Evidence map›Paper›PMID 42798530›Full record

ArticleFrontiers in neurology2026

Clinical variables provide the main predictive signal for early in-hospital rehabilitation triage after small-artery occlusion stroke: development and internal validation of an interpretable model.

Yingqi Lu, Mei Shen, Xin Chen, Youli Jiang, Yue Wang, Qiuyang Qian, Qingshi Zhao, Wei Wang

Abstract readValidation Study
In one paragraph

Article in Frontiers in neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Yingqi LuDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.
Mei ShenDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.
Xin ChenDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.
Youli JiangDepartment of Neurologic Medicine, People's Hospital of Longhua, Shenzhen, China.
Yue WangDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.
Qiuyang QianDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.
Qingshi ZhaoDepartment of Neurologic Medicine, People's Hospital of Longhua, Shenzhen, China.
Wei WangDepartment of Rehabilitation Medicine, People's Hospital of Longhua, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Small artery occlusion stroke is often mild, but some patients need rehabilitation during acute hospitalization. We developed and internally validated an interpretable model to assist early in-hospital rehabilitation screening and triage and tested whether white matter hyperintensity burden and inflammatory-metabolic biomarkers improved prediction beyond routine clinical variables. Methods: Consecutive adults with acute ischemic stroke classified as small artery occlusion were identified from a single-centre stroke registry and electronic health records. Early rehabilitation need was assessed within 48 h after admission by trained rehabilitation physicians using a structured protocol. The prespecified primary model used clinical variables. Extended models added modified Fazekas score, inflammatory metabolic biomarkers, or both. Eight regression and machine learning algorithms were compared. Internal validation used stratified tenfold cross validation with nested fivefold tuning. Performance was assessed using discrimination, precision recall, calibration, and decision curve analysis. Results: Among 411 patients, 155 had early rehabilitation need (37.7%). The clinical-variable model performed best, with an area under the receiver operating characteristic curve of 0.682 and an area under the precision recall curve of 0.591. Adding modified Fazekas score, inflammatory metabolic biomarkers, or both did not improve performance. Logistic regression performed comparably to complex algorithms. In the admission fixed-landmark sensitivity analysis, the clinical-variable model yielded an AUROC of 0.623, which decreased to 0.507 after variables directly reflecting acute neurological deficits were excluded. Leading predictors reflected neurological severity, early functional impairment, and care pathway characteristics. Conclusion: Early rehabilitation need was common among patients with small-artery occlusion stroke during acute hospitalization. Routine clinical variables provided the dominant predictive information, whereas white matter hyperintensity burden and inflammatory-metabolic biomarkers added no stable incremental value.

Indexed as

Ischemic StrokeStrokeStroke RehabilitationTriageAgedAged, 80 and overBiomarkersFemaleHumansMachine LearningMaleMiddle AgedPredictive Learning ModelsRegistriesBiomarkersclinical prediction modelearly in-hospital rehabilitationinterpretable machine learningSHAP (SHapley Additive exPlanations)small artery occlusion stroke

Identifiers

PMID42798530
PMCPMC13612356

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.