Accuracy of an algorithm in predicting upper limb functional capacity in a United States population

Abstract Objective : To determine the accuracy of an algorithm, using clinical measures only, on a sample of persons with first ever stroke in the US. It was hypothesized that algorithm accuracy would fall in a range of 70-80%. Design : Secondary analysis of prospective, observational, longitudinal cohort; two assessments were done, (1) within 48 hours to 1 week post stroke and (2) at 12 weeks post stroke. Setting : Recruited from a large acute care hospital and followed over first 6 months after stroke. Participants : Adults with first ever stroke (N=49) with paresis of the upper limb (UL) at ≤48 hours who could follow 2-step commands and were expected to return to independent living at 6 months. Intervention : NA Main Outcome Measure(s) : The overall accuracy of the algorithm with clinical measures was quantified by comparing predicted (expected) and actual (observed) categories using a correct classification rate (CCR). Results : The overall accuracy (61%) and weighted kappa (62%) were significant. Sensitivity was high for the Excellent (95%) and Poor (81%) algorithm categories. Specificity was high for the Good (82%), Limited (98%) and Poor (95%) categories. PPV was high for Poor (82%) and NPV was high for all categories. No differences in participant characteristics were found between those with accurate or inaccurate predictions. Conclusions : The results of the present study found that use of an algorithm with clinical measures only is better than chance alone (chance = 25% for each of the 4 categories) at predicting a category of UL capacity at 3 months post stroke. The moderate to high values of sensitivity, specificity, PPV and NPV demonstrates some clinical utility of the algorithm within healthcare settings in the US.