Prediction of spontaneous preterm delivery in the first trimester of pregnancy

Sananès, Nicolas; Meyer, Nicolás; Gaudineau, A.; Aïssi, Germain; Boudier, E.; Fritz, G.; Viville, B.; Nisand, I.; Langer, Bruno; Favre, R.

doi:10.1016/j.ejogrb.2013.07.042

Cited by 29 publications

(60 citation statements)

References 14 publications

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“…Besides the difference in baseline risk, Sananes et al. 26 fitted well with our population (slope = 1). Recalibration showed closer fitting to the ideal calibration line (Supporting Information Appendix S6).…”

Section: Resultssupporting

confidence: 77%

“…The model of Sananes et al. 26 had a slightly higher discrimination compared with the original cohort. All models performed better for the outcome sPTB <34 weeks of gestation.…”

Section: Resultsmentioning

confidence: 71%

“…Almost all population characteristics of Sananes et al. 26 differed considerably compared with the validation cohort. Women in the cohort of Alleman et al.…”

Section: Resultsmentioning

confidence: 94%

“…27 (5.7%) and lower in the overall population of Sananes et al. 26 (3.7%) compared with the validation cohort (4.6%). The outcome sPTB <34 weeks of gestation was comparable with our prevalence.…”

Section: Resultsmentioning

confidence: 95%

“…26 externally validated their model, but results of this validation are not reported. Alleman et al.…”

Section: Resultsmentioning

confidence: 99%

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Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Meertens

Montfort

Scheepers

et al. 2018

Acta Obstet Gynecol Scand

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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.

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Section: Resultssupporting

confidence: 77%

“…The model of Sananes et al. 26 had a slightly higher discrimination compared with the original cohort. All models performed better for the outcome sPTB <34 weeks of gestation.…”

Section: Resultsmentioning

confidence: 71%

“…Almost all population characteristics of Sananes et al. 26 differed considerably compared with the validation cohort. Women in the cohort of Alleman et al.…”

Section: Resultsmentioning

confidence: 94%

Section: Resultsmentioning

confidence: 95%

“…26 externally validated their model, but results of this validation are not reported. Alleman et al.…”

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Meertens

Montfort

Scheepers

et al. 2018

Acta Obstet Gynecol Scand

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Risk factors for spontaneous preterm birth among healthy nulliparous pregnant women in the Netherlands, a prospective cohort study

Schuster

Peelen

Hajenius

et al. 2022

Health Science Reports

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First trimester screening can predict adverse pregnancy outcomes

Sharp

Alfirević

2014

Prenatal Diagnosis

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Methods: We retrospectively reviewed and performed 4D-HDFI in 204 normal and 33 fetuses with confirmed diagnosis of cardiac anomalies of the great arteries. Cardiac volumes were available for post-analysis to obtain 4D rendered images of the great arteries. For the normal fetuses, two other traditional modalities including colour Doppler and HDFI were used to detect aortic arch and its branches and comparisons were made between each of these traditional methods and 4D-HDFI. Results: Two cases of interrupted aortic arch (IAA) type A, 3 cases of IAA type B, 5 cases of tetralogy of Fallot, 3 cases of double outlet right ventricle, 2 cases of truncus arteriosus, 2 cases of transposition of the great arteries, 2 cases of double aortic arch, 11 cases of right aortic arch with aberrant left subclavian arteries, 1 case of right aortic arch with mirror-image branching, and 2 cases of aberrant right subclavian artery were included in the current study. The 4D rendered images vividly depicted the origination, course, and the spatial relationship of the great arteries. For the normal fetuses, HDFI modality was superior to colour Doppler in detecting more brachiocephalic arteries throughout the gestational period. 4D-HDFI was the best method during the second trimester of pregnancy in identifying the greatest number of brachiocephalic arteries. Conclusions: HDFI and the advanced 4D-HDFI technique could facilitate identification of the anatomical features of the great arteries in both normal and abnormal fetuses, 4D-HDFI therefore provides additional and more precise information than conventional sonography techniques. Objectives: Prediction of pre-eclampsia (PE) before 34 and 37 weeks' at 11-14 weeks' in nulliparous women. Methods: All nulliparous women attending routine first-trimester ultrasound examination who delivered in our department between 2009 and 2016 were offered the 2016' FMF prediction model for PE before 34 and 37 weeks'. Risk assessment for PE included maternal factors, pregnancy-associated plasma protein-A (PAPP-A), mean maternal arterial blood pressure (MAP) and uterine artery Doppler (UtAPI). Risk was calculated for all nulliparous women optimally with all variables: PAPP-A + MAP + Ut IP, or pragmatically using only available parameters. Models performance was evaluated using receiver-operating characteristics (ROC) curve analysis in both groups. Results: 6,260 nulliparous women were included, 108 (1.7%) developed PE, including13 (0.2 %) and 41 (0.6%) before 34 and 37 weeks' respectively. Prediction models for PE before 34 and 37 weeks were optimal and suboptimal for 2080 (33.2%) and 4180 (66.8%) respectively. Prevalences were similar in both groups, 0.19% & 0.67% and 0.21% & 0.64%, respectively. The optimal model showed better AUC (95% CI) at predicting PE<34 and 37 weeks': 0.84 (0.66-1) V. 0.76 (0.50-1) and 0.91 (0.84-0.98) V. 0.81 (0.70-0.92), respectively. For a 10% false positive rate, the detection rate (95% CI) when using the optimal model was 20% higher than when using a suboptimal model <37 weeks'...

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Prediction of spontaneous preterm delivery in the first trimester of pregnancy

Cited by 29 publications

References 14 publications

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Risk factors for spontaneous preterm birth among healthy nulliparous pregnant women in the Netherlands, a prospective cohort study

First trimester screening can predict adverse pregnancy outcomes

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