Hamadan University of Medical Sciences Journal of Research in Health Sciences 2228-7795 25 2 2025 04 01 Predictors of Poor Sleep Quality in Heart Failure Patients: A Cross-sectional Multivariable Analysis of Clinical, Demographic, and Psychosocial Factors e00649 e00649 10.34172/jrhs.2025.184 EN Soroush Najdaghi https://orcid.org/0009-0001-4555-5718 Delaram Narimani Davani https://orcid.org/0009-0007-3881-0040 Davood Shafie https://orcid.org/0000-0002-7999-2393 Journal Article 10.34172/jrhs.2025.184 2024 10 02 2024 12 30 Background: Sleep quality is crucial in heart failure (HF) patients, yet its associations with clinical, demographic, and psychosocial factors remain underexplored. This study examined these relationships to identify predictors of poor sleep quality. Study Design: A cross-sectional study. Methods: This cross-sectional was conducted on 354 HF patients at Shahid Chamran Heart Hospital, Isfahan, Iran (September 2023-2024). Pittsburgh Sleep Quality Index (PSQI) and Hospital Anxiety and Depression Scale (HADS) were used to evaluate sleep quality, anxiety, and depression, respectively. Random forest (RF) modeling and ordinary least squares (OLS) regression identified predictors of poor sleep quality. Results: Poor sleep quality (PSQI>7) was observed in 18% of patients who were older (70.00±6.30, P<0.001) and had lower ejection fraction (EF) (23.75±12.79%, P<0.001). This group also had higher systolic blood pressure (BP 140.67±12.50 mmHg, P=0.014). Complex medication regimens, including angiotensin-converting enzyme (ACE) inhibitors, beta-blockers, and diuretics, were associated with poor sleep (P<0.001). Moreover, depression (HADS-depression: 5.36±0.70, P<0.001) and anxiety (HADS-anxiety: 4.84±1.32, P<0.001) were correlated with poor sleep. The RF model had an area under the curve of 0.79, and OLS regression (R2=0.280) highlighted New York Heart Association (NYHA) class and medication type as significant predictors. Conclusion: Overall, poor sleep quality in HF patients was related to older age, reduced cardiac function, higher blood pressure (BP), complex medication regimens, and increased anxiety and depression. Accordingly, multidimensional management strategies are needed to improve sleep outcomes.