【摘要】 Background Currently, there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF). This study aimed to investigate the frailty status of hospitalized CHF patients, identify independent risk factors significantly associated with frailty, and construct an effective risk prediction model. The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients. Methods Using convenience sampling, we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024. General demographic data were collected via questionnaires, alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL) scale. Clinical data were obtained by reviewing medical records. Participants were categorized into a frail group(n=65) and a non-frail group(n=119) based on frailty status. Clinical risk factors were compared between groups. Multivariate logistic regression was used to identify independent risk factors. A prediction model was constructed, and a receiver operating characteristic(ROC) curve was plotted to evaluate its predictive value. Results A total of 184 hospitalized CHF patients were included, with 65(35.33%) exhibiting frailty. Multivariate logistic regression analysis showed that independent risk factors for frailty included: age, ADL score, N-terminal pro-brain natriuretic peptide(NT-pro-BNP), left ventricular ejection fraction(LVEF), New York Heart Association(NYHA) class Ⅲ/Ⅳ, ≥3 comorbidities, comorbid diabetes mellitus(DM), comorbid valvular heart disease(VHD), smoking history, hemoglobin(Hb), albumin, high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), creatinine(Cr), and blood urea nitrogen(BUN). The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built. The prediction model showed quite strong predictive performance. Its area under the ROC curve was 0.904(95% CI: 0.857-0.951), with a sensitivity of 98.5% and a specificity of 85.7%. Conclusions The frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration. It provided substantial reference value for clinical management of CHF, offering a basis for early assessment, risk stratification, and targeted interventions to prevent frailty by identifying high-risk patients. [S Chin J Cardiol 2025; 26(2): 128-136]更多还原

【Abstract】 Background Currently, there is a scarcity of risk prediction models for frailty in hospitalized patients with chronic heart failure(CHF). This study aimed to investigate the frailty status of hospitalized CHF patients, identify independent risk factors significantly associated with frailty, and construct an effective risk prediction model. The goal was to provide a reference for clinical strategies in preventing and managing frailty among CHF patients. Methods Using convenience sampling, we enrolled 184 hospitalized CHF patients from a tertiary hospital between February 2022 and December 2024. General demographic data were collected via questionnaires, alongside frailty screening using the FRAIL scale and assessment of daily functioning with the Activities of Daily Living(ADL) scale. Clinical data were obtained by reviewing medical records. Participants were categorized into a frail group(n=65) and a non-frail group(n=119) based on frailty status. Clinical risk factors were compared between groups. Multivariate logistic regression was used to identify independent risk factors. A prediction model was constructed, and a receiver operating characteristic(ROC) curve was plotted to evaluate its predictive value. Results A total of 184 hospitalized CHF patients were included, with 65(35.33%) exhibiting frailty. Multivariate logistic regression analysis showed that independent risk factors for frailty included: age, ADL score, N-terminal pro-brain natriuretic peptide(NT-pro-BNP), left ventricular ejection fraction(LVEF), New York Heart Association(NYHA) class Ⅲ/Ⅳ, ≥3 comorbidities, comorbid diabetes mellitus(DM), comorbid valvular heart disease(VHD), smoking history, hemoglobin(Hb), albumin, high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-C), creatinine(Cr), and blood urea nitrogen(BUN). The aforementioned factors were incorporated into logistic regression analysis and the prediction model was built. The prediction model showed quite strong predictive performance. Its area under the ROC curve was 0.904(95% CI: 0.857-0.951), with a sensitivity of 98.5% and a specificity of 85.7%. Conclusions The frailty risk prediction model for hospitalized CHF patients demonstrated robust discriminative ability and calibration. It provided substantial reference value for clinical management of CHF, offering a basis for early assessment, risk stratification, and targeted interventions to prevent frailty by identifying high-risk patients. [S Chin J Cardiol 2025; 26(2): 128-136]更多还原