Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model.

Abstract

ObjectiveSpontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM‐UE) scale. However, this rule is criticized for overestimating the predictability of FM‐UE recovery. Our objectives were to develop a longitudinal mixture model of FM‐UE recovery, identify FM‐UE recovery subgroups, and internally validate the model predictions.MethodsWe developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient , time constant in weeks , and distribution of the initial FM‐UE scores. We fitted the model to FM‐UE measurements of 412 first‐ever ischemic stroke patients and cross‐validated endpoint predictions and FM‐UE recovery cluster assignment.ResultsThe model distinguished 5 subgroups with different recovery parameters (, , , , , , , , , ). Endpoint FM‐UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM‐UE recovery clusters was 0.79 (95% equal‐tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks.InterpretationFM‐UE recovery reflects different subgroups, each with its own recovery profile. Cross‐validation indicates that FM‐UE endpoints and FM‐UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020;87:383–393 Ann Neurol 2020;87:383–393