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Machine learning models to predict 30-day mortality for critical patients with myocardial infarction: a retrospective
Xuping Lin1, Xi Pan2, Yanfang Yang3
1Department of Spinal Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
Background:
The identification of efficient predictors for short-term mortality among patients with myocardial infarction (MI) in coronary care units (CCU) remains a challenge. This study seeks to investigate the potential of machine learning (ML) to improve risk prediction and develop a predictive model specifically tailored for 30-day mortality in critical MI patients.
Method:
This study focused on MI patients extracted from the Medical Information Mart for Intensive Care-IV database. The patient cohort was randomly stratified into derivation (n = 1,389, 70%) and validation (n = 595, 30%) groups. Independent risk factors were identified through eXtreme Gradient Boosting (XGBoost) and random decision forest (RDF) methodologies. Subsequently, multivariate logistic regression analysis was employed to construct predictive models. The discrimination, calibration and clinical utility were assessed utilizing metrics such as receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA).
Result:
A total of 1,984 patients were identified (mean [SD] age, 69.4 [13.0] years; 659 [33.2%] female). The predictive performance of the XGBoost and RDF-based models demonstrated similar efficacy. Subsequently, a 30-day mortality prediction algorithm was developed using the same selected variables, and a regression model was visually represented through a nomogram. In the validation group, the nomogram (Area Under the Curve [AUC]: 0.835, 95% Confidence Interval [CI]: [0.774-0.897]) exhibited superior discriminative capability for 30-day mortality compared to the Sequential Organ Failure Assessment (SOFA) score [AUC: 0.735, 95% CI: (0.662-0.809)]. The nomogram (Accuracy: 0.914) and the SOFA score (Accuracy: 0.913) demonstrated satisfactory calibration. DCA indicated that the nomogram outperformed the SOFA score, providing a net benefit in predicting mortality.
Conclusion:
The ML-based predictive model demonstrated significant efficacy in forecasting 30-day mortality among MI patients admitted to the CCU. The prognostic factors identified were age, blood urea nitrogen, heart rate, pulse oximetry-derived oxygen saturation, bicarbonate, and metoprolol use. This model serves as a valuable decision-making tool for clinicians.
Insights
Machine learning models effectively predict 30-day mortality in critical myocardial infarction (MI) patients. Key predictors include age, vital signs, and medication use, aiding clinical decisions.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Predicting short-term mortality in critical myocardial infarction (MI) patients in coronary care units (CCU) is challenging.
- Machine learning (ML) offers potential for improved risk prediction in these patients.
- Developing tailored predictive models for 30-day mortality in critical MI cases is crucial.
Purpose of the Study:
- To investigate the efficacy of ML in predicting 30-day mortality among critical MI patients.
- To develop and validate a predictive model for 30-day mortality using ML techniques.
- To identify key independent risk factors for short-term mortality in this patient cohort.
Main Methods:
- Utilized the Medical Information Mart for Intensive Care-IV database for patient data.
- Employed eXtreme Gradient Boosting (XGBoost) and random decision forest (RDF) for risk factor identification.
- Constructed predictive models using multivariate logistic regression and validated using ROC curves, calibration plots, and decision curve analysis (DCA).
Main Results:
- A total of 1,984 MI patients were analyzed (mean age 69.4 years, 33.2% female).
- The developed nomogram achieved an AUC of 0.835, outperforming the SOFA score (AUC: 0.735) for 30-day mortality prediction.
- The ML-based nomogram demonstrated superior discriminative capability and clinical utility compared to the SOFA score.
Conclusions:
- ML-based models are effective in predicting 30-day mortality in CCU patients with MI.
- Identified prognostic factors include age, blood urea nitrogen, heart rate, oxygen saturation, bicarbonate, and metoprolol use.
- The developed model serves as a valuable tool for clinical decision-making in managing critical MI patients.
