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Cao SS, Liu XM, Song BT, Hu YY. Interpretable machine learning models for predicting clinical pregnancies associated with surgical sperm retrieval from testes of different etiologies: a retrospective study. BMC Urol 2024; 24:156. [PMID: 39075422 PMCID: PMC11285258 DOI: 10.1186/s12894-024-01537-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 07/08/2024] [Indexed: 07/31/2024] Open
Abstract
BACKGROUND The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies. METHODS A total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model. RESULTS Combining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778-0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-müllerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy. CONCLUSIONS The XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.
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Affiliation(s)
- Shun-Shun Cao
- Pediatric Endocrinology, Genetics and Metabolism, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Xiao-Ming Liu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Bo-Tian Song
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China
| | - Yang-Yang Hu
- Reproductive Medicine Center, Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
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Cooray SD, De Silva K, Enticott JC, Dawadi S, Boyle JA, Soldatos G, Paul E, Versace VL, Teede HJ. Temporal validation and updating of a prediction model for the diagnosis of gestational diabetes mellitus. J Clin Epidemiol 2023; 164:54-64. [PMID: 37659584 DOI: 10.1016/j.jclinepi.2023.08.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/27/2023] [Accepted: 08/28/2023] [Indexed: 09/04/2023]
Abstract
OBJECTIVE The original Monash gestational diabetes mellitus (GDM) risk prediction in early pregnancy model is internationally externally validated and clinically implemented. We temporally validate and update this model in a contemporary population with a universal screening context and revised diagnostic criteria and ethnicity categories, thereby improving model performance and generalizability. STUDY DESIGN AND SETTING The updating dataset comprised of routinely collected health data for singleton pregnancies delivered in Melbourne, Australia from 2016 to 2018. Model predictors included age, body mass index, ethnicity, diabetes family history, GDM history, and poor obstetric outcome history. Model updating methods were recalibration-in-the-large (Model A), intercept and slope re-estimation (Model B), and coefficient revision using logistic regression (Model C1, original ethnicity categories; Model C2, revised ethnicity categories). Analysis included 10-fold cross-validation, assessment of performance measures (c-statistic, calibration-in-the-large, calibration slope, and expected-observed ratio), and a closed-loop testing procedure to compare models' log-likelihood and akaike information criterion scores. RESULTS In 26,474 singleton pregnancies (4,756, 18% with GDM), the original model demonstrated reasonable temporal validation (c-statistic = 0.698) but suboptimal calibration (expected-observed ratio = 0.485). Updated model C2 was preferred, with a high c-statistic (0.732) and significantly better performance in closed testing. CONCLUSION We demonstrated updating methods to sustain predictive performance in a contemporary population, highlighting the value and versatility of prediction models for guiding risk-stratified GDM care.
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Affiliation(s)
- Shamil D Cooray
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia; Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria 3168, Australia
| | - Kushan De Silva
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia; Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Joanne C Enticott
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia
| | - Shrinkhala Dawadi
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia
| | - Jacqueline A Boyle
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia; Department of Obstetrics and Gynaecology, Monash University, Clayton, Victoria 3168, Australia; Eastern Health Clinical School, Monash University, Box Hill, Victoria 3128, Australia
| | - Georgia Soldatos
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia; Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria 3168, Australia
| | - Eldho Paul
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia
| | - Vincent L Versace
- Deakin Rural Health, School of Medicine, Deakin University, Warrnambool, Victoria 3280, Australia
| | - Helena J Teede
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, Victoria 3168, Australia; Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria 3168, Australia.
