Evaluation and comparison of six GRACE models for the stratification of undifferentiated chest pain in the emergency department

HEART versus GRACE Score in Predicting the Outcomes of Patients with Acute Coronary Syndrome; a Systematic Review and Meta-Analysis

Introduction

Several scoring systems have been proposed to predict the outcomes of patients with ischemic heart disease. Global Registry of Acute Coronary Events (GRACE) and History, ECG, Age, Risk Factors, and Troponin (HEART) scores are two of the more widely used risk prediction tools in patients with acute coronary syndrome (ACS). The present systematic review and meta-analysis aimed to compare the value of GRACE and HEART scores in the outcome prediction of ACS patient.

Method

The online databases of Medline, Embase, Web of Science, and Scopus were search until September 2022 for articles directly comparing GRACE and HEART scores value in prediction of outcome in patients with ACS. GRACE score cut-offs were categorized into two groups of less than and equal to 100 and more than 100, and HEART score cut-offs were categorized into three groups of less than 4, equal to 4, and more than 4. Investigated outcomes were major adverse cardiovascular events (MACE), acute myocardial infraction (AMI) and all-cause mortality.

Results

25 articles were included. The sensitivity and specificity of the GRACE score for prediction of MACE were 0.96 and 0.26 for cut-offs of ≤ 100, and 0.58 and 0.69 for cut-offs of >100, respectively. The sensitivity and specificity of the HEART score for prediction of MACE were 0.99 and 0.16 for cut-offs less than 4, 0.93 and 0.47 for equal to 4, and 0.77 and 0.78 for cut-offs greater than 4. GRACE score was shown to be predictive of AMI with sensitivity and specificity of 0.95 and 0.29, respectively. The analysis for the value of HEART score in the prediction of AMI a sensitivity and specificity of 0.94 and 0.48, respectively. The risk scores were not found to be suitable predictors of all-cause mortality.

Conclusion

The results demonstrated the low specificity of GRACE and HEART scores in predicting the MACE, AMI and all-cause mortality, irrespective of the utilized cut-off. Considering the acceptable sensitivity of two scores in predicting the MACE and AMI, these scores were more suitable to be used as a rule-out tool for identification of ACS patients with low risk of developing adverse outcomes.

Investigation of the effectiveness of the Quick Sequential Organ Failure Assessment-Troponin scores in non- ST-elevation myocardial infarction

OBJECTIVE

A reliable predictor is needed for non-ST-elevation myocardial infarction patients with high mortality risk. The aim of this study was to assess the effectiveness of the Global Registry of Acute Coronary Events and Quick Sequential Organ Failure Assessment-Troponin (qSOFA-T) scores on in-hospital mortality rate in non-ST-elevation myocardial infarction patients.

METHODS

This is an observational and retrospective study. Patients admitted to the emergency department with acute coronary syndrome were evaluated consecutively. A total of 914 patients with non-ST-elevation myocardial infarction who met inclusion criteria were included in the study. The Global Registry of Acute Coronary Events and qSOFA scores were calculated and investigated its contribution to prognostic accuracy by adding cardiac troponin I (cTnI) concentration to the qSOFA score. The threshold value of the investigated prognostic markers was calculated by receiver operating characteristic curve analysis.

RESULTS

We found the in-hospital mortality rate to be 3.4%. The area under the receiver operating characteristic curve for Global Registry of Acute Coronary Events and qSOFA-T is 0.840 and 0.826, respectively.

CONCLUSION

The qSOFA-T score, which can be calculated easily, quickly, and inexpensively and obtained by adding the cTnI level, had excellent discriminatory power for predicting in-hospital mortality. Difficulty in calculating the Global Registry of Acute Coronary Events score, which requires a computer, can be considered a limitation of this method. Thus, patients with a high qSOFA-T score are at an increased risk of short-term mortality.

Evaluation and Comparison of the STIMUL Extended and Simplified Risk Scores for Predicting Two-Year Death in Patients Following ST-Segment Elevation Myocardial Infarction

Background and Objectives: The management of ST-segment elevation myocardial infarction (STEMI) requires a patient’s long-term risk to be estimated. The objective of this study was to develop extended and simplified models of two-year death risk estimation following STEMI that include and exclude cardiac troponins as prognostic factors and to compare their performance with each other. Materials and Methods: Extended and simplified multivariable logistic regression models were elaborated using 1103 patients with STEMI enrolled and followed up in the STIMUL (ST-segment elevation Myocardial Infarctions in Ukraine and their Lethality) registry. Results: The extended STIMUL risk score includes seven independent risk factors: age; Killip class ≥ II at admission; resuscitated cardiac arrest; non-reperfused infarct-related artery; troponin I ≥ 150.0 ng/L; diabetes mellitus; and history of congestive heart failure. The exclusion of cardiac troponin in the simplified model did not influence the predictive value of each factor. Both models divide patients into low, moderate, and high risk groups with a C-statistic of 0.89 (95% CI 0.84–0.93; p < 0.001) for the extended STIMUL model and a C-statistic of 0.86 (95% CI 0.83–0.99; p < 0.001) for the simplified model. However, the addition of the level of troponin I to the model increased its prognostic value by 10.7%. Conclusions: The STIMUL extended and simplified risk estimation models perform well in the prediction of two-year death risk following STEMI. The simplified version may be useful when clinicians do not know the value of cardiac troponins among the population of STEMI patients.

Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification

Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.