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Zhao M, Su X, Huang L. Early gestational diabetes mellitus risk predictor using neural network with NearMiss. Gynecol Endocrinol 2025; 41:2470317. [PMID: 39992231 DOI: 10.1080/09513590.2025.2470317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 06/17/2024] [Revised: 01/22/2025] [Accepted: 02/17/2025] [Indexed: 02/25/2025] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is globally recognized as a significant pregnancy-related condition, contributing to complex complications for both mothers and infants. Traditional glucose tolerance tests lack the ability to identify the risk of GDM in early pregnancy, hindering effective prevention and timely intervention during the initial stages. OBJECTIVE The primary objective of this study is to pinpoint potential risk factors for GDM and develop an early GDM risk prediction model using neural networks to facilitate GDM screening in early pregnancy. METHODS Initially, we employed statistical tests and models, including univariate and multivariate logistic regression, to identify 14 potential risk factors. Subsequently, we applied various resampling techniques alongside a multi-layer perceptron (MLP). Finally, we evaluated and compared the classification performances of the constructed models using various metric indicators. RESULTS As a result, we identified several factors in early pregnancy significantly associated with GDM (p < 0.05), including BMI, age of menarche, age, higher education, folic acid supplementation, family history of diabetes mellitus, HGB, WBC, PLT, Scr, HBsAg, ALT, ALB, and TBIL. Employing the multivariate logistic model as the baseline achieved an accuracy and AUC of 0.777. In comparison, the MLP-based model using NearMiss exhibited strong predictive performance, achieving scores of 0.943 in AUC and 0.884 in accuracy. CONCLUSIONS In this study, we proposed an innovative interpretable early GDM risk prediction model based on MLP. This model is designed to offer assistance in estimating the risk of GDM in early pregnancy, enabling proactive prevention and timely intervention.
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Affiliation(s)
- Min Zhao
- Department of Information Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiaojie Su
- Department of Information Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Lihong Huang
- Department of Information Center, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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van Eekhout JCA, Becking EC, Scheffer PG, Koutsoliakos I, Bax CJ, Henneman L, Bekker MN, Schuit E. First-Trimester Prediction Models Based on Maternal Characteristics for Adverse Pregnancy Outcomes: A Systematic Review and Meta-Analysis. BJOG 2025; 132:243-265. [PMID: 39449094 PMCID: PMC11704081 DOI: 10.1111/1471-0528.17983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 04/30/2024] [Revised: 09/10/2024] [Accepted: 10/02/2024] [Indexed: 10/26/2024]
Abstract
BACKGROUND Early risk stratification can facilitate timely interventions for adverse pregnancy outcomes, including preeclampsia (PE), small-for-gestational-age neonates (SGA), spontaneous preterm birth (sPTB) and gestational diabetes mellitus (GDM). OBJECTIVES To perform a systematic review and meta-analysis of first-trimester prediction models for adverse pregnancy outcomes. SEARCH STRATEGY The PubMed database was searched until 6 June 2024. SELECTION CRITERIA First-trimester prediction models based on maternal characteristics were included. Articles reporting on prediction models that comprised biochemical or ultrasound markers were excluded. DATA COLLECTION AND ANALYSIS Two authors identified articles, extracted data and assessed risk of bias and applicability using PROBAST. MAIN RESULTS A total of 77 articles were included, comprising 30 developed models for PE, 15 for SGA, 11 for sPTB and 35 for GDM. Discriminatory performance in terms of median area under the curve (AUC) of these models was 0.75 [IQR 0.69-0.78] for PE models, 0.62 [0.60-0.71] for SGA models of nulliparous women, 0.74 [0.72-0.74] for SGA models of multiparous women, 0.65 [0.61-0.67] for sPTB models of nulliparous women, 0.71 [0.68-0.74] for sPTB models of multiparous women and 0.71 [0.67-0.76] for GDM models. Internal validation was performed in 40/91 (43.9%) of the models. Model calibration was reported in 21/91 (23.1%) models. External validation was performed a total of 96 times in 45/91 (49.5%) of the models. High risk of bias was observed in 94.5% of the developed models and in 58.3% of the external validations. CONCLUSIONS Multiple first-trimester prediction models are available, but almost all suffer from high risk of bias, and internal and external validations were often not performed. Hence, methodological quality improvement and assessment of the clinical utility are needed.
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Affiliation(s)
| | - Ellis C. Becking
- Department of Obstetrics and Gynecology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Peter G. Scheffer
- Department of Obstetrics and Gynecology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Ioannis Koutsoliakos
- Department of Obstetrics and Gynecology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Caroline J. Bax
- Department of Obstetrics, Amsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
| | - Lidewij Henneman
- Amsterdam Reproduction and Development Research InstituteAmsterdam UMCAmsterdamThe Netherlands
- Department of Human Genetics, Amsterdam UMCLocation Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Mireille N. Bekker
- Department of Obstetrics and Gynecology, University Medical Center UtrechtUtrecht UniversityUtrechtThe Netherlands
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, Utrecht UniversityUtrechtThe Netherlands
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Jaihow S, Phasuk N, Narkkul U, Pensuksan WC, Scholand SJ, Punsawad C. Maternal and Neonatal Outcomes of Pregnant Women with Abnormal 50 g Glucose Challenge Tests in Nakhon Si Thammarat, Thailand: A Retrospective Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7038. [PMID: 37998269 PMCID: PMC10671579 DOI: 10.3390/ijerph20227038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Academic Contribution Register] [Received: 08/14/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023]
Abstract
(1) Background: An abnormal 50 g glucose challenge test (50 g GCT) during pregnancy, even without a diagnosis of gestational diabetes mellitus (GDM), may result in undesirable obstetric and neonatal outcomes. This study sought to evaluate the outcomes in pregnant women with abnormal 50 g GCT in secondary care hospitals in Thailand. (2) Methods: A total of 1129 cases of pregnant women with abnormal 50 g GCT results who delivered between January 2018 and December 2020 at Thasala, Sichon, and Thungsong hospitals were retrospectively reviewed and divided into three groups: abnormal 50 g GCT and normal 100 g oral OGTT (Group 1; n = 397 cases), abnormal 50 g GCT and one abnormal 100 g OGTT value (Group 2; n = 452 cases), and GDM (Group 3; n = 307 cases). (3) Results: Cesarean section rates in group 3 (61.9%) were statistically higher than those in groups 1 (43.6%) and 2 (49.4%) (p < 0.001). In addition, the highest rate of birth asphyxia was found in group 2 (5.9%), which was significantly higher than that in Groups 1 (1.8%) and 3 (3.3%) (p = 0.007). (4) Conclusions: Pregnant women with abnormal 50 g GCTs without a diagnosis of GDM had undesirable maternal and neonatal outcomes, as well as those who had GDM, suggesting that healthcare providers should closely monitor them throughout pregnancy and the postpartum period.