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Tanaka JRV, Sousa KHJF, Alves PJP, Guerra MJJ, Gonçalves PDB. Educational Technology on Urinary Incontinence during Pregnancy: Development and Validation of an Online Course for the Brazilian Population. AQUICHAN 2023. [DOI: 10.5294/aqui.2023.23.1.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Objective: To describe the development and validation process of an online course on urinary incontinence during pregnancy in Brazil. Materials and methods: This methodological study followed an online course’s literature search, development, and validation steps. A total of 22 specialists participated in the validation step, and the content validity index (CVI) was used. Fifty-one Physical Therapy students (target audience) also participated in the Suitability Assessment of Materials. Results: The synthesis reached in the integrative review provided the basis for the course’s theoretical content, which was regarded as suitable by the specialists regarding its content, language, presentation, stimulation/motivation, and cultural adequacy (CVI = 0.99). The target audience considered the course organized, easily understandable, engaging, and motivational, with a positive response index ranging from 84.3 % to 100 %. Conclusions: The Brazilian version of the online course was considered sufficiently adequate in content and interface quality by both specialists and the target audience.
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Cooray SD, Boyle JA, Soldatos G, Allotey J, Wang H, Fernandez-Felix BM, Zamora J, Thangaratinam S, Teede HJ. Development, validation and clinical utility of a risk prediction model for adverse pregnancy outcomes in women with gestational diabetes: The PeRSonal GDM model. EClinicalMedicine 2022; 52:101637. [PMID: 36313142 PMCID: PMC9596305 DOI: 10.1016/j.eclinm.2022.101637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with gestational diabetes mellitus (GDM) would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. We aimed to develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS A prediction model development and validation study was conducted on data from a observational cohort. Participants included all women with GDM from three metropolitan tertiary teaching hospitals in Melbourne, Australia. The development cohort comprised those who delivered between 1 July 2017 to 30 June 2018 and the validation cohort those who delivered between 1 July 2018 to 31 December 2018. The main outcome was a composite of critically important maternal and perinatal complications (hypertensive disorders of pregnancy, large-for-gestational age neonate, neonatal hypoglycaemia requiring intravenous therapy, shoulder dystocia, perinatal death, neonatal bone fracture and nerve palsy). Model performance was measured in terms of discrimination and calibration and clinical utility evaluated using decision curve analysis. FINDINGS The final PeRSonal (Prediction for Risk Stratified care for women with GDM) model included body mass index, maternal age, fasting and 1-hour glucose values (75-g oral glucose tolerance test), gestational age at GDM diagnosis, Southern and Central Asian ethnicity, East Asian ethnicity, nulliparity, past delivery of an large-for-gestational age neonate, past pre-eclampsia, GWG until GDM diagnosis, and family history of diabetes. The composite adverse pregnancy outcome occurred in 27% (476/1747) of women in the development (1747 women) and in 26% (244/955) in the validation (955 women) cohorts. The model showed excellent calibration with slope of 0.99 (95% CI 0.75 to 1.23) and acceptable discrimination (c-statistic 0.68; 95% CI 0.64 to 0.72) when temporally validated. Decision curve analysis demonstrated that the model was useful across a range of predicted probability thresholds between 0.15 and 0.85 for adverse pregnancy outcomes compared to the alternatives of managing all women with GDM as if they will or will not have an adverse pregnancy outcome. INTERPRETATION The PeRSonal GDM model comprising of routinely available clinical data shows compelling performance, is transportable across time, and has clinical utility across a range of predicted probabilities. Further external validation of the model to a more disparate population is now needed to assess the generalisability to different centres, community based care and low resource settings, other healthcare systems and to different GDM diagnostic criteria. FUNDING This work is supported by the Mothers and Gestational Diabetes in Australia 2 NHMRC funded project #1170847.
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Affiliation(s)
- Shamil D. Cooray
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton VIC 3168, Australia
- Diabetes and Endocrinology Units, Monash Health, Clayton VIC 3168, Australia
| | - Jacqueline A. Boyle
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton VIC 3168, Australia
- Monash Women's Program, Monash Health, Clayton VIC 3168, Australia
| | - Georgia Soldatos
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton VIC 3168, Australia
- Diabetes and Endocrinology Units, Monash Health, Clayton VIC 3168, Australia
| | - John Allotey
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Holly Wang
- Diabetes and Endocrinology Units, Monash Health, Clayton VIC 3168, Australia
| | | | - Javier Zamora
- WHO Collaborating Centre for Global Women's Health, Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
- CIBER Epidemiology and Public Health, 28029 Madrid, Spain
| | - Shakila Thangaratinam
- CIBER Epidemiology and Public Health, 28029 Madrid, Spain
- Birmingham Women's and Children's, NHS Foundation Trust, Birmingham, UK
| | - Helena J. Teede
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton VIC 3168, Australia
- Diabetes and Endocrinology Units, Monash Health, Clayton VIC 3168, Australia
- Corresponding author at: Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Locked Bag 29 Clayton, VIC 3168, Australia.
