1(Department of Biomedical Engineering South China University of Technology,Guangzhou 510006, China) 2(Division of Nephrology Guangdong Provincial People′s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China)
Abstract Acute kidney injury (AKI) is one of the most common critical diseases in inpatients, which has high morbidity and mortality. This study aimed to build model to predict AKI and analyze the risk factors affecting AKI occurrence, leading to timely intervene for patients with potential risk of AKI. A total of 90780 subjects (7983AKI and 82797 non-AKI, dividing training sets and independent test sets by 4:1) who stayed from 2 to 14 days were selected, and their biochemical indicators, drug use, demographic information and other clinical information were collected. Three models including logical regression, random forest and LightGBM were adopted to fulfill AKI prediction, and the performance of different models was evaluated and compared using sensitivity, specificity, the area under the characteristic working curve (AUC) and F1 value. LighGBM obtained the best performance to predict occurrence of AKI in 24 hours, the optimal F1, AUC, sensitivity, specificity was 0.800, 0.871, 0.755 and 0.987 respectively. The important factors affecting the occurrence of AKI include: Clinical features: age, the length of stay, admission department; Laboratory test: Creatinine value in the first examination, levels of sodium, potassium, chlorine, uric acid and glycosylated hemoglobin in blood; Medicine: Anti-infective drugs, non-steroidal anti-inflammatory drugs, diuretics or dehydrants, adrenergic receptor agonists, contrast agents, ACEI/ARB antihypertensive drugs as well as the types of drug used, days of drug treatment; Comorbidities: Moderate and severe nephropathy. Using the clinical information of inpatients, machine learning model can effectively predict the risk of AKI in 24 hours, reveal the important factors affecting the occurrence of AKI, and provide important basis for rational and effective treatment of inpatients and timely intervention for patients with AKI risk.
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