Abstract
Background
Current American Heart Association/American College of Cardiology guidelines recommend the GRACE (Global Registry of Acute Coronary Events) and TIMI (Thrombolysis in Myocardial Infarction) scores to assess myocardial infarction (MI) prognosis. Changes in the epidemiological characteristics of MI and the availability of new biomarkers warrant an assessment of the performance of these scores in contemporary practice. We assessed the following: (1) the performance of GRACE and TIMI to predict 1‐year mortality in a cohort of patients stratified by ST‐segment elevation MI (STEMI) and non‐STEMI (NSTEMI) and (2) the incremental discriminatory power of soluble suppression of tumorigenicity‐2, a myocardial fibrosis biomarker.
Methods and Results
Olmsted County, Minnesota, residents with incident MI (N=1401) were recruited prospectively from November 1, 2002 to December 31, 2012 (mean age, 67 years; 61% men; 79% with NSTEMI). Baseline data were used to calculate risk scores; soluble suppression of tumorigenicity‐2 was measured in stored plasma samples obtained at index MI. C‐statistics adapted to survival data were used to assess the discriminatory power of the risk scores and the improvement gained by adding other markers. During the first year of follow‐up, 190 patients (14%) died. The discriminatory performance to predict death was reasonable for GRACE and poor for TIMI, and was generally worse in those with NSTEMI versus those with STEMI. In people with NSTEMI, sequential addition of comorbidities and soluble suppression of tumorigenicity‐2 substantially improved the c‐statistic over GRACE (from 0.78 to 0.80 to 0.84) and TIMI (from 0.61 to 0.73 to 0.81), respectively (all P≤0.05).
Conclusions
Guideline‐recommended scores for risk assessment after MI underperform in contemporary community patients, particularly those with NSTEMI, which now represents most infarcts. Incorporating comorbidities and soluble suppression of tumorigenicity‐2 substantially improves risk prediction, thereby delineating opportunities to improve clinical care.
Keywords: biomarkers, mortality, myocardial infarction, risk scores
Subject Categories: Myocardial Infarction, Biomarkers, Epidemiology
Clinical Perspective
What Is New?
Major changes in the epidemiological characteristics of myocardial infarction and the availability of new biomarkers for risk stratification during the past decade call for an assessment of the performance of the GRACE (Global Registry of Acute Coronary Events) and TIMI (Thrombolysis in Myocardial Infarction) risk scores, currently recommended by American Heart Association/American College of Cardiology guidelines.
Using a community‐based cohort of patients with myocardial infarction, we demonstrated that TIMI and GRACE underperform in predicting mortality, particularly for those with non–ST‐segment elevation myocardial infarction.
Incorporating comorbidities and soluble suppression of tumorigenicity‐2, a myocardial fibrosis and remodeling biomarker, substantially improved risk prediction.
What Are the Clinical Implications?
Because accurate risk stratification after myocardial infarction is essential for informed decision making and management, these findings define an opportunity to improve clinical care.
Introduction
Recent American Heart Association/American College of Cardiology guidelines recommend using risk scores to assess prognosis in people with non–ST‐segment elevation myocardial infarction (NSTEMI)1 and ST‐segment elevation myocardial infarction (STEMI).2 Specifically, the TIMI (Thrombolysis in Myocardial Infarction) for NSTEMI,3 the TIMI for STEMI,4 and the GRACE (Global Registry of Acute Coronary Events) for both myocardial infarction (MI) types5, 6 were recommended for early risk assessment.1, 2 Because these scores were developed 2 decades ago, their performance must be reevaluated to ensure their relevance to contemporary practices. This is particularly important because major changes in the epidemiological characteristics of MI have taken place recently, characterized by a shift in case mix, improved short‐term management and secondary prevention, decreased short‐term case fatality, transitions from incident to recurrent events and from prehospital deaths to hospitalized MI, and an increasing burden of morbidity and mortality from noncardiac causes.7, 8, 9, 10, 11 In Olmsted County, Minnesota, for example, the proportion of patients with NSTEMI has increased from 60% in 1979 to 1989 to 75% in 2000 to 2006 and the average age among all those with incident MIs increased from 67 to 69 years, respectively.12 Thus, we do not know if the risk scores recommended in the guidelines, which are derived from older data, are still adequate for contemporary risk prediction.
We previously demonstrated that, among community patients with incident MI, the Charlson index13 (a general measure of comorbidity) conveys important prognostic information, incremental to those of several proposed risk stratification scores.14 Another promising variable to consider in MI risk classification is soluble suppression of tumorigenicity‐2 (sST2). sST2, a member of the interleukin‐1 receptor family, is a biomarker of myocardial fibrosis and remodeling that predicts outcomes and mortality.15 Indeed, many studies have shown a substantial prognostic impact for sST2 in patients with heart failure,16, 17, 18, 19 and in those with MI,20, 21, 22, 23 with a suggested heterogeneity in its prognostic impact between STEMI and NSTEMI.24, 25 Because these studies were mostly conducted among randomized controlled trial participants, their generalizability to community patients is uncertain,26 and the incremental value of sST2 over established risk scores remains to be established.27
The present study was designed to address these gaps in knowledge and evaluate the performance of guideline‐recommended risk scores in a contemporary community cohort of patients with MI. Specifically, we sought to do the following: (1) assess the performance of GRACE and TIMI, overall and by STEMI/NSTEMI status; and (2) examine the incremental risk stratification value, beyond that of recommended scores, of comorbidity and of the emerging sST2 biomarker.
Methods
Study Setting
This prospective community study was conducted in Olmsted County, Minnesota, under the auspices of the Rochester Epidemiology Project.28 The latter is a medical records linkage system that links the records from Olmsted County providers (Mayo Clinic, Olmsted Medical Center, and a few private providers) that provide nearly all health care to local residents. All medical diagnoses are maintained through an electronic index, and patients can be identified through their inpatient and outpatient contacts across the local providers.29 This study was approved by the appropriate institutional review boards.
MI Cohort and Mortality Follow‐Up
This cohort study, previously described in detail,8, 30 included patients with incident (first‐ever) MI from November 1, 2002 through December 31, 2012. Olmsted County residents admitted to Mayo Clinic hospitals in Rochester, MN, with a cardiac troponin T level of 0.03 ng/mL or higher were identified within 12 hours of the blood draw. Written consent was obtained from all patients, or if consent could not be granted by the patient, it was obtained from next of kin.
The validation of MI relied on standard algorithms integrating cardiac pain, electrocardiographic data, and biomarker data. According to current guidelines, each case was classified by troponin T31 as part of clinical practice, successive troponin T measurements were performed after infarction onset. A change (increase or decrease) between any 2 troponin T measurements was defined by a difference of at least 0.05 ng/mL, which is greater than the level of imprecision of the assay at all concentrations.31 Cardiac troponin T was measured with a sandwich electrochemiluminescence immunoassay on the Elecsys 2010 in the laboratories of the Department of Medicine and Pathology at Mayo Clinic.
