Symptom cluster profiles predict all-cause mortality among older adults with heart failure

Symptom cluster profiles predict all-cause mortality among older adults with heart failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Symptom cluster profiles predict all-cause mortality among older adults with heart failure Zequan Wang, Stephen Walsh, Sangchoon Jeon, Samantha Conley, Deborah Chyun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4414292/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. heart failure mortality symptom clusters symptom cluster profiles Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) results from cardiovascular conditions in conjunction with age-associated changes in cardiovascular structure and function. 1 Among the 6.7 million HF patients in the US, there was an estimated 9% annual mortality rate corresponds to around 603,000 deaths from any cause in 2020. 2 A meta-analysis revealed that the pooled one-year mortality rate for adults with chronic HF is above 10%, with a 5-year survival rate nearing 50% and a 10-year survival rate about 30%. 3 Numerous prognostic models have been developed in HF and many risk factors were associated with elevated mortality rate in HF. The most significant predictors of mortality included age, lower ejection fraction (EF), New York Heart Association functional class (NYHA), diabetes, lower systolic blood pressure (BP), lower body mass, smoking, chronic obstructive pulmonary disease (COPD), and male sex. 4 , 5 Socioeconomic (low education level, non-employment), 6 lifestyle (diet), clinical factors (heart rate and myocardial function), 7 and comorbidities (obesity), 5 also predict HF mortality. Identifying symptoms that predict death may enable earlier interventions given that symptoms often stem from the HF itself, associated comorbidities, and medical treatments. 8 Understanding the presence, severity, and interactions among symptoms is critical as they may reflect decompensation or progression of HF. For example, edema/swelling, fatigue, depressive and anxiety symptoms predicted three-month all-cause mortality among people with HF. 9 Depressive and anxiety symptoms were also predictors of mortality in multivariate analysis after adjusting for demographic and clinical covariates. 9 Although most studies focused on individual symptoms, comprehensive assessment of the characteristics of multiple symptoms and their concurrent manifestation as symptom clusters/profiles is crucial for identification of HF prognosis. 10 Only one study conducted in South Korea found that membership in a dyspneic symptom cluster (waking up breathless at night, difficulty breathing when lying flat, shortness of breath) independently predicted mortality in patients with HF during the 12-month follow-up period, after controlling for covariates ( p = 0.012). 11 For every single-unit increase in the average distress score within the dyspneic symptom cluster, the risk of cardiac death was doubled (adjusted HR = 2, 95% CI = 1.16–3.34). However, the “weary” symptom cluster (lack of energy, lack of appetite, difficulty sleeping) did not statistically significantly predict mortality. To the best of our knowledge, no investigators have examined the extent to which symptom cluster profiles predict mortality. Considering the critical role of early detection of worsening concurrent symptoms to prevent adverse events in older adults, examining the effect of symptom cluster profiles on all-cause mortality among community-dwelling older adults with HF is necessary. The purpose of this study is to explore the extent to which symptom cluster profiles are associated with all-cause mortality among U.S. community-dwelling older adults with HF, while adjusting for demographic and clinical covariates. Methods We utilized data from the Health and Retirement Study (HRS), which is a national longitudinal survey targeting U.S. residents aged 50 years and older, along with their caregivers. 12 Initiated in 1992, the HRS investigators conduct detailed biennial interviews to gather a range of socioeconomic and health-related information. These participants are recontacted every 2 years, with informed consent prior to their inclusion in the study. The protocol for data collection was approved by the institutional review board at the University of Michigan. 13 This study was reviewed and determined by exempt by our institution’s ethics committee. We analyzed both the core and exit datasets from the HRS, specifically focusing on data spanning from 2008 and 2016. Symptoms and Measures The interviews elicited the symptoms of swelling in the feet or ankles, fatigue/exhaustion, shortness of breath, and dizziness in the core surveys of 2008. These self-reported surveys have shown strong external validity. 12 For each symptom, the response options were “yes”, “no”, or “I don’t know.” Participants who selected “yes” were identified by the researchers as experiencing the symptoms. Participants were asked about their experience with pain through the question, “Are you often troubled with pain?” Those affirming were further inquired about the intensity of their pain, choosing from “mild, moderate, or severe” to describe its severity. Participants who chose yes to the first question and reported the pain as either moderate or severe were identified as experiencing significant pain. 14 Depressive symptoms were measured with the eight-item short-form of the Center for Epidemiological Studies Depression Scale (CES-D). 15 This scale asked participants to reflect on the frequency of depressive symptoms they have experienced in the last week, with scores ranging from 0 to 8. The cutoff score is 4 or higher was considered indicative of depressive symptoms. 16 Covariates and Measures Baseline demographic factors included age, sex (male vs. female), race and ethnic group (self-reported), marital status (married/partnered vs not married), body mass index (BMI), smoking status, (ever vs. never), alcohol consumption, and veteran status. BMI was computed by converting weight from pounds to kilograms and height from inches to meters, and then categorized into underweight (< 18.5), normal/healthy (18.5–24.9), overweight (25-29.9), obese (30-39.9), and morbid obesity (≥ 40). Comorbidities, total cholesterol, HDL cholesterol, systolic blood pressure (BP), BP being treated with medicines were collected using telephone and face-to-face interviews in the HRS core datasets. Comorbidities, including hypertension, diabetes, cancer, lung disease, and arthritis were obtained from the Health Condition Questionnaire in the HRS core datasets. All-cause Mortality For each wave, a status variable from the interview indicated whether the participant had passed away in the interval between the preceding wave and the current one. Participants’ deaths were ascertained using the data on the year and month of death in the exit datasets. Person-years were computed with time 0 set as the month and year of the 2008 interview, with participants being tracked up to the 2016 wave. For participants who either passed away or withdrew from the study, person-years were calculated from the beginning of the follow-up period to the month and year when mortality or dropout was documented. For those who survived through the study period, person-years were censored as of their interview date in 2016. For this study, the dependent variable is time to event from the interviews with an indicator of censorship, where 1 was event (“died”) and 0 was censoring (“lost to follow-up”). Statistical analysis We downloaded and merged the data from multiple HRS datasets, cleaned the data, and addressed missing value using SAS 9.4 (SAS Institute, Cary NC). For scales with missing responses, imputation was performed for the absent items using the Markov chain Monte Carlo method via PROC MI. Scores were computed using a combination of observed and imputed values, provided that the missing data constituted less than 30% of the total items. We computed descriptive statistics for the health history data from HRS. Framingham risk scores were computed using age, sex, smoker, total cholesterol, HDL cholesterol, systolic BP, and BP being treated with medicines. 17 We used latent class analysis (LCA) to identify subgroups of participants with HF who experience similar symptoms (fatigue, shortness of breath, pain, swelling, depressive symptoms, and dizziness) based on patterns of categorical data. The detailed methodology of LCA for baseline symptom cluster profiles at year 2008 has been reported. 18 We performed survival analysis to assess the time to death with Cox Proportional Hazard (PH) models. Hazard ratios (HR) with corresponding 95% confidence intervals (CIs) were estimated using PROC PHREG. We evaluated the assumption of proportional hazards for each covariate within the Cox proportional hazards models through the application of the standardized score process test. Survival curves across the symptom cluster profiles were estimated using Kaplan Meier estimators and compared using log-rank test. A parsimonious Cox PH model was built with risk factors for mortality (age, sex, race, BMI, alcohol consumption, smoking status, veteran status, comorbidities, and Framingham risk score). The model selection used a stepwise approach, retaining risk factors that had p -values of less than 0.10. We then added the candidate predictors (symptom cluster profiles: high burden, low burden, cardiopulmonary-depressive) into the Cox PH models and calculated adjusted HRs after controlling for selected risk covariates. No time-dependent variables were included in the models. Kaplan Meier survival analyses using PROC LIFETEST were used for analyzing time to death across different symptom cluster profiles. Results During a mean follow-up period of 62.5 months over 8 years, we included 684 participants at baseline [mean age = 74.9 (SD = 10.0) years]. Over half were female, and most were White/Caucasian (80%) (Table 1 ). The Framingham risk score indicated a moderate risk of cardiovascular risk (0.18 ± 0.15). The baseline symptom prevalence and symptom cluster profiles were reported with a 3-status LCA model, including high burden profile (pain, shortness of breath, fatigue, swelling, depressive symptoms, and dizziness), low burden profile, and cardiopulmonary-depressive profile (shortness of breath, pain, and dizziness). 