Performance of the GRACE Risk Score 2.0 Simplified Algorithm for Predicting 1-Year Death After Hospitalization for an Acute Coronary Syndrome in a Contemporary Multiracial Cohort

Abstract

The GRACE Risk Score is a well-validated tool for estimating short- and long-term risk in acute coronary syndromes (ACS). GRACE Risk Score 2.0 substitutes several variables that may be unavailable to clinicians and thus limit use of the GRACE Risk Score. GRACE Risk Score 2.0 performed well in the original GRACE cohort. We sought to validate its performance in a contemporary multiracial ACS cohort, in particular among black ACS patients. We evaluated the performance of the GRACE Risk Score 2.0 simplified algorithm for predicting 1-year mortality among 2,131 participants in TRACE-CORE, a multiracial cohort of patients discharged alive after an ACS in 2011–2013 from 6 hospitals in Massachusetts and Georgia. The median age of study participants was 61 years, 67% were men, and 16% were black. Half (51%) of the patients experienced a non–ST-segment elevation myocardial infarction (NSTEMI) and 18% an ST-segment elevation myocardial infarction (STEMI). Eighty (3.8%) patients died within 12 months of discharge. The GRACE Risk Score 2.0 simplified algorithm demonstrated excellent model discrimination for predicting 1-year mortality following hospital discharge in the TRACE-CORE cohort (c-index = 0.77). The c-index was 0.94 among patients with STEMI, 0.78 among patients with NSTEMI, and 0.87 among black ACS patients. In conclusion, the GRACE Risk Score 2.0 simplified algorithm for predicting 1-year mortality exhibited excellent model discrimination across the spectrum of ACS types and racial/ethnic subgroups and thus may be a helpful tool to guide routine clinical care for ACS patients.

A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome

“…In this article, we employed four machine learning algorithms such as RF, GBM, GLM, and DNN in the mortality-based prediction model during the 1-year clinical follow-up tracking in patients with ACS, and then, their performances were compared with GRACE Risk Score 2.0. [11][12][13][14] Normally, machine learning models are evaluated based on different performance measures such as AUC, precision, recall, accuracy, and F-score. 35 Using the test data set, we compared the performances in the mortality prediction models in accordance with AUC, precision, recall, accuracy, and F-score, as described in Table 7.…”

Importance of Endogenous Fibrinolysis in Platelet Thrombus Formation

“…The use of the Global Registry of Acute Coronary Events (GRACE) score to risk stratify patients with ACS and the QRisk2 score used in the UK are examples [ 55 , 56 ]. The use of the GRACE score has allowed hospitals to identify patients who are higher risk for whom in-patient management and earlier intervention is warranted [ 57 , 58 ]. The QRisk2 score allows the physician to incorporate simple demographics and blood tests to provide patients with their individual risk of a cardiovascular event expressed as a percentage over 10 years [ 56 ].…”

Evaluation of the impact of the GRACE risk score on the management and outcome of patients hospitalised with non-ST elevation acute coronary syndrome in the UK: protocol of the UKGRIS cluster-randomised registry-based trial

“…The GRACE risk score also shows good discriminatory performance for mortality at up to 2 years following discharge from hospital with ACS 18. The GRACE risk score has been updated (http://www.outcomes-umassmed.org/grace/) and shows good predictive accuracy of 1 year and 3 year mortality across a spectrum of ACS types 19 20…”