Evidence mapPaperPMID 42534821Full record

ArticleFrontiers in public health2026

Development and temporal validation of a prediction model for cognitive impairment in older adults with hearing loss based on the population health risk management framework.

Xianyan Xu, Mengting Li, Xuling Gao, Yan Wang, Jun Ge, Rong Zhao, Li Ma, Qiankun Liu

Abstract readValidation Study
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Article in Frontiers in public health, 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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5 · Who and what money

Authors and funding

8 authors.

Xianyan XuDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Mengting LiDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Xuling GaoDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Yan WangDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Jun GeDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Rong ZhaoDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.
Li MaSchool of Nursing, Bengbu Medical University, Bengbu, Anhui, China.
Qiankun LiuDepartment of Nursing, Bengbu First People's Hospital, Bengbu, Anhui, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Older adults with hearing loss are at increased risk of cognitive impairment, yet tailored risk stratification tools remain limited. Methods: In this cross-sectional study, 524 adults aged ≥60 years with hearing loss were enrolled between June 2023 and September 2024. Participants were divided by enrollment period into a development cohort (June 2023 to May 2024, Results: The prevalence of cognitive impairment was 40.8%. LASSO identified five key predictors: age, pure-tone average, depression, hearing-aid use, and social activities. Multivariable analysis showed that depression, older age, and higher pure-tone average were associated with a higher likelihood of cognitive impairment, whereas hearing-aid use and participation in social activities were protective factors. Among the six models, Random Forest showed the best overall performance in the validation cohort. After hyperparameter tuning, the optimized Random Forest model achieved an AUC of 0.952 in the training cohort and 0.871 in the validation cohort. In the validation cohort, the F1 score, sensitivity, Youden index, and NPV increased to 0.737, 0.779, 0.583, and 0.863, respectively. SHAP analysis indicated that pure-tone average, age, and social activities were the most influential predictors in the optimized model. Conclusion: The optimized five-variable Random Forest model demonstrated good predictive performance for cognitive impairment in older adults with hearing loss. This low-cost and interpretable tool may support early screening, risk stratification, and targeted intervention in clinical and community settings.

Indexed as

Cognitive DysfunctionHearing LossAgedAged, 80 and overCross-Sectional StudiesFemaleHumansMachine LearningMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRisk AssessmentRisk Factorscognitive impairmenthearing lossmachine learningolder adultsrisk prediction

Identifiers

PMID42534821
PMCPMC13422174

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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.