Abstract
Background:
Many lung transplant recipients fail to derive substantial improvements in functioning, health-related quality of life (HRQL), or long-term survival. Identifying modifiable factors that drive poor clinical outcomes after lung transplant is key to advancing the field. Sleep may represent one key, though rarely examined behavioral factor.
Methods:
As part of a larger longitudinal cohort study, 141 lung transplant recipients completed the 6-item Medical Outcomes Study (MOS) Sleep Scale at a single point in time concurrent with a broader survey of patient reported outcomes, including measures of functioning/disability, cognitive function depressive symptoms, generic and respiratory-specific HRQL, and health utilities. Participants also completed frailty assessments by the Short Physical Performance Battery and Fried Frailty Phenotype (FFP). Time to onset of chronic lung allograft dysfunction (CLAD) and death were derived from pulmonary function and medical record review. The MOS Sleep Scale yields a summary Sleep Problems Index (SPI); we also derived a 2-item insomnia-specific subscale. We tested associations between SPI and Insomnia on PROs and frailty by linear regression and analysis of covariance (ANCOVA) adjusting for age, sex, transplant indication and lung function. We fit Cox proportional hazards models to test the associations between SPI and Insomnia on time to CLAD and death adjusted for age, sex, and transplant indication.
Results:
We found that worse sleep by either SPI or our insomnia subscale was associated with worse depressive symptoms, cognitive function, generic and respiratory specific HRQL, physical disability, and health utilities (all p < 0.01). Poorer SPI sleep and insomnia were associated with FFP defined frailty. Notably, after adjusting for age, sex, and transplant indication, those in the worst quartile of SPI and insomnia were at markedly increased risk of CLAD (HR 2.18; 95%CI: 1.22, 3.89; p=0.01 for SPI and HR 1.96; 95%CI 1.09, 3.53; p=0.03 for insomnia). Worsening in SPI was also associated with mortality (HR: 1.29; 95%CI: 1.05, 1.58; p=0.01).
Conclusion:
Poor self-reported sleep after lung transplant appears to be a novel predictor of a range of key patient reported outcomes, physical frailty, CLAD, and death. Further research investigating the prevalence and changes in sleep during transplant is warranted as this data may inform intervention strategies to improve sleep and lung transplant outcomes.
Introduction:
Lung transplantation aims to extend survival, relieve disability, and improve health-related quality of life (HRQL).1,2 Although many do well, perioperative complications are increasing, one third of patients die within the first three post-operative years, and 20–30% of survivors do not report substantive improvements in physical functioning or HRQL.1,3–8 In response to these frequent and increasing barriers to transplant success, identifying modifiable behavioral processes that could positively support transplant recovery and improve quality of life is needed. Sleep may be one potential pathway, though its impacts on post lung transplantation outcomes are not well characterized.
Sleep plays a fundamental role in physical health, well-being, and recovery from illness. The potential for sleep disturbances after lung transplant is high, particularly during the dynamic peri- and early post-operative period that is increasingly complicated by Primary Graft Dysfunction (PGD; a form of acute post-transplant lung injury), delirium, prolonged hospital stays, post-traumatic stress, readmissions, and intense immunosuppression.6,9–13 The consequences of sleep disturbances on transplant success are likely substantial, though largely unknown. Outside the lung transplant context, sleep disturbance is associated with impaired cognition, mood dysregulation, particularly depression, and risk of frailty as well as immune dysfunction, all of which are relevant to lung transplant and have previously been shown to predict disability, chronic lung graft dysfunction (CLAD), and lung transplant mortality. A few studies to date have examined sleep after lung transplantation.14 Depending on the sleep measure used by these studies, between 30–74% of lung transplant recipients reported sleep disturbances. Despite this high prevalence of disturbed sleep, very few studies have examined the impact of sleep disturbances on clinical outcomes.14
To address these gaps, in this single-center cohort study we sought to test whether disturbed sleep after lung transplant is associated with disability, HRQL, and frailty. We additionally aimed to test whether disturbed sleep was associated with risk of CLAD onset and mortality.
Methods:
Study Design and participants.
