IntroductionPrediction models may contribute to personalized risk‐based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web‐based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds.ResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51–0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models.ConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.

BackgroundRecently, validated risk models predicting adverse obstetric outcomes combined with risk-dependent care paths have been made available for early antenatal care in the southeastern part of the Netherlands. This study will evaluate implementation progress and impact of the new approach in obstetric care.ObjectiveThe objective of this paper is to describe the design of a study evaluating the impact of implementing risk-dependent care. Validated first-trimester prediction models are embedded in daily clinical practice and combined with risk-dependent obstetric care paths.MethodsA multicenter prospective cohort study consisting of women who receive risk-dependent care is being performed from April 2017 to April 2018 (Expect Study II). Obstetric risk profiles will be calculated using a Web-based tool, the Expect prediction tool. The primary outcomes are the adherence of health care professionals and compliance of women. Secondary outcomes are patient satisfaction and cost-effectiveness. Outcome measures will be established using Web-based questionnaires. The secondary outcomes of the risk-dependent care cohort (Expect II) will be compared with the outcomes of a similar prospective cohort (Expect I). Women of this similar cohort received former care-as-usual and were prospectively included between July 1, 2013 and December 31, 2015 (Expect I).ResultsCurrently, women are being recruited for the Expect Study II, and a total of 300 women are enrolled.ConclusionsThis study will provide information about the implementation and impact of a new approach in obstetric care using prediction models and risk-dependent obstetric care paths.Trial RegistrationNetherlands Trial Register NTR4143; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4143 (Archived by WebCite at http://www.webcitation.org/6t8ijtpd9)

Background: Satisfaction of pregnancy and childbirth is an important quality measure of maternity care. Satisfaction questionnaires generally result in high scores. However, it has been argued that dissatisfaction relies on a different construct. In response to a worldwide call for obstetric care that is more woman-centered, we identified and described the contributors to suboptimal satisfaction with pregnancy and childbirth. Methods: A prospective subcohort of 739 women from a larger cohort (Expect Study I, n ¼ 2614) received a pregnancy and childbirth satisfaction questionnaire. Scores were transformed to a binary outcome whereby a score <100 points corresponded with less satisfied women. We performed a multiple logistic regression analysis to define independent perinatal factors related to suboptimal satisfaction. Results: Decreased perceived personal well-being, antenatal anxiety, and obstetrician-led care during labor were all independently associated with suboptimal pregnancy and childbirth satisfaction. No difference in satisfaction was found between antenatal care led by a midwife or an obstetrician, but midwife-led antenatal care reduced the odds of suboptimal satisfaction compared to women who were transferred to an obstetrician in the antenatal period. Antenatal anxiety was experienced by 25% of all women and is associated with decreased satisfaction scores. Discussion: Screening and treatment of women suffering from anxiety might improve pregnancy and childbirth satisfaction, but further research is necessary. Women's birthing experience may improve by reducing unnecessary secondary obstetric care.

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Background. Despite improved management, preeclampsia remains an important cause of maternal and neonatal mortality and morbidity. Low-dose aspirin (LDA) lowers the risk of preeclampsia. Although several guidelines recommend LDA prophylaxis in women at increased risk, they disagree about the definition of high risk. Recently, an externally validated prediction model for preeclampsia was implemented in a Dutch region combined with risk-based obstetric care paths. Objectives. To demonstrate the selection of a risk threshold and to evaluate the adherence of obstetric health care professionals to the prediction tool. Study Design. Using a survey (n = 136) and structured meetings among health care professionals, possible cutoff values at which LDA should be discussed were proposed. The prediction model, with chosen cutoff and corresponding risk-based care paths, was embedded in an online tool. Subsequently, a prospective multicenter cohort study (n = 850) was performed to analyze the adherence of health care professionals. Patient questionnaires, linked to the individual risk profiles calculated by the online tool, were used to evaluate adherence. Results. Health care professionals agreed upon employing a tool with a high detection rate (cutoff: 3.0%; sensitivity 75%, specificity 64%) followed by shared decision between patients and health care professionals on LDA prophylaxis. Of the 850 enrolled women, 364 women had an increased risk of preeclampsia. LDA was discussed with 273 of these women, resulting in an 81% adherence rate. Conclusion. Consensus regarding a suitable risk cutoff threshold was reached. The adherence to this recommendation was 81%, indicating adequate implementation.