A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study
BackgroundA clinical diagnosis of polycystic ovary syndrome (PCOS) can be tedious with many different required tests and examinations. Furthermore, women with PCOS have increased risks for several metabolic complications, which need long-term health management. Therefore, we attempted to establish a...
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.821368/full |
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| author | Huiyu Xu Huiyu Xu Huiyu Xu Huiyu Xu Guoshuang Feng Kannan Alpadi Yong Han Rui Yang Rui Yang Rui Yang Rui Yang Lixue Chen Lixue Chen Lixue Chen Lixue Chen Rong Li Rong Li Rong Li Rong Li Jie Qiao Jie Qiao Jie Qiao Jie Qiao |
| author_facet | Huiyu Xu Huiyu Xu Huiyu Xu Huiyu Xu Guoshuang Feng Kannan Alpadi Yong Han Rui Yang Rui Yang Rui Yang Rui Yang Lixue Chen Lixue Chen Lixue Chen Lixue Chen Rong Li Rong Li Rong Li Rong Li Jie Qiao Jie Qiao Jie Qiao Jie Qiao |
| author_sort | Huiyu Xu |
| collection | DOAJ |
| description | BackgroundA clinical diagnosis of polycystic ovary syndrome (PCOS) can be tedious with many different required tests and examinations. Furthermore, women with PCOS have increased risks for several metabolic complications, which need long-term health management. Therefore, we attempted to establish an easily applicable model to identify such women at an early stage.ObjectiveTo develop an easy-to-use tool for screening PCOS based on medical records from a large assisted reproductive technology (ART) center in China.Materials and MethodsA retrospective observational cohort from Peking University Third Hospital was used in the study. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 10-fold cross-validation was applied to construct the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity values were used to evaluate and compare the models.Design, Setting, and ParticipantsThis retrospective cohort study included 21,219 ovarian stimulation cycle records from January to December 2019 in Peking University Third Hospital.Main Outcomes and MeasuresThe main outcome was whether there was a clinical diagnosis of PCOS. The independent variables included were age, body mass index (BMI), upper limit of menstrual cycle length (UML), basal serum levels of anti-Müllerian hormone (AMH), testosterone androstenedione, antral follicle counts et al.ResultsWe have established a new mathematical model for diagnosing PCOS using serum AMH and androstenedione levels, UML, and BMI, with AUC values of 0.855 (0.838–0.870), 0.848 (0.791–0.891), 0.846 (0.812–0.875) in the training, validation, and testing sets, respectively. The contribution of each predictor to this model were: AMH 41.2%; UML 35.2%; BMI 4.3%; and androstenedione 3.7%. The top 10 groups of women most predicted to develop PCOS were demonstrated. An online tool (http://121.43.113.123:8888/) has been developed to assist Chinese ART clinics.ConclusionsThe models and online tool we established here might be helpful for screening and identifying women with undiagnosed PCOS in Asian populations and could assist in the long-term management of related metabolic disorders. |
| first_indexed | 2024-12-13T08:23:22Z |
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| spelling | doaj.art-098cfe29b4ed4a67a6f2e93a584aa48d2022-12-21T23:53:55ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-03-011310.3389/fendo.2022.821368821368A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort StudyHuiyu Xu0Huiyu Xu1Huiyu Xu2Huiyu Xu3Guoshuang Feng4Kannan Alpadi5Yong Han6Rui Yang7Rui Yang8Rui Yang9Rui Yang10Lixue Chen11Lixue Chen12Lixue Chen13Lixue Chen14Rong Li15Rong Li16Rong Li17Rong Li18Jie Qiao19Jie Qiao20Jie Qiao21Jie Qiao22Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, ChinaCenter for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, ChinaThe Predict Health, Houston, TX, United StatesHangzhou Qingguo Medical Technology Co. Ltd., Hangzhou, ChinaCenter for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, ChinaCenter for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, ChinaCenter for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, ChinaCenter for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, ChinaNational Clinical Research Center for Obstetrics and Gynecology, Beijing, ChinaKey Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, ChinaBeijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, ChinaBackgroundA clinical diagnosis of polycystic ovary syndrome (PCOS) can be tedious with many different required tests and examinations. Furthermore, women with PCOS have increased risks for several metabolic complications, which need long-term health management. Therefore, we attempted to establish an easily applicable model to identify such women at an early stage.ObjectiveTo develop an easy-to-use tool for screening PCOS based on medical records from a large assisted reproductive technology (ART) center in China.Materials and MethodsA retrospective observational cohort from Peking University Third Hospital was used in the study. Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 10-fold cross-validation was applied to construct the model. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity values were used to evaluate and compare the models.Design, Setting, and ParticipantsThis retrospective cohort study included 21,219 ovarian stimulation cycle records from January to December 2019 in Peking University Third Hospital.Main Outcomes and MeasuresThe main outcome was whether there was a clinical diagnosis of PCOS. The independent variables included were age, body mass index (BMI), upper limit of menstrual cycle length (UML), basal serum levels of anti-Müllerian hormone (AMH), testosterone androstenedione, antral follicle counts et al.ResultsWe have established a new mathematical model for diagnosing PCOS using serum AMH and androstenedione levels, UML, and BMI, with AUC values of 0.855 (0.838–0.870), 0.848 (0.791–0.891), 0.846 (0.812–0.875) in the training, validation, and testing sets, respectively. The contribution of each predictor to this model were: AMH 41.2%; UML 35.2%; BMI 4.3%; and androstenedione 3.7%. The top 10 groups of women most predicted to develop PCOS were demonstrated. An online tool (http://121.43.113.123:8888/) has been developed to assist Chinese ART clinics.ConclusionsThe models and online tool we established here might be helpful for screening and identifying women with undiagnosed PCOS in Asian populations and could assist in the long-term management of related metabolic disorders.https://www.frontiersin.org/articles/10.3389/fendo.2022.821368/fullPCOSwebsite-based toolAMHmenstrual cycle lengthBMIandrostenedione |
| spellingShingle | Huiyu Xu Huiyu Xu Huiyu Xu Huiyu Xu Guoshuang Feng Kannan Alpadi Yong Han Rui Yang Rui Yang Rui Yang Rui Yang Lixue Chen Lixue Chen Lixue Chen Lixue Chen Rong Li Rong Li Rong Li Rong Li Jie Qiao Jie Qiao Jie Qiao Jie Qiao A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study Frontiers in Endocrinology PCOS website-based tool AMH menstrual cycle length BMI androstenedione |
| title | A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study |
| title_full | A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study |
| title_fullStr | A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study |
| title_full_unstemmed | A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study |
| title_short | A Model for Predicting Polycystic Ovary Syndrome Using Serum AMH, Menstrual Cycle Length, Body Mass Index and Serum Androstenedione in Chinese Reproductive Aged Population: A Retrospective Cohort Study |
| title_sort | model for predicting polycystic ovary syndrome using serum amh menstrual cycle length body mass index and serum androstenedione in chinese reproductive aged population a retrospective cohort study |
| topic | PCOS website-based tool AMH menstrual cycle length BMI androstenedione |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.821368/full |
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