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Lin Y, Xu Z, Ding X, Chen L, Dai K. Development and validation of a clinical diagnostic model for pregnant women with renal colic in the emergency department in China: a protocol for a retrospective cohort study. BMJ Open 2022; 12:e056510. [PMID: 35501078 PMCID: PMC9062803 DOI: 10.1136/bmjopen-2021-056510] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 04/16/2022] [Indexed: 11/04/2022] Open
Abstract
INTRODUCTION Urolithiasis affects many people throughout their lives. Among the maternal population, although the morbidity of acute urolithiasis in pregnant women is unremarkable, it is the leading cause of hospitalisation during pregnancy. There is no effective clinical diagnostic tool to help doctors diagnose diseases. Our primary aim was to develop and validate a clinical prediction model based on statistical methods to predict the probability of having disease in pregnant women who visited the emergency department because of urolithiasis-induced colic. METHODS AND ANALYSIS We will use multivariate logistic regression analysis to build a multivariate regression linear model. A receiver operating characteristic curve plot and calibration plot will be used to measure the discrimination value and calibration value of the model, respectively. We will also use least absolute shrinkage and selection operator regression analysis combined with logistic regression analysis to select predictors and construct the multivariate regression model. The model will be simplified to an application that has been reported before, and users will only need to enter their clinical parameters so that risk probability is automatically derived. ETHICS AND DISSEMINATION The review and approval documents of the clinical research ethics committee have been received from the ethics committee of our hospital (The Third Affiliated Hospital of Wenzhou Medical University). We will disseminate research findings through presentations at scientific conferences and publication in peer-reviewed journals.
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Affiliation(s)
- YuZhan Lin
- Department of Clinical Laboratory, Ruian People's Hospital, Ruian, China
| | - ZhiKai Xu
- Department of Ultrasound Imaging, Ruian People's Hospital, Ruian, China
| | - XiangCui Ding
- Gynecology Department, Ruian People's Hospital, Ruian, China
| | - Lei Chen
- Department of Clinical Laboratory, Ruian People's Hospital, Ruian, China
| | - KangWei Dai
- Department of Clinical Laboratory, Ruian People's Hospital, Ruian, China
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Dai F, Mani H, Nurul SR, Tan KH. Risk stratification of women with gestational diabetes mellitus using mutually exclusive categories based on the International Association of Diabetes and Pregnancy Study Groups criteria for the development of postpartum dysglycaemia: a retrospective cohort study. BMJ Open 2022; 12:e055458. [PMID: 35177456 PMCID: PMC8860034 DOI: 10.1136/bmjopen-2021-055458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 01/31/2022] [Indexed: 11/17/2022] Open
Abstract
OBJECTIVES Women with gestational diabetes mellitus (GDM) are more predisposed to develop postpartum diabetes mellitus (DM). This study aimed to estimate the relative risk (RR) of postpartum dysglycaemia (prediabetes and DM) using mutually exclusive categories according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria cut-off points in patients with GDM, so as to establish a risk-stratification method for developing GDM management strategies. DESIGN, SETTING AND PARTICIPANTS In this retrospective cohort study, 942 women who had been diagnosed with GDM (IADPSG criteria) at 24-28 weeks of gestation from November 2016 to April 2018 underwent a 75 g oral glucose tolerance test (OGTT) at 6-12 weeks postpartum in a tertiary hospital of Singapore. Seven mutually exclusive categories (three one timepoint positive categories (fasting, 1 hour and 2 hours), three two