Comparative Analysis of the Effectiveness of Riskometer Scales in Predicting the Risk of in-Hospital Mortality in
B I Geltser1, K I Shahgeldyan2, I G Domzhalov1
1Far-East Federal University, School of Medicine, Ajax Bay, Russky Island.
Insights
The GRACE, CADILLAC, and TIMI-STe scales effectively predict in-hospital death (IHD) in ST-segment elevation myocardial infarction (STEMI) patients post-PCI. An integrated artificial neural network (ANN) model demonstrated superior accuracy in identifying high-risk patients.
Area of Science:
- Cardiology
- Medical Informatics
- Biostatistics
Background:
- ST-segment elevation myocardial infarction (STEMI) is a critical condition requiring timely intervention.
- Percutaneous coronary intervention (PCI) is a primary treatment for STEMI.
- Accurate prediction of in-hospital death (IHD) is crucial for managing STEMI patients post-PCI.
Purpose of the Study:
- To comparatively evaluate existing riskometer scales for predicting IHD in STEMI patients after PCI.
- To develop and assess novel machine learning-based models for improved IHD prediction.
- To stratify risk accurately for STEMI patients undergoing PCI.
Main Methods:
- Retrospective analysis of 4,675 STEMI patient records undergoing emergency PCI.
- Evaluation of GRACE, CADILLAC, TIMI-STe, PAMI, and RECORD scales for IHD risk stratification.
- Development of prognostic models using logistic regression, gradient boosting, and artificial neural networks (ANN).
Main Results:
- The GRACE scale exhibited the highest prognostic accuracy; PAMI showed the lowest.
- CADILLAC and TIMI-STe scales demonstrated acceptable prognostic abilities.
- The integrative ANN model, incorporating scores from five scales, outperformed logistic regression and gradient boosting in prediction accuracy.
- ANN model-based stratification identified low, medium, high, and very high IHD risk groups.
Conclusions:
- GRACE, CADILLAC, and TIMI-STe scales are advantageous for IHD risk stratification in STEMI patients post-PCI.
- The integrated ANN model offers superior prediction quality and accurate identification of high-risk STEMI patients.
- Machine learning models, particularly ANN, enhance the accuracy of IHD risk prediction in STEMI patients post-PCI.
Aim:
Comparative evaluation of the effectiveness of riskometer scales in predicting in-hospital death (IHD) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) and the development of new models based on machine learning methods.
Material And Methods:
A single-center cohort retrospective study was conducted using data from 4,675 electronic medical records of patients with STEMI (3,202 men and 1,473 women) with a median age of 63 years who underwent emergency PCI. Two groups of patients were isolated: group 1 included 318 (6.8%) patients who died in hospital; group 2 consisted of 4,359 (93.2%) patients with a favorable outcome. The GRACE, CADILLAC, TIMI-STe, PAMI, and RECORD scales were used to assess the risk of IHD. Prognostic models of IHD predicted by the sums of these scale scores were developed using single- and multivariate logistic regression, stochastic gradient boosting, and artificial neural networks (ANN). Risk of adverse events was stratified based on the ANN model data by calculating the median values of predicted probabilities of IHD in the compared groups.
Results:
Comparative analysis of the prognostic value of individual scales for the STEMI patients showed differences in the quality of the risk stratification for IHD after PCI. The GRACE scale had the highest prognostic accuracy, while the PAMI scale had the lowest accuracy. The CADILLAC and TIMI-STe scales had acceptable and comparable prognostic abilities, while the RECORD scale showed a significant proportion of false-positive results. The integrative ANN model, the predictors of which were the scores of 5 scales, was superior in the prediction accuracy to the algorithms of single- and multivariate logistic regression and stochastic gradient boosting. Based on the ANN model data, the probability of IHD was stratified into low (<0.3%), medium (0.3-9%), high (9-17%), and very high (>17%) risk groups.
Conclusion:
The GRACE, CADILLAC and TIMI-STe scales have advantages in the stratification accuracy of IHD risk in patients with STEMI after PCI compared to the PAMI and RECORD scales. The integrated ANN model that combines the prognostic resource of the five analyzed scales, had better quality criteria, and the stratification algorithm based on the data of this model was characterized by accurate identification of STEMI patients with high and very high risk of IHD after PCI.
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