18 Table 1 Demographic and Clinical Characteristics at Year-2008 (N = 684) Variables Mean (SD)/N (%) Age 74.9 (10.0) Gender Male 297 (43.4) Female 387 (56.6) Race White/Caucasian 547 (80.0) Black/African American 111 (16.2) Other/don’t know 26 (3.8) BMI Under weight ( = 40.0) 63 (9.2) Marital Status Married/Partnered 334 (48.8) Single 350 (51.2) Education High school or less 487 (71.2) College degree or above 197 (28.8) Veterans status Yes 179 (26.2) No 505 (73.8) Smoking status Yes 66 (9.6) No 618 (90.4) Alcohol consumption Yes 179 (26.2) No 505 (73.8) Hypertension Yes 576 (84.2) No 108 (15.8) Diabetes Yes 317 (46.3) No 367 (53.7) Cancer Yes 141 (20.6) No 543 (79.4) Lung disease Yes 229 (33.5) No 455 (66.5) Arthritis Yes 544 (79.5) No 140 (20.5) Total cholesterol 185.44 ± 2.45 HDL cholesterol 48.87 ± 0.79 Systolic BP 138.98 ± 3.28 BP being treated with medicines Yes 637 No 47 Framingham risk scores 0.18 ± 0.15 Over the 8 years, 421 died and 67 were lost to follow-up. From the Kaplan Meier Survival Curve, the estimate of median time-to-death was 71 (95% CI = 64, 79) months (Fig. 1 ). The survival curves diverge quickly and the survival probability dropped to approximately 30%. Figure 2 shows the Kaplan Meier estimate of time-to-death by the three baseline symptom cluster profiles. Of the 364 participants with the cardiopulmonary-depressive profile, 240 (65.9%) died (Median time = 65 months, 95% CI = 55, 73) compared to those with low burden profile who have about a 49% incidence rate of death. Meanwhile, approximately 61% of participants in high burden died (Median time = 67 months, 95% CI = 51, 90) compared to those with low burden profile. Log-rank test showed that there was a statistically significant difference in survival times between the 3 symptom cluster profiles (Log-rank = 9.13, p = 0.01). Table 2 shows the unadjusted and adjusted Cox PH Models with known risk covariates. Age, gender, smoking status, alcohol consumption, diabetes, and lung disease were significantly associated with mortality in the adjusted models. With each additional year of age, the risk of death increases by 8% (95% CI = 1.07, 1.09, p < 0.001). Notably, participants with diabetes had a 61% higher risk of death compared to those without diabetes (HR = 1.61, 95% CI = 1.32, 1.98, p < 0.001). Alcohol consumption was associated with a 29% reduction in risk of death compared to non-consumers, with an HR of 0.72 (95% CI = 0.56, 0.91, p = 0.006). Framingham risk score was not significantly associated with all-cause mortality. Table 2 Hazard ratio of time to death for known risk factors in unadjusted and adjusted Cox Proportional Hazard models (N = 684). Covariates Unadjusted Adjusted HR [95% CI] P-value HR [95% CI] P-value Age 1.07 (1.05, 1.08) < .0001 1.08 [1.07, 1.09] < .0001 Gender (male vs. female) 1.12 (0.93, 1.36) 0.230 1.28 [1.05, 1.58] 0.013 Smoking 0.93 (0.68, 1.28) 0.660 1.46 [1.05, 2.03] 0.026 Diabetes 1.20 (0.99, 1.45) 0.060 1.61 [1.32, 1.98] < .0001 Lung disease 1.41 (1.16, 1.72) < .0001 1.43 [1.17, 1.74] < .0001 Alcohol consumption 0.64 (0.51, 0.81) < .0001 0.72 [0.56, 0.91] 0.006 Arthritis 0.99 (0.78, 1.27) 0.989 Cancer 1.24 (0.99, 1.56) 0.065 Hypertension 1.01 (0.78, 1.32) 0.921 Framingham risk score 5.60 (3.34, 9.41) < .0001 Note: HR=hazard ratio; CI=confidence interval; SE= standard error We examined the baseline symptom cluster profiles after controlling for selected risk covariates (Table 2 ). Table 3 shows the adjusted HRs of the baseline symptom cluster profiles and the selected known risk covariates for prediction of mortality. Participants who belonged to the high burden profile had an adjusted HR of 1.48 (95% CI = 1.15, 1.94), and those in the respiratory-depressive distress profile had an adjusted HR of 1.44 (95% CI = 1.14, 1.80), compared to those with low burden symptom cluster profiles. Figure 3 presents the estimated survival time of individuals over time and each curve represents different symptom cluster profiles. Symptom cluster profile 1 (high burden) and symptom cluster profile 3 (respiratory-depressive distress) show the lowest survival probability over eight years compared to participants in profile 2 (low burden). Table 3 Adjusted hazard ratios for each of baseline symptom cluster profiles after controlling for selected known risk factors (N = 684). Symptom cluster profiles Adjusted HR [95% CI] P-value High burden vs. low burden 1.48 [1.15, 1.94] 0.038 Cardiopulmonary-depressive vs. low burden 1.44 [1.14, 1.80] 0.039 Age 1.07 [1.06, 1.10] < .0001 Gender (male vs. female) 1.29 [1.06, 1.58] 0.011 Diabetes 1.85 [1.43, 2.40] < .0001 Lung disease 1.59 [1.23, 2.05] 0.001 Smoking 1.45 [1.04, 2.02] 0.028 Alcohol consumption 0.75 [0.59, 0.95] 0.019 Note: HR=hazard ratio; CI=confidence interval Discussion Our study highlights that among community-dwelling older adults with HF, those with high burden (pain, swelling, shortness of breath, fatigue, depressive symptoms, and dizziness) and cardiopulmonary-depressive symptom cluster profiles (swelling, shortness of breath, and depressive symptoms) conveyed considerable risk for mortality over 8 years, after controlling for well-known demographic and clinical risk factors. Survival curves also revealed that these two symptom cluster profiles had a comparable higher risk of death than the group of low-burden symptoms. An evaluation of symptom clusters, rather than individual or isolated symptoms, may provide important and additive prognostic information beyond the established risk predictors of all-cause mortality in older adults with HF. Specific individual symptoms, such as dyspnea, could offer prognostic information when considered individually. However, evaluating symptom clusters allows us to capture the multifaceted nature of HF and its impact on older adults, by reflecting how symptoms interact and collectively contribute to unique prognostic implications. Our finding is consistent with the results of a randomized, placebo-controlled trial in patients with HF that found that moderate to severe symptoms were associated with a 43% increase in risk for all-cause mortality versus those who were only mildly symptomatic. 5 The high probabilities for shortness of breath (0.88 in the high burden profile and 1.00 in the cardiopulmonary-depressive profile), a common symptom among people with poorly compensated HF, underscores its role. One previous study also demonstrated that breathlessness (a component of the dyspneic symptom cluster) was an independent predictor of all-cause mortality, after adjusting for other risk covariates. 11 Our finding of the associations between lower alcohol consumption and an increased risk of mortality is consistent with previous studies that light/moderate alcohol consumption was associated with reduced risk of mortality in HF. 19 , 20 One study found that in community-dwelling older adults with HF, consuming up to 7 drinks per week was associated with longer survival compared to abstaining from alcohol, after adjusting for covariates. 19 One explanation is that consuming alcohol in light to moderate amounts could help protect against coronary atherosclerotic events through various mechanisms, including increasing high-density lipoprotein levels, reducing plasminogen activator inhibitor activities, and improving insulin sensitivity. 21 However, the authors acknowledge that their analysis was based on a single measure of alcohol consumption post-HF diagnosis, and they lacked data regarding the etiology of HF and left ventricular ejection fraction (LVEF). 19 Excessive alcohol consumption is widely recognized as a major cause of death and is directly correlated with an elevated risk of HF. 22 Moreover, there is inconsistent evidence regarding the impact of low to moderate alcohol consumption on mortality, with many studies being limited by small sample sizes and residual confounding factors. 23 These findings must be interpreted with cautious because we did not account for the amount or types of alcohol consumption. Therefore, we cannot dismiss the potential for confounding factors related to alcohol use and other prognostic influences affecting the observed association with improved survival. Our findings confirm the importance of conventional HF risk factors for all-cause mortality. Poor prognosis is expected to be more likely in the older age, and we found an 8% increase in risk with every one-year increase in age. Another study found that each 10-year increment over the age of 60 years increased the risk of all-cause mortality by 24% in patients with systolic HF. 5 We found that HF in men was associated with an 28% increased risk of all-cause mortality in the 8-year follow-up than in women. This finding is consistent with a study conducted in the United Kingdom that found all-cause mortality rates tended to be lower in women than men in the 75–84 year-old age group over the 17-year follow-up period. 