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Acute kidney injury (AKI) is a common complication of acute myocardial infarction (AMI) with high morbidity, mortality and lack of effective treatment, so prevention is particularly important.This study intends to use machine learning techniques to establish a precise prediction model for AKI after AMI.
AMI patients were consecutively collected from July 2011 to December 2016 in Beijing Anzhen Hospital, Capital Medical University.Collect the basic data, past medical history, laboratory indicators, treatment-related conditions, medication and other indicators of patients.Using machine learning algorithms, predictive models were built and the area under the ROC curve was calculated.
A risk prediction model for AKI after AMI was established using machine learning algorithms, and 12 important characteristics were included in the final model, which were: the number of myocardial infarctions, whether they were ST-segment elevation myocardial infarction, ventricular tachycardia, third-degree atrioventricular block, decompensated heart failure at admission, admission serum creatinine value, admission urea nitrogen value, admission CK-MB peak value, whether diuretics were used, maximum daily dose of diuretics, days of diuretic use, and whether statins were used.The model was validated in the validation set and yielded an area under the ROC curve of 0.82.The prediction model of AKI risk after AMI derived from machine learning techniques was compared with the model obtained from logistic regression analysis, and the results showed that the prediction model derived from machine learning techniques had better discriminant ability (area under the ROC curve 0.82 vs 0.79).
The prediction model of AKI risk after AMI based on machine learning technology has better discriminant ability and accuracy than the model constructed by traditional statistical methods, which can provide an effective tool for early identification of these high-risk patients, early preventive measures and reduction of morbidity in clinical practice.