We performed this study among a subset of participants of the UCSF “Breathe Again” cohort. “Breathe Again” was a single-center longitudinal prospective cohort study of 259 adults aged 18 years or older who underwent first-time lung transplantation between 2010 and 2017 which, amongst other aims, was focused on studying the impact of lung transplantation on patient-centered outcomes, including HRQL (Figure 1). This cohort is described in more detail elsewhere.15 During Breathe Again, a convenience subset sample of 141 participants were recruited to complete a pilot survey that ultimately yielded a novel lung transplant specific quality of life measure.16 Although not retained in the final measure, the initial pilot survey included a validated sleep disturbance scale. Although Breathe Again participants completed surveys before and repeatedly after transplant out to three years, the pilot survey was completed once. It is this subset of 141 participants who completed this sleep survey along with the larger Breathe Again study battery that included measures of frailty and patient reported outcomes that forms the group we analyzed herein. Breathe Again was approved by our Institutional Review Board and participants provided written informed consent.
Figure 1.
Study Flow
Conceptual Model:
We adapted a conceptual disablement model developed by sociologist Saad Nagi.17,18 Nagi posited that as diseases progress, worsening organ dysfunctions lead to impairments. Nagi defined “impairments” as quantifiable measures of organ dysfunction (e.g., pulmonary function testing, sleep measures, or assessments of frailty). As impairments worsen, disability—the impaired functioning of organs or persons in their lived environment—progresses, disability in turn, leads to poorer HRQL and death. We hypothesized that disturbed sleep might be an impairment after lung transplant that could be a risk factor for disability, depressive symptoms, poorer cognitive functioning and HRQL, frailty and death. Further, given the fundamental role of sleep in immunity as well as the possible link between cognitive impairment and depression with chronic lung allograft dysfunction (CLAD)19,20, we considered that disturbed sleep could also be a risk factor for CLAD.
Measures of Sleep:
The 84-item pilot survey included the Medical Outcomes Study Sleep Scale (MOS-Sleep).21 The MOS-Sleep is a 6-item validated measure of sleep disturbance which can be scored as an overall Sleep Problems Index (SPI) The SPI ranges from 0 to 100 with higher scores denoting worse sleep disturbances. To further understand the specific impact of insomnia, we also created an insomnia-specific subscale based on the face validity of two MOS-Sleep items (e.g., trouble falling asleep). We used these scales to test the association between both sleep disturbances and insomnia on our outcomes of interest.
Outcome Variables of Interest:
All participants in Breathe Again completed a complex study battery of patient reported battery (PROs) and frailty before and at 3, 6, 12, 18, 24, and 36 months after transplant. We selected the study measures completed after transplant concurrent to the pilot study for our outcome variables of interest.
Patient reported outcomes
The survey measures included instruments to assess functioning/disability, depressive symptoms, generic and respiratory-specific HRQL, and health utilities. In addition to the Sleep Scale, the pilot survey also included the original Medical Outcomes Study Cognitive Functioning Scale (MOS-Cog)21 that was rescaled and adapted to become the Lung Transplant Quality of Life Cognitive Limitations subscale (LTQOL-Cog).16 Functioning/disability was assessed by the Lung Transplant Valued Life Activities Scale (LT-VLA; 15 items; range 0–3; Minimally important difference [MID]: 0.3; higher scores denote worse disability. Depressive symptoms were quantified by the Geriatrics Depression Scale-15 (GDS; 15 items; range 0–15; MID: 1.65; higher scores denote worse depressive symptoms). Generic HRQL was evaluated by the RAND Medical Outcomes Study Short Form-36 Physical and Mental Composite Summary scales (SF-36 PCS and MCS; 36 items; range 0–100; MID 5; lower scores denote worse HRQL). Respiratory-specific HRQL was assessed with the Airways Questionnaire 20-Revised (AQ20-R; 20 items; range 0–20; MID 1.75; higher scores denote worse HRQL). Health utility was assessed by the EuroQol 5D (EQ5D; 5 items; range −0.11 – 1.0; MID 0.06; higher scores denote better health utility). Finally self-reported cognitive functioning was assessed by the LTQOL-Cog (6 items; range 1–5; MID: 0.47; higher scores denote worse cognitive functioning).
