A new method for predicting functional recovery of stroke patients with hemiplegia: logarithmic modelling

Abstract

Objective: To examine the validity and applicability of logarithmic modelling for predicting functional recovery of stroke patients with hemiplegia. Design: Longitudinal postal survey. Subjects: Stroke patients with hemiplegia staying in a long-term rehabilitation facility, who had been referred from acute medical service 30-60 days after onset. Methods: Functional Independence Measure (FIM) scores were periodically assessed during hospitalization. For each individual, a logarithmic formula that was scaled by an interval increase in FIM scores during the initial 2-6 weeks was used for predicting functional recovery. Results: For the study, we recruited 18 patients who showed a wide variety of disability levels on admission (FIM scores 25-107). For each patient, the predicted FIM scores derived from the logarithmic formula matched the actual change in FIM scores. The changes predicted the recovery of motor rather than cognitive functions. Regression analysis showed a close fit between logarithmic modelling and actual FIM scores (across-subject R2 = 0.945). Conclusions: Provided with two initial time-point samplings, logarithmic modelling allows accurate prediction of functional recovery for individuals. Because the modelling is mathematically simple, it can be widely applied in daily clinical practice.