timepoint positive categories (fasting+1 hour, fasting+2 hours and 1 hour+2 hours) and one three timepoint positive category (fasting+1 hour+2 hours)) were derived from the three timepoint antenatal OGTT according to the IADPSG criteria. To calculate the RRs of postpartum dysglyceamia of each mutually exclusive group, logistic regression was applied. RESULTS 924 mothers with GDM, whose mean age was 32.7±4.7 years, were mainly composed of Chinese (45.4%), Malay (21.7%) and Indian (14.3%) ethnicity. The total prevalence of postnatal dysglycaemia was 16.7% at 6-12 weeks postpartum. Stratifying subjects into seven mutually exclusive categories, the RRs of the one-time, two-time and three-time positive groups of the antenatal OGTT test were 1.0 (Ref.), 2.0 (95% CI=1.3 to 3.1; p=0.001) and 6.7 (95% CI=4.1 to 10.9; p<0.001), respectively, which could be used to categorise patients with GDM into low-risk, intermediate-risk and high-risk group. CONCLUSIONS Mutually exclusive categories could be useful for risk stratification and early management of patients with prenatal GDM. It is plausible and can be easily translated into clinical practice.
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Affiliation(s)
- Fei Dai
- Divsion of Obstetrics and Gynaecology, KK Women's and Children's Hospital, Singapore
| | - Hemaavathi Mani
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Syaza Razali Nurul
- Divsion of Obstetrics and Gynaecology, KK Women's and Children's Hospital, Singapore
| | - Kok Hian Tan
- Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore
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Zhang W, Liu Y, Wu J, Wang W, Zhou J, Guo J, Wang Q, Zhang X, Xie J, Xing Y, Hu D. Surgical Treatment is Still Recommended for Patients Over 75 Years with IA NSCLC: A Predictive Model Based on Surveillance, Epidemiology and End Results Database. Cancer Control 2022; 29:10732748221142750. [DOI: 10.1177/10732748221142750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background To determine the populations who suitable for surgical treatment in elderly patients (age ≥ 75 y) with IA stage. Methods The clinical data of NSCLC patients diagnosed from 2010 to 2015 were collected from the SEER database and divided into surgery group (SG) and no-surgery groups (NSG). The confounders were balanced and differences in survival were compared between groups using PSM (Propensity score matching, PSM). Cox regression analysis was used to screen the independent factors that affect the Cancer-specific survival (CSS). The surgery group was defined as the patients who surgery-benefit and surgery-no benefit according to the median CSS of the no-surgery group, and then randomly divided into training and validation groups. A surgical benefit prediction model was constructed in the training and validation group. Finally, the model is evaluated using a variety of methods. Results A total of 7297 patients were included. Before PSM (SG: n = 3630; NSG: n = 3665) and after PSM (SG: n = 1725, NSG: n = 1725) confirmed that the CSS of the surgery group was longer than the no-surgery group (before PSM: 82 vs. 31 months, P < .0001; after PSM: 55 vs. 39 months, P < .0001). Independent prognostic factors included age, gender, race, marrital, tumor grade, histology, and surgery. In the surgery cohort after PSM, 1005 patients (58.27%) who survived for more than 39 months were defined as surgery beneficiaries, and the 720 patients (41.73%) were defined surgery-no beneficiaries. The surgery group was divided into training group 1207 (70%) and validation group 518 (30%). Independent prognostic factors were used to construct a prediction model. In training group (AUC = .678) and validation group (AUC = .622). Calibration curve and decision curve prove that the model has better performance. Conclusions This predictive model can well identify elderly patients with stage IA NSCLC who would benefit from surgery, thus providing a basis for clinical treatment decisions.
