External Validation of a Dynamic Prediction Model for Upper Limb Function After Stroke

Abstract

ObjectiveTo externally validate the dynamic prediction model for prediction of upper limb (UL) function 6 months after stroke. The dynamic prediction model has been developed and cross-validated on data from 4 Dutch studies. DesignData from a prospective Danish cohort study were used to assess prediction accuracy. SettingA Danish neurorehabilitation hospital. ParticipantsIn this external validation study, follow-up data for 80 patients in the subacute phase after stroke (N=80), mean age 64 (SD11), 43% women, could be obtained. They were assessed at 2 weeks, 3 months, and 6 months after stroke with the Action Research Arm Test (ARAT), Fugl-Meyer Motor Assessment upper limb (FMA), and Shoulder Abduction (SA) Finger Extension (FE), (SAFE) test. InterventionNot applicable. Main Outcome MeasuresPrediction accuracy at 6 months was examined for 3 categories of ARAT (0-57 points): mild (48-57), moderate (23-47), and severe (0-22). Two individual predictions of ARAT scores at ±6 months post-stroke were computed based on, respectively, baseline (2 weeks) and 3 months ARAT, FE, SA values. The absolute individual differences between observed and predicted ARAT scores were summarized. ResultsThe prediction model performed best for patients with relatively good UL motor function, with an absolute error median (IQR) of 3 (2-9), and worst for patients with severe UL impairment, with a median (IQR) of 30 (3-39) at baseline. In general, prediction accuracy substantially improved when data obtained 3 months after stroke was included compared with baseline at 2 weeks after stroke. ConclusionWe found limited clinical usability due to the lack of prediction accuracy 2 weeks after stroke and for patients with severe UL impairments. The dynamic prediction model could probably be refined with data from biomarkers.