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Tranidou A, Tsakiridis I, Apostolopoulou A, Xenidis T, Pazaras N, Mamopoulos A, Athanasiadis A, Chourdakis M, Dagklis T. Prediction of Gestational Diabetes Mellitus in the First Trimester of Pregnancy Based on Maternal Variables and Pregnancy Biomarkers. Nutrients 2023; 16:120. [PMID: 38201950 PMCID: PMC10780503 DOI: 10.3390/nu16010120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 12/26/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Gestational diabetes mellitus (GDM) is a significant health concern with adverse outcomes for both pregnant women and their offspring. Recognizing the need for early intervention, this study aimed to develop an early prediction model for GDM risk assessment during the first trimester. Utilizing a prospective cohort of 4917 pregnant women from the Third Department of Obstetrics and Gynecology, Aristotle University of Thessaloniki, Greece, the study sought to combine maternal characteristics, obstetric and medical history, and early pregnancy-specific biomarker concentrations into a predictive tool. The primary objective was to create a series of predictive models that could accurately identify women at high risk for developing GDM, thereby facilitating early and targeted interventions. To this end, maternal age, body mass index (BMI), obstetric and medical history, and biomarker concentrations were analyzed and incorporated into five distinct prediction models. The study's findings revealed that the models varied in effectiveness, with the most comprehensive model combining maternal characteristics, obstetric and medical history, and biomarkers showing the highest potential for early GDM prediction. The current research provides a foundation for future studies to refine and expand upon the predictive models, aiming for even earlier and more accurate detection methods.
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Affiliation(s)
- Antigoni Tranidou
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
| | - Ioannis Tsakiridis
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
| | - Aikaterini Apostolopoulou
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (A.A.); (N.P.); (M.C.)
| | - Theodoros Xenidis
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
| | - Nikolaos Pazaras
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (A.A.); (N.P.); (M.C.)
| | - Apostolos Mamopoulos
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
| | - Apostolos Athanasiadis
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
| | - Michail Chourdakis
- Laboratory of Hygiene, Social & Preventive Medicine and Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (A.A.); (N.P.); (M.C.)
| | - Themistoklis Dagklis
- 3rd Department of Obstetrics and Gynecology, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece; (A.T.); (I.T.); (T.X.); (A.M.); (A.A.)
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Huang QF, Hu YC, Wang CK, Huang J, Shen MD, Ren LH. Clinical First-Trimester Prediction Models for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Biol Res Nurs 2023; 25:185-197. [PMID: 36218132 DOI: 10.1177/10998004221131993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. METHODS Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. RESULTS We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I2 ranged from 0% to 97%). CONCLUSION Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.
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Affiliation(s)
- Qi-Fang Huang
- School of Nursing, 33133Peking University, Beijing, China
| | - Yin-Chu Hu
- School of Nursing, 33133Peking University, Beijing, China
| | - Chong-Kun Wang
- School of Nursing, 33133Peking University, Beijing, China
| | - Jing Huang
- Florence Nightingale School of Nursing, 4616King's College London, London, UK
| | - Mei-Di Shen
- School of Nursing, 33133Peking University, Beijing, China
| | - Li-Hua Ren
- School of Nursing, 33133Peking University, Beijing, China
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Wu S, Li L, Hu KL, Wang S, Zhang R, Chen R, Liu L, Wang D, Pan M, Zhu B, Wang Y, Yuan C, Zhang D. A Prediction Model of Gestational Diabetes Mellitus Based on OGTT in Early Pregnancy: A Prospective Cohort Study. J Clin Endocrinol Metab 2023:7022148. [PMID: 36723990 DOI: 10.1210/clinem/dgad052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 01/11/2023] [Accepted: 01/25/2023] [Indexed: 02/02/2023]
Abstract
CONTEXT Gestational diabetes mellitus (GDM) is a common obstetric complication. Although early intervention could prevent the development of GDM, there was no consensus on early identification for women at high risk of GDM. OBJECTIVE To develop a reliable prediction model of GDM in early pregnancy. METHODS In this prospective cohort study, between 30 May 2021 and 13 August 2022, a total of 721 women were included at Women's Hospital, Zhejiang University School of Medicine. Participants were asked to complete oral glucose tolerance test (OGTT) during gestational week 7-14 for early prediction of GDM, and at week 24-28 for GDM diagnosis. Using OGTT results and baseline characteristics, logistic regression analysis was used to construct the prediction model. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, decision clinical analysis, and a nomogram were used for model performances assessment and visualization. Internal and external validation was performed to testify the stability of this model. RESULTS According to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria in early OGTT, the mean (SD) age was 30.5 ± 3.7 in low-risk participants and 31.0 ± 3.9 in high-risk participants. The area under ROC curve (AUC) of the existing criteria at week 7-14 varied from 0.705 to 0.724. Based on maternal age, pre-pregnancy BMI, and results of early OGTT, the AUC of our prediction model was 0.8720, which was validated by both internal (AUC 0.8541) and external (AUC 0.8241) validation. CONCLUSIONS The existing diagnostic criteria were unsatisfactory for early prediction of GDM. By combining early OGTT, we provided an effective prediction model of GDM in the first trimester.
