Abstract
Objective: To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively.Methods: Patients with AMI who underwent angiography therapy were enrolled and randomly divided into training cohort (75%) and validation cohort (25%). Machine learning algorithms were used to construct predictive models for CI-AKI. The predictive models were tested in a validation cohort.Results: A total of 1,495 patients with AMI were included. Of all the patients, 226 (15.1%) cases developed CI-AKI. In the validation cohort, Random Forest (RF) model with top 15 variables reached an area under the curve (AUC) of 0.82 (95% CI: 0.76–0.87), while the best logistic model had an AUC of 0.69 (95% CI: 0.62–0.76). ACEF (age, creatinine, and ejection fraction) model reached an AUC of 0.62 (95% CI: 0.53–0.71). RF model with top 15 variables achieved a high recall rate of 71.9% and an accuracy of 73.5% in the validation group. Random Forest model significantly outperformed logistic regression in every comparison.Conclusions: Machine learning algorithms especially Random Forest algorithm improves the accuracy of risk stratifying patients with AMI and should be used to accurately identify the risk of CI-AKI in AMI patients.
Highlights
Acute renal injury (AKI), always associated with a poor prognosis, may arise from a variety of diseases [1]
A total of 1,495 patients diagnosed with Acute myocardial infarction (AMI) were included in the study
The average age was 66.6 ± 13.9 years, and 71.2% of the sample were men. 66.4% of the participants had hypertension, 26.8% had diabetes, 49.8% patients had a history of smoking and 12.1% had a history of alcohol consumption
Summary
Acute renal injury (AKI), always associated with a poor prognosis, may arise from a variety of diseases [1]. Patients with AKI are more likely to develop long-term complications, including progression to chronic kidney disease, heart failure, recurrent myocardial infarction, and long-term mortality [9]. Identification of patients with AMI, who are likely to develop contrast induced acute kidney injury (CI-AKI) after an invasive treatment, will alert us to start an early therapy (e.g., iso-osmolar contrast media, fluids, pre-procedural statin) to preserve the renal function. Certain risk biomarkers [10, 11] and predictive models [12, 13] were reported to be capable of predicting the incidence of AKI. The Precision Medicine Initiative requires physicians to avoid oversimplification of medical treatments and to take individual variability into account to improve the decision-making process
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