24 However, women often present with HF at an older age than men and more often have HF preserved ejection fraction (HFpEF) and have less ischemic myocardial disease and later symptom onset. 2 , 25 Additionally, women often experience delays in diagnosis or referral for HF compared to men, and they undergo fewer diagnostic tests and medical procedures. 26 Stratifying risk and evaluating prognosis for HF in women presents a challenge to clinicians because current prognostic scores do not include assessments tailored specifically to sex or gender. Diabetes and lung disease were associated with a 61% and 46% increased risk of mortality, respectively, our findings confirm the significance of comorbid diabetes and lung disease for all-cause mortality. Previous studies showed that Chronic Obstructive Pulmonary Disease (COPD) was significantly associated with both short-term ( 10 years) all-cause mortality risks. 24 , 27 Among older adults participating in the Medicare program, those diagnosed with diabetes and HF experienced a mortality rate of 32.7 per 1000 person-years, in contrast to 3.7 per 1000 person-years for those without HF. 28 Blood glucose level may be critical, as each 18 mg/dL increase in blood glucose was associated with an adjusted 5% increase in the risk of 10-year mortality. 29 A recent meta-analysis also showed the presence of a comorbid condition could predict all-cause mortality in HF (HR 1.31; 95% CI = 1.18, 1.45). 30 Among 10 comorbid conditions, diabetes mellitus and COPD had the most significant pooled effect on mortality in HF. Our results indicate that symptom cluster profiles can be a prognostic indicator to initiate interventions and services that prevent admissions. Individual symptoms were not used routinely in prognosis decisions, much less symptom clusters/profiles in adults with HF. Non-HF specific symptoms, such as pain, anxiety, and depressive symptoms, had strong associations with adverse outcomes, such as higher hospitalization for non-CVD causes and readmission. 9 Although the symptom clusters/profiles were generated according to a close statistical relationship, our results indicate that this concept has clinical relevance. The benefit of using co-occurring symptoms as symptom clusters/profiles lies in their nature as patient-reported and often present before structured clinical assessment, such as NYHA functional class. Our findings highlight the importance of raising awareness of recognizing and managing co-occurring symptoms to reduce risks of death in routine community-setting care. Healthcare providers and clinicians should routinely record both cardiac and non-specific symptoms and proactively manage them, particularly managing conditions like shortness of breath in those identified with high symptom burdens or who exhibit cardiopulmonary and depressive symptoms. Clinicians in the community settings should intervene early on symptom clusters/profiles before transitioning to hospital settings, as part of comprehensive care, to reduce mortality risk for older adults with HF. Limitations No data were available on the cause of death. Thus, we cannot attribute this outcome only to HF. However, when considering HF as a contributing factor in U.S. death certificates, it is estimated that HF accounts for about 72–79% of deaths that are not officially reported or recognized as HF-related deaths. 2 The current literature reflects significant underreporting of deaths attributable to HF. Future research should examine the specific relationship between symptom clusters/profiles and HF-related mortality as it allows researchers and clinicians to understand the direct impact of HF. We did not account for the time-varying nature of symptom cluster profiles in the Cox model by adding symptom cluster profile transitions, which would change alongside the severity of HF. Further research is necessary to examine the optimal models to assist older adults with HF to monitor and report co-occurring symptoms and to test patient-centered prognostic models in a prospective cohort. This study also lacks data on the type of HF (preserved, reduced, or mid-range EF), its etiology, other cardiovascular conditions, and treatments, all of which are critical conditions that could influence HF outcomes. Other factors that may have been associated with symptom clusters and influenced mortality include medications used to control HF, as well as the presence of diabetes-related complications. Conclusions The presence of the high burden symptom cluster profile (pain, swelling, shortness of breath, fatigue, depressive symptoms, and dizziness) and the cardiopulmonary-depressive symptom cluster profile (shortness of breath, swelling, and depressive symptoms) are significantly associated with an increased risk of all-cause mortality over 8 years among community-dwelling older adults with HF, after adjusting for demographic and clinical risk factors. Health care providers and clinicians are encouraged to integrate symptom cluster management into their routine care to potentially reduce mortality risk among older adults with HF. Declarations Data Availability Statement The data that support the findings of this study are available from the Heart and Retirement Study (HRS). Further details and access to the data registration process are available on the HRS website at https://hrs.isr.umich.edu/about. Funding This work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Disclosures/Conflict of interests None Author Contribution Z.W. wrote the main manuscript text. S.W. and S.J. help with the statistical methods. All authors reviewed the manuscript. References Díez-Villanueva P, Jiménez-Méndez C, Alfonso F. Heart failure in the elderly. J Geriatr Cardiol. 2021;18(3):219–32. 10.11909/j.issn.1671-5411.2021.03.009 . Bozkurt B, Ahmad T, Alexander KM, et al. Heart failure epidemiology and outcomes statistics: A report of the heart failure society of america. J Card Fail. 2023;29(10):1412–51. 10.1016/j.cardfail.2023.07.006 . Jones NR, Roalfe AK, Adoki I, Hobbs FDR, Taylor CJ. Survival of patients with chronic heart failure in the community: a systematic review and meta-analysis. Eur J Heart Fail. 2019;21(11):1306–25. 10.1002/ejhf.1594 . Pocock SJ, Ariti CA, McMurray JJV, et al. Predicting survival in heart failure: a risk score based on 39372 patients from 30 studies. Eur Heart J. 2013;34(19):1404–13. 10.1093/eurheartj/ehs337 . Ford I, Robertson M, Komajda M, et al. Top ten risk factors for morbidity and mortality in patients with chronic systolic heart failure and elevated heart rate: The SHIFT risk model. Int J Cardiol. 2015;184:163–9. 10.1016/j.ijcard.2015.02.001 . Ohlsson A, Eckerdal N, Lindahl B, Hanning M, Westerling R. Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data. BMC Public Health. 2021;21(1):1040. 10.1186/s12889-021-10919-1 . Pongiglione B, Torbica A, Gale CP, Tavazzi L, Vardas P, Maggioni AP. Patient, hospital and country-level risk factors of all-cause mortality among patients with chronic heart failure: Prospective international cohort study. PLoS ONE. 2021;16(5):e0250931. 10.1371/journal.pone.0250931 . Chen J, Aronowitz P. Congestive heart failure. Med Clin North Am. 2022;106(3):447–58. 10.1016/j.mcna.2021.12.002 . Ali MR, Lam CSP, Strömberg A, et al. Symptoms and signs in patients with heart failure: association with 3-month hospitalisation and mortality. Heart. 2024;110(8):578–85. 10.1136/heartjnl-2023-323295 . Wang Z, Conley S, Redeker NS, Tocchi C. An evolutionary concept analysis in people with heart failure-symptom clusters or symptom cluster profiles? ANS Adv Nurs Sci. 2023;20. 10.1097/ANS.0000000000000495 . [Published online]. Song EK, Moser DK, Rayens MK, Lennie TA. Symptom Clusters Predict Event-Free Survival in Patients With Heart Failure. J Cardiovasc Nurs. 2010;25(4):284–91. Wallace RB, Herzog AR. Overview of the Health Measures in the Health and Retirement Study. J Hum Resour. 1995;30(1):S84–107. Health and Retirement Study. Institutional review board. Defining Excellence Simul Programs. 10.1201/9780203110744-5 . Covinsky KE, Lindquist K, Dunlop DD, Yelin E. Pain, functional limitations, and aging. J Am Geriatr Soc. 2009;57(9):1556–61. 10.1111/j.1532-5415.2009.02388.x . Radloff LS, The CES-D, Scale. A Self-Report Depression Scale for Research in the General Population. Appl Psychol Meas. 1977;1(3):385–401. Steffick DE, Wallace RB, Regula A et al. HRS/AHEAD documentation report documentation of affective functioning measures in the health and retirement study. 2000. D’Agostino RBS, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53. 10.1161/CIRCULATIONAHA.107.699579 . Wang Z, et al. Symptom Cluster Profiles among Community-residing Older Adults with Heart Failure. Findings from the U.S. Health and Retirement Study.; 2024. Sadhu JS, Novak E, Mukamal KJ, et al. Association of alcohol consumption after development of heart failure with survival among older adults in the cardiovascular health study. JAMA Netw open. 2018;1(8):e186383. 10.1001/jamanetworkopen.2018.6383 . Xi B, Veeranki SP, Zhao M, Ma C, Yan Y, Mi J. Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in U.S. Adults. J Am Coll Cardiol. 