Jeon, Sangchoon

Background: Individual symptoms are predictors of mortality in adults with heart failure (HF). However, symptom clusters may be more predictive of future risks than isolated symptoms, yet research on these symptom clusters in older adults with HF is limited. Aims: To explore the extent to which symptom cluster profiles predict all-cause mortality among community-dwelling older adults with HF, while adjusting for known risk factors. Methods: We conducted a secondary analysis of data of the 2008 to 2016 surveys of the U.S. Health and Retirement Study using latent class analysis to identify baseline cluster profiles, and survival analysis for time to death with Cox proportional hazard (Cox PH) models, and Kaplan Meier survival analyses. Results: We included 684 participants [mean age=74.9, (SD=10) years, 56.6% female, 16.2% Black/African American]. Three baseline symptom cluster profiles were identified: high-burden (pain, shortness of breath, fatigue, swelling, depressive symptoms, dizziness), low-burden, and cardiopulmonary-depressive (shortness of breath, pain, and dizziness). The estimated median time-to-death was 71 (95% CI=64, 79) months. Of the 364 participants in the cardiopulmonary-depressive profile, 240 (65.9%) died (median time= 65 months, 95% CI= 55, 73) compared to those in the low-burden profile, of which 49% died. Approximately 61% in the high burden profile died (median time=67 months, 95% CI= 51, 90). A significant difference in survival times between the 3 cluster profiles was found (Log-rank= 9.13, p = 0.01). In the adjusted Cox PH model, participants in the high symptom burden and respiratory-depressive distress profiles had adjusted HR of 1.48 (95% CI=1.15, 1.94) and 1.44 (95% CI=1.14, 1.80) compared to those in the low burden symptom cluster profile, after controlling for age, gender, smoking, and comorbidities. Conclusions: The identified symptom cluster profiles predict 8-year all-cause mortality in older adults with HF, after controlling for known risk factors. An evaluation of symptom clusters, rather than individual symptoms, may provide additive prognostic information.

Abstract Background Heart failure (HF) has a high mortality risk in older adults. Individual symptoms as predictors of mortality in HF patients; however, symptoms often manifest in clusters, which may be more predictive of future risks than isolated symptoms. However, research on symptom clusters in older adults who have HF is limited. To explore the extent to which symptom cluster profiles predict all-cause mortality among older adults with HF, while adjusting for demographic and clinical factors. Methods A secondary study was conducted using the data from the Health and Retirement Study. We measured six symptoms (fatigue, shortness of breath, pain, swelling, depressive symptoms, and dizziness), and used latent class analysis to identify baseline symptom cluster profile. We performed survival analysis for time to death with Kaplan Meier survival analyses and Cox Proportional Hazard models. Results The sample included 684 participants (mean age = 74.9 (SD = 10.0) years) who demonstrated three symptom cluster profiles (high-burden, low-burden, and cardiopulmonary-depressive). The estimated median time-to-death was 71 (95% CI= [64, 79]) months. Participants in the high symptom burden and respiratory-depressive distress profiles had adjusted hazard ratios of 1.48 (95% CI = 1.15, 1.94) and 1.44 (95% CI = 1.14, 1.80) for time to death compared to those in the low burden profile. Conclusion Symptom profiles can assist in identifying older adults with HF who are at risk for earlier mortality. Further research is needed to determine whether alleviating these symptom clusters decreases the risk of mortality.

Abstract Introduction Sleep disturbance and circadian rhythm disruptions are common among adults with chronic heart failure (HF), and are related to cognitive impairment. Although light levels are known to influence sleep quality, cognition, and the circadian system, little is known about the role of light among adults with HF. The purpose of this study is to investigate the association between light exposure and sleep, diurnal rest-activity rhythms (RAR), and cognitive function among adults with HF. Methods We retrospectively analyzed baseline data from a randomized controlled trial of cognitive behavioral therapy for insomnia for adults with HF and insomnia. We measured light levels and sleep with 24-hour wrist actigraphy and computed the circadian quotient, inter-daily stability, and intra-daily variability (strength of the RAR) using cosinor and non-parametric methods. We computed time above threshold (TAT) (100, 500, and 1000 lux), ratio of light exposure in daytime to evening (AUC ratio), and light regularity index (LRI) (20, 50, and 100 lux) to assess the intensity and consistency of light exposure patterns. Lapses in performance on the Psychomotor Vigilance Task (PVT lapse) measured cognitive function. Multiple linear regressions tested if the light exposure metrics could predict the RAR, sleep efficiency, and PVT lapses after adjusting for age, sex, and comorbidities. Results The sample included 167 participants (M age=63.8 (SD =12.9) years; 45.5%female). The AUC ratio and TAT500 were associated with a stronger circadian quotient in males (0.339 ± 0.095, p=.0005; 0.296 ± 0.085, p=.0006) compared to females (0.058 ± 0.106, p=.5800; -0.061 ± 0.128, p=.6364), after controlling for age and comorbidities. TAT500 was associated with better inter-daily stability (0.221 ± 0.077, p=.0044) than the other thresholds. Higher light exposure intensity (TAT100) (0.129 ± 0.065, p=.0492) and a greater circadian quotient (0.267±0.076, p=.0006) were associated with improved sleep efficiency. TAT100 was associated with fewer PVT lapses (-0.229 ± 0.082, p=.0061), after controlling for total sleep time, circadian quotient, and demographics. Conclusion Light exposure, particularly at certain intensities and regularity, predicted RARs, sleep efficiency, and cognitive function in adults with HF. Future tailored light interventions with varying intensities and regularity may improve HF outcomes. Support (if any)