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Affiliation(s)
- Suda Jaihow
- School of Nursing, Walailak University, Nakhon Si Thammarat 80160, Thailand;
| | - Nonthapan Phasuk
- Department of Medical Clinical Sciences, School of Medicine, Walailak University, Nakhon Si Thammarat 80160, Thailand;
- Research Center in Tropical Pathobiology, Walailak University, Nakhon Si Thammarat 80160, Thailand;
| | - Udomsak Narkkul
- Research Center in Tropical Pathobiology, Walailak University, Nakhon Si Thammarat 80160, Thailand;
- Department of Medical Sciences, School of Medicine, Walailak University, Nakhon Si Thammarat 80160, Thailand
| | | | | | - Chuchard Punsawad
- Research Center in Tropical Pathobiology, Walailak University, Nakhon Si Thammarat 80160, Thailand;
- Department of Medical Sciences, School of Medicine, Walailak University, Nakhon Si Thammarat 80160, Thailand
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4
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Identificación de factores que se asocian a alto riesgo de desarrollar diabetes gestacional. CLINICA E INVESTIGACION EN GINECOLOGIA Y OBSTETRICIA 2022. [DOI: 10.1016/j.gine.2022.100774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 11/23/2022]
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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] [Academic Contribution Register] [Received: 06/24/2021] [Revised: 07/21/2022] [Accepted: 08/18/2022] [Indexed: 11/21/2022]
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6
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Habibi N, Mousa A, Tay CT, Khomami MB, Patten RK, Andraweera PH, Wassie M, Vandersluys J, Aflatounian A, Bianco‐Miotto T, Zhou SJ, Grieger JA. Maternal metabolic factors and the association with gestational diabetes: A systematic review and meta-analysis. Diabetes Metab Res Rev 2022; 38:e3532. [PMID: 35421281 PMCID: PMC9540632 DOI: 10.1002/dmrr.3532] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 11/16/2021] [Revised: 01/10/2022] [Accepted: 02/26/2022] [Indexed: 11/10/2022]
Abstract
Gestational diabetes (GDM) is associated with several adverse outcomes for the mother and child. Higher levels of individual lipids are associated with risk of GDM and metabolic syndrome (MetS), a clustering of risk factors also increases risk for GDM. Metabolic factors can be modified by diet and lifestyle. This review comprehensively evaluates the association between MetS and its components, measured in early pregnancy, and risk for GDM. Databases (Cumulative Index to Nursing and Allied Health Literature, PubMed, Embase, and Cochrane Library) were searched from inception to 5 May 2021. Eligible studies included ≥1 metabolic factor (waist circumference, blood pressure, fasting plasma glucose (FPG), triglycerides, and high-density lipoprotein cholesterol), measured at <16 weeks' gestation. At least two authors independently screened potentially eligible studies. Heterogeneity was quantified using I2 . Data were pooled by random-effects models and expressed as odds ratio and 95% confidence intervals (CIs). Of 7213 articles identified, 40 unique articles were included in meta-analysis. In analyses adjusting for maternal age and body mass index, GDM was increased with increasing FPG (odds ratios [OR] 1.92; 95% CI 1.39-2.64, k = 7 studies) or having MetS (OR 2.52; 1.65, 3.84, k = 3). Women with overweight (OR 2.17; 95% CI 1.89, 2.50, k = 12) or obesity (OR 4.34; 95% CI 2.79-6.74, k = 9) also were at increased risk for GDM. Early pregnancy assessment of glucose or the MetS, offers a potential opportunity to detect and treat individual risk factors as an approach towards GDM prevention; weight loss for pregnant women with overweight or obesity is not recommended. Systematic review registration: PROSPERO CRD42020199225.
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Affiliation(s)
- Nahal Habibi
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Adelaide Medical SchoolUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - Aya Mousa
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash UniversityMelbourneVictoriaAustralia
| | - Chau Thien Tay
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash UniversityMelbourneVictoriaAustralia
| | - Mahnaz Bahri Khomami
- Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash UniversityMelbourneVictoriaAustralia
| | - Rhiannon K. Patten
- Institute for Health and SportVictoria UniversityMelbourneVictoriaAustralia
| | - Prabha H. Andraweera
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Adelaide Medical SchoolUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Department of Cardiology, Lyell McEwin HospitalElizabeth ValeSouth AustraliaAustralia
| | - Molla Wassie
- School of Agriculture, Food and Wine, and Waite Research Institute, University of AdelaideAdelaideSouth AustraliaAustralia
| | - Jared Vandersluys
- School of Agriculture, Food and Wine, and Waite Research Institute, University of AdelaideAdelaideSouth AustraliaAustralia
| | - Ali Aflatounian
- School of Women's and Children's Health, University of New South WalesSydneyNew South WalesAustralia
| | - Tina Bianco‐Miotto
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- School of Agriculture, Food and Wine, and Waite Research Institute, University of AdelaideAdelaideSouth AustraliaAustralia
| | - Shao J. Zhou
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- School of Agriculture, Food and Wine, and Waite Research Institute, University of AdelaideAdelaideSouth AustraliaAustralia
| | - Jessica A. Grieger
- Robinson Research InstituteUniversity of AdelaideAdelaideSouth AustraliaAustralia
- Adelaide Medical SchoolUniversity of AdelaideAdelaideSouth AustraliaAustralia
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Nwose EU, Bwititi PT, Agofure O, Oshionwu EJ, Young EE, Aganbi E, Egwenu SE, Chime HE, Gbeinbo FD, Odufu A, Okuzor JN, Okuleye A, Aninze K, Onyia IC, Ezugwu EC, Igumbor EO, Ulasi II. Prediabetes and cardiovascular complications study: Highlights on gestational diabetes, self-management and primary health care. World J Meta-Anal 2021; 9:543-556. [DOI: 10.13105/wjma.v9.i6.543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 01/28/2021] [Revised: 05/21/2021] [Accepted: 11/28/2021] [Indexed: 02/06/2023] Open
Abstract
International collaboration on the prediabetes and cardiovascular complications study started in 2013. In 2017, a reflection was reported. Incompleteness of documentation and screening of antenatal cases for gestational diabetes mellitus (GDM) was concerning. Hence, further observations have been made that warrant an update. The objective of this review is to highlight gaps between clinical knowledge and practice in GDM, diabetes self-management and primary health care (PHC) for rural dwellers. We followed a descriptive field notes method. Antenatal records of patients screened for GDM with incomplete documentation were examined to determine incompleteness of data in those that also met the criteria for GDM risk assessment. Experiences on development of a diabetes register and education and notes on behavioural change wheel were also reviewed. Other data included cross-sectional evaluation of activities of daily living at two private hospitals. Up to 29% had high GDM risk factors, which fulfilled selection criteria for laboratory screening. Demographic data was complete in all women; however, incomplete documentation was observed with as much as 98% of basic data. High levels of physical activity were found in the population, and health lectures proved effective in food choices. The workforce need for diabetes care seems underestimated, but this may be better understood with reactivation of PHC services. The observations highlight behavioural change wheel issues on GDM and PHC services that need concerted focus. Two proposals are to advance the use of a ‘risk assessment and screening sheet’ for GDM screening and enlightenment of stakeholders on the central hub role of PHC in diabetes management.