Participants were followed up through their complete medical records in the community from the index MI date to death or the most recent clinical contact through December 2014. All‐cause death was ascertained using multiple sources, including autopsy reports, death certificates filed in Olmsted County, obituary notices, and electronic death certificates obtained from the Section of Vital Statistics, Minnesota Department of Health, as previously described.8, 28
Clinical Characteristics
The medical record was reviewed to determine cardiovascular risk factors, comorbid conditions, MI characteristics, and short‐term interventions at the time of incident MI. The presence of ST‐segment elevation was ascertained using the Minnesota code of the ECG.32 Comorbidity was measured by the Charlson comorbidity index,13 which consists of 17 comorbid conditions weighted according to the degree to which they predict death. Cigarette smoking was classified as current, former, or never smoker. Clinical definitions were used to assess whether patients had hypertension, diabetes mellitus, hypercholesterolemia, or a history of heart failure. Heart rate at admission and body weight were obtained. Killip class was determined within 24 hours of the index MI. Coronary artery disease was defined angiographically. Short‐term interventions included reperfusion (thrombolytic therapy or percutaneous coronary intervention) and coronary artery bypass grafting during the initial hospitalization. All variables used for the calculation of GRACE,6 TIMI for STEMI,4 and TIMI for NSTEMI3 risk scores are listed in Table 1.
Table 1.
Variables Included in Guideline‐Recommended Scores for Post‐MI Risk Stratification
| GRACE (Range, 1–263) | TIMI‐STEMI (Range, 0–14) | TIMI‐NSTEMI (Range, 0–7) |
|---|---|---|
| Age (7 categories) | Age (2 categories) | Age ≥65 y |
| HF history | Diabetes mellitus, hypertension, or angina | 3≥CAD risk factorsa |
| Prior MI | Systolic blood pressure <100 mm Hg | Prior coronary stenosis >50% |
| Resting heart rate (7 categories) | Heart rate >100 bpm | ST‐segment deviation |
| Systolic blood pressure (7 categories) | Killip class >1 | Pre‐MI angina |
| ST‐segment deviation | Weight <67 kg | Aspirin use in past 7 d |
| Initial serum creatinine (7 categories) | LBBB or anterior ST elevation | Elevated cardiac biomarkersb |
| Elevated cardiac enzymes | Time to treatment >4 h | |
| No in‐hospital PCI |
bpm indicates beats/min; CAD, coronary artery disease; GRACE, Global Registry of Acute Coronary Events; HF, heart failure; LBBB, left bundle branch block; MI, myocardial infarction; NSTEMI, non–ST‐segment elevation MI; PCI, percutaneous coronary intervention; STEMI, ST‐segment elevation MI; and TIMI, Thrombolysis in Myocardial Infarction.
Risk factors included family history of CAD, hypertension, hypercholesterolemia, or diabetes mellitus, or being a current smoker.
Creatine kinase MB fraction and/or cardiac‐specific troponin level.
sST2 Measurement
sST2 was measured from stored plasma samples obtained using a high‐sensitivity sandwich monoclonal immunoassay (Presage ST2 assay). The antibodies used in the Presage assay were generated from a recombinant protein based on the human cDNA clone for the complete soluble sequence.33 This platform offers improved accuracy in quantifying sST2 levels, particularly at lower concentrations. This specific assay has high sensitivity; the reliability of running the Presage ST2 assay on EDTA plasma samples stored at −70°C (as per biomarker core laboratory) has been established previously.34 Calibration and standardization of this assay were performed according to the manufacturer's protocol. Previous reports document the intra‐assay and interassay coefficients of variation as <2.5% and <4.0%, respectively.33
Statistical Analysis
Baseline characteristics were compared across subgroups of sST2 (“normal” versus “high”) stratified by STEMI/NSTEMI status; they are presented as mean and SD for normally distributed continuous variables, median and 25th to 75th percentile for nonnormally distributed continuous variables, and frequencies for categorical variables. High and normal sST2 levels were defined according to published criteria,35 which are age and sex dependent. Cut points were defined as follows (values in ng/mL): women, ≤44 years, 29.5; 45 to 54 years, 34.0; 55 to 64 years, 39.3; and ≥65 years, 45.3; men, ≤44 years, 46.7; 45 to 54 years, 48.7; 55 to 64 years, 50.8; and ≥65 years, 53.0. Cox proportional hazards regression models were constructed to estimate the hazard ratios and 95% confidence intervals for all‐cause mortality associated with sST2. The latter was modeled as both a dichotomous variable (high versus normal) and a continuous variable. Because the sST2 distribution was skewed to the right, to limit the influence of extreme observations, the variable was log transformed when appropriate. Because the GRACE and TIMI scores were created for early risk assessment, follow‐up was truncated at 1 year. Several models were examined to assess the independent association of sST2 with post‐MI death: an age‐ and sex‐adjusted model; the GRACE model6; and the TIMI (for STEMI4 and NSTEMI,3 as appropriate) model. The Charlson comorbidity index (log transformed and modeled as a continuous variable) was then added to the models. Associations were examined overall and specifically by STEMI/NSTEMI status.
Discrimination (the model's ability to separate those who did and did not die during the first year of follow‐up) was assessed through c‐statistics adapted to survival data, and the difference between competing prediction models was formally tested.36 The latter step was taken to evaluate the added predictive value of a new marker by comparing predictions made using a baseline set of risk markers with predictions that also included information about the examined risk marker. The GRACE and TIMI risk scores served as baseline prediction models, on top of which the Charlson comorbidity index and sST2 (both log transformed and treated as continuous variables) were sequentially added. Calibration was assessed by the Hosmer‐Lemeshow goodness‐of‐fit test, which determines how close the predicted and observed incidence of events, as derived from logistic regression models, is over a range of scores. The tests showed acceptable calibration (P>0.05) in all the adjusted models. Analyses were performed using R statistical software, version 3.3.1 (R Development Core Team37), IBM SPSS Statistics, version 23, and SAS statistical software, version 9.4.
Results
A total of 2104 patients had incident MI validated between November 1, 2002 and December 31, 2012, of whom 1401 (66.6%) had a stored plasma sample available for sST2 analysis and were included in the study. The average (SD) age of this cohort was 67.3 (15.0) years, 61% were male, and 79% were seen with NSTEMI. The median sST2 level was 49 (25th–75th percentile, 33–103) ng/mL; 719 patients (51%) were considered to have a high sST2. The median scores (25th–75th percentile) for GRACE, TIMI‐STEMI, and TIMI‐NSTEMI were 123 (96–148), 4 (2–6), and 3 (2–4), respectively. The 703 patients who did not have sST2 measured were, on average, older (70.2 versus 67.3 years), included more women (47% versus 39%), and had more comorbidities (diabetes mellitus, 29% versus 24%; history of heart failure, 21% versus 13%) than patients with available sST2.
