Admission-based predictors of in-hospital AKI in HFrEF: a single-centre retrospective study
Year: 2026
Type: article
Study design: Observational Study
Abstract: 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.
Source: Frontiers in Physiology
Language: English
Topic: Acute Kidney Injury Research
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: Harbin Medical University
OpenAlex ID: W7221478464