Abstract Introduction Sleep disordered breathing (SDB: obstructive & central sleep apnea) and insomnia are common in heart failure (HF). However, most studies have not addressed the presence of combined SDB and insomnia (COMISA). The purposes of this study were to (1) describe COMISA among adults with stable chronic HF; (2) compare the clinical and demographic characteristics between groups with COMISA, insomnia, SDB, and normal sleep; and (3) examine the risk of COMISA in the presence of normal sleep, insomnia, SDB and other clinical and demographic characteristics. Methods Secondary analysis of an observational study of sleep among adults with stable HF. COMISA was defined as an apnea hypopnea index (AHI) > 15 with difficulty initiating or maintaining sleep or waking too early in the morning (Insomnia). We measured self-reported (Pittsburgh Sleep Quality Index) and objective sleep characteristics (polysomnography – PSG), symptoms (depression, sleepiness); used chi-square and ANOVA; and calculated the risks of COMISA for normal sleepers and with insomnia or SDB under different conditions (depression, poor sleep quality, left ventricular ejection fraction, age, race, and sex). Results The sample included 170 [(M age = 60.3 ± 16.8 years; n = 60 (35%] female. They had normal sleep (n = 37/22%); insomnia only (n = 48/28%): SDB only (n = 46/27%); and COMISA (n = 39/23%). Those with COMISA were significantly more likely to be obese (32.1%) compared to others [13.6% -29.6%, p = 0.0128]. They had the shortest total sleep time (TST) (mean = 291 ± 109 mins) and poorest sleep efficiency (M = 65.8 ± 20.5%) (p = .05). Women with insomnia were less likely to have COMISA than men (36% vs. 51%), while 74% of adults with SDB who were depressed had COMISA, compared with 41% of those with insomnia. Conclusion Risk of COMISA is associated with depression and poor sleep among adults with stable HF and SDB. Future study is needed to better understand the risks of COMISA among adults with chronic HF outcomes and to address the need for intervention that addresses it beyond a focus on only SDB or insomnia. Support (if any)

Introduction: Poor sleep contributes to outcomes in heart failure (HF). Phenotypes reflecting multi-dimensional sleep health may better predict outcomes and support more precise interventions than single measures, but few studies have used this approach. Aims: 1) Describe phenotypes of sleep health and 2) Evaluate whether sleep health phenotypes predict symptoms, cognitive function, and time to first hospitalization and emergency department (ED) visits among HF patients. Methods: We performed secondary analysis of a randomized controlled trial of cognitive behavioral therapy for insomnia compared with HF self-management education with HF patients. We measured sleep [rest-activity rhythms, sleep duration, and efficiency (2-week wrist actigraphy/follow-ups); insomnia; CPAP use; and daytime sleepiness (Epworth Scale)]; perceived stress; fatigue; anxiety; depression; pain; and cognitive ability (Patient-Reported Outcomes Measurement System: PROMIS) at baseline and 3, 6, and 12 months post-intervention and tracked hospitalizations and ED visits. We used K-means clustering to identify sleep phenotypes and generalized linear mixed models and Cox models to predict outcomes, adjusted for demographic factors. Results: Among 166 participants (M age = 63.2 (SD = 12.8) years; 57% female; 23% NYHA Class 3/4 HF; 35% EF less than 45%), there were 4 phenotypes [poor sleep (14%); short sleep/high sleep efficiency (39%); long sleep/low sleep efficiency (25%); and healthy sleep (21%)] at baseline. The healthy sleep group was older and had lower BMIs and less comorbidity and had better outcomes compared to all other phenotypes. In particular, this group had better fatigue (-2.43±0.87), pain (-1.86±1.05), anxiety (-1.50±0.89), stress (-1.46±0.69), and cognitive ability (1.76±0.80) than the poor sleep group. The poor sleep group had shorter time to adverse events compared to the other phenotypes (Hazard ratios of 0.41 to 0.56 for hospitalizations and 0.35 to 0.60 for ED visits). Conclusions: Multidimensional aspects of sleep predict HF outcomes and may improve on prediction with single measures. Future tailored sleep interventions focused on phenotypes may more precisely improve outcomes among HF patients than traditional approaches.

