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

Objective Spontaneous 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. Methods We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient r( k), time constant in weeks tau( k), 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. Results The model distinguished 5 subgroups with different recovery parameters ( r(1) = 0.09, tau(1) = 5.3, r(2) = 0.46, tau(2) = 10.1, r(3) = 0.86, tau(3) = 9.8, r(4) = 0.89, tau(4) = 2.7, r(5) = 0.93, tau(5) = 1.2). 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. Interpretation FM-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