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Affiliation(s)
- Wenting Zhang
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, P.R. China
| | - Wenyang Wang
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Qingsen Wang
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Xin Zhang
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
| | - Jun Xie
- Cancer Hospital of Anhui University of Science and Technology, Huainan, P.R. China
| | - Yingru Xing
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
- Cancer Hospital of Anhui University of Science and Technology, Huainan, P.R. China
- Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, P.R. China
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, P.R. China
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, P.R. China
- Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, P.R. China
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, P.R. China
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Minschart C, Beunen K, Benhalima K. An Update on Screening Strategies for Gestational Diabetes Mellitus: A Narrative Review. Diabetes Metab Syndr Obes 2021; 14:3047-3076. [PMID: 34262311 PMCID: PMC8273744 DOI: 10.2147/dmso.s287121] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 06/18/2021] [Indexed: 12/16/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is a frequent medical complication during pregnancy. Screening and diagnostic practices for GDM are inconsistent across the world. This narrative review includes data from 87 observational studies and randomized controlled trials (RCTs), and aims to give an overview of the current evidence on screening strategies and diagnostic criteria for GDM. Screening in early pregnancy remains controversial and studies show conflicting results on the benefit of screening and treatment of GDM in early pregnancy. Implementing the one-step "International Association of Diabetes and Pregnancy Study Groups" (IADPSG) screening strategy at 24-28 weeks often leads to a substantial increase in the prevalence of GDM, without conclusive evidence regarding the benefits on pregnancy outcomes compared to a two-step screening strategy with a glucose challenge test (GCT). In addition, RCTs are needed to investigate the impact of treatment of GDM diagnosed with IADPSG criteria on long-term maternal and childhood outcomes. Selective screening using a risk-factor-based approach could be helpful in simplifying the screening algorithm but carries the risk of missing significant proportions of GDM cases. A two-step screening method with a 50g GCT and subsequently a 75g oral glucose tolerance test (OGTT) with IADPSG could be an alternative to reduce the need for an OGTT. However, to have an acceptable sensitivity to screen for GDM with the IADPSG criteria, the threshold of the GCT should be lowered from 7.8 to 7.2 mmol/L. A pragmatic approach to screen for GDM can be implemented during the COVID-19 pandemic, using fasting plasma glucose (FPG), HbA1c or even random plasma glucose (RPG) to reduce the number of OGTTs needed. However, usual guidelines and care should be resumed as soon as the COVID pandemic is controlled.
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Affiliation(s)
- Caro Minschart
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, 3000, Belgium
| | - Kaat Beunen
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, 3000, Belgium
| | - Katrien Benhalima
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, 3000, Belgium
- Department of Endocrinology, University Hospital Gasthuisberg, KU Leuven, Leuven, 3000, Belgium
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Cooray SD, Boyle JA, Soldatos G, Thangaratinam S, Teede HJ. The Need for Personalized Risk-Stratified Approaches to Treatment for Gestational Diabetes: A Narrative Review. Semin Reprod Med 2021; 38:384-388. [PMID: 33648005 DOI: 10.1055/s-0041-1723778] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Gestational diabetes mellitus (GDM) is common and is associated with an increased risk of adverse pregnancy outcomes. However, the prevailing one-size-fits-all approach that treats all women with GDM as having equivalent risk needs revision, given the clinical heterogeneity of GDM, the limitations of a population-based approach to risk, and the need to move beyond a glucocentric focus to address other intersecting risk factors. To address these challenges, we propose using a clinical prediction model for adverse pregnancy outcomes to guide risk-stratified approaches to treatment tailored to the individual needs of women with GDM. This will allow preventative and therapeutic interventions to be delivered to those who will maximally benefit, sparing expense, and harm for those at a lower risk.
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Affiliation(s)
- Shamil D Cooray
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Diabetes Unit, Monash Health, Clayton, Victoria, Australia
| | - Jacqueline A Boyle
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Monash Women's Program, Monash Health, Clayton, Victoria, Australia
| | - Georgia Soldatos
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria, Australia
| | - Shakila Thangaratinam
- Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Helena J Teede
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.,Diabetes and Endocrinology Units, Monash Health, Clayton, Victoria, Australia
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