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Affiliation(s)
- Shan Wu
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Linghui Li
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Kai-Lun Hu
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
- Center for Reproductive Medicine, Peking University Third Hospital, Haidian District, Beijing, China
| | - Siwen Wang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Runju Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Ruixue Chen
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Le Liu
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Danni Wang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Minge Pan
- Reservation Center and Preparation Center, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
| | - Bo Zhu
- Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China
| | - Yue Wang
- Department of Gynecology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Changzheng Yuan
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
- School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Dan Zhang
- Key Laboratory of Reproductive Genetics (Ministry of Education) and Department of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China
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Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-08007-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractGestational diabetes mellitus (GDM) is one of the pregnancy complications that poses a significant risk on mothers and babies as well. GDM usually diagnosed at 22–26 of gestation. However, the early prediction is desirable as it may contribute to decrease the risk. The continuous monitoring for mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this paper is to provide comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers which are: (i) IoT Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IOT sensors to aggregate vital sings from pregnancies using invasive and noninvasive sensors. Then the vital signs transmitted to fog nodes to processed and finally stored in the cloud layer. The main contribution in this paper is located in the fog layer producing GDM module to implement two influential tasks which are: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free the cache space for the new incoming data items. The cache replacement is very important in the case of healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the incidence of GDM that may occur in the second trimester of the pregnancy. To evaluate our model, we extract data of 16,354 pregnancy women from medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data and laboratory tests was aggregated. The results of the prediction model superior the state of the art (ACC = 0.957, AUC = 0.942). Regarding to explainability, we utilized Shapley additive explanation framework to provide local and global explanation for the developed models. Overall, the proposed framework is medically intuitive, allow the early prediction of GDM with cost effective solution.
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Prediction of gestational diabetes based on explainable deep learning and fog computing. Soft comput 2022. [DOI: 10.1007/s00500-022-07420-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
AbstractGestational diabetes mellitus (GDM) is one of the pregnancy complications that endangers both mothers and babies. GDM is usually diagnosed at 22–26 weeks of gestation. However, early prediction is preferable because it may decrease the risk. The continuous monitoring of the mother’s vital signs helps in predicting any deterioration during pregnancy. The originality of this research is to provide a comprehensive framework for pregnancy women monitoring. The proposed Data Replacement and Prediction Framework consists of three layers, which are: (i) Internet of things (IoT) Layer, (ii) Fog Layer, and (iii) Cloud Layer. The first layer used IoT sensors to aggregate vital signs from pregnancies using invasive and non-invasive sensors. The vital signs are then transmitted to fog nodes to be processed and finally stored in the cloud layer. The main contribution in this research is located in the fog layer producing the GDM module to implement two influential tasks which are as follows: (i) Data Finding Methodology (DFM), and (ii) Explainable Prediction Algorithm (EPM) using DNN. First, the DFM is used to replace the unused data to free up the cache space for new incoming data items. The cache replacement is very important in the case of the healthcare system as the incoming vital signs are frequent and must be replaced continuously. Second, the EPM is used to predict the occurrence of GDM in the second trimester of the pregnancy. To evaluate our model, we extracted data from 16,354 pregnant women from the medical information mart for intensive care (MIMIC III) benchmark dataset. For each woman, vital signs, demographic data, and laboratory tests were aggregated. The results of the prediction model are superior to the state-of-the-art (ACC = 0.957, AUC = 0.942). Regarding explainability, we used Shapley additive explanation (SHAP) framework to provide local and global explanations for the developed models. Overall, the proposed framework is medically intuitive and allows the early prediction of GDM with a cost-effective solution.