Frailty
We assessed frailty by two well-validated frailty measures that emphasize physical functioning. The Short Physical Performance Battery (SPPB) is a 3-component battery of lower extremity performance measures that includes gait speed, chair stands, and balance 22,23. Each measure is scored from 0–4 with an aggregate score ranging from 0–12. Lower SPPB scores reflect increased frailty. The Fried Frailty Phenotype (FFP) is an aggregate score of five constructs: shrinking, exhaustion, low physical activity, slowness, and weakness.24 The FFP ranges from 0–5 with higher scores reflecting increased frailty.
Chronic Lung Allograft Dysfunction (CLAD)
CLAD was defined as 20% decline in forced expiratory volume in 1 second (FEV1) from post-transplant baseline that persisted for at least 3 months.25 Time to CLAD was calculated as the number of days from the date of lung transplantation until the first 20% sustained drop in FEV1.
Mortality:
Dates of death were obtained through medical record review. Survival time was calculated as the number of days from the date of lung transplantation until date of death.
Other measurements
Demographic and clinical variables were abstracted from medical records. Variables included age, gender, race/ethnicity, diagnostic indication for transplant, transplant type (single versus bilateral versus heart-lung) and all measures of lung function (forced expiratory volume in one second [FEV1; liters] and forced vital capacity [FVC; liters] after transplant.
Analytic Approach
The SPI and the insomnia subscale do not have established thresholds to define disturbed sleep or insomnia. Conceptually, any impairment in sleep could plausibly be associated with how people feel and function. Therefore, analyzed the SPI and insomnia subscale on a continuous scale (per MID worsening) and as binary variables comparing the worst quartile to the rest. Some scales, such as the SF36 and EQ5D, have established anchor-based MIDs. For many PROs, however, anchor-based MIDs are not available. In these cases, distribution-based methods are employed with one-half the observed standard deviation being the most common.26
To investigate the associations between SPI and Insomnia on PROs and frailty we used linear regression and analysis of covariance (ANCOVA) adjusted for age, sex, transplant indication and lung function. We fit Cox proportional hazards models to evaluate the associations between SPI and Insomnia with time to CLAD and time to death adjusted for age, sex, and transplant indication. Nonproportionality was tested using Schoenfeld residuals. We used Kaplan Meier methods to visualize the relationship association between the SPI and insomnia defined categorically with CLAD and death. We used the Survival Area Plot method to visualize the unadjusted association between the SPI and insomnia as continuous variables with CLAD and death.27
Analyses were performed using SAS (version 9.4, SAS Institute), and R (version 4.3.1, R Foundation).
Results:
During the Breathe Again study period, of the 392 participants enrolled, 259 underwent lung transplant. Of these, 141 completed the pilot survey that included MOS-Sleep and formed our study cohort (Table 1 and Figure 1). These 141 participants were 43% female with a mean age of 58 years (standard deviation [SD] ± 13); 75% were non-Hispanic white. Most participants underwent transplant for pulmonary fibrosis (72%) followed by non-suppurative obstructive lung diseases (15%). Participants completed the study battery used in these analyses at a median of 1.5 years after transplant (0.6, 2.4). Over the study period, 52 (37%) developed CLAD and 20 (14%) died.
Table 1.
Participant characteristics.
| No. of subjects | N = 141 |
|---|---|
| Woman, No. (%) | 60 (42.6) |
| Age, mean ± SD | 57.6 ± 12.7 |
| Age, range | 21.5 – 74.3 |
| Race/Ethnicity, No. (%) | |
| White, non-Hispanic | 105 (74.5) |
| White, Hispanic | 17 (12.1) |
| Black | 10 (7.1) |
| Asian | 8 (5.7) |
| American Indian | 1 (0.7) |
| Diagnostic indication for transplant, No. (%) | |
| Group A (e.g. Obstructive lung disease) | 21 (14.9) |
| Group B (e.g. Pulmonary Hypertension) | 5 (3.6) |
| Group C (e.g. Suppurative lung disease) | 13 (9.2) |
| Group D (e.g. Pulmonary Fibrosis) | 102 (72.3) |
| BMI (kg/m2), mean ± SD | |
| FEV1, mean (SD) | 2.5 ± 0.9 |
| FEV1 % predicted, mean (SD) | 80.5 ± 24.2 |
| FVC, mean (SD) | 3.1 ± 1.0 |
| FVC % predicted, mean (SD) | 77.1 ± 20.1 |
| Deaths within 5 years after transplant, n (%) | 20 (14.2) |
| CLAD* within 5 years after transplant, n (%) | 52 (36.9) |
| Transplant type, No. (%) | |
| Bilateral | 129 (91.5) |
| Single | 11 (7.8) |
| Heart/Lung | 1 (0.7) |
| Time post-transplant when survey was completed, Median (IQR) | 1.5 (0.6, 2.4) |
| Time point post-transplant when survey was completed, range (years) | 0.2 – 4.1 |
| ≥ 0 & ≤ 1 | 57 |
| > 1 & ≤ 2 | 43 |
| > 2 & ≤ 3 | 23 |
| >3 | 18 |
BMI = Body Mass Index; FEV1 = Forced Expiratory Volume in the first second; FVC = Forced Vital Capacity; CLAD = Chronic Lung Allograft Dysfunction. Data are presented as number of patients (percentage) or mean ± standard deviation.
