Validation of CSR model to predict stroke risk after transient ischemic attack

Sci Rep. 2022 Jan 12;12(1):604. doi: 10.1038/s41598-021-04405-2.

Abstract

It is essential to identify high risk transient ischemic attack (TIA) patients. The previous study reported that the CSR (comprehensive stroke recurrence) model, a neuroimaging model, had a high predictive ability of recurrent stroke. The aims of this study were to validate the predictive value of CSR model in TIA patients and compare the predictive ability with ABCD3-I score. Data were analyzed from the prospective hospital-based database of patients with TIA which defined by the World Health Organization time-based criteria. The predictive outcome was stroke occurrence at 90 days. The receiver-operating characteristic (ROC) curves were plotted and the C statistics were calculated as a measure of predictive ability. Among 1186 eligible patients, the mean age was 57.28 ± 12.17 years, and 474 (40.0%) patients had positive diffusion-weighted imaging (DWI). There were 118 (9.9%) patients who had stroke within 90 days. In 1186 TIA patients, The C statistic of CSR model (0.754; 95% confidence interval [CI] 0.729-0.778) was similar with that of ABCD3-I score (0.717; 95% CI 0.691-0.743; Z = 1.400; P = 0.1616). In 474 TIA patients with positive DWI, C statistic of CSR model (0.725; 95% CI 0.683-0.765) was statistically higher than that of ABCD3-I score (0.626; 95% CI 0.581-0.670; Z = 2.294; P = 0.0245). The CSR model had good predictive value for assessing stroke risk after TIA, and it had a higher predictive value than ABCD3-I score for assessing stroke risk for TIA patients with positive DWI.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Aged
  • China / epidemiology
  • Diffusion Magnetic Resonance Imaging*
  • Female
  • Humans
  • Ischemic Attack, Transient / diagnostic imaging*
  • Male
  • Middle Aged
  • Models, Statistical*
  • Prospective Studies
  • Recurrence
  • Risk Assessment
  • Stroke / epidemiology*