Shock Index Predicts Patient-Related Clinical Outcomes in Stroke.
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2018-09
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Abstract
Background The prognostic value of shock index ( SI ), heart rate divided by systolic blood pressure, in stroke for clinical outcomes other than mortality is not well understood. Methods and Results We examined the Get With The Guidelines-Stroke ( GWTG -Stroke) data to explore the usefulness of SI in predicting in-hospital outcomes in 425 808 acute stroke cases (mean age: 71.0±14.5 years; 48.8% male; 89.7% ischemic stroke and 10.3% intracerebral hemorrhage) admitted between October 2012 and March 2015. Compared with patients with SI of 0.5 to 0.7, patients with SI >0.7 (13.6% of the sample) had worse outcomes, with adjusted odds ratios of 2.00 (95% confidence interval [ CI ], 1.92-2.08) for in-hospital mortality, 1.46 (95% CI , 1.43-1.49) for longer length of hospital stay >4 days, 1.50 (95% CI , 1.47-1.54) for discharge destination other than home, 1.41 (95% CI , 1.38-1.45) for inability to ambulate independently at discharge, and 1.52 (95% CI , 1.47-1.57) for modified Rankin Scale score of 3 to 6 at discharge. Results were similar when analyses were confined to those with available National Institutes of Health Stroke Scale (NIHSS) or within individual stroke subtypes or when SI was additionally included in the models with or without blood pressure components. Every 0.1 increase in SI >0.5 was associated with significantly worse outcomes in linear spline models. The addition of SI to existing GWTG -Stroke mortality prediction models without NIHSS demonstrated modest improvement, but little to no improvement was noted in models with NIHSS . Conclusions SI calculated at the point of care may be a useful prognostic indicator to identify those with high risk of poor outcomes in acute stroke, especially in hospitals with limited experience with NIHSS assessment.
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Myint, Phyo Kyaw, Shubin Sheng, Ying Xian, Roland A Matsouaka, Mathew J Reeves, Jeffrey L Saver, Deepak L Bhatt, Gregg C Fonarow, et al. (2018). Shock Index Predicts Patient-Related Clinical Outcomes in Stroke. Journal of the American Heart Association, 7(18). p. e007581. 10.1161/jaha.117.007581 Retrieved from https://hdl.handle.net/10161/21637.
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Ying Xian
Roland Albert Matsouaka
Positions
- Associate Professor of Biostatistics & Bioinformatics
- Associate Chair for Culture, Engagement, and Impact
- Member of the Duke Clinical Research Institute
I am an Associate Professor in the Department of Biostatistics and Bioinformatics at Duke University and the Associate Chair for Culture, Engagement, and Impact in the Department of Biostatistics and Bioinformatics. I am also an Associate Director of Biostatistics and Data Science at the Duke Clinical Research Institute and member of the Consortium for the Holistic Assessment of Risk in Transplant (CHART).
The substantive areas of application of my research include biomedical,public health, and social sciences. As a DCRI faculty statistician, I collaborate with clinical researchers to better understand and treat cardiovascular diseases. I have been actively involved in the analyses of large registry data, including the Society of Thoracic Surgeons (STS) National Database, the STS and American College of Cardiology (ACC) Transcatheter Valve Therapy (TVTR) Registry, and the American Heart Association/American Stroke Association Get With The Guidelines (GTWG).
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