Correlation Analysis Between Sleep Quality and Cardiac Function Indicators in Patients with Chronic Heart Failure

Correlation Analysis Between Sleep Quality and Cardiac Function Indicators in Patients with Chronic 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 Correlation Analysis Between Sleep Quality and Cardiac Function Indicators in Patients with Chronic Heart Failure Meng-Dan Chu, Han Li, Xiao-Ying Jin, Bo-Wen Chang, Kai-Xin Peng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6776778/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 Objective: To assess the sleep quality of patients with chronic heart failure, elucidate the bidirectional relationship between heart failure and sleep quality, investigate the factors influencing sleep quality in CHF patients, and provide a scientific basis for the development of targeted intervention measures. Methods: From October 2024 to February 2025, 112 chronic heart failure patients admitted to the Fourth Affiliated Hospital of Anhui Medical University were studied. We used the PSQI to assess sleep quality and collected clinical indicators like NT-proBNP, N, L, PLT, LVEF, NYHA class, and socio-demographic data. Analysis was conducted using SPSS and Python for regression, correlation, and feature importance. Results: The study found that CHF patients had significantly higher total PSQI scores compared to domestic norms (P<0.05), with differences across all subdomains. After adjusting for confounders through multiple linear regression, NT-proBNP levels and NYHA classification were significantly positively correlated with PSQI scores (P<0.05). Diuretic therapy was identified as a potential confounder. In diuretic-treated patients, disease duration, NYHA classification, and NT-proBNP were significant predictors of total PSQI scores (P<0.05). NT-proBNP had the highest feature importance in predictive performance, and inflammatory markers like PLR and NLR also showed significant importance. Conclusion: Sleep disorders are prevalent among patients with chronic heart failure, and decreased sleep quality may further exacerbate the severity of heart failure. Sleep management may represent an important intervention direction for improving the prognosis of patients with heart failure. Heart Failure Sleep Disorders Influencing Factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heart failure (HF) remains a global health challenge, with an estimated prevalence affecting at least 26 million individuals worldwide [1]. The escalating burden of HF, driven by population aging and the rising incidence of chronic comorbidities, continues to impose substantial physiological distress on patients, degrade quality of life (QoL), and strain healthcare systems. Previous studies have identified sleep disturbances in approximately 74% of chronic HF patients [2], with disease severity serving as an independent predictor of impaired sleep quality [3]. Insomnia, a prevalent comorbidity in HF, not only exacerbates QoL deterioration [4] but may also potentiate HF progression through neurohormonal activation and inflammatory cascades [5]. However, current research inadequately addresses the mechanistic interplay between insomnia and cardiac dysfunction in chronic HF, with limited exploration of its association with key cardiac biomarkers. Existing literature predominantly focuses on the epidemiology of sleep disorders, while advanced analytics integrating multidimensional biomarkers and machine learning-driven predictive modeling remain underutilized. To address this gap, we conducted a cross-sectional investigation involving 112 consecutively enrolled chronic HF patients from our tertiary care center. The study objectives were threefold: 1) to quantify sleep quality using validated psychometric instruments; 2) to identify clinical determinants of insomnia through multivariate regression analysis; and 3) to elucidate the predictive relationship between insomnia severity and cardiac function indices via feature importance ranking in ensemble learning algorithms. This investigation provides critical insights for developing targeted sleep interventions to optimize cardiac remodeling, reduce HF readmission rates, and mitigate mortality risks. Furthermore, our machine learning framework (incorporating SHAP value interpretation) advances the pathophysiological understanding of sleep-cardiac interactions, thereby informing precision medicine strategies and enhancing holistic care paradigms in HF management. 1. Research Subjects and Methods 1.1 Research Subjects This study is a cross-sectional investigation. A total of 112 patients with chronic heart failure (CHF) admitted to the Department of General Medicine and Department of Cardiology at Chaohu Hospital Affiliated to Anhui Medical University from October 2024 to February 2025 were recruited, based on the inclusion and exclusion criteria. This study was a cross-sectional study and was reviewed and approved by the Ethics Committee of Chaohu Hospital affiliated with Anhui Medical University (Approval Number: KYXM-202405-040). All subjects signed an informed consent form. We confirm that this study was conducted in accordance with the Declaration of Helsinki. Inclusion Criteria: (1) Age between 18 and 80 years; (2) Diagnosis of chronic heart failure according to the 2024 Chinese Guidelines for the Diagnosis and Treatment of Heart Failure , with a left ventricular ejection fraction (LVEF) ≤40% (heart failure with reduced ejection fraction, HFrEF) or LVEF ≥50% (heart failure with preserved ejection fraction, HFpEF), and stable pharmacological treatment for heart failure for ≥1 month; (3) Ability to understand and comply with the study requirements and provision of informed consent. Exclusion Criteria: (1) Patients with acute heart failure or hospitalization due to acute heart failure or acute coronary syndrome (ACS) within the past month; (2) Patients diagnosed with severe psychiatric disorders such as depression, bipolar disorder, schizophrenia, or those currently using antipsychotic medications; (3) Confirmed obstructive sleep apnea (OSA) by polysomnography (apnea-hypopnea index ≥15 events/hour); (4) Patients with severe comorbidities such as recent (within 3 months) stroke, uncontrolled hypertension, or diabetic ketoacidosis; (5) Patients with estimated glomerular filtration rate (eGFR) <30 ml/min/1.73 m² or hepatic dysfunction classified as Child-Pugh B or C; (6) Patients with cancer undergoing radiotherapy, chemotherapy, or in the terminal stage of cancer; (7) Pregnant or breastfeeding women. 1.2 Research Methods 1.2.1 Demographic Data Collection Demographic data of the subjects were collected, including gender, age, ethnicity, education level, marital status, type of underlying cardiac disease, disease duration, heart function classification, Pharmacotherapy and history of other systemic diseases. 1.2.2 Insomnia Assessment The Pittsburgh Sleep Quality Index (PSQI) [6] was used to assess the sleep quality of the patients. The PSQI is a widely used self-report questionnaire designed to evaluate sleep quality over the past month. Developed by Dr. Buysse and colleagues at the University of Pittsburgh in 1989, the PSQI comprises 19 self-rated items and 5 other-rated items. The 19 self-rated items are combined into seven components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each component is scored on a scale of 0 to 3, with a total score ranging from 0 to 21. Higher scores indicate poorer sleep quality, with a score greater than 5 suggesting significant sleep difficulties. The Pittsburgh Sleep Quality Index (PSQI) is in the public domain and can be accessed at https://pubmed.ncbi.nlm.nih.gov/2748771/. 1.2.3 Laboratory and Imaging Assessments A fully automated hematology analyzer was used to measure platelet count, lymphocyte count, and neutrophil count. Plasma levels of amino terminal brain natriuretic peptide precursor (NT-proBNP) were quantitatively measured using chemiluminescence immunoassay, strictly following the manufacturer's instructions. Left ventricular ejection fraction (LVEF) was measured using a cardiac color Doppler ultrasound device in all patients. 1.3 Statistical Methods In this study, statistical software SPSS 27.0 and Python 3.13 were employed for data analysis. All data were standardized prior to analysis to ensure comparability among different variables. The Pearson correlation coefficient was used to assess the correlation between the Pittsburgh Sleep Quality Index (PSQI) and various cardiac function indicators (such as NT-proBNP and LVEF). Multivariate linear regression analysis was conducted using Ordinary Least Squares (OLS) and Least Absolute Shrinkage and Selection Operator (LASSO) regression to evaluate the impact of various factors on PSQI. The significance of the models was assessed using the F-test, while the significance of individual variables was evaluated using the t-test. The strength and direction of linear relationships among variables were measured using the Pearson correlation coefficient, and the results were displayed in the form of a heatmap. Random Forest regression was utilized to assess the impact of each variable on PSQI through a feature importance plot, and the stability of the model was verified using SHAP (SHapley Additive exPlanations). Scatter plots were employed to illustrate the relationships between key variables (such as LVEF, NT-proBNP, BMI, and NYHA classification) and PSQI, as well as the interrelationships among these variables. The significance level for all statistical tests was set at P < 0.05. 