients continue to experience large drops in FVC and DLCO compared to GAP stage I patients despite antifibrotic use.²⁷ Thus, the implication that a survival benefit from palliative care may complement that of antifibrotic therapy regardless of GAP stage is clinically relevant, and, if confirmed, may lead to a paradigm shift in the practice of pulmonologists. In view of the benefits that have been demonstrated with antifibrotics in progressive pulmonary fibrosis, it would be interesting to see if the benefits of palliative care can be replicated in this group of patients.^1,28 Well-designed prospective trials are needed to further clarify the true impact of palliative care and best care models in pulmonary fibrosis. We have laid the foundation for such trials by demonstrating a signal of survival benefit and highlighting the challenges in trial design for palliative care intervention.

Supplemental Material

Supplemental Material - The Impact of Integrated Palliative Care on Survival in Idiopathic Pulmonary Fibrosis: A Retrospective Multicenter Comparison

Supplemental Material for The Impact of Integrated Palliative Care on Survival in Idiopathic Pulmonary Fibrosis: A Retrospective Multicenter Comparison by Jenny Lu-Song, MD, FRCPC, Jeffrey A. Bakal, PhD, PStat, Sarah Younus, BSc, MPH, Onofre Moran-Mendoza, MD, PhD, Ingrid Harle, MD, CCFP, Michelle Morales, RN, Naomi Rippon, RN, Shaney L. Barratt, MD, Huzaifa Adamali, MD, and Meena Kalluri, MD, FCCP in American Journal of Hospice and Palliative Medicine®

Footnotes

Acknowledgments

We acknowledge Drs. Sharina Aldhaheri and Ghadah Alrehaili for their contributions to data collection for this study. Study data were collected and managed using REDCap¹ electronic data capture tools hosted.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Alberta Respiratory Center, grant number [SC ZJ169] and Women and Children’s Health Research Institute at the University of Alberta.

Ethical Approval

This study was approved by the Health Research Ethics Boards in Alberta (REB Pro00082981); Ontario (HSREB 6025437); and Bristol, UK (IRAS 254044).

Contributorship

Data curation S.Y., N.R., H.A.; Formal analysis J.L., J.A.B., M.K.; Writing-original draft J.L.; Figures J.L., J.A.B.; Methodology I.H., O.M.-M., S.L.B., H.A., M.K.; Conceptualization M.K.; Resources M.K.; Supervision M.K. All authors contributed to drafting, revising, and finalizing the manuscript. All authors have read and agreed to the published version of the manuscript.

ORCID iDs

Jenny Lu-Song

Meena Kalluri

Supplemental Material

Supplemental material for this article is available online.

Note

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