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Machine learning-based models for gestational diabetes mellitus prediction before 24–28 weeks of pregnancy: A review. Artif Intell Med 2022; 132:102378. [DOI: 10.1016/j.artmed.2022.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022]
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Thong EP, Ghelani DP, Manoleehakul P, Yesmin A, Slater K, Taylor R, Collins C, Hutchesson M, Lim SS, Teede HJ, Harrison CL, Moran L, Enticott J. Optimising Cardiometabolic Risk Factors in Pregnancy: A Review of Risk Prediction Models Targeting Gestational Diabetes and Hypertensive Disorders. J Cardiovasc Dev Dis 2022; 9:jcdd9020055. [PMID: 35200708 PMCID: PMC8874392 DOI: 10.3390/jcdd9020055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/30/2022] [Accepted: 02/07/2022] [Indexed: 11/16/2022] Open
Abstract
Cardiovascular disease, especially coronary heart disease and cerebrovascular disease, is a leading cause of mortality and morbidity in women globally. The development of cardiometabolic conditions in pregnancy, such as gestational diabetes mellitus and hypertensive disorders of pregnancy, portend an increased risk of future cardiovascular disease in women. Pregnancy therefore represents a unique opportunity to detect and manage risk factors, prior to the development of cardiovascular sequelae. Risk prediction models for gestational diabetes mellitus and hypertensive disorders of pregnancy can help identify at-risk women in early pregnancy, allowing timely intervention to mitigate both short- and long-term adverse outcomes. In this narrative review, we outline the shared pathophysiological pathways for gestational diabetes mellitus and hypertensive disorders of pregnancy, summarise contemporary risk prediction models and candidate predictors for these conditions, and discuss the utility of these models in clinical application.
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Affiliation(s)
- Eleanor P. Thong
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Drishti P. Ghelani
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Pamada Manoleehakul
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC 3168, Australia; (P.M.); (A.Y.)
| | - Anika Yesmin
- Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC 3168, Australia; (P.M.); (A.Y.)
| | - Kaylee Slater
- School of Health Sciences, College of Health, Medicine and Wellbeing, and Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan, NSW 2308, Australia; (K.S.); (R.T.); (C.C.); (M.H.)
| | - Rachael Taylor
- School of Health Sciences, College of Health, Medicine and Wellbeing, and Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan, NSW 2308, Australia; (K.S.); (R.T.); (C.C.); (M.H.)
| | - Clare Collins
- School of Health Sciences, College of Health, Medicine and Wellbeing, and Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan, NSW 2308, Australia; (K.S.); (R.T.); (C.C.); (M.H.)
| | - Melinda Hutchesson
- School of Health Sciences, College of Health, Medicine and Wellbeing, and Priority Research Centre for Physical Activity and Nutrition, University of Newcastle, Callaghan, NSW 2308, Australia; (K.S.); (R.T.); (C.C.); (M.H.)
| | - Siew S. Lim
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Helena J. Teede
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Cheryce L. Harrison
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Lisa Moran
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
| | - Joanne Enticott
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Clayton, VIC 3168, Australia; (E.P.T.); (D.P.G.); (S.S.L.); (H.J.T.); (C.L.H.); (L.M.)
