Risk assessment and management to prevent preterm birth

Koullali, Bouchra; Oudijk, Martijn A.; Nijman, Tobias A.J.; Mol, Ben Willem J.; Pajkrt, Eva

doi:10.1016/j.siny.2016.01.005

Cited by 151 publications

(146 citation statements)

References 74 publications

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“…Another important well‐known risk factor is cervical surgery 10, 33. However, only a minority of women will be identified as high risk by adding this predictor 11.…”

Section: Discussionmentioning

confidence: 99%

“…However, only a minority of women will be identified as high risk by adding this predictor 11. Other routine clinical parameters that may contribute to the prediction of sPTB in nulliparous women are: socioeconomic status, psychological characteristics, family history, medical history, and smoking status 10. Predictive performance of a model might be improved by taking into account biomarkers or ultrasound imaging (for example, cervical length).…”

Section: Discussionmentioning

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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“…Another important well‐known risk factor is cervical surgery 10, 33. However, only a minority of women will be identified as high risk by adding this predictor 11.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…By contrast, fewer studies consider risks factors relating to LBW in full-term births, and still fewer studies (or reviews) concentrate on a diverse range of subject populations [1,6,7]. These factors make it imperative for such data to be collated.…”

Section: Discussionmentioning

confidence: 99%

“…LBW occurs in preterm babies (less than 37 completed weeks of gestation), infants with intrauterine growth restriction (IUGR) and full-term infants (greater or equal to 37-41 completed weeks of gestation) [4,5]. Although many sources in the literature consider risk factors specifically for preterm and/or IUGR on LBW infants [1,6,7], few studies consider those factors that associate with LBW in full-term births or "term LBW". Term LBW is the focus of the present study [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Maternal Demographic and Placental Risk Factors in Term Low Birth Weight in Ghana

Aleksenko

Tettey

Richard³

et al. 2017

J Preg Child Health

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“…In addition, some neonatal clinical conditions including premature birth and low birth weight are directly associated with the occurrence of high-risk pregnancies where neonatal mortality is the main consequence (1)(2)(3) . Gestational age (GA) and newborn (NB) weight at birth are described in the literature as essential for evaluating the pattern of intrauterine growth and development, to predict the difficulties of extrauterine adaptation, diseases in the neonatal period and to estimate the risk of (neonatal) death (3)(4) .…”

Section: Introductionmentioning

confidence: 99%

Foot length measurements of newborns of high and low risk pregnancies

Salge

Rocha

Gaíva

et al. 2017

Rev. esc. enferm. USP

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Objective:Comparing foot length measurements of newborns in high and low risk pregnancies at a public hospital in Goiânia, GO, Brazil. Method: A cross-sectional study carried out between April, 2013 and May, 2015, with a sample consisting of 180 newborns; 106 infants of women from high-risk pregnancies and 74 of women from low-risk pregnancies. Data were descriptively analyzed. Foot length measurement was performed using a stiff transparent plastic ruler, graduated in millimeters. The length of both feet was measured from the tip of the hallux (big toe) to the end of the heel. Results: A statistically significant relationship was found between the foot length and newborn's weight, between the cephalic and thoracic perimeters in the high-risk group and between the cephalic perimeter in the control group. Conclusion: There is a need for creating cut-off points to identify newborns with intrauterine growth disorders using foot length.

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Risk assessment and management to prevent preterm birth

Cited by 151 publications

References 74 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

Maternal Demographic and Placental Risk Factors in Term Low Birth Weight in Ghana

Foot length measurements of newborns of high and low risk pregnancies

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