The GRACE Risk Score is a well-validated tool for estimating short- and long-term risk in acute coronary syndrome (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 in black patients with ACS. We evaluated the performance of the GRACE Risk Score 2.0 simplified algorithm for predicting 1-year mortality in 2,131 participants in Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE), a multiracial cohort of patients discharged alive after an ACS in 2011 to 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% STEMI. Eighty patients (3.8%) died within 12 months of discharge. The GRACE Risk Score 2.0 simplified algorithm demonstrated excellent model discrimination for predicting 1-year mortality after hospital discharge in the TRACE-CORE cohort (c-index = 0.77). The c-index was 0.94 in patients with STEMI, 0.78 in those with NSTEMI, and 0.87 in black patients with ACS. 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 patients with ACS.
Patients with an acute coronary syndrome (ACS) encompass subjects with diverse pathophysiological underpinnings and prognoses, but ACS risk stratification relies primarily on electrocardiographic and serum cardiac biomarker data. National practice guidelines promote use of the Global Registry of Acute Coronary Events (GRACE) Risk Score to help clinicians estimate inhospital and post-discharge risk for dying in patients with ACS. The GRACE Risk Score 1.0 estimates the risk of inhospital death and of death at 6 months after discharge. Although accurate, a major limitation to the widespread use of the GRACE Risk Score 1.0 is the inclusion of several variables, including Killip class and creatinine values at the time of the patient’s hospital presentation. GRACE Risk Score 2.0 was developed to address these limitations and evaluate the short- and long-term risk for dying after an ACS, including up to 3 years after discharge. However, the GRACE Registry included mostly white patients of European descent. Because several studies have demonstrated that race is strongly associated with differential ACS risk and quality of medical care in the United States (US), it is important to externally validate GRACE Risk Score 2.0 in a contemporary and multiracial ACS cohort. We evaluated the performance of the GRACE Risk Score 2.0 simplified algorithm for predicting 1-year mortality in the Transitions, Risks, and Actions in Coronary Events Center for Outcomes Research and Education (TRACE-CORE) cohort, a contemporary and multiracial cohort of patients with ACS surviving hospitalization and followed for 1 year after discharge in the US. Furthermore, we performed validation stratified by race and ACS subgroups.
Methods
Details of the TRACE-CORE are described elsewhere. In brief, TRACE-CORE was a multisite prospective cohort of adults surviving hospitalization with an ACS at 3 tertiary care and community medical centers in Worcester, Massachusetts (these centers capture most hospitalizations for ACS in central Massachusetts); 2 hospitals in Atlanta, Georgia (contracted to admit and treat members of a major health maintenance organization network); and 1 teaching hospital in Macon, Georgia (serving residents of central Georgia). Participating sites served a heterogeneous patient population and were selected purposely for their sociodemographic and socioeconomic diversity.
Participants with an ACS were identified by trained study staff from April 2011 to May 2013 using active surveillance methods. Adults admitted to any of the participating medical centers with electrocardiographic or cardiac biomarker criteria consistent with ACS, those who underwent urgent coronary revascularization, and symptomatic participants with >70% stenosis in a coronary artery on coronary angiography were eligible. Pregnant women, patients with dementia or receiving palliative care, those with an ACS secondary to demand ischemia, perioperative ACS cases, and those under custody of a prison system were ineligible. Sociodemographic, body mass index, clinical, laboratory, physiological, and treatment-related data from medical records of the index hospitalization were abstracted by trained research staff and validated by physicians. Patients were followed up to 12 months after discharge. All-cause mortality was ascertained from proxy reports and review of medical records augmented by review of local and national vital statistics records. The institutional review boards at each participating recruitment site approved the study. All participants provided written informed consent.
We examined the statistical performance of the GRACE Risk Score 2.0 simplified algorithm for predicting 1-year mortality after an index ACS event because information on Killip class was not collected in TRACE-CORE. Besides substituting Killip class with diuretic use within 24 hours of presentation, the published simplified algorithm substituted serum creatinine concentration with medical history of renal insufficiency at the same time. Additional variables in the simplified model are age, initial systolic blood pressure, initial pulse, cardiac arrest on admission, positive initial biomarkers, and ST deviation.
Data from TRACE-CORE participants with no missing data were used to calculate the simplified GRACE Risk Score 2.0 (validation cohort). To provide insights into differences in model performance in the validation cohort and the original GRACE cohort used to derive the GRACE risk score 2.0 simplified algorithm for predicting 1-year mortality (derivation cohort), we mapped 2 databases and compared the characteristics of 2 cohorts directly as we had access to both databases. GRACE was designed to reflect an unbiased and generalizable sample of patients with ACS hospitalized from 1999 to 2007 in 94 hospitals in 14 countries. Details of the GRACE design, recruitment, and data collection are described elsewhere.
