The TWIST Tool Predicts When Patients Will Recover Independent Walking After Stroke: An Observational Study
Overview
Rehabilitation Medicine
Affiliations
Background: The likelihood of regaining independent walking after stroke influences rehabilitation and hospital discharge planning.
Objective: This study aimed to develop and internally validate a tool to predict whether and when a patient will walk independently in the first 6 months post-stroke.
Methods: Adults with stroke were recruited if they had new lower limb weakness and were unable to walk independently. Clinical assessments were completed one week post-stroke. The primary outcome was time post-stroke by which independent walking (Functional Ambulation Category score ≥ 4) was achieved. Cox hazard regression identified predictors for achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. The cut-off and weighting for each predictor was determined using β-coefficients. Predictors were assigned a score and summed for a final TWIST score. The probability of achieving independent walking at each time point for each TWIST score was calculated.
Results: We included 93 participants (36 women, median age 71 years). Age < 80 years, knee extension strength Medical Research Council grade ≥ 3/5, and Berg Balance Test < 6, 6 to 15, or ≥ 16/56, predicted independent walking and were combined to form the TWIST prediction tool. The TWIST prediction tool was at least 83% accurate for all time points.
Conclusions: The TWIST tool combines routine bedside tests at one week post-stroke to accurately predict the probability of an individual patient achieving independent walking by 4, 6, 9, 16, or 26 weeks post-stroke. If externally validated, the TWIST prediction tool may benefit patients and clinicians by informing rehabilitation decisions and discharge planning.
Supporting Long-Term Meaningful Outcomes in Stroke Rehabilitation.
Fu V, Thompson S, Kayes N, Bright F Curr Neurol Neurosci Rep. 2025; 25(1):17.
PMID: 39899076 DOI: 10.1007/s11910-025-01403-z.
Allaart C, van Houwelingen S, Hilkens P, van Halteren A, Biesma D, Dijksman L JMIR Hum Factors. 2025; 12:e56521.
PMID: 39842003 PMC: 11799809. DOI: 10.2196/56521.
Tsujinaka R, Yoshitani T, Suzuki H, Tanaka R, Izutani Y, Morimoto K Int J Rehabil Res. 2024; 48(1):48-54.
PMID: 39621016 PMC: 11792988. DOI: 10.1097/MRR.0000000000000651.
Hu H, Sun Y, Yang Z, Che L, Cai M, Li X Front Pharmacol. 2024; 15:1445597.
PMID: 39449968 PMC: 11500078. DOI: 10.3389/fphar.2024.1445597.
Prediction of poststroke independent walking using machine learning: a retrospective study.
Tang Z, Su W, Liu T, Lu H, Liu Y, Li H BMC Neurol. 2024; 24(1):332.
PMID: 39256684 PMC: 11385990. DOI: 10.1186/s12883-024-03849-z.