Compared with patients with normal sST2 levels, patients with elevated sST2 were older, were more likely to be women, and had a higher burden of comorbidity, a worse cardiovascular risk profile, and more severe MI, regardless of STEMI/NSTEMI status. In addition, patients with a high sST2 were less likely to be prescribed aspirin, statins, and β blockers at hospital discharge (Table 2). Log sST2 was moderately correlated with GRACE (Pearson r=0.37) and weakly with TIMI (Pearson r=0.16). After a 1‐year follow‐up, 190 patients (13.6%) died (164 [14.8%] with NSTEMI and 26 [8.9%] with STEMI). Adjusted for age and sex, sST2 was a strong inverse predictor of 1‐year survival after MI, whereas STEMI/NSTEMI status was far less predictive of survival (Figure 1). Fitting different adjustment models with GRACE and TIMI risk scores and Charlson comorbidity index as covariates, the hazard ratio for death increased ≈3‐fold per 1 log‐unit increase in sST2. The associations between sST2 and death were stronger among patients with STEMI than among those with NSTEMI (Figure 2). A similar pattern (with more extreme hazard ratio estimates) was observed when treating sST2 as a dichotomous variable (data not shown). In addition, the association of sST2 with death was stronger at 30 days than at 1 year of follow‐up. For example, the age‐ and sex‐adjusted hazard ratios (95% confidence intervals) per 1 log‐unit increase in sST2 were 4.95 (3.56–6.82) at 30 days and 3.16 (2.61–3.84) at 1 year.
Table 2.
Baseline Characteristics According to sST2 Level, Overall and by STEMI/NSTEMI Presentation
| Characteristic | Overall | STEMI | NSTEMI | |||
|---|---|---|---|---|---|---|
| Normal sST2 (n=682) | High sST2 (n=719) | Normal sST2 (n=148) | High sST2 (n=143) | Normal sST2 (n=534) | High sST2 (n=576) | |
| sST2, median (25th–75th percentile), ng/mL | 32.2 (25.2–39.1) | 99.5 (62.0–200.0)a | 32.1 (24.8–41.2) | 88.2 (60.9–200.0)a | 32.3 (25.2–38.6) | 102.4 (62.5–199.8)a |
| Age, mean (SD), y | 64.6 (13.8) | 69.9 (15.5)a | 59.8 (14.0) | 63.8 (17.3)b | 65.9 (13.5) | 71.4 (14.7)a |
| Male sex, n (%) | 491 (72) | 362 (50)a | 120 (81) | 89 (62)a | 371 (70) | 273 (47)a |
| Smoking, n (%) | ||||||
| Never | 265 (39) | 294 (41) | 60 (41) | 64 (45) | 205 (38) | 230 (40) |
| Former | 270 (40) | 292 (41) | 47 (32) | 44 (31) | 223 (42) | 248 (43) |
| Current | 147 (22) | 133 (19) | 41 (28) | 35 (24) | 106 (20) | 98 (17) |
| BMI, n (%) | ||||||
| <18.5 kg/m2 (underweight) | 6 (1) | 30 (4)a | 2 (1) | 4 (3) | 4 (1) | 26 (5)a |
| 18.5–24.9 kg/m2 (normal weight) | 130 (19) | 210 (29) | 30 (20) | 35 (24) | 101 (19) | 175 (30) |
| 25.0–29.9 kg/m2 (overweight) | 281 (41) | 232 (32) | 66 (45) | 53 (37) | 215 (40) | 179 (31) |
| ≥30.0 kg/m2 (obese) | 264 (39) | 246 (34) | 50 (34) | 51 (36) | 214 (40) | 195 (34) |
| Family history of CAD, n (%) | 175 (26) | 114 (16)a | 43 (29) | 26 (18)b | 132 (25) | 88 (15)a |
| Hypertension, n (%) | 461 (68) | 532 (74)a | 85 (57) | 83 (58) | 376 (70) | 449 (78)a |
| Hyperlipidemia, n (%) | 460 (67) | 465 (65) | 92 (62) | 84 (59) | 368 (69) | 381 (66) |
| Diabetes mellitus, n (%) | 135 (20) | 199 (28)a | 20 (14) | 38 (27)a | 115 (22) | 161 (28)b |
| History of HF, n (%) | 42 (6) | 137 (19)a | 4 (3) | 14 (10)a | 38 (7) | 123 (21)a |
| History of CAD, n (%) | 87 (13) | 145 (20)a | 14 (10) | 10 (7) | 73 (14) | 135 (23)a |
| Maximum troponin T, median (25th–75th percentile), ng/mL | 0.60 (0.21–1.65) | 0.67 (0.18–2.61) | 1.76 (0.69–3.80) | 3.11 (0.95–6.63)a | 0.41 (0.17–1.19) | 0.42 (0.14–1.52) |
| Killip class >1, n (%) | 81 (12) | 234 (33)a | 26 (18) | 44 (31)b | 55 (10) | 190 (34)a |
| STEMI, n (%) | 148 (22) | 143 (20) | ··· | ··· | ··· | ··· |
| Anterior MI, n (%) | 185 (27) | 317 (44)a | 81 (55) | 94 (66) | 104 (20) | 223 (39)a |
| Reperfusion/revascularization during hospitalization, n (%) | 484 (71) | 317 (44)a | 120 (81) | 104 (73) | 364 (68) | 213 (37)a |
| Charlson index, n (%) | ||||||
| 0 | 320 (47) | 182 (25)a | 78 (53) | 53 (37)a | 242 (45) | 129 (22)a |
| 1–2 | 203 (30) | 245 (34) | 52 (35) | 48 (34) | 151 (28) | 197 (34) |
| ≥3 | 159 (23) | 292 (41) | 18 (12) | 42 (29) | 141 (26) | 250 (43) |
| eGFR, median (25th–75th percentile), mL/min per 1.73 m2 | 63.9 (53.9–76.5) | 57.2 (43.9–71.2)a | 69.0 (59.9–82.2) | 61.8 (47.7–70.9)a | 62.6 (52.7–75.6) | 56.2 (42.4–71.2)a |
| GRACE score, median (25th–75th percentile) | 112 (92–133) | 136 (104–160)a | 99 (81–121) | 109 (81–145)a | 117 (96–135) | 140 (114–162)a |
| TIMI score, median (25th–75th percentile) | 3 (2–4) | 3 (2–5)a | 3 (2–5) | 4 (2–7)a | 3 (2–4) | 3 (2–4) |
| Medication at discharge, n (%) | ||||||
| Aspirin | 627 (92.1) | 521 (78.9)a | 142 (95.9) | 123 (94.6) | 485 (91.0) | 398 (75.1)a |
| Statins | 596 (87.5) | 457 (69.2)a | 137 (92.6) | 114 (87.7) | 459 (86.1) | 343 (64.7)a |
| β Blockers | 608 (89.3) | 533 (80.8)a | 141 (95.3) | 112 (86.2)a | 467 (87.6) | 421 (79.4)a |
| ACE/ARB | 414 (60.8) | 394 (59.7) | 101 (68.2) | 101 (77.7) | 313 (58.7) | 293 (55.3) |
ACE indicates angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; GRACE, Global Registry of Acute Coronary Events; HF, heart failure; MI, myocardial infarction; NSTEMI, non–ST‐elevation MI; sST2, soluble suppression of tumorigenicity‐2; STEMI, ST‐elevation MI; and TIMI, Thrombolysis in Myocardial Infarction.
P≤0.01.
P≤0.05.
