Evidence map›Paper›PMID 42461468›Full record

ArticleAging clinical and experimental research2026

Machine learning-based prediction of hospital-associated complications after tibial fracture surgery in older patients: a nationwide Japanese database study.

Akio Shimizu, Ryota Sakamoto, Hirofumi Nagayama, Ryo Momosaki

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Article in Aging clinical and experimental research, 2026. 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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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Akio ShimizuDepartment of Rehabilitation Medicine, Mie University Graduate School of Medicine, 2-174 Edobashi, 514-8507, Tsu, Mie, Japan. a-shimizu@med.mie-u.ac.jp.ORCID http://orcid.org/0000-0003-0244-1953
Ryota SakamotoDepartment of Medical Informatics, Mie University Hospital, 2-174 Edobashi, 514-8507, Tsu, Mie, Japan.ORCID http://orcid.org/0000-0003-0411-856X
Hirofumi NagayamaDepartment of Occupational Therapy, Kanagawa University of Human Services, 1- 10-1 Heiseicho, 238-8522, Yokosuka, Kanagawa, Japan.ORCID http://orcid.org/0000-0002-1763-1330
Ryo MomosakiDepartment of Rehabilitation Medicine, Mie University Graduate School of Medicine, 2-174 Edobashi, 514-8507, Tsu, Mie, Japan.ORCID http://orcid.org/0000-0003-3274-3952

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOlder adults requiring emergency surgery for acute tibial fractures are vulnerable to hospital-associated complications (HACs), but admission-time risk stratification tools are lacking. We aimed to characterize HACs and develop both an ensemble prediction model and a simplified bedside risk score.

methodsThis retrospective cohort study used the Japanese Diagnosis Procedure Combination database provided by JMDC Inc. Patients aged ≥ 65 years emergently admitted for tibial fracture (ICD-10: S82, 2014-2025) who underwent surgery within 5 days, with stay > 5 days, were included. The primary outcome was composite HACs during index hospitalization. Missing data were handled with multiple imputation. A Super Learner ensemble was developed and evaluated on held-out test data, and a simplified scorecard was derived using analysis of variance (ANOVA)-based feature selection and Weight-of-Evidence transformation.

resultsAmong 53,186 admissions, 5,193 met eligibility criteria. HACs occurred in 851 patients (16.4%), most commonly delirium (7.5%) and falls/trauma (5.7%). The Super Learner achieved a test-set area under the receiver operating characteristic curve (AUC) of 0.740 (95% CI 0.707-0.772), higher than conventional linear logistic regression (0.724). The most influential predictors were Hospital Frailty Risk Score, dementia, Barthel Index, comorbidity burden, and days to surgery. The simplified 7-variable scorecard achieved a test-set AUC of 0.736 (95% CI 0.703-0.769), stratifying patients into five risk groups (HAC rate: 3.45%-38.64%).

conclusionsHAC risk after emergency tibial fracture surgery in older adults was driven primarily by geriatric vulnerability rather than fracture-specific factors. A simplified admission-time score may support targeted prevention, pending external validation.

Indexed as

Machine LearningPostoperative ComplicationsTibial FracturesAgedAged, 80 and overDatabases, FactualEast Asian PeopleFemaleHospitalizationHumansJapanMalePredictive Learning ModelsRetrospective StudiesRisk AssessmentRisk FactorsFrailtyHospital-associated complicationsMachine learningOlder adultsTibial fracture

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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 OpenQuestion graph.