2017;70(8):913–22. 10.1016/j.jacc.2017.06.054 . Brien SE, Ronksley PE, Turner BJ, Mukamal KJ, Ghali WA. Effect of alcohol consumption on biological markers associated with risk of coronary heart disease: systematic review and meta-analysis of interventional studies. BMJ. 2011;342:d636. 10.1136/bmj.d636 . Surma S, Gajos G. Alcohol, health loss and mortality: can wine really save the good name of moderate alcohol consumption? Pol Arch Intern Med. 2024. 10.20452/pamw.16708 . Andersson C, Schou M, Gustafsson F, Torp-Pedersen C. Alcohol intake in patients with cardiomyopathy and heart failure: consensus and controversy. Circ Heart Fail. 2022;15(8):e009459. 10.1161/CIRCHEARTFAILURE.121.009459 . Drozd M, Relton SD, Walker AMN, et al. Association of heart failure and its comorbidities with loss of life expectancy. Heart. 2021;107(17):1417–21. 10.1136/heartjnl-2020-317833 . Bozkurt B, Khalaf S. Heart Failure in Women. Methodist Debakey Cardiovasc J. 2017;13(4):216–23. 10.14797/mdcj-13-4-216 . Hayhoe B, Kim D, Aylin PP, Majeed FA, Cowie MR, Bottle A. Adherence to guidelines in management of symptoms suggestive of heart failure in primary care. Heart. 2019;105(9):678–85. 10.1136/heartjnl-2018-313971 . Mulla W, Klempfner R, Natanzon S, et al. Female gender is associated with a worse prognosis amongst patients hospitalised for de-novo acute heart failure. Int J Clin Pract. 2021;75(4):e13902. 10.1111/ijcp.13902 . Johansson I, Edner M, Dahlström U, Näsman P, Rydén L, Norhammar A. Is the prognosis in patients with diabetes and heart failure a matter of unsatisfactory management? An observational study from the Swedish Heart Failure Registry. Eur J Heart Fail. 2014;16(4):409–18. 10.1002/ejhf.44 . Itzhaki Ben Zadok O, Kornowski R, Goldenberg I, et al. Admission blood glucose and 10-year mortality among patients with or without pre-existing diabetes mellitus hospitalized with heart failure. Cardiovasc Diabetol. 2017;16(1):102. 10.1186/s12933-017-0582-y . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4414292","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":304822708,"identity":"7992dddf-868e-4a8f-90f6-6fefd2d4cdb0","order_by":0,"name":"Zequan Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJADxgcwlgSxWpgNSNbCBleJV4t8e/M2iZ87GBL7Z7dfq+apsckzZ2A+eJsHjxbGnmNlkr1nGBJn3DlTdpvnWFqxZQNbsjU+LcwSOWYSvG0MuQ03ctJu8zYcTtxwgMdMGp8WNvk3ZpJ/gVrmA7UUQ7Twf8OrhUcCaCbIlg030o8xQ21hw6tFgiet2Fq2TaJ+440cZsk5x9ISNxxmM7acg0eLfPvhjTffttkYy91If/jhTY1N4objzQ9vvMGjBQhAEQiKCB5oTDLjVw7TAgLsDwirHQWjYBSMghEJAMHpSZ0Gi0ytAAAAAElFTkSuQmCC","orcid":"","institution":"University of Connecticut","correspondingAuthor":true,"prefix":"","firstName":"Zequan","middleName":"","lastName":"Wang","suffix":""},{"id":304822711,"identity":"d1857ada-0133-49b5-acec-1ffe11e179ff","order_by":1,"name":"Stephen Walsh","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Walsh","suffix":""},{"id":304822713,"identity":"bd23e02e-cb47-44fa-941a-994dc76044fb","order_by":2,"name":"Sangchoon Jeon","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Sangchoon","middleName":"","lastName":"Jeon","suffix":""},{"id":304822714,"identity":"ec8837bf-0a03-4cd2-856a-e6f4c31fa505","order_by":3,"name":"Samantha Conley","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Samantha","middleName":"","lastName":"Conley","suffix":""},{"id":304822716,"identity":"4d0f5855-bc76-4799-bcbc-5148bd95851d","order_by":4,"name":"Deborah Chyun","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Chyun","suffix":""},{"id":304822717,"identity":"3903ceb0-2217-400a-97ad-265af8431d47","order_by":5,"name":"Nancy Redeker","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Nancy","middleName":"","lastName":"Redeker","suffix":""}],"badges":[],"createdAt":"2024-05-13 15:44:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4414292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4414292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57447818,"identity":"3342c5ad-80cb-446c-a514-3c909949f21f","added_by":"auto","created_at":"2024-05-30 19:49:04","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130247,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meier Survival Curves for time-to-mortality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4414292/v1/e1ffdc8ea407c54faf62ec2c.jpeg"},{"id":57447228,"identity":"43aa11a7-73ad-4956-aaaf-e17537968535","added_by":"auto","created_at":"2024-05-30 19:41:04","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":201397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan Meier Survival Curves for time-to-mortality by Three Baseline Symptom Cluster Profiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4414292/v1/32657e6ce94871022bed8ba4.jpeg"},{"id":57447230,"identity":"e3c47124-7185-4465-822c-db94cb3009b5","added_by":"auto","created_at":"2024-05-30 19:41:04","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":137495,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimated Survival Probability for Three Baseline Symptom Cluster Profiles.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3..jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4414292/v1/7133965a70b683e31c5205d8.jpeg"},{"id":72130912,"identity":"b4c93358-61af-4e6a-9d66-ff00f6e79bb7","added_by":"auto","created_at":"2024-12-23 04:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1070458,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4414292/v1/75592419-aedd-4b49-a128-0734fc118d77.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Symptom cluster profiles predict all-cause mortality among older adults with heart failure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) results from cardiovascular conditions in conjunction with age-associated changes in cardiovascular structure and function.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Among the 6.7\u0026nbsp;million HF patients in the US, there was an estimated 9% annual mortality rate corresponds to around 603,000 deaths from any cause in 2020.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e A meta-analysis revealed that the pooled one-year mortality rate for adults with chronic HF is above 10%, with a 5-year survival rate nearing 50% and a 10-year survival rate about 30%.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNumerous prognostic models have been developed in HF and many risk factors were associated with elevated mortality rate in HF. The most significant predictors of mortality included age, lower ejection fraction (EF), New York Heart Association functional class (NYHA), diabetes, lower systolic blood pressure (BP), lower body mass, smoking, chronic obstructive pulmonary disease (COPD), and male sex.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Socioeconomic (low education level, non-employment),\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e lifestyle (diet), clinical factors (heart rate and myocardial function),\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and comorbidities (obesity),\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e also predict HF mortality.\u003c/p\u003e \u003cp\u003eIdentifying symptoms that predict death may enable earlier interventions given that symptoms often stem from the HF itself, associated comorbidities, and medical treatments.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Understanding the presence, severity, and interactions among symptoms is critical as they may reflect decompensation or progression of HF. For example, edema/swelling, fatigue, depressive and anxiety symptoms predicted three-month all-cause mortality among people with HF.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Depressive and anxiety symptoms were also predictors of mortality in multivariate analysis after adjusting for demographic and clinical covariates.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Although most studies focused on individual symptoms, comprehensive assessment of the characteristics of multiple symptoms and their concurrent manifestation as symptom clusters/profiles is crucial for identification of HF prognosis.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Only one study conducted in South Korea found that membership in a dyspneic symptom cluster (waking up breathless at night, difficulty breathing when lying flat, shortness of breath) independently predicted mortality in patients with HF during the 12-month follow-up period, after controlling for covariates (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012).\u003csup\u003e11\u003c/sup\u003e For every single-unit increase in the average distress score within the dyspneic symptom cluster, the risk of cardiac death was doubled (adjusted HR\u0026thinsp;=\u0026thinsp;2, 95% CI\u0026thinsp;=\u0026thinsp;1.16\u0026ndash;3.34). However, the \u0026ldquo;weary\u0026rdquo; symptom cluster (lack of energy, lack of appetite, difficulty sleeping) did not statistically significantly predict mortality.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, no investigators have examined the extent to which symptom cluster profiles predict mortality. Considering the critical role of early detection of worsening concurrent symptoms to prevent adverse events in older adults, examining the effect of symptom cluster profiles on all-cause mortality among community-dwelling older adults with HF is necessary. The purpose of this study is to explore the extent to which symptom cluster profiles are associated with all-cause mortality among U.S. community-dwelling older adults with HF, while adjusting for demographic and clinical covariates.