2. Results 2.1.1 Demographic Characteristics of Chronic Heart Failure Patients and Their Impact on Sleep Quality The results indicated that among patients with chronic heart failure, there were no statistically significant differences in PSQI scores when comparing by gender, age, marital status, educational level, physical activity level, duration of heart failure, and number of comorbidities (P > 0.05). However, significant differences were observed when comparing PSQI scores with cardiac functional class, NT-proBNP levels, and the use of diuretics (P < 0.05) (Table 1). Table 1. Demographic Characteristics of Chronic Heart Failure Patients and Their Impact on Sleep Quality Indicator n Proportion (%) PSQI Score (points) t/F P sex t=1.030 0.305 male 60 53.6% 7.63±3.983 female 52 46.4% 8.44±4.327 age t=－0.64 0.949 ≤75 42 37.5% 7.98±3.885 ＞ 75 70 62.5% 8.03±4.324 BMI(kg/m 2 ) t=－0.645 0.521 ＜ 24 72 7.81±3.729 ≥24 40 8.38±4.839 Marital Status t=0.701 0.485 Unmarried,Divorced,or Widowed 6 5.4% 9.17±3.488 Married 106 94.6% 7.94±4.186 Education Level F=0.383 0.766 Primary School or Below 84 75% 7.80±4.172 Junior High School 18 16.0% 8.33±4.715 Senior High School/Vocational School 7 6.3% 7.71±4.030 College Degree or Above 3 2.7% 10.33±2.082 Physical Activity Level F=2.017 0.138 None 75 67.0% 8.43±4.256 Light 31 27.7% 7.55±3.948 Moderate 6 5.3% 5.17±2.563 Smoking Status t=1.688 0.094 Yes 22 19.6% 8.33±4.168 No 90 80.4% 6.68±3.872 Heart Failure Duration t=1.164 0.247 ≤12 months 56 50.0% 8.46±4.298 ＞ 12 months 56 50.0% 7.55±3.977 β-receptor blocker t=-1.274 0.205 Yes 75 66.9% 8.41±4.281 No 37 33.1% 7.35±3.867 Diuretics t=-2.024 0.045 Yes 91 81.2 8.44±4.193 No 21 18.8 6.43±3.682 Nocturnal cough t=-0.995 0.322 Yes 43 38.3% 8.44±4.193 No 69 61.7% 8.28±4.173 Benign Prostatic Hyperplasia (BPH) t=-1.192 0.236 Yes 8 7.2% 9.75±5.339 No 104 92.8% 7.93±4.061 Comorbidity Count F=0.675 0.644 0 4 3.6% 9.75±0.957 1 12 10.7% 7.00±3.643 2 30 26.8% 7.37±4.255 3 36 32.1% 7.97±4.205 4 19 17.0% 8.89±4.108 5 11 9.8% 8.82±5.036 NYHA Functional Class F=10.077 ＜ 0.001 Ⅱ 21 18.8% 4.80±3.354 Ⅲ 72 64.3 % 8.49±4.176 Ⅳ 19 17.0% 10.00±3.317 NT-proBNP(pg/ml) F=7.739 ＜ 0.001 ≤400 15 13.3% 4.40±2.131 400-900 8 7.1% 7.75±5.064 ≥900 89 79.4% 8.71±4.046 2.1.2 Association Analysis of PSQI with Main Analytical Factors Under Different Diuretic Usage Conditions During the analysis of the general information of patients with chronic heart failure, it was found that the use of diuretics was a significant confounding factor (P < 0.05). In order to more accurately assess the relationship between PSQI and the main analytical factors while controlling for the potential confounding effect of diuretic use, stratified analysis was further conducted using Ordinary Least Squares (OLS) regression. The study subjects were divided into two groups based on diuretic usage: the diuretic user group and the non-diuretic user group. Within each stratum, the association between PSQI and the main analytical factors was re-evaluated using OLS regression, and the effect estimates and their 95% confidence intervals were calculated for each group. The results showed that in patients using diuretics, PSQI was significantly correlated with the duration of heart failure, NYHA functional class, LVEF, NT-proBNP, NLR, and PLR (P 0.05) (Table 2). Additionally, potential multicollinearity was detected in the data. Subsequently, LASSO regression was employed to regularize the data. It was found that in patients using diuretics, the duration of heart failure, NYHA functional class, NT-proBNP, and PLR were significant predictors. In patients not using diuretics, the duration of heart failure, NYHA functional class, LVEF, and NT-proBNP were significant predictors (Table 3). Table 2 Association Analysis of PSQI with Main Analytical Factors Under Different Diuretic Usage Conditions indicators T-value P-value 95%CI NO diuretics diuretics NO diuretics diuretics NO diuretics diuretics Heart Failure Duration 0.018 -3.203 0.986 0.002 -0.046－0.046 -0.039－-0.009 NYHA 1.168 3.211 0.262 0.002 -2.080－7.049 0.996－4.109 LVEF 0.742 0.469 0.470 0.641 -24.905－51.250 -6.901－11.154 NT-proBNP 0.896 2.676 0.385 0.009 -1.578－3.842 0.506－3.435 NLR -0.220 -2.038 0.829 0.045 -0.958－0.780 -0.497－-0.006 PLR 0.223 2.450 0.827 0.016 -0.017－0.021 0.002－0.020 Note: PLR (Platelet-to-Lymphocyte Ratio), NLR (Neutrophil-to-Lymphocyte Ratio) Table 3: Significance Interpretation of LASSO Regression Analysis Predict the impact factor Heart Failure Duration NYHA LVEF NTproBNP NLR PLR Use diuretics -0.953 0.802 0.057 0.743 0.000 1.227 NO-use diuretics -0.275 1.037 0.497 0.780 0.000 -0.004 2.2 Sleep Quality in Patients with Chronic Heart Failure A total of 112 patients with chronic heart failure were included in this study, among whom 52.7% had poor sleep quality. Compared with the domestic norms [7], the PSQI scores of patients with chronic heart failure were significantly higher in all seven components (subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction) as well as the total PSQI score (P < 0.05). The total PSQI score of patients with chronic heart failure was 8.06 ± 4.162, which was significantly higher than the domestic norm (3.88 ± 2.52, P < 0.001). Among the components, the most significant increases were observed in sleep latency (1.58 ± 1.249 vs. 0.70 ± 0.86, P < 0.001) and sleep efficiency (1.14 ± 1.169 vs. 0.15 ± 0.47, P < 0.001). Significant elevations were also found in subjective sleep quality (1.49 ± 0.735 vs. 0.63 ± 0.68, P < 0.001), sleep disturbances (1.45 ± 0.534 vs. 0.90 ± 0.44, P < 0.001), use of sleep medications (0.23 ± 0.759 vs. 0.06 ± 0.24, P = 0.002), and daytime dysfunction (1.12 ± 0.617 vs. 0.73 ± 0.83, P < 0.001). These data indicate that sleep problems are common and severe among patients with chronic heart failure (Table 4). Table 4 Comparison of PSQI Scores Between Patients with Chronic Heart Failure and Domestic Norms (Mean ± SD) Group Sleep Quality Sleep Latency Sleep Duration Sleep Efficiency Sleep Disturbances Hypnotic Medication Daytime Dysfunction PSQI Total Score Domestic Norm 0.63±0.68 0.70±0.86 0.70±0.58 0.15±0.47 0.90±0.44 0.06±0.24 0.73±0.83 3.88±2.52 This Study 1.49±0.735 1.58±1.249 1.05±0.879 1.14±1.169 1.45±0.534 0.23±0.759 1.12±0.617 8.06±4.162 t-value 21.46 13.389 12.69 10.35 28.65 3.24 19.30 20.50 P-value <0.001 <0.001 <0.001 <0.001 <0.001 0.002 <0.001 <0.001 2.3 Correlation Between Clinical Characteristics and Sleep Quality Scores in Chronic Heart Failure Patients 2.3.1 Correlation Analysis After normalizing the data using standardized values, this study analyzed the association between clinical indicators and sleep quality (PSQI) in patients with chronic heart failure. The results showed that the serum NT-proBNP level (a core biomarker reflecting the severity of heart failure) had a correlation coefficient of 0.50 with the total PSQI score, indicating a statistically significant positive correlation between the two (Figure 1). This suggests that as NT-proBNP levels increase, the sleep quality score (PSQI) may mildly rise, implying that the severity of heart failure can partially explain the degree of sleep disorders. The correlation coefficient between NYHA functional class and the total PSQI score was 0.37, indicating that as the NYHA classification increases, there is a tendency for the PSQI score to increase as well. 2.3.2 Random Forest Regression Analysis Through multimodel analysis of the relationship between the Pittsburgh Sleep Quality Index (PSQI) and cardiac function indicators, NT-proBNP was identified as a key predictor of PSQI. The feature importance analysis of the Random Forest model (Figure 2) showed that NT-proBNP had the highest importance score (0.445872), followed by PLR and NLR. SHAP (SHapley Additive exPlanations) value analysis (Figures 3 and 4) further indicated that NT-proBNP had the greatest impact on model output, with a mean SHAP value of 1.66 and high stability (standard deviation of 0.36). Additionally, the SHAP contribution summary plot (Figure 4) demonstrated that higher values of NT-proBNP (red) were associated with a greater positive impact on PSQI. Overall, NT-proBNP not only reflects cardiac function status but also has a close relationship with sleep quality, suggesting its potential value in clinical assessment. 2.3.3 Scatterplot Matrix Analysis By stratifying patients according to different types of left ventricular ejection fraction (LVEF) (orange: Heart Failure with Preserved Ejection Fraction [HFpEF]; green: Heart Failure with Mid-Range Ejection Fraction [HFmrEF]; blue: Heart Failure with Reduced Ejection Fraction [HFrEF]), several key associations were identified:Patients with lower LVEF (HFrEF, blue) generally have higher PSQI total scores (indicating poorer sleep quality).Patients with reduced LVEF often have a longer duration of heart failure (blue points are concentrated on the right side of the disease duration axis).Elevated NT-proBNP levels are significantly associated with low LVEF, longer disease duration, and higher PSQI scores.The distribution of PSQI scores in patients with HFmrEF (green) and HFpEF (orange) is relatively dispersed(Figure 5). 