In the assessment of performance of a prediction model, calibration is more important than discriminative capacity. Our prediction model shows that for women with gestational hypertension or mild preeclampsia at term, distinction between low and high risk of developing postpartum hemorrhage is possible when antepartum and intrapartum variables are combined.
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.
Objective To develop and internally validate a model that predicts the outcome of an intended vaginal birth after caesarean (VBAC) for a Western European population that can be used to personalise counselling for deliveries at term.Design Registration-based retrospective cohort study.Setting Five university teaching hospitals, seven non-university teaching hospitals, and five non-university non-teaching hospitals in the Netherlands.Population A cohort of 515 women with a history of one caesarean section and a viable singleton pregnancy, without a contraindication for intended VBAC, who delivered at term.Methods Potential predictors for a vaginal delivery after caesarean section were chosen based on literature and expert opinions. We internally validated the prediction model using bootstrapping techniques.Main outcome measures Predictors for VBAC. For model validation, the area under the receiver operating characteristic curve (AUC) for discriminative capacity and calibrationper-risk-quantile for accuracy were calculated.Results A total of 371 out of 515 women had a VBAC (72%). Variables included in the model were: estimated fetal weight greater than the 90 th percentile in the third trimester; previous non-progressive labour; previous vaginal delivery; induction of labour; pre-pregnancy body mass index; and ethnicity. The AUC was 71% (95% confidence interval, 95% CI = 69-73%), indicating a good discriminative ability. The calibration plot shows that the predicted probabilities are well calibrated, especially from 65% up, which accounts for 77% of the total study population. ConclusionWe developed an appropriate Western European population-based prediction model that is aimed to personalise counselling for term deliveries.
Introduction: This study assessed the external validity of all published first trimester prediction models for the risk of preeclampsia (PE) based on routinely collected maternal predictors. Moreover, the potential utility of the best-performing models in clinical practice was evaluated. Material and Methods: Ten prediction models were systematically selected from the literature. We performed a multicenter prospective cohort study in the Netherlands between July 1, 2013, and December 31, 2015. Eligible pregnant women completed a web-based questionnaire before 16 weeks’ gestation. The outcome PE was established using postpartum questionnaires and medical records. Predictive performance of each model was assessed by means of discrimination (c-statistic) and a calibration plot. Clinical usefulness was evaluated by means of decision curve analysis and by calculating the potential impact at different risk thresholds. Results: The validation cohort contained 2,614 women of whom 76 developed PE (2.9%). Five models showed moderate discriminative performance with c-statistics ranging from 0.73 to 0.77. Adequate calibration was obtained after refitting. The best models were clinically useful over a small range of predicted probabilities. Discussion: Five of the ten included first trimester prediction models for PE showed moderate predictive performance. The best models may provide more benefit compared to risk selection as used in current guidelines.
Objective To externally validate two models from the USA (entry-to-care [ETC] and close-to-delivery [CTD]) that predict successful intended vaginal birth after caesarean (VBAC) for the Dutch population.Design A nationwide registration-based cohort study.Setting Seventeen hospitals in the Netherlands.Population Seven hundred and sixty-three pregnant women, each with one previous caesarean section and a viable singleton cephalic pregnancy without a contraindication for an intended VBAC. MethodsThe ETC model comprises the variables maternal age, prepregnancy body mass index (BMI), ethnicity, previous vaginal delivery, previous VBAC and previous nonprogressive labour. The CTD model replaces prepregnancy BMI with third-trimester BMI and adds estimated gestational age at delivery, hypertensive disease of pregnancy, cervical examination and induction of labour. We included consecutive medical records of eligible women who delivered in 2010. For validation, individual probabilities of women who had an intended VBAC were calculated.Main outcome measures Discriminative performance was assessed with the area under the curve (AUC) of the receiver operating characteristic and predictive performance was assessed with calibration plots and the Hosmer-Lemeshow (H-L) statistic.Results Five hundred and fifteen (67%) of the 763 women had an intended VBAC; 72% of these (371) had an actual VBAC. The AUCs of the ETC and CTD models were 68% (95% CI 63-72%) and 72% (95% CI 67-76%), respectively. The H-L statistic showed a P-value of 0.167 for the ETC model and P = 0.356 for the CTD model, indicating no lack of fit.Conclusion External validation of two predictive models developed in the USA revealed an adequate performance within the Dutch population.Keywords External validation, prediction, vaginal birth after caesarean.Please cite this paper as: Schoorel ENC, Melman S, van Kuijk SMJ, Grobman WA, Kwee A, Mol BWJ, Nijhuis JG, Smits LJM, Aardenburg R, de Boer K, Delemarre FMC, van Dooren IM, Franssen MTM, Kleiverda G, Kaplan M, Kuppens SMI, Lim FTH, Sikkema JM, Smid-Koopman E, Visser H, Vrouenraets FPJM, Woiski M, Hermens RPMG, Scheepers HCJ. Predicting successful intended vaginal delivery after previous caesarean section: external validation of two predictive models in a Dutch nationwide registration-based cohort with a high intended vaginal delivery rate. BJOG 2014;121:840-847.
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