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe utilized data from the Health and Retirement Study (HRS), which is a national longitudinal survey targeting U.S. residents aged 50 years and older, along with their caregivers.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Initiated in 1992, the HRS investigators conduct detailed biennial interviews to gather a range of socioeconomic and health-related information. These participants are recontacted every 2 years, with informed consent prior to their inclusion in the study. The protocol for data collection was approved by the institutional review board at the University of Michigan.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e This study was reviewed and determined by exempt by our institution\u0026rsquo;s ethics committee. We analyzed both the core and exit datasets from the HRS, specifically focusing on data spanning from 2008 and 2016.\u003c/p\u003e\n\u003ch3\u003eSymptoms and Measures\u003c/h3\u003e\n\u003cp\u003eThe interviews elicited the symptoms of swelling in the feet or ankles, fatigue/exhaustion, shortness of breath, and dizziness in the core surveys of 2008. These self-reported surveys have shown strong external validity.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e For each symptom, the response options were \u0026ldquo;yes\u0026rdquo;, \u0026ldquo;no\u0026rdquo;, or \u0026ldquo;I don\u0026rsquo;t know.\u0026rdquo; Participants who selected \u0026ldquo;yes\u0026rdquo; were identified by the researchers as experiencing the symptoms. Participants were asked about their experience with pain through the question, \u0026ldquo;Are you often troubled with pain?\u0026rdquo; Those affirming were further inquired about the intensity of their pain, choosing from \u0026ldquo;mild, moderate, or severe\u0026rdquo; to describe its severity. Participants who chose yes to the first question and reported the pain as either moderate or severe were identified as experiencing significant pain.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDepressive symptoms were measured with the eight-item short-form of the Center for Epidemiological Studies Depression Scale (CES-D).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e This scale asked participants to reflect on the frequency of depressive symptoms they have experienced in the last week, with scores ranging from 0 to 8. The cutoff score is 4 or higher was considered indicative of depressive symptoms.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCovariates and Measures\u003c/h2\u003e \u003cp\u003eBaseline demographic factors included age, sex (male vs. female), race and ethnic group (self-reported), marital status (married/partnered vs not married), body mass index (BMI), smoking status, (ever vs. never), alcohol consumption, and veteran status. BMI was computed by converting weight from pounds to kilograms and height from inches to meters, and then categorized into underweight (\u0026lt;\u0026thinsp;18.5), normal/healthy (18.5\u0026ndash;24.9), overweight (25-29.9), obese (30-39.9), and morbid obesity (\u0026ge;\u0026thinsp;40).\u003c/p\u003e \u003cp\u003eComorbidities, total cholesterol, HDL cholesterol, systolic blood pressure (BP), BP being treated with medicines were collected using telephone and face-to-face interviews in the HRS core datasets. Comorbidities, including hypertension, diabetes, cancer, lung disease, and arthritis were obtained from the Health Condition Questionnaire in the HRS core datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAll-cause Mortality\u003c/h2\u003e \u003cp\u003e For each wave, a status variable from the interview indicated whether the participant had passed away in the interval between the preceding wave and the current one. Participants\u0026rsquo; deaths were ascertained using the data on the year and month of death in the exit datasets.\u003c/p\u003e \u003cp\u003ePerson-years were computed with time 0 set as the month and year of the 2008 interview, with participants being tracked up to the 2016 wave. For participants who either passed away or withdrew from the study, person-years were calculated from the beginning of the follow-up period to the month and year when mortality or dropout was documented. For those who survived through the study period, person-years were censored as of their interview date in 2016. For this study, the dependent variable is time to event from the interviews with an indicator of censorship, where 1 was event (\u0026ldquo;died\u0026rdquo;) and 0 was censoring (\u0026ldquo;lost to follow-up\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe downloaded and merged the data from multiple HRS datasets, cleaned the data, and addressed missing value using SAS 9.4 (SAS Institute, Cary NC). For scales with missing responses, imputation was performed for the absent items using the Markov chain Monte Carlo method via PROC MI. Scores were computed using a combination of observed and imputed values, provided that the missing data constituted less than 30% of the total items.\u003c/p\u003e \u003cp\u003eWe computed descriptive statistics for the health history data from HRS. Framingham risk scores were computed using age, sex, smoker, total cholesterol, HDL cholesterol, systolic BP, and BP being treated with medicines.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e We used latent class analysis (LCA) to identify subgroups of participants with HF who experience similar symptoms (fatigue, shortness of breath, pain, swelling, depressive symptoms, and dizziness) based on patterns of categorical data. The detailed methodology of LCA for baseline symptom cluster profiles at year 2008 has been reported.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe performed survival analysis to assess the time to death with Cox Proportional Hazard (PH) models. Hazard ratios (HR) with corresponding 95% confidence intervals (CIs) were estimated using PROC PHREG. We evaluated the assumption of proportional hazards for each covariate within the Cox proportional hazards models through the application of the standardized score process test. Survival curves across the symptom cluster profiles were estimated using Kaplan Meier estimators and compared using log-rank test.\u003c/p\u003e \u003cp\u003eA parsimonious Cox PH model was built with risk factors for mortality (age, sex, race, BMI, alcohol consumption, smoking status, veteran status, comorbidities, and Framingham risk score). The model selection used a stepwise approach, retaining risk factors that had \u003cem\u003ep\u003c/em\u003e-values of less than 0.10. We then added the candidate predictors (symptom cluster profiles: high burden, low burden, cardiopulmonary-depressive) into the Cox PH models and calculated adjusted HRs after controlling for selected risk covariates. No time-dependent variables were included in the models. Kaplan Meier survival analyses using PROC LIFETEST were used for analyzing time to death across different symptom cluster profiles.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e During a mean follow-up period of 62.5 months over 8 years, we included 684 participants at baseline [mean age\u0026thinsp;=\u0026thinsp;74.9 (SD\u0026thinsp;=\u0026thinsp;10.0) years]. Over half were female, and most were White/Caucasian (80%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Framingham risk score indicated a moderate risk of cardiovascular risk (0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15). The baseline symptom prevalence and symptom cluster profiles were reported with a 3-status LCA model, including high burden profile (pain, shortness of breath, fatigue, swelling, depressive symptoms, and dizziness), low burden profile, and cardiopulmonary-depressive profile (shortness of breath, pain, and dizziness).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Clinical Characteristics at Year-2008 (N\u0026thinsp;=\u0026thinsp;684)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)/N (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.9 (10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297 (43.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387 (56.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite/Caucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (80.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack/African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/don\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnder weight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (4.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal weight (18.5\u0026ndash;24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189 (27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25.0-29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192 (28.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity (30.0-39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e208 (30.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorbid obesity (\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Partnered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (48.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e350 (51.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e487 (71.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197 (28.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVeterans status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (73.