3. Discussion Insomnia is a common complication in patients with heart failure and may be influenced by various factors, such as worsening of nocturnal symptoms, increased nocturia, medication effects, and psychological factors. These factors collectively interfere with patients' sleep quality [8]. Heart failure may also exacerbate cardiac dysfunction due to sympathetic nervous system dysfunction and inflammatory reactions caused by insomnia [9]. According to previous studies, more than 70% of patients with chronic heart failure have sleep disorders [10]. In this study, the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality of patients with chronic heart failure. The total PSQI score was 8.01 ± 4.147, which was significantly higher than the domestic norm (3.88 ± 2.52, P < 0.001), indicating that the overall sleep quality of patients was severely impaired and there were significant sleep disorders. Moreover, significant differences were found in all dimensions of the PSQI (P < 0.05), suggesting that sleep problems in patients with chronic heart failure are the result of the combined effects of multiple factors. The main issues were difficulty falling asleep and low sleep efficiency. Impaired daytime function indicates that sleep problems have affected the quality of life of patients and targeted interventions are needed. The extended sleep latency and low sleep efficiency suggest a common contradiction among patients: difficulty falling asleep and long time in bed but short actual sleep time, which may be directly related to nocturnal dyspnea and restricted body position due to heart failure symptoms [11]. In addition, significant increases in sleep quality and sleep disturbance scores (both P < 0.001) reflect that patients are easily awakened at night and frequently disturbed by symptoms such as coughing and chest tightness [8]. It is worth noting that the significant impairment of daytime function is manifested as daytime somnolence and increased fatigue, which further affects the quality of life of patients. The above results suggest that sleep disorders in patients with chronic heart failure are multidimensional, with the core issues focusing on difficulty falling asleep, low sleep efficiency, and symptom-related awakenings. Clinical interventions should prioritize optimizing nocturnal heart failure management (such as adjusting the timing of diuretic use and improving nocturnal oxygen supply), while combining sleep hygiene education to reduce bad sleep habits. For patients with refractory symptoms, individualized sleep aids should be explored under the premise of fully assessing safety to mitigate the negative impact of sleep disorders on daytime function and disease prognosis. Through standardized data analysis, it was found that serum NT-proBNP levels and NYHA classification in patients with chronic heart failure were positively correlated with the sleep quality index (PSQI total score) (the former r = 0.50, the latter r = 0.34, P < 0.05), suggesting that worsening of heart failure severity may be accompanied by deteriorating sleep quality. Patients with elevated NT-proBNP may increase the risk of sleep interruption through pathological mechanisms such as nocturnal dyspnea and fluid retention [12]. Sleep disorders may also exacerbate the progression of heart failure by activating the sympathetic nervous system [13,14], forming a bidirectional interaction. At the same time, the impact of sleep quality on diuretics should also be considered. In clinical practice, patients with significantly elevated NT-proBNP and severe conditions should strengthen nocturnal symptom management (such as optimizing diuretic therapy), while combining multidimensional assessment (including psychological status and objective sleep monitoring) to develop individualized intervention strategies. Future studies need to clarify the causal relationship between the two through longitudinal research and explore the heterogeneity in heart function classification subgroups to more accurately identify high-risk populations and improve prognosis. Based on OLS and LASSO regression analysis, NYHA classification and NT-proBNP were identified as independent predictors of sleep disorders, indicating that worsening of cardiac function and elevated levels of heart failure biomarkers can significantly exacerbate sleep disorders, which is similar to the research results of Wang et al. [15]. Whether or not diuretics are used may be an important confounding factor affecting the research results: The use of diuretics generally indicates the presence of fluid retention and relatively severe conditions. Additionally, increased urination after the use of diuretics can also affect nocturnal sleep. In the absence of diuretics, the symptoms and physiological state of heart failure patients are relatively more stable, and their sleep quality may be more affected by other factors, such as psychological status and lifestyle habits. Future studies need to combine multicenter data to explore the potential role of non-significant variables and control for confounding factors. NT-proBNP is not only a marker of the severity of heart failure [16] but also a core predictor of sleep disorders. In random forest and gradient boosting models, NT-proBNP had the highest feature importance scores (0.44 and 0.468, respectively), indicating its greatest contribution to model prediction, which was further confirmed in SHAP value analysis, further supporting its direct destructive effect on sleep in combination with the pathological and physiological state of heart failure (such as fluid retention and worsening of nocturnal symptoms) [12]. Secondly, PLR and NLR showed certain feature importance in random forest, gradient boosting, and SHAP analysis (with SHAP mean values of 0.458 for PLR and 0.442 for NLR), indicating that they have some impact on sleep quality. This suggests that inflammatory activation and metabolic abnormalities may indirectly exacerbate sleep disorders through systemic reactions [17]. The lower importance of LVEF and heart failure duration reflects the limited explanatory power of structural cardiac function indicators on sleep quality. The results show that clinical interventions should focus on NT-proBNP as a key indicator, combined with other biomarkers and clinical assessment tools, to provide patients with individualized management and intervention measures. There is a significant bidirectional relationship between heart failure and insomnia. The scatterplot matrix suggests that the worse the cardiac systolic function, the higher the NT-proBNP value, and the more severe the interference with sleep; the longer the duration of heart failure, the worse the sleep quality, which is similar to the research results of Victoria M Pak et al. [18]. Long-term cardiac dysfunction may accelerate the decline of LVEF. The elevated BNP level is significantly associated with low LVEF, long duration, and high PSQI, further confirming the bridging role of NT-proBNP between the severity of heart failure and sleep disorders. Patients with preserved or mid-range ejection fraction have higher sleep quality heterogeneity, which may be more affected by non-cardiac factors (such as comorbidities and psychological status) [19]. This finding is consistent with the core role of NT-proBNP in regression analysis but emphasizes the need to develop differentiated intervention strategies in combination with LVEF stratification. In clinical practice, patients with HFrEF should prioritize optimizing cardiac function management to improve sleep, while patients with HFpEF/HFmrEF need multidimensional assessment of sleep disorder drivers. 4. Conclusion This study demonstrates that sleep disorders are prevalent among patients with chronic heart failure, with NT-proBNP levels and NYHA classification being independent predictors. These factors primarily affect patients' sleep latency and sleep efficiency. Additionally, LVEF levels and inflammatory markers also have some impact on sleep quality. NT-proBNP levels, in particular, are not only key biomarkers for assessing the severity of heart failure but also play a bridging role between heart failure and sleep disorders. The study reveals a close link between elevated NT-proBNP levels and increased risk of sleep interruption. Moreover, sleep disorders may exacerbate cardiac dysfunction by activating the sympathetic nervous system, potentially creating a vicious cycle. This finding suggests that in the management of patients with chronic heart failure, sleep quality assessment and intervention should be important considerations, in addition to optimizing cardiac function. Therefore, in the clinical management of chronic heart failure, sleep quality should be regarded as an important therapeutic and intervention target to break the potential vicious cycle and improve patients' overall health and quality of life. 5. Limitations The main limitation of this study is its observational design, which cannot directly establish causal relationships. Therefore, future longitudinal studies are needed to further validate the association between sleep quality and cardiac function indicators. Additionally, the relatively limited sample size of this study is not sufficient to fully reveal all potential influencing factors. Future studies should expand the sample scope to enhance the representativeness and statistical power of the research. Further exploration is also needed to investigate the specific effects of psychological interventions and pharmacological treatments for sleep disorders on cardiac function improvement, in order to provide stronger evidence-based support for the comprehensive management of patients with chronic heart failure. Declarations Acknowledgments This study was funded by the Major Natural Science Research Project of Anhui Provincial Universities (No. 2023AH040088). Disclosure The authors report no confficts of interest in this work. Author Contributions Meng-Dan Chu: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing–original draft; Han Li: Formal analysis, Investigation, Software; Xiao-Ying Jin: Investigation, Methodology, Validation; Bo-Wen Chang: Data curation, Methodology; Kai-Xin Peng: Validation; Ke-Han Hu: Resources; Zeng-Feng Su: Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review & editing. Consent for publication Not Applicable. Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of Chaohu Hospital affiliated with Anhui Medical University (Approval Number: KYXM-202405-040). Availability of Data and Materials The data supporting this study's findings are available from the corresponding author upon reasonable request after publication. References SAVARESE G, LUND LH. 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Sleep and Inflammation: Bidirectional Relationship[J]. Sleep Med Clin, 2023, 18(2): 213–8. 10.1016/j.jsmc.2023.02.003 YAGGI H K PAKVMSTROUSSL, et al. Mechanisms of reduced sleepiness symptoms in heart failure and obstructive sleep apnea[J]. J Sleep Res. 2019;28(5):e12778. 10.1111/jsr.12778 . GHARAIBEH B, AL-ABSI I, ABUHAMMAD S, et al. Sleep quality among different classes of heart failure patients in Jordan: A STROBE compliant cross-sectional study[J]. Med (Baltim). 2022;101(48):e32069. 10.1097/MD.0000000000032069 . 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. 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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-6776778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481155247,"identity":"fdaeeacb-6afe-4467-b35a-0900fc161aa5","order_by":0,"name":"Meng-Dan Chu","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-Dan","middleName":"","lastName":"Chu","suffix":""},{"id":481155248,"identity":"46e61f34-e546-46b9-bfc8-c72d954d12a9","order_by":1,"name":"Han Li","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Li","suffix":""},{"id":481155249,"identity":"2fda9be0-f766-4fcb-8c61-2e6c0bf91cc3","order_by":2,"name":"Xiao-Ying Jin","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Ying","middleName":"","lastName":"Jin","suffix":""},{"id":481155250,"identity":"3d723671-5f5e-493d-b4c4-2dc24c47c98b","order_by":3,"name":"Bo-Wen Chang","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bo-Wen","middleName":"","lastName":"Chang","suffix":""},{"id":481155251,"identity":"1d69ec45-73ae-4c87-9b80-f8782b17dd9b","order_by":4,"name":"Kai-Xin Peng","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kai-Xin","middleName":"","lastName":"Peng","suffix":""},{"id":481155252,"identity":"f10d8380-b8a8-4478-b26b-e5ca229c4667","order_by":5,"name":"Ke-Han Hu","email":"","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ke-Han","middleName":"","lastName":"Hu","suffix":""},{"id":481155253,"identity":"f77e8c9d-c419-4295-8911-381559d65c88","order_by":6,"name":"Zeng-Feng Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYPACOSBmPnDgww/itRgDMVviwZk9pGnhMT7MwUaEWvkZ6Q8fF/wykDPnX/PhMAMPgzy/2AH8WgxuJCQbz+wzMLac8XbD4QILBsOZsxMIaJFIOCbN2/MnccONsxsOz+BhSDC4TUCL/IzE9t+8PQb1G26ceXCYh40ILQw3ktmYeX4YJBic72EgTovBmWfM0rwNBoYbbrAZAANZgrBf5NvTH37m+WMgb3D+8OMPH37YyPNLE3IYCDC2AQkJsEoJIpSDwR8g5j9ArOpRMApGwSgYaQAAstFJHFFDkIQAAAAASUVORK5CYII=","orcid":"","institution":"The Fourth Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zeng-Feng","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-05-29 13:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6776778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6776778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86217282,"identity":"cf1dabbc-e7e5-4de9-a2b2-eb2248c0af68","added_by":"auto","created_at":"2025-07-08 06:20:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of Correlation Between Cardiac Function Indicators and Sleep Quality Index\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/e790a18db6f2ad797e7bfbe2.png"},{"id":86217285,"identity":"82dbf0b7-384f-49e8-93dc-3d92090980f8","added_by":"auto","created_at":"2025-07-08 06:20:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28477,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance of the Random Forest Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/3dfb385a091571353b008775.png"},{"id":86218355,"identity":"79616a0e-e47d-4d88-b444-1e28cc3f2e76","added_by":"auto","created_at":"2025-07-08 06:28:27","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25738,"visible":true,"origin":"","legend":"\u003cp\u003eFeature Importance Analysis Based on SHAP Values\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/d827aa4240eccb54528f2f25.png"},{"id":86217292,"identity":"2291770b-b6e2-4aa4-865b-c849ddff507c","added_by":"auto","created_at":"2025-07-08 06:20:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55045,"visible":true,"origin":"","legend":"\u003cp\u003eSHAP Value Contribution Summary Plot\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/0d276930336a2e8874fda0b5.png"},{"id":86217300,"identity":"3799c94d-3de6-4d28-ab1e-6270800eb967","added_by":"auto","created_at":"2025-07-08 06:20:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200229,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot Matrix\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/3d3a38f0d014297218127f7c.png"},{"id":90965311,"identity":"d36f22eb-f2af-4cae-a3e2-d9cfc543ec16","added_by":"auto","created_at":"2025-09-10 06:23:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1858685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6776778/v1/5d7cba31-f08c-4872-99f5-dd7f9d12e9a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Analysis Between Sleep Quality and Cardiac Function Indicators in Patients with Chronic Heart Failure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) remains a global health challenge, with an estimated prevalence affecting at least 26 million individuals worldwide [1]. The escalating burden of HF, driven by population aging and the rising incidence of chronic comorbidities, continues to impose substantial physiological distress on patients, degrade quality of life (QoL), and strain healthcare systems. Previous studies have identified sleep disturbances in approximately 74% of chronic HF patients [2], with disease severity serving as an independent predictor of impaired sleep quality [3]. Insomnia, a prevalent comorbidity in HF, not only exacerbates QoL deterioration [4] but may also potentiate HF progression through neurohormonal activation and inflammatory cascades [5]. However, current research inadequately addresses the mechanistic interplay between insomnia and cardiac dysfunction in chronic HF, with limited exploration of its association with key cardiac biomarkers. Existing literature predominantly focuses on the epidemiology of sleep disorders, while advanced analytics integrating multidimensional biomarkers and machine learning-driven predictive modeling remain underutilized.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we conducted a cross-sectional investigation involving 112 consecutively enrolled chronic HF patients from our tertiary care center. The study objectives were threefold: 1) to quantify sleep quality using validated psychometric instruments; 2) to identify clinical determinants of insomnia through multivariate regression analysis; and 3) to elucidate the predictive relationship between insomnia severity and cardiac function indices via feature importance ranking in ensemble learning algorithms. This investigation provides critical insights for developing targeted sleep interventions to optimize cardiac remodeling, reduce HF readmission rates, and mitigate mortality risks. Furthermore, our machine learning framework (incorporating SHAP value interpretation) advances the pathophysiological understanding of sleep-cardiac interactions, thereby informing precision medicine strategies and enhancing holistic care paradigms in HF management.