SummaryPeople with heart failure (HF) experience a high symptom burden and prevalent insomnia. However, little is known about daytime symptom trajectories after cognitive behavioural therapy for insomnia (CBT‐I). In this study we describe: (1) daytime symptom trajectories among adults with insomnia and stable HF over 1 year, (2) how symptom trajectories differ between CBT‐I versus HF self‐management interventions, and (3) associations between demographic, clinical, and sleep characteristics, perceived stress, health‐related quality of life (HRQoL), functional performance and daytime symptoms trajectories. We retrospectively analysed data from a randomised controlled trial of CBT‐I versus HF self‐management (NCT0266038). We measured sleep, perceived stress, HRQoL, and functional performance at baseline and symptoms at baseline, 3, 6, and 12 months. We conducted group‐based trajectory modelling, analysis of variance, chi‐square, and proportional odds models. Among 175 participants (mean [standard deviation] age 63.0 [12.9] years, 57.1% male, 76% White), we found four daytime symptom trajectories: (A) low improving symptoms (38.3%); (B) low psychological symptoms and high improving physical symptoms (22.8%); (C) high improving symptoms (24.0%); and (D) high not improving symptoms (14.9%). The CBT‐I versus the HF self‐management group had higher odds of belonging to Group A compared to other trajectories after controlling for baseline fatigue (odds ratio = 3.27, 95% confidence interval 1.39–7.68). The difference between the CBT‐I and the HF self‐management group was not statistically significant after controlling for baseline characteristics. Group D had the highest body mass index, perceived stress, and insomnia severity and the lowest cognitive ability, HRQoL, and functional performance. Research is needed to further evaluate factors contributing to symptom trajectories.

Abstract Introduction People with heart failure (HF) have frequent hospitalizations, and emergency department (ED) visits. We examined the effects of insomnia and rest-activity rhythms (RARs) on time to hospitalizations and ED visits after controlling for other known risk factors among participants in a randomized controlled trial of cognitive behavioral therapy for insomnia compared to heart failure self-management education (attention control). Methods The analysis included 168 HF patients who had available medical records for dates of hospitalization, ED visit, transplant, and death from a randomized clinical trial for insomnia of people with HF and insomnia. The known risk factors included age, sex, New York Heart Association (NYHA) Class, ejection fraction, and comorbid conditions. We used the insomnia severity index (ISI) and evaluated the strength of RAR using the ratio of the amplitude to the MESOR estimated with cosinor analysis with activity counts from a wrist-actigraph worn for at least five consecutive days. Cox proportional hazard models were used to examine ­insomnia and the RAR metrics with the known risk factors on time to first hospitalization, ED visit, transplant, and death. Results Eighty-five (50.1%) had at least one hospitalization, and ninety-one (54.2%) participants had an ED visit. Approximately 42% of the participants were admitted to the hospital due to cardiac events. Older age, male sex, higher NYHA, and greater comorbidity were associated with earlier hospitalization or ED visits. After controlling for those risk factors, stronger RAR was associated with later time to hospitalization, and ED visit with hazard ratios (HRs) of 0.26 (95% CI=[0.07, 0.91]) and 0.25 (95% CI=[0.07, 0.89]); higher insomnia severity (ISI ≥22) was associated with shorter time to ED visits, with an HR of 2.28 (95% CI=[1.19, 4.38]). Conclusion In addition to known demographic and clinical risk factors, insomnia severity and RAR predict hospitalizations and ED visits in people with HF. Further research is necessary to assess the biological mechanism of the associations and examine whether the effects of the risk factors could be modified by improving insomnia severity and RAR in people with HF. Support (if any) This study was financially supported by grants R01NR016191 and P20NR014126 from NIH/NINR.

Abstract Introduction Cognitive behavioral therapy for insomnia (CBT-I) improves individual symptoms of fatigue and daytime sleepiness; however, little is known how CBT-I improves multiple co-occurring daytime symptoms. The purposes of this study are to (1) identify daytime symptom trajectories in adults with insomnia and stable chronic heart failure (HF) over one year, and (2) determine how symptom trajectories vary based on participation in CBT-I or HF self-management education (attention control condition). Methods We conducted a retrospective analysis of data from a randomized controlled trial that looked the sustained effects of CBT-I compared to HF self-management over one year (NCT02660385). We measured daytime symptoms (fatigue, depression, anxiety, dyspnea, pain, and daytime sleepiness) at baseline (pre-treatment) and 3, 6 and 12 months. Symptom cluster trajectory were identified using group-based trajectory modeling, and the association with CBT-I was examined using logistic regressions. Results We randomized 175 participants (M age=63.0 (12.9) years, 57% men). We found four daytime symptom cluster trajectories, (A) low improving symptoms (N = 67, 38.3%); (B) low psychological symptoms and high improving physical symptoms (N = 40, 22.8%); (C), high improving symptoms (N = 42, 24.0%); and (D) high not improving symptoms (N = 26, 14.9%). There was no statistically significant difference between CBT-I and HF self-management across 4 symptom trajectories (p = .2509). However, people who received CBT-I vs. HF self-management education had higher odds of belonging to trajectory A compared to B/C/D after controlling for baseline fatigue (OR = 3.27 95% Confidence Intervals [1.39, 7.68]). Conclusion Most participants experienced improved daytime symptom profiles over 12 months after both interventions. Participants in the CBT-I group experienced symptom improvement even when they started with low symptoms. Support (if any) National Institute of Nursing Research [R01NR016191 and P20NR014126].