Evidence map›Paper›PMID 41006503›Full record

ArticleScientific reports2025

Multistate Markov model for functional recovery in stroke: probability of state transition and prognosis prediction.

Bowen Li, Chunxiao Wan

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Bowen LiDepartment of Physical and Rehabilitation Medicine, Tianjin Medical University General Hospital, No. 154 Anshan Road, Tianjin, 300052, China.
Chunxiao WanDepartment of Physical and Rehabilitation Medicine, Tianjin Medical University General Hospital, No. 154 Anshan Road, Tianjin, 300052, China. thesissun8@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke rehabilitation involves complex transitions between different functional states. This study developed and validated a five-state Markov model to quantify state transition probabilities and predict functional outcomes in stroke patients, providing quantitative support for clinical decision-making. We analyzed 2000 functional status observations from 1000 stroke patients (baseline and follow-up) at Tianjin Medical University General Hospital. Using modified Rankin Scale scores, patients were classified into five functional states (mild/moderate/severe disability, recovery, death). A continuous-time Markov model estimated transition intensities and probabilities, with validation through observed-versus-predicted comparisons and comprehensive sensitivity analyses. The Markov model revealed dynamic transitions among functional states: the monthly transition intensity from severe to moderate disability was the highest (3.13%), while the annual cumulative probability of full recovery was highest among patients with moderate disability (15.99%). At the 12-month follow-up, 65.4%, 59.6%, and 49.0% of patients with mild, moderate, and severe disability, respectively, remained in their original state, while 14.3%, 16.0%, and 14.5% achieved full recovery. One-way and probabilistic sensitivity analyses indicated that the model was robust to parameter variations, with narrow 95% confidence intervals. The multi-stata Markov model provided insights into the dynamic process of functional recovery after stroke in a specialized setting,offering a methodological framework that may support clinical prognosis assessment with appropriate validation. Patients with moderate disability exhibited the highest recovery potential, while those with severe disability demonstrated the fastest improvement rate, suggesting that individualized rehabilitation strategies may be considered tailored to different functional states.

Indexed as

Markov ChainsRecovery of FunctionStrokeStroke RehabilitationAdultAgedFemaleHumansMaleMiddle AgedProbabilityPrognosisFunctional recoveryMarkov modelPrognosis predictionStrokeTransition probability

Identifiers

PMID41006503
PMCPMC12475012

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