Dataset Information

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.

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.

SUBMITTER: Sun L

PROVIDER: S-EPMC7691423 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.

Sun Ling L Zhu Wenwu W Chen Xin X Jiang Jianguang J Ji Yuan Y Liu Nan N Xu Yajing Y Zhuang Yi Y Sun Zhiqin Z Wang Qingjie Q Zhang Fengxiang F

Frontiers in medicine 20201113

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 pat ...[more]

PMID: 33282893

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Project description:ImportanceAccurate prediction of adverse outcomes after acute myocardial infarction (AMI) can guide the triage of care services and shared decision-making, and novel methods hold promise for using existing data to generate additional insights.ObjectiveTo evaluate whether contemporary machine learning methods can facilitate risk prediction by including a larger number of variables and identifying complex relationships between predictors and outcomes.Design, setting, and participantsThis cohort study used the American College of Cardiology Chest Pain-MI Registry to identify all AMI hospitalizations between January 1, 2011, and December 31, 2016. Data analysis was performed from February 1, 2018, to October 22, 2020.Main outcomes and measuresThree machine learning models were developed and validated to predict in-hospital mortality based on patient comorbidities, medical history, presentation characteristics, and initial laboratory values. Models were developed based on extreme gradient descent boosting (XGBoost, an interpretable model), a neural network, and a meta-classifier model. Their accuracy was compared against the current standard developed using a logistic regression model in a validation sample.ResultsA total of 755 402 patients (mean [SD] age, 65 [13] years; 495 202 [65.5%] male) were identified during the study period. In independent validation, 2 machine learning models, gradient descent boosting and meta-classifier (combination including inputs from gradient descent boosting and a neural network), marginally improved discrimination compared with logistic regression (C statistic, 0.90 for best performing machine learning model vs 0.89 for logistic regression). Nearly perfect calibration in independent validation data was found in the XGBoost (slope of predicted to observed events, 1.01; 95% CI, 0.99-1.04) and the meta-classifier model (slope of predicted-to-observed events, 1.01; 95% CI, 0.99-1.02), with more precise classification across the risk spectrum. The XGBoost model reclassified 32 393 of 121 839 individuals (27%) and the meta-classifier model reclassified 30 836 of 121 839 individuals (25%) deemed at moderate to high risk for death in logistic regression as low risk, which were more consistent with the observed event rates.Conclusions and relevanceIn this cohort study using a large national registry, none of the tested machine learning models were associated with substantive improvement in the discrimination of in-hospital mortality after AMI, limiting their clinical utility. However, compared with logistic regression, XGBoost and meta-classifier models, but not the neural network, offered improved resolution of risk for high-risk individuals.

Project description:BackgroundEarly identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).MethodsA total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git) . The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set.ResultsThree ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42-7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01-4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98-4.56) are significant factors related to arrhythmia after AMI.ConclusionsWe used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.

Project description:Background:Contrast-induced acute kidney injury (CI-AKI) is a common complication of coronary angiography (CAG), which is associated with worse prognosis. Some studies indicated ?-blockers could preserve renal function among patients with acute myocardial infarction (AMI), but the relationship between ?-blockers and CI-AKI has not been well documented among patients with AMI who were undergoing CAG or percutaneous coronary intervention (PCI). Methods:In this prospective, observational study, 1,309 AMI patients who were undergoing CAG or PCI were consecutively recruited between January 2010 and December 2013. Patients were assigned into ?-blockers group (n=1,074) or non-?-blockers group (n=235) according to use or non-use of ?-blockers (including metoprolol tartrate/metoprolol succinate/Bisoprolol Fumarate) within 24 hours of perioperative period. CI-AKI was defined as an absolute increase of >0.5 mg/dL from baseline serum creatinine (SCr) within 48-72 hours after contrast medium (CM) exposure. Results:The overall incidence of CI-AKI was 247/1,309 (18.9%).After multivariate adjusting, a total of 10 variables were related to CI-AKI, including ?-blockers [?-blockers group vs. non-?-blockers group: odds ratio (OR) =0.520; 95% confidence interval (CI), 0.291-0.930; P=0.027], age, diabetes mellitus, estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, left ventricular ejection fraction (LVEF) <40%, use of intra-aortic balloon pump (IABP), peri-hypotension, emergent PCI, coronary lesions and CM dose >200 mL. During the mean follow-up of 2.35±0.99 years, the ?-blockers group was significantly associated with lower rates of mortality [?-blockers group vs. non-?-blockers group: adjusted hazard ratio (HR) =0.43; 95% CI, 0.27-0.71; P=0.001] among patients with AMI. Conclusions:Use of ?-blockers within 24 hours of perioperative period may be associated with lower rates of CI-AKI and long-term mortality among patients with AMI who are undergoing CAG or PCI. Trial registration:PRECOMIN, ClinicalTrials.gov NCT01400295.

Dataset Information

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.

Publications

Machine Learning to Predict Contrast-Induced Acute Kidney Injury in Patients With Acute Myocardial Infarction.

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