We found consistent and statistically significant associations between disturbed sleep, either as a continuous variable or dichotomized as worst versus the top three quartiles, and all patient-reported outcomes of interest, after adjusting for age, sex, transplant indication, and allograft function (Table 2). For example, for each MID worsening in the Sleep Problems Index (SPI) participants reported worse depressive symptoms (GDS: 0.63; 95%CI: 0.32, 0.94; p < 0.01; MID = 1.65), generic mental HRQL (SF36 MCS: −2.04; 95%CI: −2.75, −1.33; p <0.01; MID =5), and health utilities (EQ5D: −0.03; 95%CI: −0.04, −0.02; p<0.01; MID =0.06). Similarly, participants in worst quartile of SPI reported substantially worse respiratory-specific HRQL (AQ20-R: 3.53; 95%CI: 1.79, 5.26; p<0.01; MID = 1.75), cognitive functioning (LTQOL-Cog: 0.63; 95%CI: 0.31, 0.96; p <0.01; MID = 0.47), and generic physical HRQL (SF36-PCS: −6.99; 95%CI: −10.67, −3.30; p <0.01; MID = 5).
Table 2:
Association between sleep problem index with patient-centered outcomes.
| Outcome Variable (MID) | Conceptual Domain measured | Parameter Estimate* SPI MID = 8 |
P-value |
|---|---|---|---|
|
LT-VLA MID = 0.3 |
Physical functioning/disability | Per MID worsening: 0.05 (0.02, 0.09) |
<0.01 |
| Worst quartile: 0.16 (−0.01, 0.32) |
0.06 | ||
|
GDS MID = 1.65 |
Depression | Per MID worsening: 0.63 (0.32, 0.94) |
<0.01 |
| Worst quartile: 2.35 (0.97, 3.73) |
<0.01 | ||
|
Cognitive Limitations MID = 0.47 |
Cognitive Impairments | Per MID worsening: 0.17 (0.10, 0.24) |
<0.01 |
| Worst quartile: 0.63 (0.31, 0.96) |
<0.01 | ||
|
SF36PCS MID = 5 |
Generic physical HRQL | Per MID worsening: 1.93 (−2.7, −1.15) |
<0.01 |
| Worst quartile: −6.99 (−10.67, −3.30) |
<0.01 | ||
|
SF36MCS MID = 5 |
Generic mental HRQL | Per MID worsening: −2.04 (−2.75, −1.33) |
<0.01 |
| Worst quartile: −8.64 (−11.96, −5.31) |
<0.01 | ||
|
AQ20R MID = 1.75 |
Respiratory-specific | Per MID worsening: 0.91 (0.52, 1.30) |
<0.01 |
| Worst quartile: 3.53 (1.79, 5.26) |
<0.01 | ||
|
EQ5D MID = 0.06 |
Health utility | Per MID worsening: −0.03 (−0.04, −0.02) |
<0.01 |
| Worst quartile: −0.11 (−0.17, −0.05) |
<0.01 | ||
|
SPPB MID = 1 |
Physical Frailty | Per MID worsening: −0.07 (−0.22, 0.08) |
0.34 |
| Worst quartile: 0.09 (−0.60, 0.79) |
0.79 | ||
|
FFP MID = 1 |
Physical Frailty | Per MID worsening: 0.13 (0.04, 0.21) |
<0.01 |
| Worst quartile: 0.52 (0.14, 0.90) |
0.01 |
Adjusted for age, sex, transplant indication, and FEV1
MID = minimally important difference; LT-VLA = Lung Transplant Valued Life Activities scale; GDS = Geriatric Depression Scale; SF36PCS = Short Form 36 Physical Component Summary Scales; SF36MCS = Short Form 36 Mental Component Summary Scales; AQ20R = Airways Questionnaire 20- Revised; EQ5D = EuroQoL 5 Dimensions (5-level version); SPPB= Short Physical Performance Battery; FFP = Fried Frailty Phenotype.
