Prediction of long-term recurrent ischemic stroke: the added value of non-contrast CT, CT perfusion, and CT angiography
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DIAGNOSTIC NEURORADIOLOGY
Prediction of long-term recurrent ischemic stroke: the added value of non-contrast CT, CT perfusion, and CT angiography Frans Kauw 1,7 & Jacoba P. Greving 2 & Richard A. P. Takx 1 & Hugo W. A. M. de Jong 1 & Wouter J. Schonewille 3 & Jan A. Vos 4 & Marieke J. H. Wermer 5 & Marianne A. A. van Walderveen 6 & L. Jaap Kappelle 7 & Birgitta K. Velthuis 1 & Jan W. Dankbaar 1 & On behalf of the Dutch acute stroke study (DUST) investigators Received: 17 June 2020 / Accepted: 16 August 2020 # The Author(s) 2020
Abstract Purpose The aim of this study was to evaluate whether the addition of brain CT imaging data to a model incorporating clinical risk factors improves prediction of ischemic stroke recurrence over 5 years of follow-up. Methods A total of 638 patients with ischemic stroke from three centers were selected from the Dutch acute stroke study (DUST). CT-derived candidate predictors included findings on non-contrast CT, CT perfusion, and CT angiography. Fiveyear follow-up data were extracted from medical records. We developed a multivariable Cox regression model containing clinical predictors and an extended model including CT-derived predictors by applying backward elimination. We calculated net reclassification improvement and integrated discrimination improvement indices. Discrimination was evaluated with the optimism-corrected c-statistic and calibration with a calibration plot. Results During 5 years of follow-up, 56 patients (9%) had a recurrence. The c-statistic of the clinical model, which contained male sex, history of hyperlipidemia, and history of stroke or transient ischemic attack, was 0.61. Compared with the clinical model, the extended model, which contained previous cerebral infarcts on non-contrast CT and Alberta Stroke Program Early CT score greater than 7 on mean transit time maps derived from CT perfusion, had higher discriminative performance (c-statistic 0.65, P = 0.01). Inclusion of these CT variables led to a significant improvement in reclassification measures, by using the net reclassification improvement and integrated discrimination improvement indices. Conclusion Data from CT imaging significantly improved the discriminatory performance and reclassification in predicting ischemic stroke recurrence beyond a model incorporating clinical risk factors only. Keywords Brain infarction . Recurrent event . Risk factor . Computed tomography . Survival analysis Subject terms Ischemic Stroke . Complications . Risk Factors . Computerized tomography . Prognosis
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00234-020-02526-5) contains supplementary material, which is available to authorized users. * Frans Kauw [email protected]
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Department of Radiology, St. Antonius Hospital, Nieuwegein, The Netherlands
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Department of Radiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
Department of Neurology, Leiden University Medical Center, Leiden, The Netherla
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