Anneke Kwee

Background: Improvement in the accuracy of identifying women who are at risk to develop gestational diabetes mellitus (GDM) is warranted, since timely diagnosis and treatment improves the outcomes of this common pregnancy disorder. Although prognostic models for GDM are externally validated and outperform current risk factor based selective approaches, there is little known about the impact of such models in day-to-day obstetric care. Methods: A prognostic model was implemented as a directive clinical prediction rule, classifying women as low- or high-risk for GDM, with subsequent distinctive care pathways including selective midpregnancy testing for GDM in high-risk women in a prospective multicenter birth cohort comprising 1073 pregnant women without pre-existing diabetes and 60 obstetric healthcare professionals included in nine independent midwifery practices and three hospitals in the Netherlands (effectiveness-implementation hybrid type 2 study). Model performance (c-statistic) and implementation outcomes (acceptability, adoption, appropriateness, feasibility, fidelity, penetration, sustainability) were evaluated after 6 months by indicators and implementation instruments (NoMAD; MIDI). Results: The adherence to the prognostic model (c-statistic 0.85 (95%CI 0.81–0.90)) was 95% (n = 1021). Healthcare professionals scored 3.7 (IQR 3.3–4.0) on implementation instruments on a 5-point Likert scale. Important facilitators were knowledge, willingness and confidence to use the model, client cooperation and opportunities for reconfiguration. Identified barriers mostly related to operational and organizational issues. Regardless of risk-status, pregnant women appreciated first-trimester information on GDM risk-status and lifestyle advice to achieve risk reduction, respectively 89% (n = 556) and 90% (n = 564)). Conclusions: The prognostic model was successfully implemented and well received by healthcare professionals and pregnant women. Prognostic models should be recommended for adoption in guidelines.

Objectives: To evaluate whether (1) first‐trimester prognostic models for gestational diabetes mellitus (GDM) outperform the currently used single risk factor approach, and (2) a first‐trimester random venous glucose measurement improves model performance.Design: Prospective population‐based multicentre cohort.Setting: Thirty‐one independent midwifery practices and six hospitals in the Netherlands.Population: Women recruited before 14 weeks of gestation without pre‐existing diabetes.Methods: The single risk factor approach (presence of at least one risk factor: BMI ≥30 kg/m², previous macrosomia, history of GDM, positive first‐degree family history of diabetes, non‐western ethnicity) was compared with the four best performing models in our previously published external validation study (Gabbay‐Benziv 2014, Nanda 2011, Teede 2011, van Leeuwen 2010) with and without the addition of glucose.Main outcome measures: Discrimination was assessed by c‐statistics, calibration by calibration plots, added value of glucose by the likelihood ratio chi‐square test, net benefit by decision curve analysis and reclassification by reclassification plots.Results: Of the 3723 women included, a total of 181 (4.9%) developed GDM. The c‐statistics of the prognostic models were higher, ranging from 0.74 to 0.78 without glucose and from 0.78 to 0.80 with glucose, compared with the single risk factor approach (0.72). Models showed adequate calibration, and yielded a higher net benefit than the single risk factor approach for most threshold probabilities. Teede 2011 performed best in the reclassification analysis.Conclusions: First‐trimester prognostic models seem to outperform the currently used single risk factor approach in screening for GDM, particularly when glucose was added as a predictor.Tweetable Prognostic models seem to outperform the currently used single risk factor approach in screening for gestational diabetes.