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Affiliation(s)
- Ezekiel Uba Nwose
- Department of Public and Community Health, Novena University, Kwale 322107, Nigeria
- School of Dentistry and Medical Sciences, Charles Sturt University, Wagga campus, New South Wales 2650, Australia
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
| | - Phillip Taderera Bwititi
- School of Dentistry and Medical Sciences, Charles Sturt University, Wagga campus, New South Wales 2650, Australia
| | - Otovwe Agofure
- Department of Public and Community Health, Novena University, Kwale 322107, Nigeria
| | - Echinei Jacob Oshionwu
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
- California Department of State Hospital, Stockton, CA 95215, United States
| | - Ekenechukwu Esther Young
- Department of Medicine, College of Medicine, University of Nigeria, Ituku-Ozalla campus, Enugu 402109, Nigeria
| | - Eferhire Aganbi
- Biochemistry Department, Delta State University, Abraka 330105, Nigeria
| | | | - Helen Egoyibo Chime
- Department of Public and Community Health, Novena University, Kwale 322107, Nigeria
| | | | - Alex Odufu
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
| | - John Nwakaego Okuzor
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
- Department of Clinical Laboratory Services, Texas Health (HMH HEB), Bedford, TX 76022, United States
| | - Azuka Okuleye
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
| | - Kennedy Aninze
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
- Clinic Department, Donak Hospital, Kwale 2539083, Nigeria
| | - Innocent Chuks Onyia
- Global Medical Research and Development Organization (GMRDO) group, Abbi Delta State 322107, Nigeria
- Clinic Department, U-Turn Hospital, U-Turn Abule Egba 100276, Nigeria
| | - Euzebus Chinonye Ezugwu
- Department of Medicine, College of Medicine, University of Nigeria, Ituku-Ozalla campus, Enugu 402109, Nigeria
| | | | - Ifeoma Isabel Ulasi
- Department of Medicine, College of Medicine, University of Nigeria, Ituku-Ozalla campus, Enugu 402109, Nigeria
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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: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution 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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Wang Y, Ge Z, Chen L, Hu J, Zhou W, Shen S, Zhu D, Bi Y. Risk Prediction Model of Gestational Diabetes Mellitus in a Chinese Population Based on a Risk Scoring System. Diabetes Ther 2021; 12:1721-1734. [PMID: 33993435 PMCID: PMC8179863 DOI: 10.1007/s13300-021-01066-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 03/14/2021] [Accepted: 04/21/2021] [Indexed: 01/04/2023] Open
Abstract
INTRODUCTION Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system. METHODS This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively. RESULTS In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916). CONCLUSION The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.
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Affiliation(s)
- Yanmei Wang
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China
| | - Zhijuan Ge
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China
| | - Lei Chen
- Department of Endocrinology, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou, China
| | - Jun Hu
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China
| | - Wenting Zhou
- Department of Endocrinology, Medical School of Southeast University Nanjing Drum Tower Hospital, Nanjing, China
| | - Shanmei Shen
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China
| | - Dalong Zhu
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China.
| | - Yan Bi
- Department of Endocrinology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing, 210008, China.
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Gao S, Leng J, Liu H, Wang S, Li W, Wang Y, Hu G, Chan JCN, Yu Z, Zhu H, Yang X. Development and validation of an early pregnancy risk score for the prediction of gestational diabetes mellitus in Chinese pregnant women. BMJ Open Diabetes Res Care 2020; 8:8/1/e000909. [PMID: 32327440 PMCID: PMC7202751 DOI: 10.1136/bmjdrc-2019-000909] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 09/14/2019] [Revised: 02/25/2020] [Accepted: 03/15/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To develop and validate a set of risk scores for the prediction of gestational diabetes mellitus (GDM) before the 15th gestational week using an established population-based prospective cohort. METHODS From October 2010 to August 2012, 19 331 eligible pregnant women were registered in the three-tiered antenatal care network in Tianjin, China, to receive their antenatal care and a two-step GDM screening. The whole dataset was randomly divided into a training dataset (for development of the risk score) and a test dataset (for validation of performance of the risk score). Logistic regression was performed to obtain coefficients of selected predictors for GDM in the training dataset. Calibration was estimated using Hosmer-Lemeshow test, while discrimination was checked using area under the receiver operating characteristic curve (AUC) in the test dataset. RESULTS In the training dataset (total=12 887, GDM=979 or 7.6%), two risk scores were developed, one only including predictors collected at the first antenatal care visit for early prediction of GDM, like maternal age, body mass index, height, family history of diabetes, systolic blood pressure, and alanine aminotransferase; and the other also including predictors collected during pregnancy, that is, at the time of GDM screening, like physical activity, sitting time at home, passive smoking, and weight gain, for maximum performance. In the test dataset (total=6444, GDM=506 or 7.9%), the calibrations of both risk scores were acceptable (both p for Hosmer-Lemeshow test >0.25). The AUCs of the first and second risk scores were 0.710 (95% CI: 0.680 to 0.741) and 0.712 (95% CI: 0.682 to 0.743), respectively (p for difference: 0.9273). CONCLUSION Both developed risk scores had adequate performance for the prediction of GDM in Chinese pregnant women in Tianjin, China. Further validations are needed to evaluate their performance in other populations and using different methods to identify GDM cases.