Figure 1.
Age‐ and sex‐adjusted survival after myocardial infarction (MI) in mutually exclusive groups defined by ST‐segment elevation MI (STEMI) vs non‐STEMI (NSTEMI) presentation and normal vs high soluble suppression of tumorigenicity‐2 (sST2) measurement.
Figure 2.
Association between soluble suppression of tumorigenicity‐2 (sST2) and 1‐year mortality after myocardial infarction (MI), overall and by ST‐segment elevation MI (STEMI)/non‐STEMI (NSTEMI) subtype, applying different adjustment approaches. Hazard ratios (HRs; 95% confidence intervals [CIs]) are reported per 1 log‐unit increase in sST2. Charlson comorbidity index is log transformed and modeled as a continuous variable. GRACE indicates Global Registry of Acute Coronary Events; and TIMI, Thrombolysis in Myocardial Infarction.
The overall discriminatory ability in predicting death was reasonable for the GRACE score (c‐statistic=0.80) and poor for the TIMI score (c‐statistic=0.63), which was outperformed by a model with only age and sex (c‐statistic=0.74). Inclusion of the Charlson comorbidity index and sST2 contributed incrementally to the models' discriminatory power (Figure 3; Table 3). In general, the discriminatory power was better in those with STEMI than in those with NSTEMI for both scores, GRACE and TIMI. In patients with NSTEMI, sequential addition of the Charlson comorbidity index and sST2 markedly improved the c‐statistic over GRACE (from 0.78 to 0.80 to 0.84) and TIMI (from 0.61 to 0.73 to 0.81), respectively. In patients with STEMI, only sST2 significantly improved discrimination over both risk scores. Notably, in patients with both STEMI and NSTEMI, the model, including age, sex, Charlson index, and sST2, had a higher c‐statistic than either GRACE or TIMI, alone or when augmented by the Charlson index (Table 3). Stratified by sex, a better discriminatory ability was found in men (Table 4) than in women (Table 5), overall and for both STEMI and NSTEMI, which was consistent throughout all models. Last, in a sensitivity analysis, the category‐less net reclassification index was assessed, showing a substantial improvement with the addition of sST2 over both TIMI (net reclassification index=0.449, P=0.005) and GRACE (net reclassification index=0.397, P=0.03), thus supporting the results of the main analysis.
Figure 3.
Receiver operating characteristic curves for the predicted probabilities of selected risk scores before (green line) and after the addition of Charlson comorbidity index (blue dashed line) and soluble suppression of tumorigenicity‐2 (sST2; red dashed line). The models are GRACE (Global Registry of Acute Coronary Events; top panel), TIMI (Thrombolysis in Myocardial Infarction; middle panel), and age and sex (bottom panel).
Table 3.
Discriminatory Power of Prediction Models for Men and Women Combined
| Variables Added | C‐Statistic (95% Confidence Interval) | ||
|---|---|---|---|
| GRACE | TIMIa | Age and Sex | |
| Overall: 1401 subjects (190 deaths) | |||
| None | 0.80 (0.77–0.83) | 0.63 (0.59–0.67) | 0.74 (0.70–0.77) |
| Charlson index | 0.82 (0.79–0.84)b | 0.75 (0.72–0.78)c | 0.79 (0.76–0.82)c |
| sST2 | 0.86 (0.84–0.88)c | 0.83 (0.81–0.85)c | 0.85 (0.82–0.87)c |
| STEMI only: 291 subjects (26 deaths) | |||
| None | 0.89 (0.84–0.93) | 0.80 (0.72–0.89) | 0.78 (0.69–0.88) |
| Charlson index | 0.90 (0.83–0.96) | 0.85 (0.76–0.93) | 0.84 (0.75–0.92) |
| sST2 | 0.94 (0.90–0.97)b | 0.91 (0.87–0.96)b | 0.92 (0.88–0.96)b |
| NSTEMI only: 1110 subjects (164 deaths) | |||
| None | 0.78 (0.74–0.81) | 0.61 (0.57–0.65) | 0.72 (0.68–0.76) |
| Charlson index | 0.80 (0.76–0.83)b | 0.73 (0.69–0.77)c | 0.77 (0.74–0.81)c |
| sST2 | 0.84 (0.81–0.87)c | 0.81 (0.79–0.84)c | 0.83 (0.80–0.86)c |
sST2 and Charlson comorbidity index are log transformed and modeled as continuous variables, along with age. GRACE indicates Global Registry of Acute Coronary Events; NSTEMI, non–ST‐elevation myocardial infarction; sST2, soluble suppression of tumorigenicity‐2; STEMI, ST‐elevation myocardial infarction; and TIMI, Thrombolysis in Myocardial Infarction.
Specific TIMI scores were used for STEMI/NSTEMI, as appropriate.
P≤0.05 for comparison with previous (ie, above) model.
P≤0.01 for comparison with previous (ie, above) model.
Table 4.
Discriminatory Power of Prediction Models for Men
| Variables Added | C‐Statistic (95% Confidence Interval) | ||
|---|---|---|---|
| GRACE | TIMIa | Age | |
| Overall: 853 subjects (99 deaths) | |||
| None | 0.84 (0.81–0.87) | 0.68 (0.63–0.73) | 0.77 (0.73–0.82) |
| Charlson index | 0.85 (0.82–0.88) | 0.80 (0.76–0.83)b | 0.82 (0.78–0.86)b |
| sST2 | 0.88 (0.85–0.91)b | 0.85 (0.82–0.88)b | 0.87 (0.84–0.90)b |
| STEMI only: 209 subjects (14 deaths) | |||
| None | 0.91 (0.87–0.96) | 0.82 (0.69–0.95) | 0.79 (0.66–0.92) |
| Charlson index | 0.92 (0.83–1.00) | 0.87 (0.74–0.99) | 0.86 (0.73–0.99) |
| sST2 | 0.96 (0.91–1.00)c | 0.94 (0.88–1.00) | 0.95 (0.90–0.99) |
| NSTEMI only: 644 subjects (85 deaths) | |||
| None | 0.81 (0.78–0.85) | 0.65 (0.60–0.71) | 0.75 (0.71–0.80) |
| Charlson index | 0.83 (0.79–0.87) | 0.78 (0.73–0.83)b | 0.81 (0.76–0.85)b |
| sST2 | 0.86 (0.82–0.89)c | 0.84 (0.80–0.87)b | 0.85 (0.81–0.89)b |
sST2 and Charlson comorbidity index are log transformed and modeled as continuous variables, along with age. GRACE indicates Global Registry of Acute Coronary Events; NSTEMI, non–ST‐elevation myocardial infarction; sST2, soluble suppression of tumorigenicity‐2; STEMI, ST‐elevation myocardial infarction; and TIMI, Thrombolysis in Myocardial Infarction.
Specific TIMI scores were used for STEMI/NSTEMI, as appropriate.
P≤0.01 for comparison with previous (ie, above) model.
P≤0.05 for comparison with previous (ie, above) model.
Table 5.