Insomnia symptoms were also strongly associated with our PROs of interest after adjusting for prespecified confounders (Table 3). For each MID worsening in the MOS-Sleep insomnia subscale, participants reported worse depressive symptoms (GDS: 0.58; 95%CI: 0.25, 0.90; p < 0.01; MID = 1.65) and respiratory specific HRQL (AQ20-R: 1.06; 95%CI: 0.67, 1.44; p<0.01; MID = 1.75). Moreover, those in the worst quartile reported worse generic physical HRQL (SF36-PCS: −6.57; 95%CI: −10.28, −2.86; p <0.01; MID = 5) and cognitive functioning (LTQOL-Cog: 0.71; 95%CI: 0.39, 1.03; p <0.01; MID = 0.47).
Table 3:
Association between insomnia and patient-centered outcomes
| Outcome Variable (MID) | Conceptual Domain measured | Parameter Estimate* Insomnia subscale MID = 12 |
P-value |
|---|---|---|---|
|
VLA MID = 0.3 |
Physical functioning/disability | Per MID worsening: 0.05 (0.02, 0.09) |
<0.01 |
| Worst quartile: 0.22 (0.06, 0.38) |
0.01 | ||
|
GDS MID = 1.65 |
Depression | Per MID worsening: 0.58 (0.25, 0.90) |
<0.01 |
| Worst quartile: 2.33 (0.96, 3.70) |
<0.01 | ||
|
Cognitive Limitations MID = 0.47 |
Cognitive impairments | Per MID worsening: 0.18 (0.11, 0.25) |
<0.01 |
| Worst quartile: 0.71 (0.39, 1.03) |
<0.01 | ||
|
SF36PCS MID = 5 |
Generic physical HRQL | Per MID worsening: −1.76 (−2.59, −0.93) |
<0.01 |
| Worst quartile: −6.57 (−10.28, −2.86) |
<0.01 | ||
|
SF36MCS MID = 5 |
Generic mental HRQL | Per MID worsening: −1.98 (−2.74, −1.22) |
<0.01 |
| Worst quartile: −8.26 (−11.62, −4.90) |
<0.01 | ||
|
AQ20R MID = 1.75 |
Respiratory-specific | Per MID worsening: 1.06 (0.67, 1.44) |
<0.01 |
| Worst quartile: 4.47 (2.87, 6.07) |
<0.01 | ||
|
EQ5D MID = 0.06 |
Health utility | Per MID worsening: −0.02 (−0.04, −0.01) |
<0.01 |
| Worst quartile: −0.08 (−0.14, −0.02) |
<0.01 | ||
|
SPPB MID = 1 |
Physical Frailty | Per MID worsening: −0.05 (−0.21, 0.11) |
0.54 |
| Worst quartile: 0.13 (−0.57, 0.82) |
0.72 | ||
|
FFP MID = 1 |
Physical Frailty | Per MID worsening: 0.12 (0.03, 0.21) |
<0.01 |
| Worst quartile: 0.43 (0.03, 0.82) |
0.03 |
Adjusted for age, sex, transplant indication, and FEV1
MID = minimally important difference; LT-VLA = Lung Transplant Valued Life Activities scale; GDS = Geriatric Depression Scale; SF36PCS = Short Form 36 Physical Component Summary Scales; SF36MCS = Short Form 36 Mental Component Summary Scales; AQ20R = Airways Questionnaire 20-Revised; EQ5D = EuroQoL 5 Dimensions (5-level version); SPPB= Short Physical Performance Battery; FFP = Fried Frailty Phenotype.