The cesarean section (CS) rate has increased over recent decades with poor guideline adherence as a possible cause. The objective of this study was to explore barriers and facilitators for delivering optimal care as described in clinical practice guidelines. Key recommendations from evidence-based guidelines were used as a base to explore barriers and facilitators for delivering optimal CS care in The Netherlands. Both focus group and telephone interviews among 29 different obstetrical professionals were performed. Transcripts from the interviews were analysed. Barriers and facilitators were identified and categorised in six domains according to the framework developed by Grol: the guideline recommendations (I), the professional (II), the patient (III), the social context (IV), the organizational context (V) and the financial/legislation context (VI). Most barriers were found in the professional and organizational domain. Barriers mentioned by healthcare professionals were disagreement with specific guideline recommendations, and hesitation to allow women to be part of the decision making process. Other barriers are lack of adequately trained personal staff, lack of collaboration between professionals, and lack of technical equipment. Clear facilitators and barriers for guideline adherence were identified in all domains. Several barriers may be addressed by using decision aids on mode of birth or prediction models to individualise care in women in whom both planned vaginal birth and CS are equal options. In women with an intended vaginal birth, adequate staffing and the availability of both fetal blood sampling and epidural analgesia are important.

Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality. MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible. Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias. Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis. Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

To improve early risk-identification in pregnancy, research on prediction models for common pregnancy complications is ongoing. Therefore, it was the aim of this study to explore pregnant women’s perceptions, preferences and needs regarding prediction models for first trimester screening for common pregnancy complications, such as preeclampsia, to support future implementation. Ten focus groups (of which five with primiparous and five with multiparous women) were conducted (n = 45). Six focus groups were conducted in urban regions and four in rural regions. All focus group discussions were audio taped and NVIVO was used in order to facilitate the thematic analysis conducted by the researchers. Women in this study had a positive attitude towards first trimester screening for preeclampsia using prediction models. Reassurance when determined as low-risk was a major need for using the test. Self-monitoring, early recognition and intensive monitoring were considered benefits of using prediction models in case of a high-risk. Women acknowledged that high-risk determination could cause (unnecessary) anxiety, but it was expected that personal and professional interventions would level out this anxiety. Women in this study had positive attitudes towards preeclampsia screening. Self-monitoring, together with increased alertness of healthcare professionals, would enable them to take active actions to improve pregnancy outcomes. This attitude enhances the opportunities for prevention, early recognition and treatment of preeclampsia and probably other adverse pregnancy outcomes.

Objective We assessed the influence of external factors on false-positive, false-negative, and invalid fibronectin results in the prediction of spontaneous delivery within 7 days. Methods We studied symptomatic women between 24 and 34 weeks' gestational age. We performed uni- and multivariable logistic regression to estimate the effect of external factors (vaginal soap, digital examination, transvaginal sonography, sexual intercourse, vaginal bleeding) on the risk of false-positive, false-negative, and invalid results, using spontaneous delivery within 7 days as the outcome. Results Out of 708 women, 237 (33%) had a false-positive result; none of the factors showed a significant association. Vaginal bleeding increased the proportion of positive fetal fibronectin (fFN) results, but was significantly associated with a lower risk of false-positive test results (odds ratio [OR], 0.22; 95% confidence intervals [CI], 0.12–0.39). Ten women (1%) had a false-negative result. None of the investigated factors was significantly associated with a significantly higher risk of false-negative results. Twenty-one tests (3%) were invalid; only vaginal bleeding showed a significant association (OR, 4.5; 95% CI, 1.7–12). Conclusion The effect of external factors on the performance of qualitative fFN testing is limited, with vaginal bleeding as the only factor that reduces its validity.

Meta-analyses are typically triggered by a (potentially false-significant) finding in one of the preceding primary studies. We studied consequences of meta-analysis investigating effects when primary studies that triggered such meta-analysis are also included. We analytically determined the bias of the treatment effect estimates obtained by meta-analysis, conditional on the number of included primary and false-significant studies. The type I error rate and power of the meta-analysis were assessed using simulations. We applied a method for bias-correction, by subtracting an analytically derived bias from the treatment effect estimated in meta-analysis. Bias in meta-analytical effects and type I error rates increased when increasing numbers of primary studies with false-significant effects were included. When 20% of the primary studies showed false-significant effects, the bias was 0.33 (z-score) instead of 0, and the type I error rate was 23% instead of 5%. After applying a bias-correction, the type I error rate became indeed 5%. Inclusion of primary studies with false-significant effects leads to biased effect estimates and inflated type I error rates in the meta-analysis, depending on the number of false-significant studies. This bias can be adjusted for.