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Affiliation(s)
- Si Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin, China
| | - Junhong Leng
- Department of Child Health, Tianjin Women and Children's Health Center, Tianjin, China
| | - Hongyan Liu
- Department of Child Health, Tianjin Women and Children's Health Center, Tianjin, China
| | - Shuo Wang
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Weiqin Li
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Yue Wang
- Department of Child Health, Tianjin Women and Children's Health Center, Tianjin, China
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Prince of Wales Hospital-International Diabetes Federation Centre of Education, The Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhijie Yu
- Population Cancer Research Program and Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Hong Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin, China
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin Medical University, Tianjin, China
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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: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Academic Contribution 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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12
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Heestermans T, Payne B, Kayode GA, Amoakoh-Coleman M, Schuit E, Rijken MJ, Klipstein-Grobusch K, Bloemenkamp K, Grobbee DE, Browne JL. Prognostic models for adverse pregnancy outcomes in low-income and middle-income countries: a systematic review. BMJ Glob Health 2019; 4:e001759. [PMID: 31749995 PMCID: PMC6830054 DOI: 10.1136/bmjgh-2019-001759] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 06/17/2019] [Revised: 09/09/2019] [Accepted: 10/05/2019] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Ninety-nine per cent of all maternal and neonatal deaths occur in low-income and middle-income countries (LMIC). Prognostic models can provide standardised risk assessment to guide clinical management and can be vital to reduce and prevent maternal and perinatal mortality and morbidity. This review provides a comprehensive summary of prognostic models for adverse maternal and perinatal outcomes developed and/or validated in LMIC. METHODS A systematic search in four databases (PubMed/Medline, EMBASE, Global Health Library and The Cochrane Library) was conducted from inception (1970) up to 2 May 2018. Risk of bias was assessed with the PROBAST tool and narratively summarised. RESULTS 1741 articles were screened and 21 prognostic models identified. Seventeen models focused on maternal outcomes and four on perinatal outcomes, of which hypertensive disorders of pregnancy (n=9) and perinatal death including stillbirth (n=4) was most reported. Only one model was externally validated. Thirty different predictors were used to develop the models. Risk of bias varied across studies, with the item 'quality of analysis' performing the least. CONCLUSION Prognostic models can be easy to use, informative and low cost with great potential to improve maternal and neonatal health in LMIC settings. However, the number of prognostic models developed or validated in LMIC settings is low and mirrors the 10/90 gap in which only 10% of resources are dedicated to 90% of the global disease burden. External validation of existing models developed in both LMIC and high-income countries instead of developing new models should be encouraged. PROSPERO REGISTRATION NUMBER CRD42017058044.
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Affiliation(s)
- Tessa Heestermans
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Beth Payne
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
- Women's Health Research Institute, School of Population and Public Health, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Gbenga Ayodele Kayode
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
- International Research Centre of Excellence, Institute of Human Virology, Abuja, Nigeria
| | - Mary Amoakoh-Coleman
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
- Noguchi Memorial Research Institute for Medical Research, University of Ghana, Legon, Ghana
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Marcus J Rijken
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Woman and Baby, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kerstin Klipstein-Grobusch
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
- Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg-Braamfontein, South Africa
| | - Kitty Bloemenkamp
- Division of Woman and Baby, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Diederick E Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Joyce L Browne
- Julius Global Health, Julius Center for Health Sciences and Primary Care, Universitair Medisch Centrum Utrecht, Utrecht University, Utrecht, The Netherlands
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13
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Talasaz ZH, Sadeghi R, Askari F, Dadgar S, Vatanchi A. First trimesters Pregnancy-Associated Plasma Protein-A levels value to Predict Gestational diabetes Mellitus: A systematic review and meta-analysis of the literature. Taiwan J Obstet Gynecol 2018; 57:181-189. [PMID: 29673658 DOI: 10.1016/j.tjog.2018.02.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Accepted: 08/29/2017] [Indexed: 01/07/2023] Open
Abstract
Detecting pregnant women at risk of diabetes in first months can help them by early intervention for delaying or preventing onset of GDM. In this study, we aimed to assess the Predictive value of first trimester Pregnancy related plasma protein-A (PAPP-A) levels for detecting Gestational diabetes Mellitus (GDM). This systematic review and meta-analysis was conducted through probing in databases. PubMed, Scopus, Medline and Google scholar citations were searched to find the published papers from 1974 to 2017. Studies were considered eligible if they were cohorts, case-control studies, reported GDM result, not other types, conducted on singleton pregnancy, measured Serum pregnancy associated plasma protein A in the first trimester and evaluated the relation of first trimester pregnancy associated plasma protein-A and GDM. Two reviewers independently assessed the quality with Newcastle-Ottawa and extracted data in the Pre-defined checklist. Analysis of the data was carried out by "Comprehensive Meta-analysis Version 2 (CAM)" and Metadisc software. 17 articles have our inclusion criteria and were considered in our systematic review, 5 studies included in Meta-analysis. Meta-analysis of these articles showed that the predictive value of PAPP-A for GDM has 55% sensitivity (53-58), 90% (89-90) specificity, LR + 2.48 (0.83-7.36) and LR - 0.70 (0.45-1.09) with 95% confidence intervals. In our study PAPP-A has low predictive accuracy overall, but it may be useful when combined with other tests, and this is an active part for future research. One limitation of our study is significant heterogeneity because of different adjusted variables and varied diagnostic criteria.
