Development and validation of outcome prediction models for acute kidney injury patients undergoing continuous renal replacement therapy
Bo Li, Yan Huo, Kun Zhang, Limin Chang, Haohua Zhang, Xinrui Wang, Leying Li, Zhenjie Hu
Abstract
Object This study aimed to develop and validate a set of practical predictive tools that reliably estimate the 28-day prognosis of acute kidney injury patients undergoing continuous renal replacement therapy. Methods The clinical data of acute kidney injury patients undergoing continuous renal replacement therapy were extracted from the Medical Information Mart for Intensive Care IV database with structured query language and used as the development cohort. An all-subset regression was used for the model screening. Predictive models were constructed via a logistic regression, and external validation of the models was performed using independent external data. Results Clinical prediction models were developed with clinical data from 1,148 patients and validated with data from 121 patients. The predictive model based on seven predictors (age, vasopressor use, red cell volume distribution width, lactate, white blood cell count, platelet count, and phosphate) exhibited good predictive performance, as indicated by a C-index of 0.812 in the development cohort, 0.811 in the internal validation cohort and 0.768 in the external validation cohort. Conclusions The model reliably predicted the 28-day prognosis of acute kidney injury patients undergoing continuous renal replacement therapy. The predictive items are readily available, and the web-based prognostic calculator ( https://libo220284.shinyapps.io/DynNomapp/ ) can be used as an adjunctive tool to support the management of patients.