\u003c/p\u003e"},{"header":"1. Research Subjects and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.1 Research Subjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a cross-sectional investigation. A total of 112 patients with chronic heart failure (CHF) admitted to the Department of General Medicine and Department of Cardiology at Chaohu Hospital Affiliated to Anhui Medical University from October 2024 to February 2025 were recruited, based on the inclusion and exclusion criteria. This study was a cross-sectional study and was reviewed and approved by the Ethics Committee of Chaohu Hospital affiliated with Anhui Medical University (Approval Number: KYXM-202405-040). All subjects signed an informed consent form. We confirm that this study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInclusion Criteria:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Age between 18 and 80 years;\u003c/p\u003e\n\u003cp\u003e(2) Diagnosis of chronic heart failure according to the \u003cem\u003e2024 Chinese Guidelines for the Diagnosis and Treatment of Heart Failure\u003c/em\u003e, with a left ventricular ejection fraction (LVEF) \u0026le;40% (heart failure with reduced ejection fraction, HFrEF) or LVEF \u0026ge;50% (heart failure with preserved ejection fraction, HFpEF), and stable pharmacological treatment for heart failure for \u0026ge;1 month;\u003c/p\u003e\n\u003cp\u003e(3) Ability to understand and comply with the study requirements and provision of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eExclusion Criteria:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Patients with acute heart failure or hospitalization due to acute heart failure or acute coronary syndrome (ACS) within the past month;\u003c/p\u003e\n\u003cp\u003e(2) Patients diagnosed with severe psychiatric disorders such as depression, bipolar disorder, schizophrenia, or those currently using antipsychotic medications;\u003c/p\u003e\n\u003cp\u003e(3) Confirmed obstructive sleep apnea (OSA) by polysomnography (apnea-hypopnea index \u0026ge;15 events/hour);\u003c/p\u003e\n\u003cp\u003e(4) Patients with severe comorbidities such as recent (within 3 months) stroke, uncontrolled hypertension, or diabetic ketoacidosis;\u003c/p\u003e\n\u003cp\u003e(5) Patients with estimated glomerular filtration rate (eGFR) \u0026lt;30 ml/min/1.73 m\u0026sup2; or hepatic dysfunction classified as Child-Pugh B or C;\u003c/p\u003e\n\u003cp\u003e(6) Patients with cancer undergoing radiotherapy, chemotherapy, or in the terminal stage of cancer;\u003c/p\u003e\n\u003cp\u003e(7) Pregnant or breastfeeding women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.2 Research Methods\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.2.1 Demographic Data Collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic data of the subjects were collected, including gender, age, ethnicity, education level, marital status, type of underlying cardiac disease, disease duration, heart function classification, Pharmacotherapy and history of other systemic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.2.2 Insomnia Assessment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Pittsburgh Sleep Quality Index (PSQI) [6] was used to assess the sleep quality of the patients. The PSQI is a widely used self-report questionnaire designed to evaluate sleep quality over the past month. Developed by Dr. Buysse and colleagues at the University of Pittsburgh in 1989, the PSQI comprises 19 self-rated items and 5 other-rated items. The 19 self-rated items are combined into seven components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each component is scored on a scale of 0 to 3, with a total score ranging from 0 to 21. Higher scores indicate poorer sleep quality, with a score greater than 5 suggesting significant sleep difficulties. The Pittsburgh Sleep Quality Index (PSQI) is in the public domain and can be accessed at https://pubmed.ncbi.nlm.nih.gov/2748771/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.2.3 Laboratory and Imaging Assessments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA fully automated hematology analyzer was used to measure platelet count, lymphocyte count, and neutrophil count. Plasma levels of amino terminal brain natriuretic peptide precursor (NT-proBNP) were quantitatively measured using chemiluminescence immunoassay, strictly following the manufacturer\u0026apos;s instructions. Left ventricular ejection fraction (LVEF) was measured using a cardiac color Doppler ultrasound device in all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e1.3 Statistical Methods\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, statistical software SPSS 27.0 and Python 3.13 were employed for data analysis. All data were standardized prior to analysis to ensure comparability among different variables. The Pearson correlation coefficient was used to assess the correlation between the Pittsburgh Sleep Quality Index (PSQI) and various cardiac function indicators (such as NT-proBNP and LVEF). Multivariate linear regression analysis was conducted using Ordinary Least Squares (OLS) and Least Absolute Shrinkage and Selection Operator (LASSO) regression to evaluate the impact of various factors on PSQI. The significance of the models was assessed using the F-test, while the significance of individual variables was evaluated using the t-test. The strength and direction of linear relationships among variables were measured using the Pearson correlation coefficient, and the results were displayed in the form of a heatmap. Random Forest regression was utilized to assess the impact of each variable on PSQI through a feature importance plot, and the stability of the model was verified using SHAP (SHapley Additive exPlanations). Scatter plots were employed to illustrate the relationships between key variables (such as LVEF, NT-proBNP, BMI, and NYHA classification) and PSQI, as well as the interrelationships among these variables. The significance level for all statistical tests was set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1.1 Demographic Characteristics of Chronic Heart Failure Patients and Their Impact on Sleep Quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results indicated that among patients with chronic heart failure, there were no statistically significant differences in PSQI scores when comparing by gender, age, marital status, educational level, physical activity level, duration of heart failure, and number of comorbidities (P \u0026gt; 0.05). However, significant differences were observed when comparing PSQI scores with cardiac functional class, NT-proBNP levels, and the use of diuretics (P \u0026lt; 0.05) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographic Characteristics of Chronic Heart Failure Patients and Their Impact on Sleep Quality\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProportion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSQI Score (points)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et/F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=1.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003emale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e53.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.63\u0026plusmn;3.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e46.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.44\u0026plusmn;4.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=－0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e37.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.98\u0026plusmn;3.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＞\u003c/strong\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e62.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.03\u0026plusmn;4.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=－0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.81\u0026plusmn;3.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.38\u0026plusmn;4.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnmarried,Divorced,or Widowed\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9.17\u0026plusmn;3.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarried\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e94.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.94\u0026plusmn;4.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF=0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary School or Below\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.80\u0026plusmn;4.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJunior High School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e16.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.33\u0026plusmn;4.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSenior High School/Vocational School\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.71\u0026plusmn;4.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollege Degree or Above\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e2.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10.33\u0026plusmn;2.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical Activity Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF=2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; None\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e67.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.43\u0026plusmn;4.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e27.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.55\u0026plusmn;3.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e5.17\u0026plusmn;2.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=1.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e19.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.33\u0026plusmn;4.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e80.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.68\u0026plusmn;3.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeart Failure Duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026le;12 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.46\u0026plusmn;4.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e＞\u003c/strong\u003e\u003cstrong\u003e12 months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.55\u0026plusmn;3.