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e618 (90.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e505 (73.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (84.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e317 (46.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e367 (53.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e543 (79.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLung disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229 (33.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455 (66.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArthritis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e544 (79.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystolic BP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.98\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP being treated with medicines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFramingham risk scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOver the 8 years, 421 died and 67 were lost to follow-up. From the Kaplan Meier Survival Curve, the estimate of median time-to-death was 71 (95% CI\u0026thinsp;=\u0026thinsp;64, 79) months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The survival curves diverge quickly and the survival probability dropped to approximately 30%. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Kaplan Meier estimate of time-to-death by the three baseline symptom cluster profiles. Of the 364 participants with the cardiopulmonary-depressive profile, 240 (65.9%) died (Median time\u0026thinsp;=\u0026thinsp;65 months, 95% CI\u0026thinsp;=\u0026thinsp;55, 73) compared to those with low burden profile who have about a 49% incidence rate of death. Meanwhile, approximately 61% of participants in high burden died (Median time\u0026thinsp;=\u0026thinsp;67 months, 95% CI\u0026thinsp;=\u0026thinsp;51, 90) compared to those with low burden profile. Log-rank test showed that there was a statistically significant difference in survival times between the 3 symptom cluster profiles (Log-rank\u0026thinsp;=\u0026thinsp;9.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the unadjusted and adjusted Cox PH Models with known risk covariates. Age, gender, smoking status, alcohol consumption, diabetes, and lung disease were significantly associated with mortality in the adjusted models. With each additional year of age, the risk of death increases by 8% (95% CI\u0026thinsp;=\u0026thinsp;1.07, 1.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, participants with diabetes had a 61% higher risk of death compared to those without diabetes (HR\u0026thinsp;=\u0026thinsp;1.61, 95% CI\u0026thinsp;=\u0026thinsp;1.32, 1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Alcohol consumption was associated with a 29% reduction in risk of death compared to non-consumers, with an HR of 0.72 (95% CI\u0026thinsp;=\u0026thinsp;0.56, 0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Framingham risk score was not significantly associated with all-cause mortality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratio of time to death for known risk factors in unadjusted and adjusted Cox Proportional Hazard models (N\u0026thinsp;=\u0026thinsp;684).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.05, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 [1.07, 1.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male vs. female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.93, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28 [1.05, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.68, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.46 [1.05, 2.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20 (0.99, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61 [1.32, 1.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41 (1.16, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.43 [1.17, 1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.51, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 [0.56, 0.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.78, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.99, 1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.78, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFramingham risk score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.60 (3.34, 9.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003eNote: HR=hazard ratio; CI=confidence interval; SE= standard error\n \u003c/p\u003e \u003cp\u003eWe examined the baseline symptom cluster profiles after controlling for selected risk covariates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the adjusted HRs of the baseline symptom cluster profiles and the selected known risk covariates for prediction of mortality. Participants who belonged to the high burden profile had an adjusted HR of 1.48 (95% CI\u0026thinsp;=\u0026thinsp;1.15, 1.94), and those in the respiratory-depressive distress profile had an adjusted HR of 1.44 (95% CI\u0026thinsp;=\u0026thinsp;1.14, 1.80), compared to those with low burden symptom cluster profiles. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the estimated survival time of individuals over time and each curve represents different symptom cluster profiles. Symptom cluster profile 1 (high burden) and symptom cluster profile 3 (respiratory-depressive distress) show the lowest survival probability over eight years compared to participants in profile 2 (low burden).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted hazard ratios for each of baseline symptom cluster profiles after controlling for selected known risk factors (N\u0026thinsp;=\u0026thinsp;684).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom cluster profiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted HR [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh burden vs. low burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48 [1.15, 1.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopulmonary-depressive\u003c/p\u003e \u003cp\u003evs. low burden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.44 [1.14, 1.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 [1.06, 1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male vs. female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 [1.06, 1.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.85 [1.43, 2.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.59 [1.23, 2.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45 [1.04, 2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75 [0.59, 0.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003eNote: HR=hazard ratio; CI=confidence interval \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eOur study highlights that among community-dwelling older adults with HF, those with high burden (pain, swelling, shortness of breath, fatigue, depressive symptoms, and dizziness) and cardiopulmonary-depressive symptom cluster profiles (swelling, shortness of breath, and depressive symptoms) conveyed considerable risk for mortality over 8 years, after controlling for well-known demographic and clinical risk factors. Survival curves also revealed that these two symptom cluster profiles had a comparable higher risk of death than the group of low-burden symptoms. An evaluation of symptom clusters, rather than individual or isolated symptoms, may provide important and additive prognostic information beyond the established risk predictors of all-cause mortality in older adults with HF.\u003c/p\u003e \u003cp\u003eSpecific individual symptoms, such as dyspnea, could offer prognostic information when considered individually. However, evaluating symptom clusters allows us to capture the multifaceted nature of HF and its impact on older adults, by reflecting how symptoms interact and collectively contribute to unique prognostic implications. Our finding is consistent with the results of a randomized, placebo-controlled trial in patients with HF that found that moderate to severe symptoms were associated with a 43% increase in risk for all-cause mortality versus those who were only mildly symptomatic.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The high probabilities for shortness of breath (0.88 in the high burden profile and 1.00 in the cardiopulmonary-depressive profile), a common symptom among people with poorly compensated HF, underscores its role. One previous study also demonstrated that breathlessness (a component of the dyspneic symptom cluster) was an independent predictor of all-cause mortality, after adjusting for other risk covariates.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur finding of the associations between lower alcohol consumption and an increased risk of mortality is consistent with previous studies that light/moderate alcohol consumption was associated with reduced risk of mortality in HF.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e One study found that in community-dwelling older adults with HF, consuming up to 7 drinks per week was associated with longer survival compared to abstaining from alcohol, after adjusting for covariates.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e One explanation is that consuming alcohol in light to moderate amounts could help protect against coronary atherosclerotic events through various mechanisms, including increasing high-density lipoprotein levels, reducing plasminogen activator inhibitor activities, and improving insulin sensitivity.