Influencing factors and profile differences in death attitudes of advance care planning readiness among patients with chronic heart failure
A latent profile analysis
Bibliographic Data
| ID | 22090647 |
|---|---|
| Authors | Jian Lin (0000-0003-0436-0858, Huazhong University of Science and Technology), Mengying Yang (Central Hospital of Wuhan), Ruijia Xu (Central Hospital of Wuhan), Yunchao Deng (Central Hospital of Wuhan), Ting Huang (0000-0003-4186-8714, Central Hospital of Wuhan) |
| Year | 2026 |
| Volume | 14 |
| Pages | 1825995-1825995 |
| Publication date | 2026-04-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2026.1825995 |
| PMID | 42136611 |
| OpenAlex | W7159628475 |
| Language | EN |
| References cited | 36 |
Objective A cross-sectional study was conducted in a tertiary A-level general hospital in Wuhan, China. The study aimed to explore the latent profiles of advance care planning (ACP) readiness among patients with chronic heart failure (CHF), analyze the influencing factors of different profiles, and compare the differences in death attitudes between profiles. Methods From February to December 2025, a total of 313 CHF patients were recruited by convenience sampling from the Department of Cardiovascular Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology. The participants were investigated using a general information questionnaire, the ACP Readiness Scale, the Death Attitude Profile-Revised, and the Social Support Rating Scale. Latent profile analysis (LPA) was conducted using Mplus 8.3 to identify latent profiles of ACP readiness. Binary logistic regression was used to explore the influencing factors of profile membership, and the Bolck-Croon-Hagenaars (BCH) method was employed to compare differences in death attitudes between the latent profiles. Results Two latent profiles of ACP readiness were identified: the “low ACP readiness” group (56.87%) and the “high ACP readiness” group (43.13%). Binary logistic regression showed that higher educational level, longer disease course, higher NYHA functional class, and greater social support were statistically significant positive predictors of belonging to the “high ACP readiness” group ( P 0.05). Conclusion There is significant heterogeneity in ACP readiness among CHF patients. Educational level, disease course, NYHA functional class, and social support are important factors influencing profile membership. The two profiles differ significantly in fear of death, death avoidance, and natural acceptance. Healthcare providers should develop targeted intervention strategies based on the characteristics of different profiles and strengthen death education to enhance patients' ACP readiness
Advance care planning · Cause of death · Disease · Latent class model · Logistic regression · Regression analysis · Social support · Chronic Disease Management Strategies · Heart Failure Treatment and Management · Palliative Care and End-of-Life Issues
| Highly cited | No |
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