AIMS: The differentiation of type 1 and type 2 myocardial infarction (T1MI, T2MI) is important, but challenging in the emergency department. We aimed to investigate the clinical characteristics and cardiovascular outcome of T2MI patients and to develop a clinical decision tool to differentiate T1MI and T2MI patients. METHODS AND RESULTS: We prospectively enrolled 1548 patients with suspected MI. All patients were followed for up to 2 years to assess mortality. We used logistic regression with backward step-down selection to determine the most important predictors of T2MI. Based on these regression coefficients, we developed a diagnostic prediction model (score) to diagnose T2MI. T2MI was the final diagnosis of 99 patients. Patients with T2MI showed a high 1-year mortality rate (13.8%), which equals that of T1MI patients (9.4%). Female sex (Beta 1.27 [95% confidence interval; CI 0.67-1.90]), not having radiating chest pain (Beta 1.62 [CI 0.96-2.34]) and a baseline high-sensitivity troponin I concentration ≤ 40.8 ng/L (Beta 1.30 [CI 0.74-1.89]) were the strongest predictors for T2MI. Their combination resulted in an area under the curve of 0.71 to discriminate T1MI and T2MI. The binary score based on this model assigns one point to each of the predictors. Patients with the highest score value of 3 had a 72% probability of T2MI. CONCLUSION: T2MI patients are a heterogeneous population with high-cardiovascular risk. A score based on laboratory and clinical parameters might help to differentiate T1MI and T2MI patients. The additional use of this score in clinical routine needs to be investigated prospectively. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02355457). Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: The differentiation of type 1 and type 2 myocardial infarction (T1MI, T2MI) is important, but challenging in the emergency department. We aimed to investigate the clinical characteristics and cardiovascular outcome of T2MI patients and to develop a clinical decision tool to differentiate T1MI and T2MI patients. METHODS AND RESULTS: We prospectively enrolled 1548 patients with suspected MI. All patients were followed for up to 2 years to assess mortality. We used logistic regression with backward step-down selection to determine the most important predictors of T2MI. Based on these regression coefficients, we developed a diagnostic prediction model (score) to diagnose T2MI. T2MI was the final diagnosis of 99 patients. Patients with T2MI showed a high 1-year mortality rate (13.8%), which equals that of T1MI patients (9.4%). Female sex (Beta 1.27 [95% confidence interval; CI 0.67-1.90]), not having radiating chest pain (Beta 1.62 [CI 0.96-2.34]) and a baseline high-sensitivity troponin I concentration ≤ 40.8 ng/L (Beta 1.30 [CI 0.74-1.89]) were the strongest predictors for T2MI. Their combination resulted in an area under the curve of 0.71 to discriminate T1MI and T2MI. The binary score based on this model assigns one point to each of the predictors. Patients with the highest score value of 3 had a 72% probability of T2MI. CONCLUSION: T2MI patients are a heterogeneous population with high-cardiovascular risk. A score based on laboratory and clinical parameters might help to differentiate T1MI and T2MI patients. The additional use of this score in clinical routine needs to be investigated prospectively. TRIAL REGISTRATION: www.clinicaltrials.gov (NCT02355457). Published on behalf of the European Society of Cardiology. All rights reserved.
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