Dynamic aspect of functional recovery after stroke using a multistate model.
OBJECTIVE
To estimate time to functional recovery and quantify the effects of significant prognostic factors affecting the dynamic change of 3-state functional outcome after stroke.


METHODS
Modeling of clinical predictions.


METHODS
Referral center.


METHODS
One hundred eleven patients with first-time ischemic stroke.


METHODS
Not applicable.


METHODS
Serial Barthel Index scores at onset, 2 weeks, and 1, 2, 4, and 6 months poststroke. The severity of disability was classified into 3 functional states: poor functional state (PFS) for Barthel Index scores from 0 to 40, moderate functional state (MFS) for scores from 45 to 80, and good functional state (GFS) for scores greater than 80. A 3-state Markov regression model together with Bayesian acyclic graphic underpinning was used to estimate transition parameters and mean time to functional recovery between states and to predict the probability of functional recovery by using Gibbs sampling technique.


RESULTS
The mean total recovery time was 3.1 months for patients with PFS at baseline and 1.3 months for patients with MFS at baseline. The mean recovery times to different functional states were also estimated. Age predominantly affected the probabilities of MFS to GFS transitions, younger patients had faster transition rates (rate ratio, 4.51; 95% confidence interval [CI], 2.72-7.40); but age had only borderline effects on PFS to MFS transitions. In contrast, infarct size exerted substantial effects on PFS to MFS transitions: small-size infarct correlated with a higher transition rate (rate ratio, 10.17; 95% CI, 5.25-20.13), whereas only a borderline effect on MFS to GFS transitions was found. The baseline functional state significantly affected the MFS to GFS transitions.


CONCLUSIONS
By using a multistate model, overall and patient-specific mean time to functional recovery to different functional states can be estimated and the effect of clinical predictors on functional transitions can be precisely quantified to predict patient-specific probability of functional recovery.
Pan
Shin-Liang
SL
Department of Physical Medicine and Rehabilitation, National Taiwan University College of Medicine and University Hospital, Taipei, Taiwan.
Lien
I-Nan
IN
Yen
Ming-Fang
MF
Lee
Ti-Kai
TK
Chen
Tony Hsiu-Hsi
TH
D000367:Age Factors; D000368:Aged; D001921:Brain / Q000473:pathology; D004185:Disability Evaluation; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D011379:Prognosis; D020127:Recovery of Function / Q000502:physiology*; D012720:Severity of Illness Index; D020521:Stroke / Q000473:pathology / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D014057:Tomography, X-Ray Computed