Abstract
BACKGROUND: We aimed to identify the affecting features of persistent acute kidney injury (pAKI) for patients in intensive care units (ICU). METHODS: The Medical Information Mart for Intensive Care IV (MIMIC-IV) database and eICU Collaborative Research Database (eICU-CRD) were used to identify AKI patients with and without duration of more than 48 hours. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM-RFE) were utilized to screen for the significant clinical indexes associated with pAKI. Predictive nomogram was created based on the above informative parameters to predict the probability of pAKI. RESULTS: LASSO regression and SVM-RFE revealed that serum albumin, chronic kidney disease, AKI stage, sequential organ failure assessment score, lactate and renal replacement therapy during the first day were significantly associated with pAKI in the training cohort. The predictive nomogram based on the six predictors exhibited good predictive performance as calculated by C-index 0.730 (95% CI 0.710-0.749) in the training group, 0.702 (95% CI 0.672-0.722) in the internal validation set and 0.704 (0.677-0.731) in the external validation cohort for the prediction of pAKI. Moreover, the predictive nomogram exhibited not only encouraging calibration ability, but also great clinical utility in the training group, in the internal validation group as well as in the external validation cohort. CONCLUSION: Serum albumin, CKD, AKI stage, SOFA score, lactate, RRT during the first day were closely associated with pAKI in patients in ICU. The predictive nomogram for pAKI manifested good predictive ability for the identification of ICU patients with pAKI.