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Affiliation(s)
- Zahra Hadizadeh Talasaz
- Student Research Committee, Department of Midwifery, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ramin Sadeghi
- Nuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fariba Askari
- Student Research Committee, Department of Midwifery, School of Nursing and Midwifery, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Salmeh Dadgar
- Faculty of Medicine, Obstetrics & Gynecology Department, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Atiyeh Vatanchi
- Faculty of Medicine, Obstetrics & Gynecology Department, Mashhad University of Medical Sciences, Mashhad, Iran
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Farrar D, Simmonds M, Griffin S, Duarte A, Lawlor DA, Sculpher M, Fairley L, Golder S, Tuffnell D, Bland M, Dunne F, Whitelaw D, Wright J, Sheldon TA. The identification and treatment of women with hyperglycaemia in pregnancy: an analysis of individual participant data, systematic reviews, meta-analyses and an economic evaluation. Health Technol Assess 2018; 20:1-348. [PMID: 27917777 DOI: 10.3310/hta20860] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is associated with a higher risk of important adverse outcomes. Practice varies and the best strategy for identifying and treating GDM is unclear. AIM To estimate the clinical effectiveness and cost-effectiveness of strategies for identifying and treating women with GDM. METHODS We analysed individual participant data (IPD) from birth cohorts and conducted systematic reviews to estimate the association of maternal glucose levels with adverse perinatal outcomes; GDM prevalence; maternal characteristics/risk factors for GDM; and the effectiveness and costs of treatments. The cost-effectiveness of various strategies was estimated using a decision tree model, along with a value of information analysis to assess where future research might be worthwhile. Detailed systematic searches of MEDLINE® and MEDLINE In-Process & Other Non-Indexed Citations®, EMBASE, Cumulative Index to Nursing and Allied Health Literature Plus, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database, Maternity and Infant Care database and the Cochrane Methodology Register were undertaken from inception up to October 2014. RESULTS We identified 58 studies examining maternal glucose levels and outcome associations. Analyses using IPD alone and the systematic review demonstrated continuous linear associations of fasting and post-load glucose levels with adverse perinatal outcomes, with no clear threshold below which there is no increased risk. Using IPD, we estimated glucose thresholds to identify infants at high risk of being born large for gestational age or with high adiposity; for South Asian (SA) women these thresholds were fasting and post-load glucose levels of 5.2 mmol/l and 7.2 mmol/l, respectively and for white British (WB) women they were 5.4 and 7.5 mmol/l, respectively. Prevalence using IPD and published data varied from 1.2% to 24.2% (depending on criteria and population) and was consistently two to three times higher in SA women than in WB women. Lowering thresholds to identify GDM, particularly in women of SA origin, identifies more women at risk, but increases costs. Maternal characteristics did not accurately identify women with GDM; there was limited evidence that in some populations risk factors may be useful for identifying low-risk women. Dietary modification additional to routine care reduced the risk of most adverse perinatal outcomes. Metformin (Glucophage,® Teva UK Ltd, Eastbourne, UK) and insulin were more effective than glibenclamide (Aurobindo Pharma - Milpharm Ltd, South Ruislip, Middlesex, UK). For all strategies to identify and treat GDM, the costs exceeded the health benefits. A policy of no screening/testing or treatment offered the maximum expected net monetary benefit (NMB) of £1184 at a cost-effectiveness threshold of £20,000 per quality-adjusted life-year (QALY). The NMB for the three best-performing strategies in each category (screen only, then treat; screen, test, then treat; and test all, then treat) ranged between -£1197 and -£1210. Further research to reduce uncertainty around potential longer-term benefits for the mothers and offspring, find ways of improving the accuracy of identifying women with GDM, and reduce costs of identification and treatment would be worthwhile. LIMITATIONS We did not have access to IPD from populations in the UK outside of England. Few observational studies reported longer-term associations, and treatment trials have generally reported only perinatal outcomes. CONCLUSIONS Using the national standard cost-effectiveness threshold of £20,000 per QALY it is not cost-effective to routinely identify pregnant women for treatment of hyperglycaemia. Further research to provide evidence on longer-term outcomes, and more cost-effective ways to detect and treat GDM, would be valuable. STUDY REGISTRATION This study is registered as PROSPERO CRD42013004608. FUNDING The National Institute for Health Research Health Technology Assessment programme.
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Affiliation(s)
- Diane Farrar
- Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK.,Department of Health Sciences, University of York, York, UK
| | - Mark Simmonds
- Centre for Reviews and Dissemination, University of York, York, UK
| | - Susan Griffin
- Centre for Health Economics, University of York, York, UK
| | - Ana Duarte
- Centre for Health Economics, University of York, York, UK
| | - Debbie A Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.,School of Social and Community Medicine, University of Bristol, Bristol, UK
| | - Mark Sculpher
- Centre for Health Economics, University of York, York, UK
| | - Lesley Fairley
- Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK
| | - Su Golder
- Department of Health Sciences, University of York, York, UK
| | - Derek Tuffnell
- Bradford Women's and Newborn Unit, Bradford Teaching Hospitals, Bradford, UK
| | - Martin Bland
- Department of Health Sciences, University of York, York, UK
| | - Fidelma Dunne
- Galway Diabetes Research Centre (GDRC) and School of Medicine, National University of Ireland, Galway, Republic of Ireland
| | - Donald Whitelaw
- Department of Diabetes & Endocrinology, Bradford Teaching Hospitals, Bradford, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals, Bradford, UK
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15
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Huhn EA, Rossi SW, Hoesli I, Göbl CS. Controversies in Screening and Diagnostic Criteria for Gestational Diabetes in Early and Late Pregnancy. Front Endocrinol (Lausanne) 2018; 9:696. [PMID: 30538674 PMCID: PMC6277591 DOI: 10.3389/fendo.2018.00696] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 06/30/2018] [Accepted: 11/05/2018] [Indexed: 01/14/2023] Open
Abstract
This review serves to evaluate the screening and diagnostic strategies for gestational diabetes and overt diabetes in pregnancy. We focus on the different early screening and diagnostic approaches in first trimester including fasting plasma glucose, random plasma glucose, oral glucose tolerance test, hemoglobin A1c, risk prediction models and biomarkers. Early screening for gestational diabetes is currently not recommended since the potential benefits and harms of early detection and subsequent treatment need to be further evaluated in randomized controlled trials.
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Affiliation(s)
- Evelyn A. Huhn
- Department of Obstetrics and Gynaecology, University Hospital Basel, Basel, Switzerland
- *Correspondence: Evelyn A. Huhn
| | - Simona W. Rossi
- Department of Biomedicine, University of Basel and University Hospital Basel, Basel, Switzerland
| | - Irene Hoesli
- Department of Obstetrics and Gynaecology, University Hospital Basel, Basel, Switzerland
| | - Christian S. Göbl
- Division of Obstetrics and Feto-Maternal Medicine, Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
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Adam S, Rheeder P. Selective Screening Strategies for Gestational Diabetes: A Prospective Cohort Observational Study. J Diabetes Res 2017; 2017:2849346. [PMID: 29201921 PMCID: PMC5671730 DOI: 10.1155/2017/2849346] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 06/14/2017] [Revised: 09/08/2017] [Accepted: 09/27/2017] [Indexed: 11/18/2022] Open
Abstract
AIM We aimed to develop a prediction model for the diagnosis of gestational diabetes and to evaluate the performance of published prediction tools on our population. METHODS We conducted a cohort study on nondiabetic women < 26 weeks gestation at a level 1 clinic in Johannesburg, South Africa. At recruitment, participants completed a questionnaire and random basal glucose and HbA1c were evaluated. A 75 g 2-hour OGTT was scheduled between 24-28 weeks gestation, as per FIGO guidelines. A score was derived using multivariate logistic regression. Published scoring systems were tested by deriving ROC curves. RESULTS In 554 women, RBG, BMI, and previous baby ≥ 4000 g were significant risk factors included for GDM, which were used to derive a nomogram-based score. The logistic regression model for prediction of GDM had R2 0.143, Somer's Dxy rank correlation 0.407, and Harrell's c-score 0.703. HbA1c did not improve predictive value of the nomogram at any threshold (e.g,. at probability > 10%, 25.6% of cases were detected without the HbA1c, and 25.8% of cases would have been detected with the HbA1c). The 9 published scoring systems performed poorly. CONCLUSION We propose a nomogram-based score that can be used at first antenatal visit to identify women at high risk of GDM.