Categorical variables are reported as frequencies and percentages, and continuous variables as medians with interquartile ranges. Differences in the baseline characteristics, management, and outcomes of patients in the validation versus derivation cohorts were examined using the chi-square test or Fisher’s exact test for categorical variables and the Wilcoxon rank-sum test for continuous variables. Data were censored at the last contact (in survivors) up to 1 year after the index discharge. The survival rate within 1-year post-discharge was estimated using the Kaplan-Meier method. The log-rank test was used to compare the survival rates between 2 cohorts and among racial subgroups in the validation cohort.
The validations were conducted in the overall TRACE-CORE validation cohort and in subgroups stratified by ACS diagnosis (STEMI, non-STEMI [NSTEMI], unstable angina) and race (black, non-black, white). To be consistent with the methods used to derive the GRACE Risk Score 2.0, Cox proportional hazards regression models were also used to validate the model. As we only intended to validate GRACE Risk Score 2.0, the proportional hazard assumption for each risk factor in TRACE-CORE was not assessed. Continuous variables in the TRACE-CORE validation were modeled using restricted cubic spline functions with the knots as defined in GRACE Risk Score 2.0, but we report categorical estimates to reflect the general shapes of these functions for presentation. In addition, the original GRACE Risk Score 2.0 report presented a mixture of estimates for the full model variables plus the 2 substitute variables from the simplified algorithm. Therefore, to make comparisons equitable, we report estimates generated from a single model containing the listed factors using the derivation cohort and the validation cohort. The c-index, calculated using the Harrell macro for Cox regression, was used to assess model discrimination. Although we intended to assess the goodness of fit (calibration) using the May-Hosmer method, owing to the small number of post-discharge deaths in TRACE-CORE, we could only form 2 risk groups based on this method. We did not, therefore, report model calibration. Instead, we reported the individual risk predictor estimates from the derivation and validation cohorts.
To guard against the possibility of overfitting the GRACE model to smaller data sets, we conducted a sensitivity analysis in which we evaluated model discrimination by computing a risk score ( X′ˆβ
X ′ β ˆ
) for each TRACE-CORE patient, using GRACE-derived model estimates ( ˆβs
β ˆ s
). We then refit the Cox models in TRACE-CORE data using the risk score as sole covariate and recomputed c indexes. C indexes, thus, computed ensure the model was not overfit to TRACE-CORE data as the score is a single degree of freedom variable whose estimates derive from a different (i.e., GRACE) data set.
All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, North Carolina), and statistical significance level was prespecified as α = 0.05 (2 sided).
Results
Of the 2,174 patients enrolled in TRACE-CORE in 2011 to 2013, 2,131 patients (98%) had no missing data needed to calculate the simplified GRACE Risk Score 2.0 (validation cohort). Most patients in the validation cohort were white (77%), 16% were black, and 7% were other races. A small percentage of the validation cohort (3.2%) considered themselves to be Hispanic/Latino ethnicity. The comparison on baseline characteristics of the derivation (GRACE) and validation (TRACE-CORE) cohorts are presented in Table 1 .
