Clinical interventions in aging · 2026
Identification of Risk Factors and Prediction Models for Acute Kidney Injury in Hospitalized Elderly Patients with Heart Failure.
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Liu Mingyu, Qiu Dongxia, Qiu Shuang, Huang Wanqing, Qi Lemuge, Zhou Jinjie, Gong Xiaolan, Tao Yongyuan, Bai Jiuxu, Cao Ning
Аннотация
BACKGROUND: Elderly heart failure (HF) patients have an increased risk of acute kidney injury (AKI) during hospitalization. Identifying risk factors and developing a prediction model are clinically important.
STUDY METHOD: We retrospectively enrolled 7076 elderly HF patients hospitalized from January 2019 to December 2023. All predictors were restricted to data available within 24 hours of admission and prior to AKI occurrence. Candidate predictors were first screened by univariate analysis (P<0.05) and then further refined by LASSO regression, and the selected variables were ultimately entered into multivariable logistic regression. Thirteen machine learning models were constructed and compared to identify the optimal model. Model performance was evaluated using the area under the ROC curve, calibration curves, and decision curve analysis.
RESULTS: AKI occurred in 796 patients (11.2%). The final model included diuretic use, hypertension, diabetes, valvular heart disease, COPD, CKD, admission cystatin C, and LVEF. Among the 13 machine learning models, logistic regression performed best in the validation set (AUC = 0.804, 95% CI: 0.777-0.830). Calibration was acceptable in both the training and validation sets, with Hosmer-Lemeshow P values of 0.561 and 0.605, calibration slopes of 0.896 and 0.835, and Brier scores of 0.078 and 0.082, respectively. Decision curve analysis confirmed its clinical utility. A nomogram was constructed for model visualization.
CONCLUSION: In this single-center study with internal validation only, the prediction model demonstrated favorable performance for in-hospital AKI in elderly HF patients and may serve as a candidate tool for early risk stratification. External validation in independent cohorts is required before routine clinical implementation.
STUDY METHOD: We retrospectively enrolled 7076 elderly HF patients hospitalized from January 2019 to December 2023. All predictors were restricted to data available within 24 hours of admission and prior to AKI occurrence. Candidate predictors were first screened by univariate analysis (P<0.05) and then further refined by LASSO regression, and the selected variables were ultimately entered into multivariable logistic regression. Thirteen machine learning models were constructed and compared to identify the optimal model. Model performance was evaluated using the area under the ROC curve, calibration curves, and decision curve analysis.
RESULTS: AKI occurred in 796 patients (11.2%). The final model included diuretic use, hypertension, diabetes, valvular heart disease, COPD, CKD, admission cystatin C, and LVEF. Among the 13 machine learning models, logistic regression performed best in the validation set (AUC = 0.804, 95% CI: 0.777-0.830). Calibration was acceptable in both the training and validation sets, with Hosmer-Lemeshow P values of 0.561 and 0.605, calibration slopes of 0.896 and 0.835, and Brier scores of 0.078 and 0.082, respectively. Decision curve analysis confirmed its clinical utility. A nomogram was constructed for model visualization.
CONCLUSION: In this single-center study with internal validation only, the prediction model demonstrated favorable performance for in-hospital AKI in elderly HF patients and may serve as a candidate tool for early risk stratification. External validation in independent cohorts is required before routine clinical implementation.