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Affiliation(s)
- Sumaiya Adam
- Department of Obstetrics and Gynecology, University of Pretoria, Pretoria, South Africa
| | - Paul Rheeder
- Department of Internal Medicine, University of Pretoria, Pretoria, South Africa
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17
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Chi Z, Zhang S, Wang Y, Yang L, Yang Y, Li X. Research of gestational diabetes mellitus risk evaluation method. Technol Health Care 2017; 24 Suppl 2:S499-503. [PMID: 27163310 DOI: 10.3233/thc-161174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Gestational diabetes mellitus (GDM) is not easily detected. The difficulty in detecting GDM is largely due to the late onset of clinical symptoms as well as the various complications that result from GDM [1]. OBJECTIVE GDM greatly influences both mother and child. Therefore, the purpose of this study was to reduce the morbidity of GDM. METHODS In this study, risk factors that influence GDM were selected through statistical analysis. Multivariable logistic regression analysis was used to obtain the regression equation and Odds Ratio (OR) value. The risk score of each factor was obtained according to the OR value. RESULTS The score of every pregnant woman could be very intuitively used to show the risk of getting GDM. CONCLUSION Through the above methods, a comprehensive risk evaluation method of detecting GDM was developed.
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Affiliation(s)
- Zhenyu Chi
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, China
| | - Song Zhang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, China
| | - Yang Wang
- Shenzhen Huada Gene Research Institute, Shenzhen, China
| | - Lin Yang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, China
| | - Yimin Yang
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, China
| | - Xuwen Li
- College of Life Science and Bio-engineering, Beijing University of Technology, Beijing, China
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18
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Farrar D, Simmonds M, Bryant M, Lawlor DA, Dunne F, Tuffnell D, Sheldon TA. Risk factor screening to identify women requiring oral glucose tolerance testing to diagnose gestational diabetes: A systematic review and meta-analysis and analysis of two pregnancy cohorts. PLoS One 2017; 12:e0175288. [PMID: 28384264 PMCID: PMC5383279 DOI: 10.1371/journal.pone.0175288] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 12/15/2016] [Accepted: 03/23/2017] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Easily identifiable risk factors including: obesity and ethnicity at high risk of diabetes are commonly used to indicate which women should be offered the oral glucose tolerance test (OGTT) to diagnose gestational diabetes (GDM). Evidence regarding these risk factors is limited however. We conducted a systematic review (SR) and meta-analysis and individual participant data (IPD) analysis to evaluate the performance of risk factors in identifying women with GDM. METHODS We searched MEDLINE, Medline in Process, Embase, Maternity and Infant Care and the Cochrane Central Register of Controlled Trials (CENTRAL) up to August 2016 and conducted additional reference checking. We included observational, cohort, case-control and cross-sectional studies reporting the performance characteristics of risk factors used to identify women at high risk of GDM. We had access to IPD from the Born in Bradford and Atlantic Diabetes in Pregnancy cohorts, all pregnant women in the two cohorts with data on risk factors and OGTT results were included. RESULTS Twenty nine published studies with 211,698 women for the SR and a further 14,103 women from two birth cohorts (Born in Bradford and the Atlantic Diabetes in Pregnancy study) for the IPD analysis were included. Six studies assessed the screening performance of guidelines; six examined combinations of risk factors; eight evaluated the number of risk factors and nine examined prediction models or scores. Meta-analysis using data from published studies suggests that irrespective of the method used, risk factors do not identify women with GDM well. Using IPD and combining risk factors to produce the highest sensitivities, results in low specificities (and so higher false positives). Strategies that use the risk factors of age (>25 or >30) and BMI (>25 or 30) perform as well as other strategies with additional risk factors included. CONCLUSIONS Risk factor screening methods are poor predictors of which pregnant women will be diagnosed with GDM. A simple approach of offering an OGTT to women 25 years or older and/or with a BMI of 25kg/m2 or more is as good as more complex risk prediction models. Research to identify more accurate (bio)markers is needed. Systematic Review Registration: PROSPERO CRD42013004608.
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Affiliation(s)
- Diane Farrar
- Bradford Institute for Health Research, Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom
- Department of Health Sciences, University of York, York, United Kingdom
| | - Mark Simmonds
- Centre for Reviews and Dissemination, University of York, York, United Kingdom
| | - Maria Bryant
- Bradford Institute for Health Research, Bradford Institute for Health Research, Bradford Royal Infirmary, Bradford, United Kingdom
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, United Kingdom
| | - Debbie A. Lawlor
- MRC Integrative Epidemiology Unit at the University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
| | - Fidelma Dunne
- Galway Diabetes Research Centre (GDRC) and School of Medicine, National University of Ireland, Galway, Republic of Ireland
| | - Derek Tuffnell
- Bradford Women’s and Newborn Unit, Bradford, United Kingdom
| | - Trevor A. Sheldon
- Department of Health Sciences, University of York, York, United Kingdom
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Kleinrouweler CE, Cheong-See FM, Collins GS, Kwee A, Thangaratinam S, Khan KS, Mol BWJ, Pajkrt E, Moons KG, Schuit E. Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016; 214:79-90.e36. [PMID: 26070707 DOI: 10.1016/j.ajog.2015.06.013] [Citation(s) in RCA: 125] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 02/11/2015] [Revised: 05/20/2015] [Accepted: 06/01/2015] [Indexed: 12/18/2022]
Abstract
Health care provision is increasingly focused on the prediction of patients' individual risk for developing a particular health outcome in planning further tests and treatments. There has been a steady increase in the development and publication of prognostic models for various maternal and fetal outcomes in obstetrics. We undertook a systematic review to give an overview of the current status of available prognostic models in obstetrics in the context of their potential advantages and the process of developing and validating models. Important aspects to consider when assessing a prognostic model are discussed and recommendations on how to proceed on this within the obstetric domain are given. We searched MEDLINE (up to July 2012) for articles developing prognostic models in obstetrics. We identified 177 papers that reported the development of 263 prognostic models for 40 different outcomes. The most frequently predicted outcomes were preeclampsia (n = 69), preterm delivery (n = 63), mode of delivery (n = 22), gestational hypertension (n = 11), and small-for-gestational-age infants (n = 10). The performance of newer models was generally not better than that of older models predicting the same outcome. The most important measures of predictive accuracy (ie, a model's discrimination and calibration) were often (82.9%, 218/263) not both assessed. Very few developed models were validated in data other than the development data (8.7%, 23/263). Only two-thirds of the papers (62.4%, 164/263) presented the model such that validation in other populations was possible, and the clinical applicability was discussed in only 11.0% (29/263). The impact of developed models on clinical practice was unknown. We identified a large number of prognostic models in obstetrics, but there is relatively little evidence about their performance, impact, and usefulness in clinical practice so that at this point, clinical implementation cannot be recommended. New efforts should be directed toward evaluating the performance and impact of the existing models.
