Key result
Five-variable admission model predicts in-hospital acute kidney injury with an AUC of ~0.81.
Why the study?
A concise prediction model using only admission data for in-hospital AKI risk in HFrEF patients was lacking.
Cohort (n=1,071)
No
Effect estimate: AUC-ROC 0.81 (95% CI 0.77-0.85)
A parsimonious five-variable model using only presentation data (age, furosemide-equivalent dose, hypotension, RDW, and eGFR) can accurately predict early in-hospital AKI in patients admitted with HFrEF.
May enable early AKI risk stratification in HFrEF admissions; leaves open external validation and outcome impact trials.
Objective This study aimed to construct and internally validate a concise prediction model for in-hospital acute kidney injury (AKI) among patients admitted with heart failure with reduced ejection fraction (HFrEF). The model was limited to information available at hospital presentation. Methods We retrospectively analyzed 1,071 HFrEF hospitalizations at the Fourth Affiliated Hospital of Harbin Medical University from September 2020 to September 2022. The outcome was incident AKI during the index admission, defined using a modified KDIGO (48-h serum creatinine) criterion, based on an increase of ≥26.5 µmol/L within 48 h. Candidate variables measured at admission were screened with least absolute shrinkage and selection operator (LASSO) logistic regression using the 1−SE rule. Bootstrap stability and clinical parsimony were then considered to preserve temporal validity and bedside usability. Internal validation was performed using 5-fold stratified out-of-fold cross-validation. Results In-hospital AKI occurred in 118 of the 1,071 patients (11.0%). Five key on-admission clinical parameters were identified as the strongest predictors: age, furosemide-equivalent dose, hypotension on presentation, RDW, eGFR. In internal validation, the combination of these five strictly admission-based parameters achieved an AUC-ROC of 0.81 [95% bootstrap confidence interval (CI): 0.77–0.85], a precision-recall AUC (PR-AUC) of 0.47, a Brier score of 0.08, a calibration intercept of −0.02, and a calibration slope of 0.98. Decision curve analysis demonstrated clinical utility with a positive net benefit across most clinically plausible decision thresholds. Conclusion A five-predictor model based entirely on admission data can provide interpretable early risk stratification for in-hospital AKI in patients hospitalized with HFrEF. By excluding post-admission treatment responses and weight change, the model reduces reverse-causality bias and may support renal safety decisions at the time of presentation.
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Dai et al. (2026) conducted a cohort in Heart failure with reduced ejection fraction (HFrEF) (n=1,071). Five-variable on-admission clinical prediction model (age, furosemide-equivalent dose, hypotension, RDW, eGFR) was evaluated on New-onset acute kidney injury (AKI) during the index hospitalization (AUC-ROC 0.81, 95% CI 0.77-0.85). A five-variable prediction model based strictly on admission data (age, furosemide-equivalent dose, hypotension, RDW, and eGFR) accurately predicted early in-hospital acute kidney injury with an AUC-ROC of 0.81.
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