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

Abstract

Objective: To 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. Design: Data from a prospective Danish cohort study were used to assess prediction accuracy. Setting: A Danish neurorehabilitation hospital. Participants: In 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. Intervention: Not applicable. Main Outcome Measures: Prediction 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. Results: The 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. Conclusion: We 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.

Keywords

• Stroke
• Upper limb
• Neurological rehabilitation
• Algorithms