The Senior Hospitalized Adults' Risk Prediction Score for Acute Kidney Injury: Derivation and External Validation From an Observational Cohort

AIM: Acute kidney injury (AKI) is a serious complication in older patients. A practical prediction model may aid in identifying high-risk patients for early intervention. METHODS: We included hospitalized patients aged ≥ 65 years (2018-2020) with available serum creatinine (Scr). Data from 2018 to 2019 were used for model development, and data from 2020 were used for validation. The primary outcome was hospital-acquired AKI. A multivariable logistic regression model was built using baseline, latest, and trend-based variables. Four models were constructed using the least absolute shrinkage and selection operator (LASSO) and stepwise selection, and the Senior Hospitalized Adults' AKI Risk Prediction (SHARP) score was derived as a simplified scoring system. Performance was evaluated using the area under the receiver-operating characteristic curve (AUC), calibration curves, and the Hosmer-Lemeshow test. RESULTS: Among 14 576 derivation and 7328 validation admissions, AKI incidence was 6.2% and 5.3%, respectively. The model incorporated eight risk factors: heart failure, chronic kidney disease, baseline prealbumin, baseline Scr, highest B-type natriuretic peptide, highest blood urea nitrogen, highest C-reactive protein, latest heart rate. It showed strong discrimination (AUC: 0.86-0.87) and good calibration (Hosmer-Lemeshow p > 0.05). The simplified SHARP score maintained excellent performance, effectively stratifying AKI risk. In the validation cohort, the SHARP score predicted AKI with a median lead time of 40.73, 38.64, and 24.05 h for low, moderate, and high-risk groups, respectively. CONCLUSION: The SHARP score allows clinicians to quickly assess the risk of AKI in older hospitalized patients. Future studies should focus on external validation to confirm its predictive accuracy and generalizability.