Prediction of Hospital Mortality in Patients with ST Segment Elevation Myocardial Infarction: Evolution of Risk
B I Geltser1, I G Domzhalov2, K I Shakhgeldyan3
1MD, DSc, Professor, Corresponding Member of the Russian Academy of Science, Deputy Director for Science of the School of Medicine and Life Sciences; Far Eastern Federal University, 10 Village Ayaks, Island Russkiy, Vladivostok, 690922, Russia.
Insights
Predicting hospital mortality in ST-segment elevation myocardial infarction (STEMI) is crucial. This review compares traditional scales with new machine learning tools for better risk stratification.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- Risk stratification for hospital mortality in ST-segment elevation myocardial infarction (STEMI) is vital for patient care.
- Existing prognostic scales (GRACE, CADILLAC, TIMI) are widely used but may lack precision.
- The search for novel predictors and advanced risk assessment tools is ongoing.
Purpose of the Study:
- To systematically review and compare the predictive value of classical prognostic scales and novel machine learning-based risk tools for hospital mortality in STEMI patients.
- To highlight the importance of developing explainable artificial intelligence (AI) algorithms for clinical trust.
Main Methods:
- Systematic literature review of scientific data.
- Analysis of studies evaluating classical prognostic scales (e.g., GRACE, CADILLAC, TIMI risk score for STEMI, RECORD).
- Evaluation of new risk measurement tools based on machine learning techniques.
Main Results:
- Classical prognostic scales offer established risk stratification for STEMI patients.
- Emerging machine learning techniques show potential for more detailed and accurate risk prediction.
- Studies increasingly focus on identifying new predictors to refine patient status-to-outcome relationships.
Conclusions:
- Both traditional scales and machine learning tools play a role in STEMI mortality risk stratification.
- Developing explainable AI prognostic algorithms is essential for clinical adoption and trust.
- Further research is needed to integrate advanced AI tools into routine clinical practice for improved patient outcomes.
Abstract:
Risk stratification of hospital mortality in patients with ST segment elevation myocardial infarction on the electrocardiogram is an important part of the specialized medical care provision. The systematic review presents scientific literature data characterizing the predictive value of both classical prognostic scales (GRACE, CADDILLAC, TIMI risk score for STEMI, RECORD, etc.) and new risk measurement tools developed on the basis of modern machine learning techniques. Most studies on this issue are often focused on the search for new predictors of adverse events, which allow to detail the relations between indicators of the clinical and functional status of patients and the end point of the study. Here, an important task is to develop hospital mortality prognostic algorithms characterized by explainable artificial intelligence and trusted by doctors.
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