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;-receptor blocker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=-1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Yes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e66.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.41\u0026plusmn;4.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e33.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.35\u0026plusmn;3.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiuretics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=-2.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.44\u0026plusmn;4.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e6.43\u0026plusmn;3.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNocturnal cough\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003et=-0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e38.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.44\u0026plusmn;4.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e61.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.28\u0026plusmn;4.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign Prostatic Hyperplasia (BPH)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;t=-1.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9.75\u0026plusmn;5.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e92.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.93\u0026plusmn;4.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidity Count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF=0.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e3.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9.75\u0026plusmn;0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e10.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.00\u0026plusmn;3.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e26.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.37\u0026plusmn;4.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e32.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.97\u0026plusmn;4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.89\u0026plusmn;4.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e9.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.82\u0026plusmn;5.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNYHA Functional Class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF=10.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Ⅱ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e18.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4.80\u0026plusmn;3.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eⅢ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e64.3 %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.49\u0026plusmn;4.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eⅣ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e17.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10.00\u0026plusmn;3.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNT-proBNP(pg/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eF=7.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026le;400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e13.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4.40\u0026plusmn;2.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e400-900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e7.75\u0026plusmn;5.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;900\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e79.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8.71\u0026plusmn;4.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2 Association Analysis of PSQI with Main Analytical Factors Under Different Diuretic Usage Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the analysis of the general information of patients with chronic heart failure, it was found that the use of diuretics was a significant confounding factor (P \u0026lt; 0.05). In order to more accurately assess the relationship between PSQI and the main analytical factors while controlling for the potential confounding effect of diuretic use, stratified analysis was further conducted using Ordinary Least Squares (OLS) regression. The study subjects were divided into two groups based on diuretic usage: the diuretic user group and the non-diuretic user group. Within each stratum, the association between PSQI and the main analytical factors was re-evaluated using OLS regression, and the effect estimates and their 95% confidence intervals were calculated for each group. The results showed that in patients using diuretics, PSQI was significantly correlated with the duration of heart failure, NYHA functional class, LVEF, NT-proBNP, NLR, and PLR (P \u0026lt; 0.05). In contrast, in patients not using diuretics, PSQI showed no significant correlation with the duration of heart failure, NYHA functional class, or other factors (P \u0026gt; 0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003eAdditionally, potential multicollinearity was detected in the data. Subsequently, LASSO regression was employed to regularize the data. It was found that in patients using diuretics, the duration of heart failure, NYHA functional class, NT-proBNP, and PLR were significant predictors. In patients not using diuretics, the duration of heart failure, NYHA functional class, LVEF, and NT-proBNP were significant predictors (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Association Analysis of PSQI with Main Analytical Factors Under Different Diuretic Usage Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eindicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eT-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNO diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ediuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNO diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ediuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNO diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ediuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHeart Failure Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.046－0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.039－-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.080－7.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.996－4.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-24.905－51.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.901－11.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.578－3.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.506－3.435\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.958－0.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.497－-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.017－0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002－0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote: PLR (Platelet-to-Lymphocyte Ratio), NLR (Neutrophil-to-Lymphocyte Ratio)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Significance Interpretation of LASSO Regression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003ePredict the impact factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eHeart Failure Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNYHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNTproBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eUse diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.953\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.802\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.743\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.227\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eNO-use diuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.275\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.497\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.780\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Sleep Quality in Patients with Chronic Heart Failure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 112 patients with chronic heart failure were included in this study, among whom 52.7% had poor sleep quality. Compared with the domestic norms [7], the PSQI scores of patients with chronic heart failure were significantly higher in all seven components (subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction) as well as the total PSQI score (P \u0026lt; 0.05). The total PSQI score of patients with chronic heart failure was 8.06 \u0026plusmn; 4.162, which was significantly higher than the domestic norm (3.88 \u0026plusmn; 2.52, P \u0026lt; 0.001). Among the components, the most significant increases were observed in sleep latency (1.58 \u0026plusmn; 1.249 vs. 0.70 \u0026plusmn; 0.86, P \u0026lt; 0.001) and sleep efficiency (1.14 \u0026plusmn; 1.169 vs. 0.15 \u0026plusmn; 0.47, P \u0026lt; 0.001). Significant elevations were also found in subjective sleep quality (1.49 \u0026plusmn; 0.735 vs. 0.63 \u0026plusmn; 0.68, P \u0026lt; 0.001), sleep disturbances (1.45 \u0026plusmn; 0.534 vs. 0.90 \u0026plusmn; 0.44, P \u0026lt; 0.001), use of sleep medications (0.23 \u0026plusmn; 0.759 vs. 0.06 \u0026plusmn; 0.24, P = 0.002), and daytime dysfunction (1.12 \u0026plusmn; 0.617 vs. 0.73 \u0026plusmn; 0.83, P \u0026lt; 0.001). These data indicate that sleep problems are common and severe among patients with chronic heart failure (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026nbsp; Comparison of PSQI Scores Between Patients with Chronic Heart Failure and Domestic Norms (Mean \u0026plusmn; SD)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Latency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Disturbances\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypnotic Medication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaytime Dysfunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSQI Total Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomestic Norm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.63\u0026plusmn;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.70\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.15\u0026plusmn;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.90\u0026plusmn;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.73\u0026plusmn;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3.88\u0026plusmn;2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThis Study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.49\u0026plusmn;0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.58\u0026plusmn;1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.05\u0026plusmn;0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.14\u0026plusmn;1.