| Variable | GRACE ∗ (derivation cohort) (n = 33,890) | TRACE-CORE (validation cohort) (n = 2,131) | p value |
|---|---|---|---|
| ACS diagnosis | <0.001 | ||
| STEMI | 12,334 (36.4%) | 378 (17.7%) | |
| NSTEMI | 12,564 (37.1%) | 1,085 (50.9%) | |
| Unstable angina pectoris | 8,992 (26.5%) | 668 (31.4%) | |
| Demographics | |||
| Men | 22,782 (67.5%) | 1,421 (66.7%) | 0.44 |
| Age (years) | 66 (56–76) | 61 (53–69) | <0.001 |
| Body mass index (kg/m 2 ) | 27.0 (24.3–30.3) | 29.4 (26.2–33.9) | <0.001 |
| Medical history (≤6 months before index event) | |||
| Angina pectoris | 15,163 (44.8%) | 880 (41.3%) | 0.002 |
| Myocardial infarction | 10,167 (30.1%) | 578 (27.1%) | 0.004 |
| Heart failure | 3,389 (10.1%) | 300 (14.1%) | <0.001 |
| Percutaneous coronary intervention | 6,490 (19.2%) | 611 (28.7%) | <0.001 |
| Coronary artery bypass graft | 4,220 (12.5%) | 367 (17.2%) | <0.001 |
| Hypertension | 21,703 (64.3%) | 1,616 (75.8%) | <0.001 |
| Hyperlipidemia | 17,177 (50.9%) | 1,468 (68.9%) | <0.001 |
| Atrial fibrillation | 2,580 (7.6%) | 174 (8.2%) | 0.38 |
| TIA/stroke | 2,834 (8.4%) | 193 (9.1%) | 0.29 |
| Peripheral artery disease | 3,017 (8.9%) | 216 (10.1%) | 0.06 |
| Prosthetic valve replacement † | 309 (0.9%) | 24 (1.1%) | 0.33 |
| Smoker (former or current) | 19,194 (56.8%) | 1,348 (63.3%) | <0.001 |
| Diabetes mellitus | 8,628 (25.5%) | 802 (37.6%) | <0.001 |
| Renal insufficiency ‡ | 2,537 (7.5%) | 237 (11.1%) | <0.001 |
| Major surgery § | 2,434 (7.2%) | 18 (0.8%) | <0.001 |
| Major bleeding ¶ | 367 (1.1%) | 36 (1.7%) | 0.010 |
| Venous thromboembolism | 609 (1.8%) | 61 (2.9%) | 0.001 |
| Family history of CAD | 9,475 (28.2%) | 1,123 (52.7%) | <0.001 |
| Presentation characteristics | |||
| Transfer-in patients | 4,330 (12.8%) | 814 (38.2%) | <0.001 |
| Pulse (beats/min) | 76 (65–90) | 75 (65–88) | 0.002 |
| Systolic blood pressure (mmHg) | 140 (120–160) | 140 (124–157) | 0.24 |
| Diastolic blood pressure (mmHg) | 80 (70–90) | 79 (69–90) | 0.95 |
| Cardiac arrest | 641 (1.9%) | 18 (0.8%) | 0.001 |
| Initial cardiac biomarker positive | 17,293 (51.0%) | 1,414 (66.4%) | <0.001 |
| ST deviation on presentation | 17,856 (52.7%) | 459 (21.5%) | <0.001 |
| Initial serum creatinine (mg/dL) | 1.0 (0.9–1.3) | 0.98 (0.8–1.2) | <0.001 |
| In-hospital procedures | |||
| Cardiac catheterization | 22,052 (65.4%) | 2,008 (94.3%) | <0.001 |
| Percutaneous coronary intervention | 14,290 (42.3%) | 1,429 (67.3%) | <0.001 |
| Coronary artery bypass grafting | 1,524 (4.8%) | 279 (13.1%) | <0.001 |
| Thrombolytics | 3,544 (10.6%) | 30 (1.4%) | <0.001 |
| In-hospital events | |||
| CHF/pulmonary edema | 3,787 (11.2%) | 41 (1.9%) | <0.001 |
| Cardiogenic shock | 1,195 (3.5%) | 20 (0.9%) | <0.001 |
| Cardiac arrest/ventricular fibrillation | 1,374 (4.1%) | 21 (1.0%) | <0.001 |
| Atrial fibrillation/flutter | 2,420 (7.2%) | 166 (7.8%) | 0.28 |
| Sustained ventricular tachycardia | 819 (2.4%) | 84 (3.9%) | <0.001 |
| Thrombocytopenia | 74 (0.2%) | 6 (0.3%) | 0.56 |
| Venous thromboembolism | 97 (0.3%) | 5 (0.2%) | 0.65 |
| Acute renal failure | 1,289 (3.8%) | 118 (5.5%) | <0.001 |
| Myocardial infarction >24 hours after arrival/reinfarction | 785 (2.3%) | 5 (0.2%) | <0.001 |
| Stroke | 203 (0.6%) | 9 (0.4%) | 0.29 |
| Major bleeding | 749 (2.2%) | 29 (1.4%) | 0.007 |
| Discharge status among hospital survivors | |||
| Home | 26,617 (76.9%) | 2,049 (96.2%) | <0.001 |
| Transfer to another acute facility | 4,055 (11.7%) | 5 (0.2%) | |
| AMA/self-discharge | 254 (0.8%) | 3 (0.1%) | |
| Other | 1,545 (4.8%) | 74 (3.5%) | |
| Length of hospital stay (days) | 5 (3–8) | 3 (2–5) | <0.001 |
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