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e However, the authors acknowledge that their analysis was based on a single measure of alcohol consumption post-HF diagnosis, and they lacked data regarding the etiology of HF and left ventricular ejection fraction (LVEF).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eExcessive alcohol consumption is widely recognized as a major cause of death and is directly correlated with an elevated risk of HF.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Moreover, there is inconsistent evidence regarding the impact of low to moderate alcohol consumption on mortality, with many studies being limited by small sample sizes and residual confounding factors.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e These findings must be interpreted with cautious because we did not account for the amount or types of alcohol consumption. Therefore, we cannot dismiss the potential for confounding factors related to alcohol use and other prognostic influences affecting the observed association with improved survival.\u003c/p\u003e \u003cp\u003eOur findings confirm the importance of conventional HF risk factors for all-cause mortality. Poor prognosis is expected to be more likely in the older age, and we found an 8% increase in risk with every one-year increase in age. Another study found that each 10-year increment over the age of 60 years increased the risk of all-cause mortality by 24% in patients with systolic HF.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e We found that HF in men was associated with an 28% increased risk of all-cause mortality in the 8-year follow-up than in women. This finding is consistent with a study conducted in the United Kingdom that found all-cause mortality rates tended to be lower in women than men in the 75\u0026ndash;84 year-old age group over the 17-year follow-up period.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e However, women often present with HF at an older age than men and more often have HF preserved ejection fraction (HFpEF) and have less ischemic myocardial disease and later symptom onset.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Additionally, women often experience delays in diagnosis or referral for HF compared to men, and they undergo fewer diagnostic tests and medical procedures.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Stratifying risk and evaluating prognosis for HF in women presents a challenge to clinicians because current prognostic scores do not include assessments tailored specifically to sex or gender.\u003c/p\u003e \u003cp\u003eDiabetes and lung disease were associated with a 61% and 46% increased risk of mortality, respectively, our findings confirm the significance of comorbid diabetes and lung disease for all-cause mortality. Previous studies showed that Chronic Obstructive Pulmonary Disease (COPD) was significantly associated with both short-term (\u0026lt;\u0026thinsp;10 years) and long-term (\u0026gt;\u0026thinsp;10 years) all-cause mortality risks.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Among older adults participating in the Medicare program, those diagnosed with diabetes and HF experienced a mortality rate of 32.7 per 1000 person-years, in contrast to 3.7 per 1000 person-years for those without HF.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Blood glucose level may be critical, as each 18 mg/dL increase in blood glucose was associated with an adjusted 5% increase in the risk of 10-year mortality.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e A recent meta-analysis also showed the presence of a comorbid condition could predict all-cause mortality in HF (HR 1.31; 95% CI\u0026thinsp;=\u0026thinsp;1.18, 1.45).\u003csup\u003e30\u003c/sup\u003e Among 10 comorbid conditions, diabetes mellitus and COPD had the most significant pooled effect on mortality in HF.\u003c/p\u003e \u003cp\u003eOur results indicate that symptom cluster profiles can be a prognostic indicator to initiate interventions and services that prevent admissions. Individual symptoms were not used routinely in prognosis decisions, much less symptom clusters/profiles in adults with HF. Non-HF specific symptoms, such as pain, anxiety, and depressive symptoms, had strong associations with adverse outcomes, such as higher hospitalization for non-CVD causes and readmission.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Although the symptom clusters/profiles were generated according to a close statistical relationship, our results indicate that this concept has clinical relevance. The benefit of using co-occurring symptoms as symptom clusters/profiles lies in their nature as patient-reported and often present before structured clinical assessment, such as NYHA functional class.\u003c/p\u003e \u003cp\u003eOur findings highlight the importance of raising awareness of recognizing and managing co-occurring symptoms to reduce risks of death in routine community-setting care. Healthcare providers and clinicians should routinely record both cardiac and non-specific symptoms and proactively manage them, particularly managing conditions like shortness of breath in those identified with high symptom burdens or who exhibit cardiopulmonary and depressive symptoms. Clinicians in the community settings should intervene early on symptom clusters/profiles before transitioning to hospital settings, as part of comprehensive care, to reduce mortality risk for older adults with HF.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eNo data were available on the cause of death. Thus, we cannot attribute this outcome only to HF. However, when considering HF as a contributing factor in U.S. death certificates, it is estimated that HF accounts for about 72\u0026ndash;79% of deaths that are not officially reported or recognized as HF-related deaths.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The current literature reflects significant underreporting of deaths attributable to HF. Future research should examine the specific relationship between symptom clusters/profiles and HF-related mortality as it allows researchers and clinicians to understand the direct impact of HF.\u003c/p\u003e \u003cp\u003eWe did not account for the time-varying nature of symptom cluster profiles in the Cox model by adding symptom cluster profile transitions, which would change alongside the severity of HF. Further research is necessary to examine the optimal models to assist older adults with HF to monitor and report co-occurring symptoms and to test patient-centered prognostic models in a prospective cohort. This study also lacks data on the type of HF (preserved, reduced, or mid-range EF), its etiology, other cardiovascular conditions, and treatments, all of which are critical conditions that could influence HF outcomes. Other factors that may have been associated with symptom clusters and influenced mortality include medications used to control HF, as well as the presence of diabetes-related complications.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe presence of the high burden symptom cluster profile (pain, swelling, shortness of breath, fatigue, depressive symptoms, and dizziness) and the cardiopulmonary-depressive symptom cluster profile (shortness of breath, swelling, and depressive symptoms) are significantly associated with an increased risk of all-cause mortality over 8 years among community-dwelling older adults with HF, after adjusting for demographic and clinical risk factors. Health care providers and clinicians are encouraged to integrate symptom cluster management into their routine care to potentially reduce mortality risk among older adults with HF.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Heart and Retirement Study (HRS). Further details and access to the data registration process are available on the HRS website at https://hrs.isr.umich.edu/about.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures/Conflict of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZ.W. wrote the main manuscript text. S.W. and S.J. help with the statistical methods. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eD\u0026iacute;ez-Villanueva P, Jim\u0026eacute;nez-M\u0026eacute;ndez C, Alfonso F. Heart failure in the elderly. J Geriatr Cardiol. 2021;18(3):219\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11909/j.issn.1671-5411.2021.03.009\u003c/span\u003e\u003cspan address=\"10.11909/j.issn.1671-5411.2021.03.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBozkurt B, Ahmad T, Alexander KM, et al. Heart failure epidemiology and outcomes statistics: A report of the heart failure society of america. J Card Fail. 2023;29(10):1412\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cardfail.2023.07.006\u003c/span\u003e\u003cspan address=\"10.1016/j.cardfail.2023.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones NR, Roalfe AK, Adoki I, Hobbs FDR, Taylor CJ. Survival of patients with chronic heart failure in the community: a systematic review and meta-analysis. Eur J Heart Fail. 2019;21(11):1306\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ejhf.1594\u003c/span\u003e\u003cspan address=\"10.1002/ejhf.1594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePocock SJ, Ariti CA, McMurray JJV, et al. Predicting survival in heart failure: a risk score based on 39372 patients from 30 studies. Eur Heart J. 2013;34(19):1404\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehs337\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehs337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord I, Robertson M, Komajda M, et al. Top ten risk factors for morbidity and mortality in patients with chronic systolic heart failure and elevated heart rate: The SHIFT risk model. Int J Cardiol. 