1. Development and validation of a least absolute shrinkage and selection operator-based prediction model for depression in adolescents with polycystic ovary syndrome.
- Author
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DING Rui, TAN Hui-wen, LIU Ying, YAN Xin, GUO Yun-mei, and WANG Lian-hong
- Subjects
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DEPRESSION in adolescence , *POLYCYSTIC ovary syndrome , *GERIATRIC Depression Scale , *PREDICTION models , *RECEIVER operating characteristic curves , *DECISION making - Abstract
Objective To establish a depression prediction model for adolescents with Polycystic Ovary Syndrome (PCOS) and validate the model. Methods Patients' data were collected from the gynecological clinic of Affiliated Hospital of Zunyi Medical University according to the item pool of risk factors for depression in adolescents with PCOS. Data collected between October 2021 and September 2022. In this study, R software (version 4.2.1) was used to perform regression analysis by the least absolute shrinkage and selection operator (LASSO), so as to screen out strong risk factors related to depression in adolescents with PCOS. These risk factors were then incorporated into logistic regression to develop a depression warning model in adolescents with PCOS. The model has been visualized by nomogram and has been verified both internally and externally. The predicted effect of the model was evaluated through discrimination, specificity and sensitivity. Decision curve analysis was used to analyze the clinical effect of the model. Results The model was as follows: depression risk = 1/(1 + exp-(-4. 055 + 0.221 x sleep + 0.729 x hormonal contraceptive use + 0.920 x hirsutism + 0.079 x illness perception -0.058 x social support + 1.049 x (luteinizing hormone/follicle stimulating hormone) ≥2)). The area under the ROC curve for this model was 0.881. The optimal cut-off value on the ROC curve was 0.278, corresponding to a high specificity and sensitivity of 76.2% and 88.0%, respectively. The corrected area under the ROC curve obtained was 0.867. In addition, the result of decision curve analysis showed that the model could provide effective evidence support for clinical decision-making. The area under the ROC curve obtained from external validation was 0.871. Conclusion In this study, an early warning model of depression risk in adolescents with PCOS was constructed. It can effectively identify people at high risk of depression in adolescents with PCOS at an early stage, thus providing a theoretical basis for the implementation of comprehensive and effective risk prevention measures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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