Kissela, B., Schneider, A., Kleindorfer, D., Khoury, J., Miller, R., Alwell, K., Woo, D., Szaflarski, J., Gebel, J., Moomaw, C., Pancioli, A., Jauch, E., Shukla, R. and Broderick, J. (2004) Stroke in a Biracial Population: The Excess Burden of Stroke among Blacks. Stroke, 35, 426-431.
https://doi.org/10.1161/01.STR.0000110982.74967.39

has been cited by the following article:

• TITLE: Predicting Mortality and Functional Outcomes after Ischemic Stroke: External Validation of a Prognostic Model

  AUTHORS: Achala Vagal, Heidi Sucharewv, Christopher Lindsell, Dawn Kleindorfer, Kathleen Alwell, Charles J. Moomaw, Daniel Woo, Matthew Flaherty, Pooja Khatri, Opeolu Adeoye, Simona Ferioli, Jason Mackey, Sharyl Martini, Felipe De Los Rios La Rosa F., Brett Kissela

  KEYWORDS: Ischaemic Stroke, Epidemiology, Stroke, Leukoaraiosis, African American, Stroke Prevalence

  JOURNAL NAME: Journal of Behavioral and Brain Science, Vol.8 No.10, October 30, 2018

  ABSTRACT: Background: We previously developed predictive models for 3-month mortality and modified Rankin Score (mRS) after ischemic stroke. Aim: The aim was to test model validity for 3-month mortality and mRS after ischemic stroke in two independent data sets. Methods: Our derivation models used data from 451 subjects with ischemic stroke in 1999 enrolled in the Greater Cincinnati/Northern Kentucky Stroke Study (GCKNSS). We utilized two separate cohorts of ischemic strokes through GCKNSS (460 in 2005 and 504 in 2010) to assess external validity by utilizing measures of agreement between predicted and observed values, calibration, and discrimination using Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. Results: The 3-month mortality model performed well in the validation datasets with an average prediction error (Brier score) of 0.045 for 2005 and 0.053 for 2010 and excellent discrimination with an area under the curve of 0.86 (95% CI: 0.79, 0.93) for 2005 and 0.84 (0.76, 0.92) for 2010. Predicted 3-month mRS also performed well in the validation datasets with R2 of 0.57 for 2005 and 0.50 for 2010 and a root mean square error of 0.85 for 2005 and 1.05 for 2010. Predicted mRS tended to be higher than actual in both validation datasets. Re-estimation of the model parameters for age and severe white matter hyperintensity in both 2005 and 2010, and for diabetes in 2005, improved predictive accuracy. Conclusions: Our previously developed stroke models performed well in two study periods, suggesting validity of the model predictions.