- Correspondence:
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Kotzaeridi G, Blätter J, Eppel D, Rosicky I, Mittlböck M, Yerlikaya-Schatten G, Schatten C, Husslein P, Eppel W, Huhn EA, Tura A, Göbl CS. Performance of early risk assessment tools to predict the later development of gestational diabetes. Eur J Clin Invest 2021; 51:e13630. [PMID: 34142723 PMCID: PMC9285036 DOI: 10.1111/eci.13630] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Revised: 05/17/2021] [Accepted: 05/25/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Several prognostic models for gestational diabetes mellitus (GDM) are provided in the literature; however, their clinical significance has not been thoroughly evaluated, especially with regard to application at early gestation and in accordance with the most recent diagnostic criteria. This external validation study aimed to assess the predictive accuracy of published risk estimation models for the later development of GDM at early pregnancy. METHODS In this cohort study, we prospectively included 1132 pregnant women. Risk evaluation was performed before 16 + 0 weeks of gestation including a routine laboratory examination. Study participants were followed-up until delivery to assess GDM status according to the IADPSG 2010 diagnostic criteria. Fifteen clinical prediction models were calculated according to the published literature. RESULTS Gestational diabetes mellitus was diagnosed in 239 women, that is 21.1% of the study participants. Discrimination was assessed by the area under the ROC curve and ranged between 60.7% and 76.9%, corresponding to an acceptable accuracy. With some exceptions, calibration performance was poor as most models were developed based on older diagnostic criteria with lower prevalence and therefore tended to underestimate the risk of GDM. The highest variable importance scores were observed for history of GDM and routine laboratory parameters. CONCLUSIONS Most prediction models showed acceptable accuracy in terms of discrimination but lacked in calibration, which was strongly dependent on study settings. Simple biochemical variables such as fasting glucose, HbA1c and triglycerides can improve risk prediction. One model consisting of clinical and laboratory parameters showed satisfactory accuracy and could be used for further investigations.
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Affiliation(s)
- Grammata Kotzaeridi
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Julia Blätter
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Daniel Eppel
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Ingo Rosicky
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Martina Mittlböck
- Center of Medical Statistics, Informatics, and Intelligent Systems, Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria
| | | | - Christian Schatten
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Peter Husslein
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Eppel
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Evelyn A Huhn
- Department of Obstetrics and Gynaecology, University Hospital Basel, Basel, Switzerland
| | - Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padova, Italy
| | - Christian S Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
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van Hoorn F, Koster MPH, Kwee A, Groenendaal F, Franx A, Bekker MN. Implementation of a first-trimester prognostic model to improve screening for gestational diabetes mellitus. BMC Pregnancy Childbirth 2021; 21:298. [PMID: 33849467 PMCID: PMC8045273 DOI: 10.1186/s12884-021-03749-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 03/19/2021] [Indexed: 12/23/2022] Open
Abstract
Background Improvement in the accuracy of identifying women who are at risk to develop gestational diabetes mellitus (GDM) is warranted, since timely diagnosis and treatment improves the outcomes of this common pregnancy disorder. Although prognostic models for GDM are externally validated and outperform current risk factor based selective approaches, there is little known about the impact of such models in day-to-day obstetric care. Methods A prognostic model was implemented as a directive clinical prediction rule, classifying women as low- or high-risk for GDM, with subsequent distinctive care pathways including selective midpregnancy testing for GDM in high-risk women in a prospective multicenter birth cohort comprising 1073 pregnant women without pre-existing diabetes and 60 obstetric healthcare professionals included in nine independent midwifery practices and three hospitals in the Netherlands (effectiveness-implementation hybrid type 2 study). Model performance (c-statistic) and implementation outcomes (acceptability, adoption, appropriateness, feasibility, fidelity, penetration, sustainability) were evaluated after 6 months by indicators and implementation instruments (NoMAD; MIDI). Results The adherence to the prognostic model (c-statistic 0.85 (95%CI 0.81–0.90)) was 95% (n = 1021). Healthcare professionals scored 3.7 (IQR 3.3–4.0) on implementation instruments on a 5-point Likert scale. Important facilitators were knowledge, willingness and confidence to use the model, client cooperation and opportunities for reconfiguration. Identified barriers mostly related to operational and organizational issues. Regardless of risk-status, pregnant women appreciated first-trimester information on GDM risk-status and lifestyle advice to achieve risk reduction, respectively 89% (n = 556) and 90% (n = 564)). Conclusions The prognostic model was successfully implemented and well received by healthcare professionals and pregnant women. Prognostic models should be recommended for adoption in guidelines. Supplementary Information The online version contains supplementary material available at 10.1186/s12884-021-03749-x.
