Evidence map›Paper›PMID 41073115›Full record

Observational studyBMJ open2025

Development and validation of a model for predicting depression risk in primary palmar hyperhidrosis: a cross-sectional retrospective observational study.

Xiaoyu Wang, Yuqing Huang

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In one paragraph

Observational study in BMJ open, 2025. 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

2 authors.

Xiaoyu WangDepartment of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China.ORCID http://orcid.org/0009-0002-2092-0376
Yuqing HuangDepartment of Thoracic Surgery, Beijing Haidian Hospital, Beijing, China huangyuqing555@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePrimary palmar hyperhidrosis (PPH), characterised by excessive palm sweating, significantly impacts patients' physiology, psychology, self-esteem, work, life and social interactions. The incidence of depression is higher among PPH patients. Timely detection of key predictive factors and the development of risk prediction models are crucial for effective intervention and treatment in this patient group.

designWe conducted an in-depth analysis of clinical data from 926 PPH patients treated at the Thoracic Surgery Department of Beijing Haidian Hospital between 2016 and 2021. We used the Boruta algorithm alongside the Backward Elimination strategy to select predictive factors and constructed five machine-learning models. By evaluating these models' performance, we determined the optimal one. Additionally, we introduced the Shapley Additive exPlanations method to enhance the interpretability of this optimal model.

resultsThe Personality Diagnostic Questionnaire-4 score, Self-Rating Anxiety Scale score, family history, quality of life excluding PPH, onset age and the age when PPH begins to impact life (Impact age) are six predictive factors for depression in PPH patients. The support vector machine (SVM) model performs more comprehensively through model validation. In the validation set, the area under the curve is 0.798 (95% CI: 0.737 to 0.859), with a Brier score of 0.1451 (95% CI: 0.1233 to 0.1716), accuracy of 0.7184, sensitivity of 0.775, specificity of 0.699 and F1 score of 0.585.

conclusionsThese findings can enhance our understanding of depression in PPH patients, and the SVM model is a valuable screening tool for assessing the risk of depression in PPH patients.

Indexed as

DepressionHyperhidrosisAdultCross-Sectional StudiesFemaleHumansMachine LearningMaleMiddle AgedQuality of LifeRetrospective StudiesRisk AssessmentRisk FactorsSupport Vector MachineYoung AdultDepression & mood disordersMachine LearningRisk Factors

Identifiers

PMID41073115
PMCPMC12517024

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