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

OBJECTIVE

Primary 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.

DESIGN

We 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.

RESULTS

The 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.

CONCLUSIONS

These 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.