Development and Validation of a Prediction Model for Acute Kidney Injury Among Patients With Acute Decompensated Heart Failure - Wang, Lei; Zhao, Yun-Tao (Frontiers in Cardiovascular Medicine, 2021)
Background Acute kidney injury (AKI) is an adverse event that carries significant morbidity among patients with acute decompensated heart failure (ADHF). We planned to develop a parsimonious model which is simple enough to use in clinical practice to predict the risk of acute kidney injury occurrence. Methods 650 ADHF patients were enrolled in this study. Data for each patient were collected from the medical records. We took three different approaches of variable selection to derive four multivariable logistic regression model. We selected six candidate predictors which lead to a relatively stable outcome in different models to derive the final prediction model. The prediction model was verified through the use of the C-Statistics, the calibration curve. Results AKI occurred in 42.8% of the patients. Advanced age, diabetes, previous renal dysfunction, high baseline creatinine, high B-type natriuretic peptide and hypoalbuminemia were the strongest predictors for AKI. The prediction model showed moderate discrimination C-Statistics: 0.766 (95% CI, 0.729–0.803) and good identical calibration. Conclusion In this study, we developed a prediction model and nomogram to estimate the risk of AKI among patients with ADHF. It may help clinical physicians detect AKI and manage it promptly.