Accuracy of Physiotherapist Predictions for Independent Walking After Stroke.

Abstract

The use of prediction tools in stroke rehabilitation research and clinical practice is increasing, but it is not clear whether these prediction tools out-perform clinician predictions. This study aimed to compare physiotherapist predictions for independent walking with the Time to Walking Independently after STroke (TWIST) prediction tool. Adults with new lower limb weakness and unable to walk independently (Functional Ambulation Category [FAC] < 4) were recruited. At 1 week post-stroke, the treating physiotherapist was asked to predict whether their patient would achieve independent walking by 4, 6, 9, 12, 16, or 26 weeks, or remain dependent. Predictions were also made using the TWIST prediction tool, but not shared. Binary logistic regressions were conducted with the time independent walking was achieved as the dependent variable and independent variables were the physiotherapist and TWIST predictions. Ninety-one participants were included (median age 71 years, 36 [40%] female). Most participants (67 [74%]) were non-ambulatory (FAC = 0) at 1-week post-stroke. Thirty-seven physiotherapists were recruited. Physiotherapists made accurate predictions for time taken to achieve independent walking for 39 participants (43%). Prediction accuracy was not related to physiotherapist confidence or years of stroke-specific experience. TWIST out-performed physiotherapist predictions (Physiotherapists 76%-77%, TWIST 86%-88% accurate) for participants who achieved independent walking by 4, 6, and 9 weeks post-stroke. Accuracy of physiotherapist and TWIST predictions was similar for 16 and 26 weeks post-stroke. The TWIST prediction tool is more accurate than physiotherapists at predicting whether a patient will achieve independent walking by 4, 6, or 9 weeks post-stroke, but not for 16 or 26 weeks post-stroke. TWIST may be useful to inform early rehabilitation and discharge planning. Clinical Trial Registration-URL: www.anzctr.org.au Unique Identifier: ACTRN12617001434381.