Key result
Cox proportional hazards modeling achieved a C-index of 0.7408 and mean time-dependent AUC of 0.7506 for heart failure mortality, with serum creatinine and age as key predictors.
Why the study?
This study was conducted to analyze survival outcomes and identify significant clinical risk factors in heart failure patients using the Cox Proportional Hazards model.
Does a Cox Proportional Hazards survival analysis model identify significant clinical predictors of mortality and estimate individualized survival probabilities in patients aged ≥40 years with NYHA class III-IV heart failure and left ventricular systolic dysfunction?
Cohort (n=299)
Yes
Does a Cox Proportional Hazards survival analysis model identify significant clinical predictors of mortality and estimate individualized survival probabilities in patients aged ≥40 years with NYHA class III-IV heart failure and left ventricular systolic dysfunction?
Cox proportional hazards survival analysis identifies age, serum creatinine, hypertension, anaemia, creatinine phosphokinase, and ejection fraction as significant predictors of mortality in heart failure patients, offering continuous time-dependent survival estimation and risk stratification superior to static binary classification.
Answers are grounded in this paper and cite their sources.
May support creatinine-based HF risk models; leaves open prospective validation before clinical use.
This study applies the Cox Proportional Hazards model to analyze survival outcomes in heart failure patients using the Heart Failure Clinical Records dataset. Kaplan–Meier analysis used to estimate survival probabilities, Cox regression employed to identify significant clinical risk factors. The fitted full-cohort model yielded apparent Concordance Index of 0.7408, whereas held-out test evaluation from stratified 75:25 train–test split yielded a mean time-dependent AUC of 0.7506 and Integrated Brier Score of 0.1658. Age, serum creatinine, high blood pressure, anaemia, creatinine phosphokinase, and ejection fraction were identified as significant predictors of mortality. The results indicate that survival analysis provides more comprehensive prognostic information than binary classification because it estimates both mortality risk and patient survival probability. This approach also support patient risk stratification and facilitate more targeted clinical decision-making. This approach provides interpretable framework for patient risk profiling and individualized survival estimation, but external validation is required before clinical use. Penelitian ini menerapkan model Cox Proportional Hazards untuk menganalisis ketahanan hidup pasien gagal jantung menggunakan dataset Heart Failure Clinical Records. Analisis Kaplan–Meier digunakan untuk mengestimasi probabilitas ketahanan hidup, regresi Cox digunakan untuk mengidentifikasi faktor risiko klinis yang signifikan. Model full-cohort menghasilkan apparent Concordance Index sebesar 0,7408, evaluasi pada held-out test set dari stratified 75:25 train–test split menghasilkan mean time-dependent AUC sebesar 0,7506 dan Integrated Brier Score sebesar 0,1658. Usia, serum creatinine, hipertensi, anemia, creatinine phosphokinase, dan ejection fraction menjadi prediktor signifikan terhadap mortalitas. Hasil penelitian menunjukkan bahwa analisis survival memberikan informasi prognosis yang lebih komprehensif dibandingkan klasifikasi biner karena mampu mengestimasi risiko kematian dan probabilitas ketahanan hidup. Pendekatan ini juga dapat mendukung stratifikasi risiko pasien serta pengambilan keputusan klinis yang lebih terarah. Pendekatan ini memberikan kerangka yang dapat diinterpretasikan untuk profil risiko dan estimasi survival individual, tetapi memerlukan validasi eksternal sebelum digunakan untuk mendukung keputusan klinis.
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Abdillah et al. (2026) conducted a cohort in Heart Failure (NYHA Class III or IV with left ventricular systolic dysfunction) (n=299). Clinical risk factors (serum creatinine, age, high blood pressure, anaemia, creatinine phosphokinase, ejection fraction) modeled via Cox Proportional Hazards vs. Lower-risk clinical profiles / absence of risk factors was evaluated on All-cause mortality during follow-up (evaluated via Cox regression for serum creatinine per 1 mg/dL increase) (HR 1.3710, 95% CI 1.1963-1.5713, p=<0.001). Cox proportional hazards modeling achieved a C-index of 0.7408 and mean time-dependent AUC of 0.7506 for heart failure mortality, with serum creatinine and age as key predictors.
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