Discriminatory Power of Prediction Models for Women
| Variables Added | C‐Statistic (95% Confidence Interval) | ||
|---|---|---|---|
| GRACE | TIMIa | Age | |
| Overall: 548 subjects (91 deaths) | |||
| None | 0.75 (0.69–0.80) | 0.57 (0.51–0.62) | 0.69 (0.63–0.75) |
| Charlson index | 0.77 (0.72–0.83) | 0.68 (0.63–0.74)b | 0.74 (0.68–0.79)b |
| sST2 | 0.83 (0.79–0.86)b | 0.80 (0.76–0.84)b | 0.81 (0.78–0.85)b |
| STEMI only: 82 subjects (12 deaths) | |||
| None | 0.82 (0.65–0.98) | 0.71 (0.55–0.87) | 0.70 (0.53–0.86) |
| Charlson index | 0.85 (0.73–0.98) | 0.78 (0.64–0.92) | 0.79 (0.67–0.91) |
| sST2 | 0.88 (0.79–0.97) | 0.87 (0.77–0.97) | 0.87 (0.77–0.97) |
| NSTEMI only: 466 subjects (79 deaths) | |||
| None | 0.73 (0.67–0.80) | 0.55 (0.48–0.63) | 0.69 (0.62–0.75) |
| Charlson index | 0.76 (0.70–0.82) | 0.67 (0.61–0.73)b | 0.73 (0.67–0.80)b |
| sST2 | 0.81 (0.77–0.86)b | 0.78 (0.74–0.83)b | 0.81 (0.76–0.86)b |
sST2 and Charlson comorbidity index are log transformed and modeled as continuous variables, along with age. GRACE indicates Global Registry of Acute Coronary Events; NSTEMI, non–ST‐elevation myocardial infarction; sST2, soluble suppression of tumorigenicity‐2; STEMI, ST‐elevation myocardial infarction; and TIMI, Thrombolysis in Myocardial Infarction.
Specific TIMI scores were used for STEMI/NSTEMI, as appropriate.
P≤0.01 for comparison with previous (ie, above) model.
Discussion
In this contemporary community cohort of patients with first MI, the American Heart Association/American College of Cardiology guideline‐recommended GRACE and TIMI risk scores underperformed in predicting 1‐year survival, particularly for NSTEMI and in women. This is of substantial importance because NSTEMI accounts for up to 80% of patients with MI, and women represent a large proportion of patients treated for NSTEMI. Adding the Charlson comorbidity index to the risk scores improved their discriminatory performance, particularly for TIMI, which showed poor discrimination otherwise. Adding sST2 had a substantial incremental impact on the discriminatory power, regardless of the risk score or MI type. In general, a model with age, sex, comorbidity, and sST2 had superior discriminatory ability compared with either GRACE or TIMI, overall and for both patients with STEMI and patients with NSTEMI. This superiority persisted even after the Charlson comorbidity index was added to the GRACE and TIMI scores, demonstrating the large predictive power of sST2 in acute MI. Thus, regardless of hypothetical therapeutic targets,38, 39 sST2 appears as a promising prognostic indicator.
Early assessment of risk after MI guides initial clinical evaluation and treatment and, thereby, influences the acuity, intensity, duration, and location of care. It can provide the patient and family with a more informed sense of potential outcome.1, 2, 3, 4, 5 The American Heart Association/American College of Cardiology recommend using validated scores for risk stratification in acute coronary syndrome, taking into consideration that physicians who rely on subjective assessment of risk may fail to consider important prognostic factors. Higher risk scores generally imply that higher‐intensity treatments may be appropriate within the context of the patient's health status. At present, either GRACE5, 6 or TIMI3, 4 risk scores are advocated by the guidelines.1, 2 Both risk scores were developed on the basis of patients recruited during the late 1990s and early 2000s. Since then, widespread major changes in the epidemiological characteristics and management of MI have taken place and been extensively reported. These changes included a major decline in the incidence of STEMI, an increasing proportion of NSTEMI, improved short‐term treatment and secondary prevention measures, reduced short‐term case fatality rates, and an increasing burden of morbidity and mortality from noncardiovascular causes.7, 8, 9, 10, 11, 12 In this context, we relied on a prospective contemporary cohort of community‐dwelling patients with validated MI to assess risk scores' performance. Our results indicate that the GRACE score outperformed the TIMI score in predicting death at 1 year. The superiority of GRACE, previously reported,40 may be attributable to several factors. First, the TIMI risk scores were designed for early risk assessment: 14 days for NSTEMI3 and 30 days for STEMI.4 The GRACE score was designed for both in‐hospital5 and 6‐month6 risk assessments, notwithstanding several studies showing good longer‐term predictive performance for both TIMI and GRACE.4, 41, 42, 43, 44, 45 The TIMI score for NSTEMI in our cohort showed particularly poor discriminatory power (inferior to that of a model including only age and sex), possibly because, unlike the other scores, which used all‐cause mortality as the primary end point, it also included recurrent MI and severe ischemia requiring urgent revascularization in its composite outcome. Second, the 2 TIMI scores are simpler to use than the GRACE score, because they were designed for easy bedside application without the aid of a computer as, “further refinement of the model produces unattractive levels of complexity (p. 841).”3 The GRACE score, on the other hand, is more complex and requires more data, with the rationale that most clinicians have personal digital devices, making the use of more sophisticated and more accurate models practical.5 Notably, it was previously suggested that risk prediction models with a c‐statistic of 0.6 to 0.7 are of limited clinical value, whereas those with a c‐statistic between 0.7 and 0.8 have modest value.46 Third, the selection of patients in whom GRACE and TIMI were derived was different. Although the TIMI risk score was originally developed in nearly 15 000 patients with STEMI from the InTIME II (Intravenous nPA for Treatment of Infarcting Myocardium Early II) trial,4 and the TIMI for unstable angina/NSTEMI was developed from 2 phase 3, international, randomized, controlled trials,3 the GRACE model was developed from a multinational registry of population‐based patients involving 94 hospitals in 14 countries. The GRACE model was thus designed to reflect an unbiased and generalizable sample that predicts mortality risk across the spectrum of patients being seen with acute coronary syndrome; hence, it is likely more generalizable to other community settings, such as ours.