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Affiliation(s)
- Fieke van Hoorn
- Department of Obstetrics and Gynaecology, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, Utrecht, 3584 EA, the Netherlands
| | - Maria P H Koster
- Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre Rotterdam, Doctor Molewaterplein 40, Rotterdam, 3015 GD, the Netherlands
| | - Anneke Kwee
- Department of Obstetrics and Gynaecology, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, Utrecht, 3584 EA, the Netherlands
| | - Floris Groenendaal
- Department of Neonatology, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, Utrecht, 3584 EA, the Netherlands
| | - Arie Franx
- Department of Obstetrics and Gynaecology, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, Utrecht, 3584 EA, the Netherlands.,Department of Obstetrics and Gynaecology, Erasmus MC, University Medical Centre Rotterdam, Doctor Molewaterplein 40, Rotterdam, 3015 GD, the Netherlands
| | - Mireille N Bekker
- Department of Obstetrics and Gynaecology, University Medical Centre Utrecht, Utrecht University, Lundlaan 6, Utrecht, 3584 EA, the Netherlands.
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van Montfort P, Scheepers HCJ, van Dooren IMA, Meertens LJE, Wynants L, Zelis M, Zwaan IM, Spaanderman MEA, Smits LJM. Adherence rates to a prediction tool identifying women with an increased gestational diabetes risk: An implementation study. Int J Gynaecol Obstet 2021; 154:85-91. [PMID: 33277691 PMCID: PMC8247415 DOI: 10.1002/ijgo.13517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 09/27/2020] [Accepted: 12/03/2020] [Indexed: 11/09/2022]
Abstract
OBJECTIVE The best screening strategy for gestational diabetes mellitus (GDM) remains a topic of debate. Several organizations made a statement in favor of universal screening, but the volume of oral glucose tolerance tests (OGTT) required may burden healthcare systems. As a result, many countries still rely on selective screening using a checklist of risk factors, but reported diagnostic characteristics vary. Moreover, women's discomfort due to an OGTT is often neglected. Since 2017, obstetric healthcare professionals in a Dutch region assessed women's GDM risk with a prediction model and counseled those with an increased risk regarding an OGTT. METHODS From 2017 to 2018, 865 women were recruited in a multicenter prospective cohort. RESULTS In total, 385 women (48%) had an increased predicted GDM risk. Of all women, 78% reported that their healthcare professional discussed their GDM risk. Predicted GDM risks were positively correlated with conducting an OGTT. CONCLUSION Implementation of a GDM prediction model resulted in moderate rates of OGTTs performed in general, but high rates in high-risk women. As 25% of women experienced discomfort from the OGTT, a selective screening strategy based on a prediction model with a high detection rate may be an interesting alternative to universal screening. STUDY COHORT REGISTRATION Netherlands Trial Register: NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143.
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Affiliation(s)
- Pim van Montfort
- Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Hubertina C J Scheepers
- Department of Obstetrics and Gynecology, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ivo M A van Dooren
- Department of Obstetrics and Gynecology, Sint Jans Gasthuis Weert, Weert, The Netherlands
| | - Linda J E Meertens
- Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
| | - Laure Wynants
- Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Maartje Zelis
- Department of Obstetrics and Gynecology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Iris M Zwaan
- Department of Obstetrics and Gynecology, Laurentius Hospital, Roermond, The Netherlands
| | - Marc E A Spaanderman
- Department of Obstetrics and Gynecology, Sint Jans Gasthuis Weert, Weert, The Netherlands
| | - Luc J M Smits
- Department of Epidemiology, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands
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11
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Zhang Z, Yang L, Han W, Wu Y, Zhang L, Gao C, Jiang K, Liu Y, Wu H. Machine Learning Prediction Models for Gestational Diabetes Mellitus: A meta- analysis (Preprint). J Med Internet Res 2020; 24:e26634. [PMID: 35294369 PMCID: PMC8968560 DOI: 10.2196/26634] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/11/2021] [Accepted: 12/10/2021] [Indexed: 12/20/2022] Open
Abstract
