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

Abstract

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.

Incidence, risk factors and consequences of preterm birth – findings from a multicentric observational study in Nepal

“…Further, mothers with a history of smoking had higher risk for preterm births. Other studies have also shown similar associations for preterm births [4,[27][28][29]. Also, the risks were higher for mothers who did not use clean fuel.…”

A prediction model of surgical site infection after instrumented thoracolumbar spine surgery in adults

“…The model has an AUC of 0.72 (95% CI 0.65-0.79). This is considered to be moderate and is comparable to prediction models from other clinical disciplines with an AUC range from 0.54 to 0.73 [22,23]. Bear in mind that the model is used for prediction of future events, compared to diagnostic models that estimate the probability of the presence or absence of an outcome in the present time.…”

Ultrasound Assessment of Cervical Length in the First Trimester of Pregnancy to Predict Preterm Birth

“…To further improve the forecasting model, using the latest knowledge of the development model and potential risk factors, it is necessary to contribute to the personal risk assessment of spontaneous preterm delivery. (15)…”