2015;184:163\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijcard.2015.02.001\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2015.02.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhlsson A, Eckerdal N, Lindahl B, Hanning M, Westerling R. Non-employment and low educational level as risk factors for inequitable treatment and mortality in heart failure: a population-based cohort study of register data. BMC Public Health. 2021;21(1):1040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-021-10919-1\u003c/span\u003e\u003cspan address=\"10.1186/s12889-021-10919-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePongiglione B, Torbica A, Gale CP, Tavazzi L, Vardas P, Maggioni AP. Patient, hospital and country-level risk factors of all-cause mortality among patients with chronic heart failure: Prospective international cohort study. PLoS ONE. 2021;16(5):e0250931. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0250931\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0250931\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Aronowitz P. Congestive heart failure. Med Clin North Am. 2022;106(3):447\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mcna.2021.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.mcna.2021.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli MR, Lam CSP, Str\u0026ouml;mberg A, et al. Symptoms and signs in patients with heart failure: association with 3-month hospitalisation and mortality. Heart. 2024;110(8):578\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/heartjnl-2023-323295\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2023-323295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Conley S, Redeker NS, Tocchi C. An evolutionary concept analysis in people with heart failure-symptom clusters or symptom cluster profiles? ANS Adv Nurs Sci. 2023;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/ANS.0000000000000495\u003c/span\u003e\u003cspan address=\"10.1097/ANS.0000000000000495\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. [Published online].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong EK, Moser DK, Rayens MK, Lennie TA. Symptom Clusters Predict Event-Free Survival in Patients With Heart Failure. J Cardiovasc Nurs. 2010;25(4):284\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallace RB, Herzog AR. Overview of the Health Measures in the Health and Retirement Study. J Hum Resour. 1995;30(1):S84\u0026ndash;107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealth and Retirement Study. Institutional review board. Defining Excellence Simul Programs. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1201/9780203110744-5\u003c/span\u003e\u003cspan address=\"10.1201/9780203110744-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCovinsky KE, Lindquist K, Dunlop DD, Yelin E. Pain, functional limitations, and aging. J Am Geriatr Soc. 2009;57(9):1556\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1532-5415.2009.02388.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1532-5415.2009.02388.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadloff LS, The CES-D, Scale. A Self-Report Depression Scale for Research in the General Population. Appl Psychol Meas. 1977;1(3):385\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteffick DE, Wallace RB, Regula A et al. HRS/AHEAD documentation report documentation of affective functioning measures in the health and retirement study. 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026rsquo;Agostino RBS, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.107.699579\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.107.699579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, et al. Symptom Cluster Profiles among Community-residing Older Adults with Heart Failure. Findings from the U.S. Health and Retirement Study.; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSadhu JS, Novak E, Mukamal KJ, et al. Association of alcohol consumption after development of heart failure with survival among older adults in the cardiovascular health study. JAMA Netw open. 2018;1(8):e186383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2018.6383\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2018.6383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXi B, Veeranki SP, Zhao M, Ma C, Yan Y, Mi J. Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in U.S. Adults. J Am Coll Cardiol. 2017;70(8):913\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2017.06.054\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2017.06.054\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrien SE, Ronksley PE, Turner BJ, Mukamal KJ, Ghali WA. Effect of alcohol consumption on biological markers associated with risk of coronary heart disease: systematic review and meta-analysis of interventional studies. BMJ. 2011;342:d636. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.d636\u003c/span\u003e\u003cspan address=\"10.1136/bmj.d636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSurma S, Gajos G. Alcohol, health loss and mortality: can wine really save the good name of moderate alcohol consumption? Pol Arch Intern Med. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20452/pamw.16708\u003c/span\u003e\u003cspan address=\"10.20452/pamw.16708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson C, Schou M, Gustafsson F, Torp-Pedersen C. Alcohol intake in patients with cardiomyopathy and heart failure: consensus and controversy. Circ Heart Fail. 2022;15(8):e009459. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCHEARTFAILURE.121.009459\u003c/span\u003e\u003cspan address=\"10.1161/CIRCHEARTFAILURE.121.009459\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrozd M, Relton SD, Walker AMN, et al. Association of heart failure and its comorbidities with loss of life expectancy. Heart. 2021;107(17):1417\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/heartjnl-2020-317833\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2020-317833\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBozkurt B, Khalaf S. Heart Failure in Women. Methodist Debakey Cardiovasc J. 2017;13(4):216\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.14797/mdcj-13-4-216\u003c/span\u003e\u003cspan address=\"10.14797/mdcj-13-4-216\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHayhoe B, Kim D, Aylin PP, Majeed FA, Cowie MR, Bottle A. Adherence to guidelines in management of symptoms suggestive of heart failure in primary care. Heart. 2019;105(9):678\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/heartjnl-2018-313971\u003c/span\u003e\u003cspan address=\"10.1136/heartjnl-2018-313971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulla W, Klempfner R, Natanzon S, et al. Female gender is associated with a worse prognosis amongst patients hospitalised for de-novo acute heart failure. Int J Clin Pract. 2021;75(4):e13902. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijcp.13902\u003c/span\u003e\u003cspan address=\"10.1111/ijcp.13902\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohansson I, Edner M, Dahlstr\u0026ouml;m U, N\u0026auml;sman P, Ryd\u0026eacute;n L, Norhammar A. Is the prognosis in patients with diabetes and heart failure a matter of unsatisfactory management? An observational study from the Swedish Heart Failure Registry. Eur J Heart Fail. 2014;16(4):409\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ejhf.44\u003c/span\u003e\u003cspan address=\"10.1002/ejhf.44\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItzhaki Ben Zadok O, Kornowski R, Goldenberg I, et al. Admission blood glucose and 10-year mortality among patients with or without pre-existing diabetes mellitus hospitalized with heart failure. Cardiovasc Diabetol. 2017;16(1):102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-017-0582-y\u003c/span\u003e\u003cspan address=\"10.1186/s12933-017-0582-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure, mortality, symptom clusters, symptom cluster profiles","lastPublishedDoi":"10.21203/rs.3.rs-4414292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4414292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHeart 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe sample included 684 participants (mean age\u0026thinsp;=\u0026thinsp;74.9 (SD\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;1.15, 1.94) and 1.44 (95% CI\u0026thinsp;=\u0026thinsp;1.14, 1.80) for time to death compared to those in the low burden profile.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSymptom 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.\u003c/p\u003e","manuscriptTitle":"Symptom cluster profiles predict all-cause mortality among older adults with heart failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-30 19:40:54","doi":"10.21203/rs.3.rs-4414292/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3186def6-5053-4698-aeaf-f2a1f68a0103","owner":[],"postedDate":"May 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T04:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-30 19:40:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4414292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4414292","identity":"rs-4414292","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}