Dynamic Aspect of Functional Recovery After Stroke Using a Multistate Model

Shin Liang Pan, I. Nan Lien, Ming Fang Yen, Ti Kai Lee, Tony Hsiu Hsi Chen

研究成果: 雜誌貢獻文章

13 引文 (Scopus)

摘要

Pan SL, Lien IN, Yen MF, Lee TK, Chen THH. 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. Design: Modeling of clinical predictions. Setting: Referral center. Participants: One hundred eleven patients with first-time ischemic stroke. Interventions: Not applicable. Main Outcome Measure: 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.
原文英語
頁(從 - 到)1054-1060
頁數7
期刊Archives of Physical Medicine and Rehabilitation
89
發行號6
DOIs
出版狀態已發佈 - 六月 2008
對外發佈Yes

指紋

Stroke
Confidence Intervals
Patient Transfer
Referral and Consultation
Outcome Assessment (Health Care)

ASJC Scopus subject areas

  • Rehabilitation

引用此文

Dynamic Aspect of Functional Recovery After Stroke Using a Multistate Model. / Pan, Shin Liang; Lien, I. Nan; Yen, Ming Fang; Lee, Ti Kai; Chen, Tony Hsiu Hsi.

於: Archives of Physical Medicine and Rehabilitation, 卷 89, 編號 6, 06.2008, p. 1054-1060.

研究成果: 雜誌貢獻文章

Pan, Shin Liang ; Lien, I. Nan ; Yen, Ming Fang ; Lee, Ti Kai ; Chen, Tony Hsiu Hsi. / Dynamic Aspect of Functional Recovery After Stroke Using a Multistate Model. 於: Archives of Physical Medicine and Rehabilitation. 2008 ; 卷 89, 編號 6. 頁 1054-1060.
@article{870f3f96442b4a6092ac5baf3253188e,
title = "Dynamic Aspect of Functional Recovery After Stroke Using a Multistate Model",
abstract = "Pan SL, Lien IN, Yen MF, Lee TK, Chen THH. 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. Design: Modeling of clinical predictions. Setting: Referral center. Participants: One hundred eleven patients with first-time ischemic stroke. Interventions: Not applicable. Main Outcome Measure: 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.",
keywords = "Activities of daily living, Cerebrovascular accident, Markov chains, Rehabilitation, Risk factors, Stochastic processes",
author = "Pan, {Shin Liang} and Lien, {I. Nan} and Yen, {Ming Fang} and Lee, {Ti Kai} and Chen, {Tony Hsiu Hsi}",
year = "2008",
month = "6",
doi = "10.1016/j.apmr.2007.10.032",
language = "English",
volume = "89",
pages = "1054--1060",
journal = "Archives of Physical Medicine and Rehabilitation",
issn = "0003-9993",
publisher = "W.B. Saunders Ltd",
number = "6",

}

TY - JOUR

T1 - Dynamic Aspect of Functional Recovery After Stroke Using a Multistate Model

AU - Pan, Shin Liang

AU - Lien, I. Nan

AU - Yen, Ming Fang

AU - Lee, Ti Kai

AU - Chen, Tony Hsiu Hsi

PY - 2008/6

Y1 - 2008/6

N2 - Pan SL, Lien IN, Yen MF, Lee TK, Chen THH. 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. Design: Modeling of clinical predictions. Setting: Referral center. Participants: One hundred eleven patients with first-time ischemic stroke. Interventions: Not applicable. Main Outcome Measure: 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.

AB - Pan SL, Lien IN, Yen MF, Lee TK, Chen THH. 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. Design: Modeling of clinical predictions. Setting: Referral center. Participants: One hundred eleven patients with first-time ischemic stroke. Interventions: Not applicable. Main Outcome Measure: 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.

KW - Activities of daily living

KW - Cerebrovascular accident

KW - Markov chains

KW - Rehabilitation

KW - Risk factors

KW - Stochastic processes

UR - http://www.scopus.com/inward/record.url?scp=43949112616&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=43949112616&partnerID=8YFLogxK

U2 - 10.1016/j.apmr.2007.10.032

DO - 10.1016/j.apmr.2007.10.032

M3 - Article

C2 - 18503799

AN - SCOPUS:43949112616

VL - 89

SP - 1054

EP - 1060

JO - Archives of Physical Medicine and Rehabilitation

JF - Archives of Physical Medicine and Rehabilitation

SN - 0003-9993

IS - 6

ER -