Multimarker Risk Stratification in Patients With Acute Myocardial Infarction

Abstract

Background: Several biomarkers have individually been shown to be useful for risk stratification in patients with acute myocardial infarction (MI). The optimal multimarker strategy remains undefined.

Methods and results: Biomarkers representing different pathobiological axes were studied, including myocardial stress/structural changes (NT-pro B-type natriuretic peptide [NT-proBNP], midregional proatrial natriuretic peptide [MR-proANP], suppression of tumorigenicity 2 [ST2], galectin-3, midregional proadrenomedullin [MR-proADM], and copeptin), myonecrosis (troponin T), and inflammation (myeloperoxidase [MPO], high sensitivity C-reactive protein [hsCRP], pregnancy-associated plasma protein A [PAPP-A], and growth-differentiation factor-15 [GDF-15]), in up to 1258 patients from Clopidogrel as Adjunctive Reperfusion Therapy-Thrombolysis in Myocardial Infarction 28 (CLARITY-TIMI 28), a randomized trial of clopidogrel in ST-elevation MI (STEMI). Patients were followed for 30 days. Biomarker analyses were adjusted for traditional clinical variables. Forward step-wise selection was used to assess a multimarker strategy. After adjustment for clinical variables and using a dichotomous cutpoint, 7 biomarkers were each significantly associated with a higher odds of cardiovascular death or heart failure (HF) through 30 days, including NT-proBNP (adjusted odds ratio [ORadj], 2.54; 95% CI, 1.47-4.37), MR-proANP (2.18; 1.27-3.76), ST2 (2.88; 1.72-4.81), troponin T (4.13; 1.85-9.20), MPO (2.75; 1.20-6.27), hsCRP (1.96, 1.17-3.30), and PAPP-A (3.04; 1.17-7.88). In a multimarker model, 3 biomarkers emerged as significant and complementary predictors of cardiovascular death or HF: ST2 (ORadj, 2.87; 1.61-5.12), troponin T (2.34; 1.09-5.01 and 4.13, 1.85-9.20, respectively for intermediate and high levels), and MPO (2.49; 1.04-5.96). When added to the TIMI STEMI Risk Score alone, the multimarker risk score significantly improved the C-statistic (area under the curve, 0.75 [95% CI, 0.69-0.81] to 0.82 [0.78-0.87]; P=0.001), net reclassification index (0.93; P<0.001), and integrated discrimination index (0.09; P<0.001).

Conclusions: In patients with STEMI, a multimarker strategy that combines biomarkers across pathobiological axes of myocardial stress, myocyte necrosis, and inflammation provides incremental prognostic information for prediction of cardiovascular death or HF.

Keywords: ST‐elevation myocardial infarction; Thrombolysis in Myocardial Infarction risk score; biomarkers; multimarker; prognosis.

Figure 1

When all markers were combined in a model, 3 candidate markers remained significantly associated with higher odds of CV death or HF at 30 days in patients with STEMI after multivariable adjustment. Troponin T was modeled into 3 groups: undetectable as referent then tertiles (T) of detectable troponin T with tertile 1 and 2 collapsed. CV indicates cardiovascular; HF, heart failure; MPO, myeloperoxidase; OR, odds ratio; STEMI, ST‐elevation myocardial infarction.

Figure 2

An integeric risk score was created using the 3 markers that were significantly associated with the odds CV death or HF including ST2, MPO, and troponin T. A step‐wise increase in the incidence of 30‐day CV death or HF was observed with increasing multimarker risk score. CV indicates cardiovascular; HF, heart failure; MPO, myeloperoxidase; OR, odds ratio.

Figure 3

The multimarker risk score provided incremental information for risk stratification to the TIMI risk score for STEMI. CV indicates cardiovascular; DM, diabetes mellitus; HF, heart failure; HR, hazard ratio; HTN, hypertension; LBBB, left bundle branch block; SBP, systolic blood pressure; STE, ST elevation; STEMI, ST‐elevation myocardial infarction; TIMI, Thrombolysis in Myocardial Infarction.