Scores, such as GRACE and TIMI, enable a more systematic approach to risk stratification, theoretically superior to subjective risk assessment. However, the ability of the scoring systems to discriminate patients' risks is not optimal, partly because of the stochastic nature of cardiovascular events and the difficulty in predicting outcome based on risk assessment at a single point in time.47 Nevertheless, we were able to demonstrate substantial improvement in risk stratification in those with STEMI and NSTEMI by adding comorbidity and sST2, a myocardial fibrosis and remodeling biomarker, to the models. As the burden of coronary disease shifts toward older age groups and short‐term case fatality is improving constantly,7, 8, 9, 10, 11, 12 MI is increasingly becoming a disease of elderly people, such that the impact of comorbidity on outcome becomes increasingly important. In this context, women in our cohort, who were, on average, 7 years older than men (72 versus 65 years), were less accurately risk stratified by both GRACE and TIMI. This may not be surprising, because standardized risk scores have typically been developed and validated in younger patients. As to sST2, the concept of augmenting risk scoring systems by adding biomarkers is particularly appealing.3, 48 Indeed, risk scores include focused clinical dimensions and biomarkers capture distinct aspects of MI pathophysiological characteristics that may provide additional information. A variety of biomarkers were examined for their value in risk assessment, including high‐sensitivity C‐reactive protein, B‐type natriuretic peptide, NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide), and growth differentiation factor 15 (GDF‐15). The results of these investigations were somewhat disappointing.43, 49, 50 This may be partly because of the relatively high correlations (Pearson r>0.5) of GRACE with B‐type natriuretic peptide, NT‐proBNP, and GDF‐15.47, 49 Recently, however, Widera et al,47 studying 1122 patients with NSTEMI or unstable angina, have shown that measurements of GDF‐15 and NT‐proBNP on admission enhance the predictive value of GRACE. Adjustment of the GRACE score by GDF‐15 increased the c‐statistic from 0.79 to 0.85, similar to the improvement observed on adjusting the GRACE by NT‐proBNP. We report herein an improvement over GRACE of similar magnitude by the addition of sST2. sST2 is part of the interleukin‐1 receptor family related to cardiac mechanical strain. Expressed by cardiomyocytes and cardiac fibroblasts, an excess of circulating sST2 leads to the binding and subsequent reduced bioavailability of the circulating cardioprotective ligand interleukin‐33. This ligand reduces myocardial fibrosis, prevents myocyte hypertrophy, reduces apoptosis, and improves myocardial function.51 A possible pathophysiological importance of sST2 in infarct remodeling was further suggested by Weir et al,52 who reported an association of sST2 with infarct magnitude/evolution over 24 weeks of observation in patients with acute MI with resultant left ventricle systolic dysfunction. Clinically, the prognostic value of sST2 in MI was demonstrated in various settings.20, 21, 22, 23, 24, 25, 53 More important sST2 only weakly correlated with other biomarkers of myocardial injury, inflammatory activation, and hemodynamic stress.21 Furthermore, sST2 has a modest correlation with GRACE and a weak correlation with TIMI risk scores, as shown herein.
Thus, sST2 conveys prognostic information likely reflecting pathways distinct from those detected by established biomarkers.21 Furthermore, interleukin‐33/sST2 not only represents a promising cardiovascular biomarker, but also a novel mechanism of intramyocardial fibroblast‐cardiomyocyte communication that may prove to be a therapeutic target for the prevention of heart failure and death after MI.39
Some limitations of our study should be acknowledged to aid in data interpretation. The racial and ethnic composition of the study population (predominantly white) may limit the generalizability to groups not adequately represented. In this context, one third of the patients during the study period did not have sST2 measured. These patients tended to be older and to have more comorbid conditions than patients who had sST2 measured. Because all the analyses were performed using the same sample, the results of the additional candidate predictors (including sST2) over GRACE and TIMI may be overoptimistic.47 We examined only sST2 to the exclusion of other biomarkers. Yet, the correlations between sST2 and several other biomarkers were shown to be weak to moderate, at most. As a consequence, their confounding potential is negligible. Whether other biomarkers, such as NT‐proBNP and GDF‐15, provide incremental predictive value over sST2 necessitates a multimarker approach. Finally, we have limited power when analyzing the data for patients with STEMI because of few deaths in this group.
Conclusion
In conclusion, in this community cohort of contemporary patients with MI followed up for mortality, the GRACE and TIMI scoring systems, recommended by current guidelines, had a reasonable‐to‐good discriminatory capacity in patients with STEMI but only poor‐to‐moderate value in patients with NSTEMI. Addition of comorbidity and, particularly, of sST2 markedly improved risk prediction. Because accurate risk stratification is essential for informed decision making and management, these findings define an opportunity to improve clinical care.
Sources of Funding
This study was supported by the National Institutes of Health, Bethesda, MD (R01 HL120957), and made possible by the Rochester Epidemiology Project, Rochester, MN (R01 AG034676), from the National Institute on Aging, Bethesda, MD. The funding sources played no role in the design, conduct, or reporting of this study.
Disclosures
Sarano has a research grant from Edwards Lifescience, outside of the submitted work. Jaffe reports consulting for Beckman, Coulter, Siemens, Abbott, Roche, Alere, NeurogenomeX, Sphingotec, Single, and Novartis, outside of the submitted work. Roger consults for Sanofi, outside of the submitted work. All other authors report no conflicts of interest.
Acknowledgments
We thank Susan Stotz, RN, Ellen Koepsell, RN, and Deborah Strain for their study support.
(J Am Heart Assoc. 2017;6:e005958 DOI: 10.1161/JAHA.117.005958.)
References
- 1. Amsterdam EA, Wenger NK, Brindis RG, Casey DE Jr, Ganiats TG, Holmes DR Jr, Jaffe AS, Jneid H, Kelly RF, Kontos MC, Levine GN, Liebson PR, Mukherjee D, Peterson ED, Sabatine MS, Smalling RW, Zieman SJ. 2014 AHA/ACC guideline for the management of patients with non‐ST‐elevation acute coronary syndromes: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2014;64:e139–e228. [DOI] [PubMed] [Google Scholar]
