Dataset Information

Type 2 Myocardial Infarction: Evolving Approaches to Diagnosis and Risk-Stratification.

ABSTRACT:

Background

Type 2 myocardial infarction (T2MI) is frequently encountered in clinical practice and associated with adverse outcomes.

Content

T2MI occurs most frequently due to noncoronary etiologies that alter myocardial oxygen supply and/or demand. The diagnosis of T2MI is often confused with acute nonischemic myocardial injury, in part because of difficulties in delineating the nature of symptoms and misunderstandings about disease categorization. The use of objective features of myocardial ischemia using electrocardiographic (ECG) or imaging abnormalities may facilitate more precise T2MI diagnosis. High-sensitivity cardiac troponin (hs-cTn) assays allow rapid MI diagnosis and risk stratification, yet neither maximum nor delta values facilitate differentiation of T2MI from T1MI. Several investigational biomarkers have been evaluated for T2MI, but none have robust data. There is interest in evaluating risk profiles among patients with T2MI. Clinically, the magnitude of maximum and delta cTn values as well as the presence and magnitude of ischemia on ECG or imaging is used to indicate disease severity. Scoring systems such as GRACE, TIMI, and TARRACO have been evaluated, but all have limited to modest performance, with substantial variation in time intervals used for risk-assessment and endpoints used.

Summary

The diagnosis of T2MI requires biomarker evidence of acute myocardial injury and clear clinical evidence of acute myocardial ischemia without atherothrombosis. T2MIs are most often caused by noncoronary etiologies that alter myocardial oxygen supply and/or demand. They are increasingly encountered in clinical practice and associated with poor short- and long-term outcomes. Clinicians require novel biomarker or imaging approaches to facilitate diagnosis and risk-stratification.

SUBMITTER: Chapman AR

PROVIDER: S-EPMC7793229 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSeveral 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 resultsBiomarkers 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).ConclusionsIn 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.

Project description:BackgroundMany mortality risk scoring tools exist among patients with ST-elevation Myocardial Infarction (STEMI). A risk stratification model that evaluates STEMI prognosis more simply and rapidly is preferred in clinical practice.Methods and findingsWe developed a simple stratification model for blood examination by using the STEMI data of AMI-Kyoto registry in the derivation set (n = 1,060) and assessed its utility for mortality prediction in the validation set (n = 521). We selected five variables that significantly worsen in-hospital mortality: white blood cell count, hemoglobin, C-reactive protein, creatinine, and blood sugar levels at >10,000/μL, <10 g/dL, >1.0 mg/dL, >1.0 mg/dL, and >200 mg/dL, respectively. In the derivation set, each of the five variables significantly worsened in-hospital mortality (p < 0.01). We developed the risk stratification model by combining laboratory variables that were scored based on each beta coefficient obtained using multivariate analysis and divided three laboratory groups. We also found a significant trend in the in-hospital mortality rate for three laboratory groups. Therefore, we assessed the utility of this model in the validation set. The prognostic discriminatory capacity of our laboratory stratification model was comparable to that of the full multivariable model (c-statistic: derivation set vs validation set, 0.81 vs 0.74). In addition, we divided all cases (n = 1,581) into three thrombolysis in myocardial infarction (TIMI) risk index groups based on an In TIME II substudy; the cases were further subdivided based on this laboratory model. The high laboratory group had significantly high in-hospital mortality rate in each TIMI risk index group (trend of in-hospital mortality; p < 0.01).ConclusionsThis laboratory stratification model can predict in-hospital mortality of STEMI simply and rapidly and might be useful for predicting in-hospital mortality of STEMI by further subdividing the TIMI risk index.