Background Gestational diabetes mellitus (GDM) is a common endocrine metabolic disease, involving a carbohydrate intolerance of variable severity during pregnancy. The incidence of GDM-related complications and adverse pregnancy outcomes has declined, in part, due to early screening. Machine learning (ML) models are increasingly used to identify risk factors and enable the early prediction of GDM. Objective The aim of this study was to perform a meta-analysis and comparison of published prognostic models for predicting the risk of GDM and identify predictors applicable to the models. Methods Four reliable electronic databases were searched for studies that developed ML prediction models for GDM in the general population instead of among high-risk groups only. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias of the ML models. The Meta-DiSc software program (version 1.4) was used to perform the meta-analysis and determination of heterogeneity. To limit the influence of heterogeneity, we also performed sensitivity analyses, a meta-regression, and subgroup analysis. Results A total of 25 studies that included women older than 18 years without a history of vital disease were analyzed. The pooled area under the receiver operating characteristic curve (AUROC) for ML models predicting GDM was 0.8492; the pooled sensitivity was 0.69 (95% CI 0.68-0.69; P<.001; I2=99.6%) and the pooled specificity was 0.75 (95% CI 0.75-0.75; P<.001; I2=100%). As one of the most commonly employed ML methods, logistic regression achieved an overall pooled AUROC of 0.8151, while non–logistic regression models performed better, with an overall pooled AUROC of 0.8891. Additionally, maternal age, family history of diabetes, BMI, and fasting blood glucose were the four most commonly used features of models established by the various feature selection methods. Conclusions Compared to current screening strategies, ML methods are attractive for predicting GDM. To expand their use, the importance of quality assessments and unified diagnostic criteria should be further emphasized.
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Affiliation(s)
- Zheqing Zhang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Luqian Yang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Wentao Han
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Yaoyu Wu
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Linhui Zhang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Chun Gao
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Kui Jiang
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
| | - Yun Liu
- Department of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing, China
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Huiqun Wu
- Department of Medical Informatics, Medical School of Nantong University, Nantong, China
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12
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Meertens LJE, Scheepers HCJ, van Kuijk SMJ, Roeleveld N, Aardenburg R, van Dooren IMA, Langenveld J, Zwaan IM, Spaanderman MEA, van Gelder MMHJ, Smits LJM. External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study. Acta Obstet Gynecol Scand 2020; 99:891-900. [PMID: 31955406 PMCID: PMC7317858 DOI: 10.1111/aogs.13811] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 11/14/2019] [Accepted: 12/14/2019] [Indexed: 11/29/2022]
Abstract
Introduction We performed an independent validation study of all published first trimester prediction models, containing non‐invasive predictors, for the risk of gestational diabetes mellitus. Furthermore, the clinical potential of the best performing models was evaluated. Material and methods Systemically selected prediction models from the literature were validated in a Dutch prospective cohort using data from Expect Study I and PRIDE Study. The predictive performance of the models was evaluated by discrimination and calibration. Clinical utility was assessed using decision curve analysis. Screening performance measures were calculated at different risk thresholds for the best model and compared with current selective screening strategies. Results The validation cohort included 5260 women. Gestational diabetes mellitus was diagnosed in 127 women (2.4%). The discriminative performance of the 12 included models ranged from 68% to 75%. Nearly all models overestimated the risk. After recalibration, agreement between the observed outcomes and predicted probabilities improved for most models. Conclusions The best performing prediction models showed acceptable performance measures and may enable more personalized medicine‐based antenatal care for women at risk of developing gestational diabetes mellitus compared with current applied strategies.
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Affiliation(s)
- Linda J E Meertens
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
| | - Hubertina C J Scheepers
- Department of Obstetrics and Gynecology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment (KEMTA), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Nel Roeleveld
- Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert Aardenburg
- Department of Obstetrics and Gynecology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Ivo M A van Dooren
- Department of Obstetrics and Gynecology, Sint Jans Gasthuis Weert, Weert, The Netherlands
| | - Josje Langenveld
- Department of Obstetrics and Gynecology, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Iris M Zwaan
- Department of Obstetrics and Gynecology, Laurentius Hospital, Roermond, The Netherlands
| | - Marc E A Spaanderman
- Department of Obstetrics and Gynecology, School for Oncology and Developmental Biology (GROW), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marleen M H J van Gelder
- Department for Health Evidence, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luc J M Smits
- Department of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands
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