Comparison Between Timi and Grace Scores as a Predictor for Short- And Long-Term Outcome in Patients With Acute St-Elevation Myocardial Infarction

Chimparlee, Nitinan; Chaipromprasit, Jarkarpun; Athisakul, Siriporn; Lertsuwunseri, Vorarit; Buddhari, Wacin; Udayachalerm, Wasan; Srimahachota, Suphot

doi:10.1016/s0735-1097(18)30787-3

Cited by 2 publications

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“…The TIMI risk score was originally developed to estimate 30 days mortality risk. In the absence of a more convenient risk score system, it has since been exploited to predict in-hospital, 30 days and 1-year mortality post-STEMI in other Asian countries as well as Mexico [ 2 , 36 – 38 ]. This is despite its moderate accuracy for risk prediction in Asians with an AUC of 0.78 [ 3 ].…”

Section: Discussionmentioning

confidence: 99%

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

et al. 2021

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Background Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

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Section: Discussionmentioning

confidence: 99%

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

et al. 2021

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“…The TIMI score’s simplicity is recognized in current guidelines and is frequently used in Asian hospitals for risk assessment of patients with ACS. The TIMI risk score, originally established to predict 30-day mortality, is used in Asian hospitals to predict in-hospital, 30-day, and 1-year mortality post ACS [ 39 , 63 – 65 ]. Correia et al reported that the TIMI score is better than GRACE score calibration because it has more variables associated with mortality, a balanced distribution of low, intermediate, and high-risk patients, and more accurate estimation [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

et al. 2022

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Background Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

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Comparison Between Timi and Grace Scores as a Predictor for Short- And Long-Term Outcome in Patients With Acute St-Elevation Myocardial Infarction

Cited by 2 publications

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Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach

In-hospital mortality risk stratification of Asian ACS patients with artificial intelligence algorithm

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