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.45\u0026plusmn;0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.23\u0026plusmn;0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e8.06\u0026plusmn;4.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e21.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e13.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e12.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e28.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e19.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e20.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Correlation Between Clinical Characteristics and Sleep Quality Scores in Chronic Heart Failure Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.1 Correlation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter normalizing the data using standardized values, this study analyzed the association between clinical indicators and sleep quality (PSQI) in patients with chronic heart failure. The results showed that the serum NT-proBNP level (a core biomarker reflecting the severity of heart failure) had a correlation coefficient of 0.50 with the total PSQI score, indicating a statistically significant positive correlation between the two (Figure 1). This suggests that as NT-proBNP levels increase, the sleep quality score (PSQI) may mildly rise, implying that the severity of heart failure can partially explain the degree of sleep disorders. The correlation coefficient between NYHA functional class and the total PSQI score was 0.37, indicating that as the NYHA classification increases, there is a tendency for the PSQI score to increase as well.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e2.3.2 Random Forest Regression Analysis\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThrough multimodel analysis of the relationship between the Pittsburgh Sleep Quality Index (PSQI) and cardiac function indicators, NT-proBNP was identified as a key predictor of PSQI. The feature importance analysis of the Random Forest model (Figure 2) showed that NT-proBNP had the highest importance score (0.445872), followed by PLR and NLR. SHAP (SHapley Additive exPlanations) value analysis (Figures 3 and 4) further indicated that NT-proBNP had the greatest impact on model output, with a mean SHAP value of 1.66 and high stability (standard deviation of 0.36). Additionally, the SHAP contribution summary plot (Figure 4) demonstrated that higher values of NT-proBNP (red) were associated with a greater positive impact on PSQI. Overall, NT-proBNP not only reflects cardiac function status but also has a close relationship with sleep quality, suggesting its potential value in clinical assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.3 Scatterplot Matrix Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy stratifying patients according to different types of left ventricular ejection fraction (LVEF) (orange: Heart Failure with Preserved Ejection Fraction [HFpEF]; green: Heart Failure with Mid-Range Ejection Fraction [HFmrEF]; blue: Heart Failure with Reduced Ejection Fraction [HFrEF]), several key associations were identified:Patients with lower LVEF (HFrEF, blue) generally have higher PSQI total scores (indicating poorer sleep quality).Patients with reduced LVEF often have a longer duration of heart failure (blue points are concentrated on the right side of the disease duration axis).Elevated NT-proBNP levels are significantly associated with low LVEF, longer disease duration, and higher PSQI scores.The distribution of PSQI scores in patients with HFmrEF (green) and HFpEF (orange) is relatively dispersed(Figure 5).\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eInsomnia is a common complication in patients with heart failure and may be influenced by various factors, such as worsening of nocturnal symptoms, increased nocturia, medication effects, and psychological factors. These factors collectively interfere with patients' sleep quality [8]. Heart failure may also exacerbate cardiac dysfunction due to sympathetic nervous system dysfunction and inflammatory reactions caused by insomnia [9]. According to previous studies, more than 70% of patients with chronic heart failure have sleep disorders [10]. In this study, the Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality of patients with chronic heart failure. The total PSQI score was 8.01 ± 4.147, which was significantly higher than the domestic norm (3.88 ± 2.52, P \u0026lt; 0.001), indicating that the overall sleep quality of patients was severely impaired and there were significant sleep disorders. Moreover, significant differences were found in all dimensions of the PSQI (P \u0026lt; 0.05), suggesting that sleep problems in patients with chronic heart failure are the result of the combined effects of multiple factors. The main issues were difficulty falling asleep and low sleep efficiency. Impaired daytime function indicates that sleep problems have affected the quality of life of patients and targeted interventions are needed. The extended sleep latency and low sleep efficiency suggest a common contradiction among patients: difficulty falling asleep and long time in bed but short actual sleep time, which may be directly related to nocturnal dyspnea and restricted body position due to heart failure symptoms [11]. In addition, significant increases in sleep quality and sleep disturbance scores (both P \u0026lt; 0.001) reflect that patients are easily awakened at night and frequently disturbed by symptoms such as coughing and chest tightness [8]. It is worth noting that the significant impairment of daytime function is manifested as daytime somnolence and increased fatigue, which further affects the quality of life of patients. The above results suggest that sleep disorders in patients with chronic heart failure are multidimensional, with the core issues focusing on difficulty falling asleep, low sleep efficiency, and symptom-related awakenings. Clinical interventions should prioritize optimizing nocturnal heart failure management (such as adjusting the timing of diuretic use and improving nocturnal oxygen supply), while combining sleep hygiene education to reduce bad sleep habits. For patients with refractory symptoms, individualized sleep aids should be explored under the premise of fully assessing safety to mitigate the negative impact of sleep disorders on daytime function and disease prognosis.\u003c/p\u003e\n\u003cp\u003eThrough standardized data analysis, it was found that serum NT-proBNP levels and NYHA classification in patients with chronic heart failure were positively correlated with the sleep quality index (PSQI total score) (the former r = 0.50, the latter r = 0.34, P \u0026lt; 0.05), suggesting that worsening of heart failure severity may be accompanied by deteriorating sleep quality. Patients with elevated NT-proBNP may increase the risk of sleep interruption through pathological mechanisms such as nocturnal dyspnea and fluid retention [12]. Sleep disorders may also exacerbate the progression of heart failure by activating the sympathetic nervous system [13,14], forming a bidirectional interaction. At the same time, the impact of sleep quality on diuretics should also be considered. In clinical practice, patients with significantly elevated NT-proBNP and severe conditions should strengthen nocturnal symptom management (such as optimizing diuretic therapy), while combining multidimensional assessment (including psychological status and objective sleep monitoring) to develop individualized intervention strategies. Future studies need to clarify the causal relationship between the two through longitudinal research and explore the heterogeneity in heart function classification subgroups to more accurately identify high-risk populations and improve prognosis.