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Prediction of myocardial infarction in patients with transient ischaemic attack
Author(s) -
Vilanova M. B.,
MauriCapdevila G.,
Sanahuja J.,
Quilez A.,
PiñolRipoll G.,
Begué R.,
Gil M. I.,
CodinaBarios M. C.,
Benabdelhak I.,
Purroy F.
Publication year - 2015
Publication title -
acta neurologica scandinavica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.967
H-Index - 95
eISSN - 1600-0404
pISSN - 0001-6314
DOI - 10.1111/ane.12291
Subject(s) - medicine , hazard ratio , myocardial infarction , stroke (engine) , cardiology , proportional hazards model , confidence interval , etiology , prospective cohort study , incidence (geometry) , mechanical engineering , physics , optics , engineering
Background Determinants of risk of myocardial infarction ( MI ) after transient ischaemic attack ( TIA ) are not well defined. The aim of our study was to determine the risk and risk factors for MI after TIA . Methods We prospectively recruited patients within 24 h of transient ischaemic cerebrovascular events between October 2006 and January 2013. A total of 628 TIA patients were followed for six months or more. MI and stroke recurrence ( SR ) were recorded. The duration and typology of clinical symptoms, vascular risk factors and aetiological work‐ups were prospectively recorded and established prognostic scores ( ABCD 2, ABCD 2I, ABCD 3I, Essen Stroke Risk Score, California Risk Score and Stroke Prognosis Instrument) were calculated. Results Twenty‐eight (4.5%) MI and 68 (11.0%) recurrent strokes occurred during a median follow‐up period of 31.2 months (16.1–44.9). In Cox proportional hazards multivariate analyses, we identify previous coronary heart disease ( CHD ) (hazard ratio [ HR ] 5.65, 95% confidence interval [ CI ] 2.45–13.04, P < 0.001) and sex male ( HR 2.72, 95% CI 1.02–7.30, P = 0.046) as independent predictors of MI . Discrimination for the prognostic scores only ranged from 0.60 to 0.71. The incidence of MI did not vary among the different aetiological subtypes. Positive diffusion weighted imaging ( DWI ) (7.5% vs 2.5%, P = 0.007), and ECG abnormalities (Q wave or ST ‐T wave changes) (13.6% vs 3.6%, P = 0.001) were associated to MI . Conclusion According to our results, discrimination was poor for all previous risk prediction models evaluated. Variables such as previous CHD , male sex, DWI and ECG abnormalities should be considered in new prediction models.