Poorer sleep and insomnia symptoms were also generally associated with worse physical functioning and frailty by FFP (Tables 2 and 3). For example, each MID worsening in SPI was associated with disability (LT-VLA: 0.05; 95%CI: 0.02, 0.09; p <0.01; MID = 0.3) and frailty (FFP: 0.13; 95%CI: 0.04, 0.21; p <0.01). Each MID worsening in insomnia was also associated with disability (LT-VLA: 0.05; 95%CI: 0.02, 0.09; p <0.01; MID = 0.3) and frailty (FFP: 0.12; 95%CI: 0.03, 0.21; p <0.01). Sleep did not appear to be significantly associated with frailty by SPPB.
Finally, poorer sleep and insomnia were also associated with the development of CLAD and, death after lung transplantation. For example, after adjusting for age, sex, and transplant indication, each MID worsening in sleep by SPI was associated with a 1.14-fold increased risk of CLAD (HR 1.14; 95%CI: 1.00, 1.30; p =0.04) and a 1.29-fold increased risk of death (HR: 1.29; 95%CI: 1.05, 1.58; p = 0.01). The direction and magnitude of the associations were relatively consistent across analyses, although the strength of associations between binary SPI and insomnia with death after transplant did not reach statistical significance in tests. Results are detailed in Table 4 and in Figures 2 and 3.
Table 4.
Association between sleep problem index and insomnia subscale with CLAD and death adjusting for age, sex, and transplant indication.
| Time to CLAD Hazards Ratio | Time to Death Hazards Ratio | ||
|---|---|---|---|
|
Sleep Problem Index (SPI) SPI MID = 8 |
Per MID worsening | 1.14 (1.00, 1.30) p = 0.04 |
1.29 (1.05, 1.58) p = 0.01 |
| Worst quartile | 2.18 (1.22, 3.89) p = 0.01 |
2.12 (0.84, 5.35) p= 0.11 |
|
|
Insomnia Insomnia MID = 12 |
Per MID worsening | 1.21 (1.05, 1.39) p = 0.01 |
1.16 (0.93, 1.44) p = 0.20 |
| Worst quartile | 1.96 (1.09, 3.53) p = 0.03 |
0.68 (0.22, 2.10) p = 0.51 |
Figure 2.
Kaplan Meier estimate of the association of disturbed sleep by MOS-Sleep Problems Index (panel A) and insomnia-specific subscale (panel B) defined as categorical variables on time to CLAD. Survival Area Plot illustrating the association of disturbed sleep by MOS-Sleep Problems Index (panel C) and insomnia-specific subscale (panel D) defined as continuous variables on time to CLAD.
Figure 3.
Kaplan Meier estimate of the association of disturbed sleep by MOS-Sleep Problems Index (panel A) and insomnia-specific subscale (panel B) defined as categorical variables on time to death. Survival Area Plot illustrating the association of disturbed sleep by MOS-Sleep Problems Index (panel C) and insomnia-specific subscale (panel D) defined as continuous variables on time to death.
Discussion
In this single-center study of 141 lung transplant recipients, we found that disturbed sleep and insomnia were strongly associated with worse cognitive functioning, depressive symptoms and poorer HRQL. We also found that disturbed sleep and insomnia were associated with disability and frailty as well as increased risk of CLAD and all-cause mortality. These associations remained statistically significant after accounting for important covariates, including allograft function.
Growing evidence emphasizes the critical role sleep plays in physical and mental health outcomes in healthy and medically-ill populations. However, little is known about the impact sleep has on patient-reported outcomes in lung transplant recipients. In this regard, poorer sleep has been associated with greater depressive symptoms, and poorer scores on the general mental health component of the HRQL in lung transplant patients.28–31 Outside the lung transplant setting, sleep is associated with frailty risk and cognitive functioning.32–36 For example, a pooled analysis of the existing literature supports short and excessively long sleep duration, as well as a longer sleep onset latency as significant risk factors for development of frailty.35 Further, experimental and epidemiologic data supports consistent links between poor sleep and impaired cognition, including difficulties in executive functioning, memory, and attention processes.37 Cognitive dysfunction following major cardiothoracic and lung transplant surgery is common and, in lung transplant is associated with increased risk of mortality.19,38–40 There is increasing concern that sleep disturbance during this important recovery period may contribute to future cognitive outcomes.