- 2. O'Gara PT, Kushner FG, Ascheim DD, Casey DE Jr, Chung MK, de Lemos JA, Ettinger SM, Fang JC, Fesmire FM, Franklin BA, Granger CB, Krumholz HM, Linderbaum JA, Morrow DA, Newby LK, Ornato JP, Ou N, Radford MJ, Tamis‐Holland JE, Tommaso CL, Tracy CM, Woo YJ, Zhao DX, Anderson JL, Jacobs AK, Halperin JL, Albert NM, Brindis RG, Creager MA, DeMets D, Guyton RA, Hochman JS, Kovacs RJ, Ohman EM, Stevenson WG, Yancy CW. 2013 ACCF/AHA guideline for the management of ST‐elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2013;127:e362–e425. [DOI] [PubMed] [Google Scholar]
- 3. Antman EM, Cohen M, Bernink PJ, McCabe CH, Horacek T, Papuchis G, Mautner B, Corbalan R, Radley D, Braunwald E. The TIMI risk score for unstable angina/non‐ST elevation MI: a method for prognostication and therapeutic decision making. JAMA. 2000;284:835–842. [DOI] [PubMed] [Google Scholar]
- 4. Morrow DA, Antman EM, Charlesworth A, Cairns R, Murphy SA, de Lemos JA, Giugliano RP, McCabe CH, Braunwald E. TIMI risk score for ST‐elevation myocardial infarction: a convenient, bedside, clinical score for risk assessment at presentation: an intravenous nPA for treatment of infarcting myocardium early II trial substudy. Circulation. 2000;102:2031–2037. [DOI] [PubMed] [Google Scholar]
- 5. Granger CB, Goldberg RJ, Dabbous O, Pieper KS, Eagle KA, Cannon CP, Van De Werf F, Avezum A, Goodman SG, Flather MD, Fox KA. Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med. 2003;163:2345–2353. [DOI] [PubMed] [Google Scholar]
- 6. Eagle KA, Lim MJ, Dabbous OH, Pieper KS, Goldberg RJ, Van de Werf F, Goodman SG, Granger CB, Steg PG, Gore JM, Budaj A, Avezum A, Flather MD, Fox KA. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6‐month postdischarge death in an international registry. JAMA. 2004;291:2727–2733. [DOI] [PubMed] [Google Scholar]
- 7. Kostis WJ, Deng Y, Pantazopoulos JS, Moreyra AE, Kostis JB. Trends in mortality of acute myocardial infarction after discharge from the hospital. Circ Cardiovasc Qual Outcomes. 2010;3:581–589. [DOI] [PubMed] [Google Scholar]
- 8. Roger VL, Weston SA, Gerber Y, Killian JM, Dunlay SM, Jaffe AS, Bell MR, Kors J, Yawn BP, Jacobsen SJ. Trends in incidence, severity, and outcome of hospitalized myocardial infarction. Circulation. 2010;121:863–869. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Yeh RW, Sidney S, Chandra M, Sorel M, Selby JV, Go AS. Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med. 2010;362:2155–2165. [DOI] [PubMed] [Google Scholar]
- 10. Gerber Y, Weston SA, Jiang R, Roger VL. The changing epidemiology of myocardial infarction in Olmsted County, Minnesota, 1995–2012. Am J Med. 2015;128:144–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Rosamond WD, Chambless LE, Heiss G, Mosley TH, Coresh J, Whitsel E, Wagenknecht L, Ni H, Folsom AR. Twenty‐two‐year trends in incidence of myocardial infarction, coronary heart disease mortality, and case fatality in 4 US communities, 1987–2008. Circulation. 2012;125:1848–1857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Gerber Y, Melton LJ III, Weston SA, Roger VL. Association between myocardial infarction and fractures: an emerging phenomenon. Circulation. 2011;124:297–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. [DOI] [PubMed] [Google Scholar]
- 14. Singh M, Reeder GS, Jacobsen SJ, Weston S, Killian J, Roger VL. Scores for post‐myocardial infarction risk stratification in the community. Circulation. 2002;106:2309–2314. [DOI] [PubMed] [Google Scholar]
- 15. Januzzi JL Jr. ST2 as a cardiovascular risk biomarker: from the bench to the bedside. J Cardiovasc Transl Res. 2013;6:493–500. [DOI] [PubMed] [Google Scholar]
- 16. Bayes‐Genis A, de Antonio M, Vila J, Penafiel J, Galan A, Barallat J, Zamora E, Urrutia A, Lupon J. Head‐to‐head comparison of 2 myocardial fibrosis biomarkers for long‐term heart failure risk stratification: ST2 versus galectin‐3. J Am Coll Cardiol. 2014;63:158–166. [DOI] [PubMed] [Google Scholar]
- 17. Ky B, French B, McCloskey K, Rame JE, McIntosh E, Shahi P, Dries DL, Tang WH, Wu AH, Fang JC, Boxer R, Sweitzer NK, Levy WC, Goldberg LR, Jessup M, Cappola TP. High‐sensitivity ST2 for prediction of adverse outcomes in chronic heart failure. Circ Heart Fail. 2011;4:180–187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Mueller T, Dieplinger B, Gegenhuber A, Poelz W, Pacher R, Haltmayer M. Increased plasma concentrations of soluble ST2 are predictive for 1‐year mortality in patients with acute destabilized heart failure. Clin Chem. 2008;54:752–756. [DOI] [PubMed] [Google Scholar]
- 19. Rehman SU, Mueller T, Januzzi JL Jr. Characteristics of the novel interleukin family biomarker ST2 in patients with acute heart failure. J Am Coll Cardiol. 2008;52:1458–1465. [DOI] [PubMed] [Google Scholar]
- 20. Dhillon OS, Narayan HK, Khan SQ, Kelly D, Quinn PA, Squire IB, Davies JE, Ng LL. Pre‐discharge risk stratification in unselected STEMI: is there a role for ST2 or its natural ligand IL‐33 when compared with contemporary risk markers? Int J Cardiol. 2013;167:2182–2188. [DOI] [PubMed] [Google Scholar]
- 21. Kohli P, Bonaca MP, Kakkar R, Kudinova AY, Scirica BM, Sabatine MS, Murphy SA, Braunwald E, Lee RT, Morrow DA. Role of ST2 in non‐ST‐elevation acute coronary syndrome in the MERLIN‐TIMI 36 trial. Clin Chem. 2012;58:257–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Sabatine MS, Morrow DA, Higgins LJ, MacGillivray C, Guo W, Bode C, Rifai N, Cannon CP, Gerszten RE, Lee RT. Complementary roles for biomarkers of biomechanical strain ST2 and N‐terminal prohormone B‐type natriuretic peptide in patients with ST‐elevation myocardial infarction. Circulation. 2008;117:1936–1944. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Jenkins WS, Roger VL, Jaffe AS, Weston SA, AbouEzzeddine OF, Jiang R, Manemann SM, Enriquez‐Sarano M. Prognostic value of soluble ST2 after myocardial infarction: a community perspective. Am J Med. 2017;130:1112.e9–1112.e15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Demyanets S, Speidl WS, Tentzeris I, Jarai R, Katsaros KM, Farhan S, Krychtiuk KA, Wonnerth A, Weiss TW, Huber K, Wojta J. Soluble ST2 and interleukin‐33 levels in coronary artery disease: relation to disease activity and adverse outcome. PLoS One. 2014;9:e95055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Richards AM, Di Somma S, Mueller T. ST2 in stable and unstable ischemic heart diseases. Am J Cardiol. 2015;115:48B–58B. [DOI] [PubMed] [Google Scholar]
- 26. Steg PG, Lopez‐Sendon J, Lopez de Sa E, Goodman SG, Gore JM, Anderson FA Jr, Himbert D, Allegrone J, Van de Werf F. External validity of clinical trials in acute myocardial infarction. Arch Intern Med. 2007;167:68–73. [DOI] [PubMed] [Google Scholar]
