The Need for Personalized Risk-Stratified Approaches to Treatment for Gestational Diabetes: A Narrative Review

2021 ◽  
Author(s):  
Shamil D. Cooray ◽  
Jacqueline A. Boyle ◽  
Georgia Soldatos ◽  
Shakila Thangaratinam ◽  
Helena J. Teede
Keyword(s):  
Gestational Diabetes ◽  
Pregnancy Outcomes ◽  
Population Based ◽  
Adverse Pregnancy Outcomes ◽  
Therapeutic Interventions ◽  
Clinical Prediction Model ◽  
Increased Risk ◽  
Adverse Pregnancy ◽  
The Individual ◽  
Personalized Risk

AbstractGestational diabetes mellitus (GDM) is common and is associated with an increased risk of adverse pregnancy outcomes. However, the prevailing one-size-fits-all approach that treats all women with GDM as having equivalent risk needs revision, given the clinical heterogeneity of GDM, the limitations of a population-based approach to risk, and the need to move beyond a glucocentric focus to address other intersecting risk factors. To address these challenges, we propose using a clinical prediction model for adverse pregnancy outcomes to guide risk-stratified approaches to treatment tailored to the individual needs of women with GDM. This will allow preventative and therapeutic interventions to be delivered to those who will maximally benefit, sparing expense, and harm for those at a lower risk.

scholarly journals Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e038845
Author(s):  
Shamil D. Cooray ◽  
Jacqueline A. Boyle ◽  
Georgia Soldatos ◽  
Javier Zamora ◽  
Borja M. Fernández Félix ◽  
...  
Keyword(s):  
Gestational Diabetes ◽  
Prediction Model ◽  
Pregnancy Outcomes ◽  
Absolute Risk ◽  
Clinical Care ◽  
Adverse Pregnancy Outcomes ◽  
Therapeutic Interventions ◽  
Multivariable Logistic Regression Model ◽  
Adverse Pregnancy ◽  
Development And Validation

IntroductionGestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM.Methods and analysisWe undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators.Ethics and disseminationThis study was approved by the Human Research Ethics Committee of Monash Health (RES-19–0000713 L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals.Trial registration detailsSystematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results.

scholarly journals Schizophrenia and offspring's risk for adverse pregnancy outcomes and infant death

2008 ◽  
Vol 193 (4) ◽  
pp. 311-315 ◽  
Author(s):  
Emma Nilsson ◽  
Christina M. Hultman ◽  
Sven Cnattingius ◽  
Petra Otterblad Olausson ◽  
Camilla Björk ◽  
...  
Keyword(s):  
Infant Death ◽  
Pregnancy Outcomes ◽  
Risk Behaviour ◽  
Population Based ◽  
Adverse Pregnancy Outcomes ◽  
Maternal Behaviour ◽  
Swedish Population ◽  
Maternal Risk ◽  
Increased Risk ◽  
Adverse Pregnancy

BackgroundWomen with schizophrenia are at increased risk for adverse pregnancy outcomes. It is not known whether offspring born to fathers with schizophrenia also have an increased risk.AimsTo evaluate paternal and maternal influences on the association between schizophrenia and pregnancy outcomes.MethodA record linkage including 2 million births was made using Swedish population-based registers. The risk for adverse pregnancy outcomes was evaluated through logistic regression.ResultsOffspring with a mother or father with schizophrenia faced a doubled risk of infant mortality, which could not be explained by maternal behaviour alone during pregnancy. Excess infant death risk was largely attributable to post-neonatal death. Maternal factors (e.g. smoking) explained most of the other risks of adverse pregnancy outcomes among both mothers and fathers with schizophrenia.ConclusionsThe risks to offspring whose fathers had schizophrenia suggest that, in addition to maternal risk behaviour, nonoptimal social and/or parenting circumstances are of importance.

Increased risk of adverse pregnancy outcomes for women with migraines: A nationwide population-based study

Cephalalgia ◽  
2009 ◽  
Vol 30 (4) ◽  
pp. 433-438 ◽  
Author(s):  
H-M Chen ◽  
S-F Chen ◽  
Y-H Chen ◽  
H-C Lin
Keyword(s):  
Pregnancy Outcomes ◽  
Low Birthweight ◽  
Preterm Births ◽  
Population Based ◽  
Adverse Pregnancy Outcomes ◽  
Multivariate Logistic Regression ◽  
Population Based Study ◽  
Increased Risk ◽  
Preterm Babies ◽  
Adverse Pregnancy

Using a 3-year nationwide population-based database, this study aims to examine the risk of adverse pregnancy outcomes in women with migraines, including low birthweight (LBW), preterm birth, infants born small for gestational age, Caesarean section (CS) and pre-eclampsia. We identified a total of 4911 women with migraines who gave birth from 2001 to 2003, together with 24 555 matched women as a comparison cohort. Multivariate logistic regression analyses showed that after adjusting for potential confounders, the odds ratios were 1.16 [95% confidence intervals (CI) = 1.03–1.31, P = 0.014] for LBW, 1.24 (95% CI = 1.13–1.39, P < 0.001) for preterm births, 1.16 (95% CI = 1.07–1.24, P < 0.001) for CS and 1.34 (95% CI = 1.02–1.77, P = 0.027) for pre-eclampsia for women with migraines compared with unaffected mothers. We conclude that women with migraines were at increased risk of having LBW, preterm babies, pre-eclampsia and delivery by CS, compared with unaffected mothers.