The cross-sectional nature of these findings precludes causal inference testing or mechanisms. While the associations between disturbed sleep and cognitive, psychological, and HRQL outcomes may be more direct, the associations with physical impairments including frailty and CLAD may be less-so. Behaviorally-driven factors such as impaired motivation to exercise regularly and consume a balanced diet could drive disability and frailty whereas depression and cognitive impairment could impact medication adherence, thereby increasing risk of CLAD. It is also plausible that sleep’s essential role in immune system dysregulation from disturbed sleep via enhanced systemic inflammation41,42 and accelerated biological aging43,44 could identify biological links between sleep and disability, frailty and CLAD. While plausible, these potential explanations should be considered speculative until more definitive research is performed.
The present study has several notable strengths. First, this is one of the largest studies of sleep and patient reported outcomes in lung transplant. A recent scoping review of the literature examining sleep quality following transplant identified nine original research studies with sample sizes ranging from 20 to 219 participants. This study is the second largest to dateand the first to link measures of sleep quality to outcomes beyond mental health. Notably, this is the first-in-field study to identify associations between sleep and physical disability, frailty, CLAD, and even death after lung transplant, raising the possibility that sleep disturbance may serve as an important modifiable behavioral pathway to improve quality life and clinical outcomes among lung transplant recipients.
Despite the strengths of this study, there were also important limitations. This cohort was not originally designed to study the impact of sleep disturbances on outcomes after lung transplantation. The convenience sampling strategy resulted in a group of participants whose sleep was queried over a range of early post-operative years. We lack information on how sleep disturbances change over the early post-operative period. In prior work by our team and by others, other factors after transplant improve for many in the first post-operative year. Whether those with persistently poor sleep experience different clinical outcomes than those whose sleep worsens or improves over time is unknown. Further, our measures of sleep were collected concurrent to other PROs precluding our ability to test for causality. Thus, it remains unclear whether disturbed sleep caused depressive symptoms, poorer HRQL, and other PROs or whether the reverse may be true. Understanding the directionality of these relationships are key to further efforts including designing interventions aimed at improving patient-centered outcomes. Although our patient population is similar to other U.S. transplant programs, our findings were derived from a single center. Whether our findings can be generalized outside of our center is unknown. We also did not perform objective tests of sleep, which precludes us from determining which type of sleep impairment drove patient-reports. This has important treatment implications as cognitive behavioral and pharmacologic interventions used to treat problems like insomnia are less effective for sleep disordered breathing conditions. While our insomnia subscale has face validity, we did not use a validated insomnia-specific measure. Thus, our findings related to insomnia should be interpreted with caution. Finally, although this cohort represents one of the largest studies of sleep after lung transplantation, it, nevertheless, is relatively modest in size.
Future efforts to study sleep longitudinally in the peri- and early post-operative period may help shed light on the causal role of sleep in lung transplant recovery. If our findings are confirmed, treatments for disturbed sleep attributable to sleep disorders like insomnia and obstructive sleep apnea are effective, relatively easy to implement, and could be incorporated into post-transplant care both in person and remotely. Further, if our findings linking disturbed sleep with CLAD and mortality are confirmed, efforts to disambiguate their behavioral or immunological underpinnings could shed new insights into important outcomes in lung transplantation.
Acknowledgements:
The authors are deeply appreciative of the time our patient participants provided to support this research and members of the UCSF clinical Advanced Lung Disease and Transplant program
Funding:
JPS: NHLBI K23HL111115, U01HL163242, U01HL145435. JRG: R01 HL151552 VA CDA IK2CX002011; CYH: U01HL163242
Footnotes
Author Conflicts of Interest:
• JPS: Consulting fees from XVIVO; Scientific Advisory Board: Mallinckrodt Pharmaceuticals; DSMB: Krystal Biotech
• SRH: Consulting fees from AI Therapeutics and CareDx; Scientific Advisory Board: CareDx
• JK: DSMB Lung Bioengineering
• JRG: Scientific Advisory Board and Research Funding: Theravance Biopharma
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