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Hod M, Kapur A, Sacks DA, Hadar E, Agarwal M, Di Renzo GC, Roura LC, McIntyre HD, Morris JL, Divakar H. The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management, and care . Int J Gynaecol Obstet 2015;131 Suppl 3:S173-S211. [PMID: 29644654 DOI: 10.1016/s0020-7292(15)30033-3] [Citation(s) in RCA: 527] [Impact Index Per Article: 52.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 02/07/2023]
Affiliation(s)
- Moshe Hod
- Division of Maternal Fetal Medicine, Rabin Medical Center, Tel Aviv University, Petah Tikva, Israel
| | - Anil Kapur
- World Diabetes Foundation, Gentofte, Denmark
| | - David A Sacks
- Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA
| | - Eran Hadar
- Helen Schneider Hospital for Women, Rabin Medical Center, Petah Tikva, Israel.,Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Mukesh Agarwal
- Department of Pathology, UAE University, Al Ain, United Arab Emirates
| | - Gian Carlo Di Renzo
- Centre of Perinatal and Reproductive Medicine, Department of Obstetrics and Gynecology, University of Perugia, Perugia, Italy
| | - Luis Cabero Roura
- Maternal Fetal Medicine Unit, Vall d'Hebron University Hospital, Barcelona, Spain
| | | | - Jessica L Morris
- International Federation of Gynecology and Obstetrics, London, UK
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5. Diagnosing gestational diabetes mellitus. Int J Gynaecol Obstet 2015. [DOI: 10.1016/s0020-7292(15)30013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 11/26/2022]
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Correa PJ, Vargas JF, Sen S, Illanes SE. Prediction of gestational diabetes early in pregnancy: targeting the long-term complications. Gynecol Obstet Invest 2014; 77:145-9. [PMID: 24401480 DOI: 10.1159/000357616] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 11/29/2012] [Accepted: 11/28/2013] [Indexed: 11/19/2022]
Abstract
Gestational diabetes (GD), defined as carbohydrate intolerance with onset or first recognition during pregnancy, has a prevalence of 7% and is a growing problem worldwide. Infants born to mothers with GD are more likely to be large for gestational age, incur traumatic birth injury, require a stay in the intensive care unit and develop postnatal metabolic disturbances. As the worldwide epidemic of obesity worsens, more women are entering pregnancy with metabolic alterations and preexisting insulin resistance, which is heightened by the hormonal milieu of pregnancy. The Hyperglycemia Adverse Pregnancy Outcome (HAPO) study has clearly shown that GD-related complications correlate with glycemic control. We will review the current understanding of the physiology of GD and the screening and treatment guidelines that are commonly utilized in clinical care. In addition, we will discuss the need for development of multiparametric models combining maternal clinical risk factors and biomarkers early in pregnancy to better stratify and predict risk of GD-related complications and offer targeted intervention.
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Affiliation(s)
- Paula J Correa
- Department of Obstetrics and Gynecology, Faculty of Medicine, Universidad de los Andes, Santiago, Chile
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Lovati E, Beneventi F, Simonetta M, Laneri M, Quarleri L, Scudeller L, Albonico G, Locatelli E, Cavagnoli C, Tinelli C, Spinillo A, Corazza GR. Gestational diabetes mellitus: including serum pregnancy-associated plasma protein-A testing in the clinical management of primiparous women? A case-control study. Diabetes Res Clin Pract 2013; 100:340-7. [PMID: 23642968 DOI: 10.1016/j.diabres.2013.04.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 12/11/2012] [Revised: 02/18/2013] [Accepted: 04/08/2013] [Indexed: 12/13/2022]
Abstract
AIMS To assess pregnancy-associated plasma protein A (PAPP-A) correlation with GDM and its usefulness in predicting GDM in primiparous women. METHODS First trimester data related to 307 pregnant women affected by GDM and 366 control pregnant women were retrieved from a computer data base and integrated with ad hoc data. Clinical data were recorded at delivery. A logistic model was used to analyze the association between first trimester data and subsequent clinical outcomes. We derived a risk score using both classical risk factors for GDM and PAPP-A. RESULTS Diabetic and control women were significantly different in terms of age (p<0.001), BMI (p<0.001), weight (p<0.001), family history of diabetes (p<0.001), PAPP-A concentration and PAPP-A corrected multiple of the median (MoM) (p<0.001). The ROC-AUC of the clinical risk score was 0.60 (95%CI 0.56-0.64), the adjusted score including PAPP-A MoM was 0.70 (95%CI 0.66-0.74). CONCLUSIONS Low PAPP-A was strongly associated with GDM and lower values were found in diabetic women needing insulin therapy. Adding PAPP-A to first trimester screening could improve the prediction of women at high risk who will develop GDM. Further studies are needed to validate the applicability of our findings in different populations and settings.
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Affiliation(s)
- Elisabetta Lovati
- First Department of Medicine, IRCCS Fondazione Policlinico San Matteo, University of Pavia, Pavia, Italy.
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24
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Cosson E, Benbara A, Pharisien I, Nguyen MT, Revaux A, Lormeau B, Sandre-Banon D, Assad N, Pillegand C, Valensi P, Carbillon L. Diagnostic and prognostic performances over 9 years of a selective screening strategy for gestational diabetes mellitus in a cohort of 18,775 subjects. Diabetes Care 2013; 36:598-603. [PMID: 23150287 PMCID: PMC3579341 DOI: 10.2337/dc12-1428] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We aimed to evaluate a selective screening strategy for gestational diabetes mellitus (GDM) based on the presence of risk factors: BMI ≥25 kg/m(2), age ≥35 years, family history of diabetes, personal history of GDM, or birth of a child with macrosomia. RESEARCH DESIGN AND METHODS Of 20,630 deliveries between 2002 and 2010, we selected 18,775 deliveries in women with no known diabetes and for whom all risk factors were known. GDM was universally screened and defined as fasting plasma glucose level ≥5.3 mmol/L and/or 2-h postload (75 g) glucose level ≥7.8 mmol/L. RESULTS The prevalence of at least one risk factor has increased since 2002 (P < 0.001) from 51.7 to 61.5%, with no change in the GDM prevalence (mean 14.4%, intention to screen). At least one risk factor was present in 58.5% of women who represented 65.3% of all those with GDM. The presence of risk factors was significantly associated with GDM (odds ratio 1.4 [95% CI 1.3-1.5], P < 0.001) and with GDM-related events (preeclampsia/large for gestational age/dystocia) (P < 0.001) with the following incidences: no GDM/no risk factor 8.8%, no GDM/risk factor 11.1%, GDM/no risk factor 16.7%, and GDM/risk factor 18.2%. CONCLUSIONS The presence of risk factors increased during the last decade. This condition is predictive of GDM and GDM-related events. However, a selective screening would lead to missing one-third of the women with GDM who, even without risk factors, had more events than women without GDM. Therefore, these data stand against the present selective screening currently proposed in the French guidelines.