scholarly journals Burden, risk factors and outcomes associated with gestational diabetes in a population-based cohort of pregnant women from North India

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Stuti Bahl ◽  
Neeta Dhabhai ◽  
Sunita Taneja ◽  
Pratima Mittal ◽  
Rupali Dewan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Trial ◽  
Caesarean Section ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Pregnancy Outcomes ◽  
Population Based ◽  
Adverse Pregnancy Outcomes ◽  
Oral Glucose ◽  
Adverse Pregnancy

Abstract Background The burden of gestational diabetes mellitus (GDM) appears to be increasing in India and may be related to the double burden of malnutrition. The population-based incidence and risk factors of GDM, particularly in lower socio-economic populations, are not known. We conducted analyses on data from a population-based cohort of pregnant women in South Delhi, India, to determine the incidence of GDM, its risk factors and association with adverse pregnancy outcomes (stillbirth, preterm birth, large for gestational age babies) and need for caesarean section. Methods We analyzed data from the intervention group of the Women and Infants Integrated Interventions for Growth Study (WINGS), an individually randomized factorial design trial. An oral glucose tolerance test (OGTT) was performed at the time of confirmation of pregnancy, and for those who had a normal test (≤140 mg), it was repeated at 24–28 and at 34–36 weeks. Logistic regression was performed to ascertain risk factors associated with GDM. Risk ratios (RR) were calculated to find association between GDM and adverse pregnancy outcomes and need for caesarean section. Results 19.2% (95% CI: 17.6 to 20.9) pregnant women who had at least one OGTT were diagnosed to have GDM. Women who had prediabetes at the time of confirmation of pregnancy had a significantly higher risk of developing GDM (RR 2.08, 95%CI 1.45 to 2.97). Other risk factors independently associated with GDM were woman’s age (adjusted OR (AOR) 1.10, 95% CI 1.06 to 1.15) and BMI (AOR 1.04, 95% CI 1.01 to 1.07). Higher maternal height was found to be protective factor for GDM (AOR 0.98, 95% CI 0.96 to 1.00). Women with GDM, received appropriate treatment did not have an increase in adverse outcomes and no increased need for caesarean section Conclusions A substantial proportion of pregnant women from a low to mid socio-economic population in Delhi had GDM, with older age, higher BMI and pre-diabetes as important risk factors. These findings highlight the need for interventions for prevention and provision of appropriate management of GDM in antenatal programmes. Clinical trial registration Clinical Trial Registry – India, #CTRI/2017/06/008908 (http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=19339&EncHid=&userName=society%20for%20applied%20studies).

scholarly journals Burden, Risk Factors and Outcomes Associated with Gestational Diabetes in A Population-Based Cohort of Pregnant Women from North India

2021 ◽  
Author(s):  
Stuti Bahl ◽  
Neeta Dhabhai ◽  
Sunita Taneja ◽  
Pratima Mittal ◽  
Rupali Dewan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Clinical Trial ◽  
Caesarean Section ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Pregnancy Outcomes ◽  
Population Based ◽  
Adverse Pregnancy Outcomes ◽  
Oral Glucose ◽  
Adverse Pregnancy

Abstract Background: The burden of gestational diabetes mellitus (GDM) appears to be increasing in India and may be related to the double burden of malnutrition. The population-based incidence and risk factors of GDM, particularly in lower socio-economic populations, are not known. We conducted analyses on data from a population-based cohort of pregnant women in South Delhi, India, to determine the incidence of GDM, its risk factors and association with adverse pregnancy outcomes (stillbirth, preterm birth, large for gestational age babies) and need for caesarean section. Methods: We analyzed data from the intervention group of the Women and Infants Integrated Interventions for Growth Study (WINGS), an individually randomized factorial design trial. An oral glucose tolerance test (OGTT) was performed at the time of confirmation of pregnancy, and for those who had a normal test (≤140 mg), it was repeated at 24-28 and at 34-36 weeks. Logistic regression was performed to ascertain risk factors associated with GDM. Risk ratios (RR) were calculated to find association between GDM and adverse pregnancy outcomes and need for caesarean section. Results: 19.2% (95% CI: 17.6 to 20.9) pregnant women who had at least one OGTT were diagnosed to have GDM. Women who had prediabetes at the time of confirmation of pregnancy had a significantly higher risk of developing GDM (RR 2.08, 95%CI 1.45 to 2.97). Other risk factors independently associated with GDM were woman’s age (adjusted OR (AOR) 1.10, 95% CI 1.06 to 1.15) and BMI (AOR 1.04, 95% CI 1.01 to 1.07). Higher maternal height was found to be protective factor for GDM (AOR 0.98, 95% CI 0.96 to 1.00). Women with GDM, received appropriate treatment did not have an increase in adverse outcomes. However, GDM increased the need for caesarean section (RR 1.17, 95% CI 1.01 to 1.36).Conclusions: A substantial proportion of pregnant women from a low to mid socio-economic population in Delhi had GDM, with older age, higher BMI and pre-diabetes as important risk factors. These findings highlight the need for interventions for prevention and provision of appropriate management of GDM in antenatal programmes.Clinical Trial registration: Clinical Trial Registry – India, #CTRI/2017/06/008908 (http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=19339&EncHid=&userName=society%20for%20applied%20studies)

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