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Clinical validation of a model predicting the risk of preterm delivery

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Listed:
  • Yohann Dabi
  • Sophie Nedellec
  • Claire Bonneau
  • Blandine Trouchard
  • Roman Rouzier
  • Alexandra Benachi

Abstract

Objectives: To validate a model predicting the risk of threatened preterm delivery and to establish the optimal threshold for this risk scoring system. Materials and methods: Two cohorts were studied: one of singleton pregnancies without preterm premature rupture of membranes (PPROM) and no cervical cerclage (cohort 1) and one of twin pregnancies without PPROM and no cervical cerclage (cohort 2). Patients were included from January 1st 2013 until December 31st 2013 by the Regional Perinatal Network of Ile de France with patients transferred because of threatened preterm delivery at 22 to 32 weeks of gestation. The individual probability of delivery within 48 hours of admission was calculated using the nomogram for every patient. Discrimination and calibration of the nomogram as well as the optimal threshold were determined using R studio. Results: The nomogram accurately predicted obstetric outcome. Discrimination and calibration were excellent, with an area under the curve (AUC) of 0.88 (95% CI 0.86–0.90) for cohort 1 and 0.73 (95% CI 0.66–0.80) for cohort 2. The optimal threshold would be 15% for cohort 1 and 10% for cohort 2. Using these thresholds, the performance characteristics of the nomogram were: sensitivity 80% (cohort 1) and 69% (cohort 2), negative predictive value 94.8% (cohort 1) and 91.3% (cohort 2). Use of the nomogram would avoid 253 unnecessary transfers in cohort 1. Conclusions: The nomogram was efficient and clinically relevant in our high risk population. A threshold set at 15% would help minimize the risk of preterm deliveries in singleton pregnancies and should reduce unnecessary, costly and stressful in utero transfer.

Suggested Citation

  • Yohann Dabi & Sophie Nedellec & Claire Bonneau & Blandine Trouchard & Roman Rouzier & Alexandra Benachi, 2017. "Clinical validation of a model predicting the risk of preterm delivery," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-11, February.
  • Handle: RePEc:plo:pone00:0171801
    DOI: 10.1371/journal.pone.0171801
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