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Affiliation(s)
- Emmanuel Cosson
- Centre de Recherche en Nutrition Humaine d’Ile-de-France, Department of Endocrinology-Diabetology-Nutrition, Jean Verdier Hospital, Assistance Publique-Hôpitaux de Paris, Sorbonne Paris Cité, Paris 13 University, Bondy, France.
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25
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Göbl CS, Bozkurt L, Rivic P, Schernthaner G, Weitgasser R, Pacini G, Mittlböck M, Bancher-Todesca D, Lechleitner M, Kautzky-Willer A. A two-step screening algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting gestational diabetes mellitus. Diabetologia 2012; 55:3173-81. [PMID: 23001377 DOI: 10.1007/s00125-012-2726-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Received: 07/08/2012] [Accepted: 08/06/2012] [Indexed: 10/27/2022]
Abstract
AIMS/HYPOTHESIS It is currently not clear how to construct a time- and cost-effective screening strategy for gestational diabetes mellitus (GDM). Thus, we elaborated a simple screening algorithm combining (1) fasting plasma glucose (FPG) measurement; and (2) a multivariable risk estimation model focused on individuals with normal FPG levels to decide if a further OGTT is indicated. METHODS A total of 1,336 women were prospectively screened for several risk factors for GDM within a multicentre study conducted in Austria. Of 714 women (53.4%) who developed GDM using recent diagnostic guidelines, 461 were sufficiently screened with FPG. A risk prediction score was finally developed using data from the remaining 253 women with GDM and 622 healthy women. The screening algorithm was validated with a further 258 pregnant women. RESULTS A risk estimation model including history of GDM, glycosuria, family history of diabetes, age, preconception dyslipidaemia and ethnic origin, in addition to FPG, was accurate for detecting GDM in participants with normal FPG. Including an FPG pretest, the receiver operating characteristic AUC of the screening algorithm was 0.90 (95% CI 0.88, 0.91). A cut-off value of 0.20 was able to differentiate between low and intermediate risk for GDM with a high sensitivity. Comparable results were seen with the validation cohort. Moreover, we demonstrated an independent association between values derived from the risk estimation and macrosomia in offspring (OR 3.03, 95% CI 1.79, 5.19, p < 0.001). CONCLUSIONS/INTERPRETATION This study demonstrates a new concept for accurate but cheap GDM screening. This approach should be further evaluated in different populations to ensure an optimised diagnostic algorithm.
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Affiliation(s)
- C S Göbl
- Department of Gynecology and Obstetrics, Division of Feto-Maternal Medicine, Medical University of Vienna, Vienna, Austria
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26
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Gill PK, Choo WY, Bulgiba AM. How useful is clinical scoring in reducing the need for gestational diabetes screening? Int J Diabetes Dev Ctries 2012. [DOI: 10.1007/s13410-012-0068-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Academic Contribution Register] [Indexed: 11/29/2022] Open
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27
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Bertozzi S, Londero AP, Salvador S, Grassi T, Fruscalzo A, Driul L, Marchesoni D. Influence of the couple on hypertensive disorders during pregnancy: A retrospective cohort study. Pregnancy Hypertens 2011; 1:156-63. [PMID: 26104497 DOI: 10.1016/j.preghy.2011.01.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Received: 12/16/2010] [Accepted: 01/28/2011] [Indexed: 10/18/2022]
Abstract
OBJECTIVE Our study investigates a possible couple predisposition for pregnancy-related hypertensive disorders (PRHDs). MATERIALS AND METHODS We selected 350 women with PRHDs and a random control cohort without PRHDs. We analyzed their clinical files and asked them and their partners about clinical information and family history for some common pathologies. Statistical bivariate and multivariate analysis was performed by R, considering significant p<0.05. RESULTS Familial history reveals in cases more maternal grandparents hypertension and thrombophilia, and paternal, personal and familial, thrombophilia history than in controls. By multivariate analysis, the occurrence of PRHDs is influenced by stress, maternal BMI, maternal chronic hypertension, pre-pregnancy diabetes mellitus, nulliparity, maternal grandmother and grandfather hypertension; and academic degrees is a protective factor. Selecting only multipara, PRHDs correlate with advanced maternal age, higher maternal BMI, chronic hypertension, longer interpregnancy interval, stress, previous pregnancies affected by PRHDs, and paternal, personal and familial, thrombophilia history. Moreover the multivariate logistic regression models considering parents familial and personal history results are accurate to predict PRHDs with an AUC of 79% in the general population and 82% among multiparous women. CONCLUSIONS The couple should be evaluated together for PRHDs risk, both parents familial history should be considered in PRHDs screening programs, and further studies are required, in a society continuously changing its characteristics and habits.
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Affiliation(s)
- Serena Bertozzi
- Department of Surgery, AOU "SM della Misericordia" of Udine, 33100 Udine, Italy
| | - Ambrogio P Londero
- Clinic of Obstetrics and Gynecology, AOU "SM della Misericordia" of Udine, 33100 Udine, Italy
| | | | - Tiziana Grassi
- Clinic of Obstetrics and Gynecology, AOU "SM della Misericordia" of Udine, 33100 Udine, Italy
| | - Arrigo Fruscalzo
- Frauenklinik, Mathias-Spital, Frankenburgstr. 31, 48431 Rheine, Germany
| | - Lorenza Driul
- Clinic of Obstetrics and Gynecology, AOU "SM della Misericordia" of Udine, 33100 Udine, Italy
| | - Diego Marchesoni
- Clinic of Obstetrics and Gynecology, AOU "SM della Misericordia" of Udine, 33100 Udine, Italy
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Hiéronimus S, Le Meaux JP. Intérêt du dépistage du diabète gestationnel et comparaison des stratégies ciblée et systématique. ACTA ACUST UNITED AC 2010; 39:S200-13. [DOI: 10.1016/s0368-2315(10)70047-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Academic Contribution Register] [Indexed: 11/26/2022]
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