How Strong the Predictive Ability of the Variable Age and Stroke Severity for Patients Presented Late

Abstract Background and purpose: Age and stroke severity as 2 independent predictors have been included in many well-known prediction models. However, whether the model consisting of the 2 variables derived from early arrival group could bring equal clinical benefit for those patients presented late was unclear. This study aimed to investigate the performance of this transformation. Methods: We enrolled ischemic stroke patients admitted to our stroke center within 3 days after symptom onset from January 1, 2018 to March 31, 2019.These patients were divided into 2 groups, early arrival group within 6 hours after onset and late arrival group between 6 hours and 3 days. Two multivariate logistic regression models were developed consisting of the variable age and stroke severity. The primary outcome was the unfavorable outcome which defined as modified Rankin Scale score of 3-6. The differences of the performance of the models were compared through 3 aspects (discrimination, calibration, and clinical utility). Results: Five-hundred seventeen ischemic stroke patients were included in our study. There were 258 patients reached in our stroke center within 6 hours while 259 patients were not. The area under the curve were .78 (95% confidence interval .70-.87) for the model developed in the early arrival group and .82 (95% confidence interval .73-.90) for the model developed in the late arrival group respectively. The models calibrated well in the late arrival group. As for clinical utility, the net benefit of the model developed in the early group was only slightly lower than the model developed in the late arrival group. Conclusions: The prediction model consisting of the variable age and stroke severity derived from the early arrival group patients had the potential to be applied directly in the patients presented late.