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. 2022 Sep 6:13:984653.
doi: 10.3389/fpsyt.2022.984653. eCollection 2022.

Development and validation of a prediction model for depression in adolescents with polycystic ovary syndrome: A study protocol

Rui Ding  1   2 , Heng Zhou  3 , Xin Yan  1   2 , Ying Liu  1   2 , Yunmei Guo  1   2 , Huiwen Tan  1   2 , Xueting Wang  1   2 , Yousha Wang  1   2 , Lianhong Wang  1   2
Affiliations

Development and validation of a prediction model for depression in adolescents with polycystic ovary syndrome: A study protocol

Rui Ding et al. Front Psychiatry. .

Abstract

Introduction: The high prevalence and severity of depression in adolescents with polycystic ovary syndrome (PCOS) is a critical health threat that must be taken seriously. The identification of high-risk groups for depression in adolescents with PCOS is essential to preventing its development and improving its prognosis. At present, the routine screening of depression in adolescents with PCOS is mainly performed using scales, and there is no early identification method for high-risk groups of PCOS depression in adolescents. It is necessary to use a warning model to identify high-risk groups for depression with PCOS in adolescents.

Methods and analysis: Model development and validation will be conducted using a retrospective study. The study will involve normal adolescent girls as the control group and adolescent PCOS patients as the experimental group. We will collect not only general factors such as individual susceptibility factors, biological factors, and psychosocial environmental factors of depression in adolescence, but will also examine the pathological factors, illness perception factors, diagnosis and treatment factors, and symptom-related factors of PCOS, as well as the outcome of depression. LASSO will be used to fit a multivariate warning model of depression risk. Data collected between January 2022 and August 2022 will be used to develop and validate the model internally, and data collected between September 2022 and December 2022 will be used for external validation. We will use the C-statistic to measure the model's discrimination, the calibration plot to measure the model's risk prediction ability for depression, and the nomogram to visualize the model.

Discussion: The ability to calculate the absolute risk of depression outcomes in adolescents with PCOS would enable early and accurate predictions of depression risk among adolescents with PCOS, and provide the basis for the formulation of depression prevention and control strategies, which have important theoretical and practical implications.

Trial registration number: [ChiCTR2100050123]; Pre-results.

Keywords: LASSO regression; adolescents; depression; polycystic ovary syndrome; prediction model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Simulation map of risk factors for depression in adolescents with PCOS.
Figure 2
Figure 2
Simulation diagram for developing and validating a LASSO-based model to predict depression in adolescents with PCOS.
See this image and copyright information in PMC

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