Hysterectomy as a predictor of depression: A comprehensive analysis using logistic regression and machine learning

Yafeng Wang, Shiyang Wei, Shuaifang Wei, Yalan Li, Xuefeng Jiang
In: Journal of Affective Disorders · 2025 · vol. 391 , pp. 119980 · doi:10.1016/j.jad.2025.119980 · PMID:40738336 · W4412686779
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This study utilized logistic regression and machine learning to analyze hysterectomy as a predictor of depression.

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Abstract

BackgroundAn increasing number of studies have shown that there is an inseparable connection between hysterectomy and occurrence of depression, and the impact on patient's mental health cannot be ignored. Therefore, this study utilized the National Health Interview Survey (NHIS) database to explore correlation between hysterectomy and depression.MethodsData of this study were derived from NHIS 2023. After excluding samples, differences in baseline characteristics of participants between depression group and control group were explored by creating a baseline table. Next, correlation between hysterectomy and depression was analyzed by constructing adjusted logistic regression models. Subsequently, correlation between hysterectomy and depression in different populations was further confirmed through logistic regression analysis. Meanwhile, whether hysterectomy had clinical predictive value for depression was evaluated by plotting receiver operating characteristic (ROC) curve and constructing k-nearest neighbors (KNN) machine learning model.ResultsA total of 14,327 female participants were included in this study. A total of 2281 and 12,046 participants in depression group and control group, respectively. Baseline characteristic analysis showed that, except for education, significant differences were observed in remaining covariates between two groups. Among them, the 671 participants had both depression and had undergone hysterectomy. Subsequently, hysterectomy was significantly associated with depression in three models, and this association was not affected by other covariates. In addition, hysterectomy was a risk factor for depression (odds ratio (OR) = 1.32, 95 % confidence interval (CI) = 1.17-1.50, p < 0.001). Based on model 3, area under the curve (AUC) value of ROC curve was 0.797. KNN analysis indicated hysterectomy ranked high, suggesting that hysterectomy had relatively good predictive value for depression.ConclusionThis study confirmed a robust correlation between hysterectomy and depression, thereby offering useful references for future research.

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