External validation of prognostic models to predict stillbirth using the International Prediction of Pregnancy Complications (IPPIC) Network database: an individual participant data meta-analysis

Objective Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta-analysis to assess their predictive performance. Methods MEDLINE, EMBASE, DH-DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C-statistic, and calibration was assessed using calibration plots, calibration slope and calibration-in-the-large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random-effects meta-analysis. Clinical utility was assessed using net benefit. Results Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one-fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta-analysis, the models had summary C-statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.Peer reviewe

External validation of prognostic models predicting pre-eclampsia : individual participant data meta-analysis

Abstract Background Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. Methods IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. Results Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model’s calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions appeared small and limited to probability thresholds between 5 and 7%. Conclusions The evaluated models had modest predictive performance, with key limitations such as poor calibration (likely due to overfitting in the original development datasets), substantial heterogeneity, and small net benefit across settings. The evidence to support the use of these prediction models for pre-eclampsia in clinical decision-making is limited. Any models that we could not validate should be examined in terms of their predictive performance, net benefit, and heterogeneity across multiple UK settings before consideration for use in practice. Trial registration PROSPERO ID: CRD42015029349

High fasting plasma glucose during early pregnancy: A review about early gestational diabetes mMellitus

Fasting plasma glucose (FPG) is nowadays routinely measured during early pregnancy to detect preexisting diabetes (FPG >= 7 mmol/L). This screening has concomitantly led to identify early intermediate hyperglycemia, defined as FPG in the 5.1 to 6.9 mmol/L range, also early gestational diabetes mellitus (eGDM). Early FPG has been associated with poor pregnancy outcomes, but the recommendation by the IADPSG to refer women with eGDM for immediate management is more pragmatic than evidence based. Although eGDM is characterized by insulin resistance and associated with classical risk factors for type 2 diabetes and incident diabetes after delivery, it is not necessarily associated with preexisting prediabetes. FPG >= 5.1 mmol/L in early pregnancy is actually poorly predictive of gestational diabetes mellitus diagnosed after 24 weeks of gestation. An alternative threshold should be determined but may vary according to ethnicity, gestational age, and body mass index. Finally, observational data suggest that early management of intermediate hyperglycemia may improve prognosis, through reduced gestational weight gain and potential early introduction of hypoglycemic agents. Considering all these issues, we suggest an algorithm for the management of eGDM based on early FPG levels that would be measured in case of risk factors. Nevertheless, interventional randomized trials are still missing

Screening for dysglycaemia during pregnancy: Proposals conciliating International Association of Diabetes and Pregnancy Study Group (IADPSG) and US National Institutes of Health (NIH) panels

The International Association of Diabetes and Pregnancy Study Group (IADPSG) has proposed that blood glucose levels for the diagnosis of gestational diabetes mellitus (GDM) be the values associated with a 1.75-fold increase in the risk of neonatal complications in the Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study. However, this recommendation was not adopted by the US National Institutes of Health (NIH) panel as it would have been responsible for a huge increase in the prevalence of GDM with no clear evidence of a reduction of events at such blood glucose values. Considering this aspect, we now propose the use of a blood glucose threshold combination associated with an odds-ratio of 2.0 for neonatal disorders [fasting plasma glucose (FPG) >= 95 mg/dL, or a 1-h glucose value after a 75-g oral glucose tolerance test (OGTT) >= 191 mg/dL or a 2-h glucose value >= 162 mg/dL] for GDM diagnosis. This would lead to a lower prevalence of GDM and concentrate medical resources on those with the highest risk of complications. This would also allow the use of a similar FPG value for both the diagnosis and therapeutic target of GDM. The IADPSG also proposed screening for dysglycaemia during early pregnancy, using FPG measurement with a similar threshold after 24 weeks of gestation. We propose the same strategy considering an FPG value >= 95 mg/dL as abnormal, but only after confirmatory measurements. We also believe that an OGTT should not be used before 24 weeks of gestation as normal values during that time are as yet unknown

The role of integrins in human embryo implantation

Integrins are adhesion molecules present in endometrial, decidual, and extravillous cytotrophoblast (EVCT) cells. They participate in cell-cell adhesion as well as in adhesion between cells and components of the extracellular matrix, and they play an important role in the endometrial phenotype change that occurs during the secretory phase, the first stage of implantation. At the beginning of pregnancy, the change in integrin expression is synchronized with the trophoblast attachment (embryo-endometrium interactions with integrins v3, 41, 61, and 71) and the embryo's invasion of the decidua (integrins 64511141 switch from proliferative to endovascular EVCT). Several diseases, including preeclampsia, intrauterine growth retardation caused by vascular problems and defective luteal phases, may be explained by anomalies in integrin patterns