- 27. Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, Go AS, Harrell FE Jr, Hong Y, Howard BV, Howard VJ, Hsue PY, Kramer CM, McConnell JP, Normand SL, O'Donnell CJ, Smith SC Jr, Wilson PW. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119:2408–2416. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Melton LJ III. History of the Rochester epidemiology project. Mayo Clin Proc. 1996;71:266–274. [DOI] [PubMed] [Google Scholar]
- 29. St Sauver JL, Grossardt BR, Yawn BP, Melton LJ III, Rocca WA. Use of a medical records linkage system to enumerate a dynamic population over time: the Rochester epidemiology project. Am J Epidemiol. 2011;173:1059–1068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Roger VL, Killian JM, Weston SA, Jaffe AS, Kors J, Santrach PJ, Tunstall‐Pedoe H, Jacobsen SJ. Redefinition of myocardial infarction: prospective evaluation in the community. Circulation. 2006;114:790–797. [DOI] [PubMed] [Google Scholar]
- 31. Alpert JS, Thygesen K, Antman E, Bassand JP. Myocardial infarction redefined: a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol. 2000;36:959–969. [DOI] [PubMed] [Google Scholar]
- 32. Kors JA, Crow RS, Hannan PJ, Rautaharju PM, Folsom AR. Comparison of computer‐assigned Minnesota codes with the visual standard method for new coronary heart disease events. Am J Epidemiol. 2000;151:790–797. [DOI] [PubMed] [Google Scholar]
- 33. Dieplinger B, Januzzi JL Jr, Steinmair M, Gabriel C, Poelz W, Haltmayer M, Mueller T. Analytical and clinical evaluation of a novel high‐sensitivity assay for measurement of soluble ST2 in human plasma: the Presage ST2 assay. Clin Chim Acta. 2009;409:33–40. [DOI] [PubMed] [Google Scholar]
- 34. Broch K, Ueland T, Nymo SH, Kjekshus J, Hulthe J, Muntendam P, McMurray JJ, Wikstrand J, Cleland JG, Aukrust P, Gullestad L. Soluble ST2 is associated with adverse outcome in patients with heart failure of ischaemic aetiology. Eur J Heart Fail. 2012;14:268–277. [DOI] [PubMed] [Google Scholar]
- 35. Coglianese EE, Larson MG, Vasan RS, Ho JE, Ghorbani A, McCabe EL, Cheng S, Fradley MG, Kretschman D, Gao W, O'Connor G, Wang TJ, Januzzi JL. Distribution and clinical correlates of the interleukin receptor family member soluble ST2 in the Framingham Heart Study. Clin Chem. 2012;58:1673–1681. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36. Uno H, Tian L, Cai T, Kohane IS, Wei LJ. A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data. Stat Med. 2013;32:2430–2442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. R Core Team . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria: URL http://www.R-project.org/. [Google Scholar]
- 38. Pascual‐Figal DA, Januzzi JL. The biology of ST2: the International ST2 Consensus Panel. Am J Cardiol. 2015;115:3B–7B. [DOI] [PubMed] [Google Scholar]
- 39. Kakkar R, Lee RT. The IL‐33/ST2 pathway: therapeutic target and novel biomarker. Nat Rev Drug Discov. 2008;7:827–840. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. de Araujo Goncalves P, Ferreira J, Aguiar C, Seabra‐Gomes R. TIMI, PURSUIT, and GRACE risk scores: sustained prognostic value and interaction with revascularization in NSTE‐ACS. Eur Heart J. 2005;26:865–872. [DOI] [PubMed] [Google Scholar]
- 41. Eggers KM, Kempf T, Venge P, Wallentin L, Wollert KC, Lindahl B. Improving long‐term risk prediction in patients with acute chest pain: the Global Registry of Acute Coronary Events (GRACE) risk score is enhanced by selected nonnecrosis biomarkers. Am Heart J. 2010;160:88–94. [DOI] [PubMed] [Google Scholar]
- 42. Kozieradzka A, Kaminski KA, Maciorkowska D, Olszewska M, Dobrzycki S, Nowak K, Kralisz P, Prokopczuk P, Musial WJ. GRACE, TIMI, Zwolle and CADILLAC risk scores: do they predict 5‐year outcomes after ST‐elevation myocardial infarction treated invasively? Int J Cardiol. 2011;148:70–75. [DOI] [PubMed] [Google Scholar]
- 43. Meune C, Drexler B, Haaf P, Reichlin T, Reiter M, Meissner J, Twerenbold R, Stelzig C, Freese M, Winkler K, Mueller C. The GRACE score's performance in predicting in‐hospital and 1‐year outcome in the era of high‐sensitivity cardiac troponin assays and B‐type natriuretic peptide. Heart. 2011;97:1479–1483. [DOI] [PubMed] [Google Scholar]
- 44. Yan AT, Yan RT, Tan M, Casanova A, Labinaz M, Sridhar K, Fitchett DH, Langer A, Goodman SG. Risk scores for risk stratification in acute coronary syndromes: useful but simpler is not necessarily better. Eur Heart J. 2007;28:1072–1078. [DOI] [PubMed] [Google Scholar]
- 45. Ilkhanoff L, O'Donnell CJ, Camargo CA, O'Halloran TD, Giugliano RP, Lloyd‐Jones DM. Usefulness of the TIMI Risk Index in predicting short‐ and long‐term mortality in patients with acute coronary syndromes. Am J Cardiol. 2005;96:773–777. [DOI] [PubMed] [Google Scholar]
- 46. Ohman EM, Granger CB, Harrington RA, Lee KL. Risk stratification and therapeutic decision making in acute coronary syndromes. JAMA. 2000;284:876–878. [DOI] [PubMed] [Google Scholar]
- 47. Widera C, Pencina MJ, Meisner A, Kempf T, Bethmann K, Marquardt I, Katus HA, Giannitsis E, Wollert KC. Adjustment of the GRACE score by growth differentiation factor 15 enables a more accurate appreciation of risk in non‐ST‐elevation acute coronary syndrome. Eur Heart J. 2012;33:1095–1104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Abu‐Assi E, Ferreira‐Gonzalez I, Ribera A, Marsal JR, Cascant P, Heras M, Bueno H, Sanchez PL, Aros F, Marrugat J, Garcia‐Dorado D, Pena‐Gil C, Gonzalez‐Juanatey JR, Permanyer‐Miralda G. Do GRACE (Global Registry of Acute Coronary Events) risk scores still maintain their performance for predicting mortality in the era of contemporary management of acute coronary syndromes? Am Heart J. 2010;160:826–834.e821–823. [DOI] [PubMed] [Google Scholar]
- 49. Ang DS, Wei L, Kao MP, Lang CC, Struthers AD. A comparison between B‐type natriuretic peptide, global registry of acute coronary events (GRACE) score and their combination in ACS risk stratification. Heart. 2009;95:1836–1842. [DOI] [PubMed] [Google Scholar]
- 50. Fernandez‐Berges D, Bertomeu‐Gonzalez V, Sanchez PL, Cruz‐Fernandez JM, Arroyo R, Barriales Alvarez V, Carrasco Sanchez FJ, Dalli E, Castro Beiras A, Kaski JC. Clinical scores and patient risk stratification in non‐ST elevation acute coronary syndrome. Int J Cardiol. 2011;146:219–224. [DOI] [PubMed] [Google Scholar]
- 51. Vasile VC, Jaffe AS. Emerging biomarkers for acute heart conditions. Curr Opin Cardiol. 2014;29:312–318. [DOI] [PubMed] [Google Scholar]
- 52. Weir RA, Miller AM, Murphy GE, Clements S, Steedman T, Connell JM, McInnes IB, Dargie HJ, McMurray JJ. Serum soluble ST2: a potential novel mediator in left ventricular and infarct remodeling after acute myocardial infarction. J Am Coll Cardiol. 2010;55:243–250. [DOI] [PubMed] [Google Scholar]
- 53. Shimpo M, Morrow DA, Weinberg EO, Sabatine MS, Murphy SA, Antman EM, Lee RT. Serum levels of the interleukin‐1 receptor family member ST2 predict mortality and clinical outcome in acute myocardial infarction. Circulation. 2004;109:2186–2190. [DOI] [PubMed] [Google Scholar]