Evidence map›Paper›PMID 41686554›Full record

ArticleMedicine2026

Influencing factors of fracture in elderly ankle fracture patients in the emergency department and the construction of nomogram prediction model.

Yu-Lei Mao, Yong-Kang Wei

Abstract read
In one paragraph

Article in Medicine, 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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0citing papers in PubMed
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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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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Yu-Lei MaoYa'an Hospital of Traditional Chinese Medicine.ORCID 0009-0007-8859-6898
Yong-Kang WeiChengdu Xinhua Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to analyze the independent risk factors of elderly ankle fracture patients in the emergency department and to construct the corresponding prediction model in order to improve the ability of the clinic to identify the high-risk individuals, so as to carry out the preventive and interventional work more effectively.

methodsThis study employed a retrospective cohort design, including 300 elderly ankle fracture patients who visited the emergency department of our hospital between January 2020 and December 2024 (observation group), as well as 300 non-fractured elderly patients from the same period (control group), matched according to predefined criteria. Possibly relevant risk factors were screened by univariate and multivariate statistical analyses, and a nomogram prediction model was built accordingly. In order to assess the discriminative power, calibration and clinical utility of the model, the receiver operating characteristic curve was plotted and the area under the curve was calculated, and the goodness-of-fit of the model was evaluated using the Hosmer-Lemeshow test. To improve the stability of the prediction tool, Bootstrap method and ten-fold cross-validation were introduced in the study for internal validation, and the calibration graph and decision curve were combined to assess its potential for application in actual clinical practice.

resultsmultifactorial logistic regression analysis showed that living alone (OR, 2.914; 95% CI, 1.336-8.847), osteoporosis (OR, 4.044; 95% CI, 1.416-11.127), body mass index <18.5kg/m2 or body mass index >24kg/m2 (OR, 3.599; 95% CI, 2.047-9.490), cognitive dysfunction (OR, 3.901; 95% CI, 1.612-7.829), and fracture history (OR, 4.291; 95% CI, 1.310-8.046) were all independent risk factors for emergency geriatric ankle fractures.

conclusionThe prediction model constructed in this study has good accuracy and operability, and can be used as an intuitive tool for clinicians to conduct individualized risk assessment, which helps to identify high-risk elderly patients at an early stage and promote the implementation of targeted prevention and treatment measures.

Indexed as

Ankle FracturesEmergency Service, HospitalNomogramsAgedAged, 80 and overFemaleHumansLogistic ModelsMalePrediction AlgorithmsRetrospective StudiesRisk FactorsROC Curveankleelderlyemergency departmentfracturenomogram

Identifiers

PMID41686554
PMCPMC12908838

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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.