Prediction and Clinically Important Factors of Acute Kidney Injury Non-recovery - Liu, Chien-Liang; Tain, You-Lin; Lin, Yun-Chun; Hsu, Chien-Ning (Frontiers in Medicine, 2022)
Objective: This study aimed to identify phenotypic clinical features associated with acute kidney injury (AKI) to predict nonrecovery from AKI at hospital discharge after AKI hospitalization using electronic health record data. Methods: Data for hospitalized patients in the Acute Kidney Injury Recovery Evaluation Study were derived from a large healthcare delivery system in Taiwan between January 2011 and December 2017. Living patients with AKI nonrecovery were used to derive and validate multiple feature predictive models. Sixty-four candidate variables, including demographic characteristics, comorbidities, healthcare services utilization, laboratory values, and nephrotoxic medication use, were measured within 1 year before the index admission and during hospitalization for AKI. Results: Among the top 20 important features in the predictive model, 8 features had a positive effect on AKI nonrecovery prediction: AKI during hospitalization, serum creatinine level at admission, receipt of dialysis during hospitalization, baseline comorbidity of cancer, AKI at admission, baseline lymphocyte count, and baseline potassium and low-density lipoprotein cholesterol levels. The predicted AKI nonrecovery risk model using the eXtreme Gradient Boosting algorithm achieved an area under the receiver operating characteristic curve statistic of 0.807, discrimination with a sensitivity of 0.724, and a specificity of 0.738 in the temporal validation cohort. Conclusion: The machine learning model approach can accurately predict AKI nonrecovery using routinely collected health data in clinical practice. These results suggest that multifactorial risk factors are involved in AKI nonrecovery, requiring patient-centered risk assessments and promotion of post-discharge AKI care to prevent AKI complications.