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. 2014:2014:242171.
doi: 10.1155/2014/242171. Epub 2014 Mar 5.

A bioclinical pattern for the early diagnosis of cardioembolic stroke

Bruno Zecca  1 , Clara Mandelli  1 , Alberto Maino  2 , Chiara Casiraghi  3 , Giovanbattista Bolla  4 , Dario Consonni  5 , Paola Santalucia  6 , Giuseppe Torgano  1
Affiliations

A bioclinical pattern for the early diagnosis of cardioembolic stroke

Bruno Zecca et al. Emerg Med Int. 2014.

Abstract

Background and Scope. Early etiologic diagnosis of ischemic stroke subtype guides acute management and treatment. We aim to evaluate if plasma biomarkers can predict stroke subtypes in the early phase from stroke onset. Methods. Plasma N-terminal prohormone of brain natriuretic peptide (NT-proBNP), D-dimer, C-reactive protein, serum albumin, and globulin levels have been investigated in 114 consecutive patients presenting at the emergency room within 6 hours of the ischemic stroke onset. Plasma levels of biomarkers have been correlated with stroke aetiology (based on TOAST criteria) by multivariable logistic regression analysis, adjusted for several covariates. Results. Of the 114 patients, 34 (30%) had cardioembolic stroke, 27 (23%) atherothrombotic stroke, 19 (17%) lacunar stroke, and 34 (30%) stroke of undetermined origin. Patients with cardioembolic stroke had significantly higher levels of NT-proBNP and lower globulin/albumin (G/A) ratio compared with the other subgroups. At multiple logistic regression NT-proBNP > 200 pg/mL, G/A ratio > 0.70, and NIHSS score were independent predictors of cardioembolic stroke with high accuracy of the model, either including (AUC, 0.91) or excluding (AUC, 0.84) atrial fibrillation. Conclusions. A prediction model that includes NT-proBNP, G/A ratio, and NIHSS score can be useful for the early etiologic diagnosis of ischemic stroke.

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Figures

Figure 1
Figure 1
Receiver operating characteristic (ROC) curves for selected clinical variables among 114 patients (34 with cardioembolic stroke and 80 with stroke of other origins), alone or in combination in multiple logistic regression models. (a) Model with atrial fibrillation; (b) model without atrial fibrillation. See Table 3 for model specification.
See this image and copyright information in PMC

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