Development of a multivariate clinical prediction model for the diagnosis of mild stroke/TIA in physician first-contact patient settings

OBJECTIVE To develop a clinical prediction model for diagnosing mild stroke/transient ischemic attack (TIA) in first-contact patient settings. DESIGN: Retrospective study design utilizing logistic regression modeling of patient clinical symptoms collected from patient chart histories and referral data. SETTING: Regional fast-track TIA clinic on Vancouver Island, Canada, accepting referrals from emergency departments (ED) and general practice (GP). PARTICIPANTS: Model development: 4187 ED and GP referred patients from 2008–2011 who were assessed at the TIA clinic. Temporal hold-out validation: 1953 ED and GP referred patients from 2012–2013 assessed at the same clinic. OUTCOMES: Diagnosis of mild stroke/TIA by clinic neurologists. RESULTS: 123 candidate predictors were assessed using univariate feature selection for inclusion in the model, and culminated in the selection of 50 clinical features. Post-hoc investigation of the selected predictors revealed 12 clinically relevant interaction terms. Model performance on the temporal hold-out validation set achieved a sensitivity/specificity of 71.8% / 72.8% using the ROC01 cutpoint (≥ 0.662), and an AUC of 79.9% (95% CI, 77.9%–81.9%). In comparison, the ABCD2 score (≥ 4) achieved a sensitivity/specificity of 70.4% / 54.5% and an AUC of 67.5% (95% CI, 65.2%–69.9%). The logistic regression model demonstrated good calibration on the hold-out set (β 0 = −0.257); β linear = 1.047). CONCLUSIONS The developed diagnostic model performs better than the ABCD2 score at diagnosing mild stroke/TIA on the basis of clinical symptoms. The model has the potential to replace the use of the prognostic ABCD2 score in diagnostic medical contexts in which the ABCD2 score is currently used, such as patient triage.