\u003c/p\u003e\n\u003cp\u003eBased on OLS and LASSO regression analysis, NYHA classification and NT-proBNP were identified as independent predictors of sleep disorders, indicating that worsening of cardiac function and elevated levels of heart failure biomarkers can significantly exacerbate sleep disorders, which is similar to the research results of Wang et al. [15]. Whether or not diuretics are used may be an important confounding factor affecting the research results: The use of diuretics generally indicates the presence of fluid retention and relatively severe conditions. Additionally, increased urination after the use of diuretics can also affect nocturnal sleep. In the absence of diuretics, the symptoms and physiological state of heart failure patients are relatively more stable, and their sleep quality may be more affected by other factors, such as psychological status and lifestyle habits. Future studies need to combine multicenter data to explore the potential role of non-significant variables and control for confounding factors.\u003c/p\u003e\n\u003cp\u003eNT-proBNP is not only a marker of the severity of heart failure [16] but also a core predictor of sleep disorders. In random forest and gradient boosting models, NT-proBNP had the highest feature importance scores (0.44 and 0.468, respectively), indicating its greatest contribution to model prediction, which was further confirmed in SHAP value analysis, further supporting its direct destructive effect on sleep in combination with the pathological and physiological state of heart failure (such as fluid retention and worsening of nocturnal symptoms) [12]. Secondly, PLR and NLR showed certain feature importance in random forest, gradient boosting, and SHAP analysis (with SHAP mean values of 0.458 for PLR and 0.442 for NLR), indicating that they have some impact on sleep quality. This suggests that inflammatory activation and metabolic abnormalities may indirectly exacerbate sleep disorders through systemic reactions [17]. The lower importance of LVEF and heart failure duration reflects the limited explanatory power of structural cardiac function indicators on sleep quality. The results show that clinical interventions should focus on NT-proBNP as a key indicator, combined with other biomarkers and clinical assessment tools, to provide patients with individualized management and intervention measures.\u003c/p\u003e\n\u003cp\u003eThere is a significant bidirectional relationship between heart failure and insomnia. The scatterplot matrix suggests that the worse the cardiac systolic function, the higher the NT-proBNP value, and the more severe the interference with sleep; the longer the duration of heart failure, the worse the sleep quality, which is similar to the research results of Victoria M Pak et al. [18]. Long-term cardiac dysfunction may accelerate the decline of LVEF. The elevated BNP level is significantly associated with low LVEF, long duration, and high PSQI, further confirming the bridging role of NT-proBNP between the severity of heart failure and sleep disorders. Patients with preserved or mid-range ejection fraction have higher sleep quality heterogeneity, which may be more affected by non-cardiac factors (such as comorbidities and psychological status) [19]. This finding is consistent with the core role of NT-proBNP in regression analysis but emphasizes the need to develop differentiated intervention strategies in combination with LVEF stratification. In clinical practice, patients with HFrEF should prioritize optimizing cardiac function management to improve sleep, while patients with HFpEF/HFmrEF need multidimensional assessment of sleep disorder drivers.\u003c/p\u003e\n\n"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study demonstrates that sleep disorders are prevalent among patients with chronic heart failure, with NT-proBNP levels and NYHA classification being independent predictors. These factors primarily affect patients' sleep latency and sleep efficiency. Additionally, LVEF levels and inflammatory markers also have some impact on sleep quality. NT-proBNP levels, in particular, are not only key biomarkers for assessing the severity of heart failure but also play a bridging role between heart failure and sleep disorders. The study reveals a close link between elevated NT-proBNP levels and increased risk of sleep interruption. Moreover, sleep disorders may exacerbate cardiac dysfunction by activating the sympathetic nervous system, potentially creating a vicious cycle. This finding suggests that in the management of patients with chronic heart failure, sleep quality assessment and intervention should be important considerations, in addition to optimizing cardiac function. Therefore, in the clinical management of chronic heart failure, sleep quality should be regarded as an important therapeutic and intervention target to break the potential vicious cycle and improve patients' overall health and quality of life.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eThe main limitation of this study is its observational design, which cannot directly establish causal relationships. Therefore, future longitudinal studies are needed to further validate the association between sleep quality and cardiac function indicators. Additionally, the relatively limited sample size of this study is not sufficient to fully reveal all potential influencing factors. Future studies should expand the sample scope to enhance the representativeness and statistical power of the research. Further exploration is also needed to investigate the specific effects of psychological interventions and pharmacological treatments for sleep disorders on cardiac function improvement, in order to provide stronger evidence-based support for the comprehensive management of patients with chronic heart failure.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Major Natural Science Research Project of Anhui Provincial Universities (No. 2023AH040088).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no confficts of interest in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeng-Dan Chu: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing\u0026ndash;original draft; Han Li: Formal analysis, Investigation, Software; Xiao-Ying Jin: Investigation, Methodology, Validation; Bo-Wen Chang: Data curation, Methodology; Kai-Xin Peng: Validation; Ke-Han Hu: Resources; Zeng-Feng Su: Conceptualization, Funding acquisition, Project administration, Supervision, Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethics Committee of Chaohu Hospital affiliated with Anhui Medical University (Approval Number: KYXM-202405-040).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are available from the corresponding author upon reasonable request after publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSAVARESE G, LUND LH. 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Med (Baltim). 2022;101(48):e32069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000032069\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000032069\" 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, Sleep Disorders, Influencing Factors","lastPublishedDoi":"10.21203/rs.3.rs-6776778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6776778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To assess the sleep quality of patients with chronic heart failure, elucidate the bidirectional relationship between heart failure and sleep quality, investigate the factors influencing sleep quality in CHF patients, and provide a scientific basis for the development of targeted intervention measures.\u003cbr\u003e\n\u003cstrong\u003eMethods: \u003c/strong\u003eFrom October 2024 to February 2025, 112 chronic heart failure patients admitted to the Fourth Affiliated Hospital of Anhui Medical University were studied. We used the PSQI to assess sleep quality and collected clinical indicators like NT-proBNP, N, L, PLT, LVEF, NYHA class, and socio-demographic data. Analysis was conducted using SPSS and Python for regression, correlation, and feature importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study found that CHF patients had significantly higher total PSQI scores compared to domestic norms (P\u0026lt;0.05), with differences across all subdomains. After adjusting for confounders through multiple linear regression, NT-proBNP levels and NYHA classification were significantly positively correlated with PSQI scores (P\u0026lt;0.05). Diuretic therapy was identified as a potential confounder. In diuretic-treated patients, disease duration, NYHA classification, and NT-proBNP were significant predictors of total PSQI scores (P\u0026lt;0.05). NT-proBNP had the highest feature importance in predictive performance, and inflammatory markers like PLR and NLR also showed significant importance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Sleep disorders are prevalent among patients with chronic heart failure, and decreased sleep quality may further exacerbate the severity of heart failure. Sleep management may represent an important intervention direction for improving the prognosis of patients with heart failure.\u003c/p\u003e","manuscriptTitle":"Correlation Analysis Between Sleep Quality and Cardiac Function Indicators in Patients with Chronic Heart Failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 06:20:23","doi":"10.21203/rs.3.rs-6776778/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":"b68b409b-19a6-493d-b2c7-5f649043e613","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-10T06:23:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 06:20:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6776778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6776778","identity":"rs-6776778","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}