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Full length article| Volume 266, P1-6, November 2021

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Use of artificial intelligence to predict mean time to delivery following cervical ripening with dinoprostone vaginal insert

      Abstract

      Objective

      To validate a mathematical model to predict the mean time to delivery (TTD) following cervical ripening with dinoprostone vaginal insert (DVI), and assess its impact on the risk of nocturnal deliveries.

      Methods

      We performed a case-control retro-prospective study at Angers University Hospital. In the control group, we retrospectively included 405 patients who underwent cervical ripening with DVI between 01/2015 and 09/2016. Based on the delivery outcomes, we developed a mathematical model that integrates all the factors influencing TTD following cervical ripening with DVI. In the study group, we prospectively included 223 patients who underwent cervical ripening with DVI between 11/2017 and 11/2018. The timing of insertion was calculated using the mathematical model developed in the control group, in order to prevent the occurrence of nocturnal deliveries.

      Results

      The calculated mean TTD was significantly shorter than the real mean TTD (21h46 min ± 3h28 min versus 25h38 min ± 12h10 min, p < 0.001), and for 44% of patients, there was at least 10 h difference between the two. The real TTD (25h38 min ± 12H10 min versus 20h39 min ± 10h49, p < 0.001), and the rate of nocturnal deliveries (30.5% versus 21.2%, p = 0.01) were significantly higher in the study group compared to the control group.

      Conclusion

      The mathematical model did not help predicting TTD following cervical ripening with DVI, and or reducing the number of nocturnal deliveries.

      Keywords

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      References

        • Blondel B.
        • Lelong N.
        • Kermarrec M.
        • Goffinet F.
        Results from the French National Perinatal Surveys.
        J Gynecol Obstet Biol Reprod (Paris). 2012; 41: e1-e15https://doi.org/10.1016/j.jgyn.2012.04.014
      1. European Perinatal Health Report 2010 - Euro-Peristat n.d. https://www.europeristat.com/reports/european-perinatal-health-report-2010.html (accessed August 1, 2020).

        • Blanc-Petitjean P.
        • Salomé M.
        • Dupont C.
        • Crenn-Hebert C.
        • Gaudineau A.
        • Perrotte F.
        • et al.
        Overview of induction of labor practices in France.
        Gynecol Obstet Fertil Senol. 2019; 47: 555-561https://doi.org/10.1016/j.gofs.2019.05.002
        • Goffinet F.
        • Dreyfus M.
        • Carbonne B.
        • Magnin G.
        • Cabrol D.
        Survey of the practice of cervical ripening and labor induction in France.
        J Gynecol Obstet Biol Reprod (Paris). 2003; 32: 638-646
      2. Goffinet F, Humbert R, Clerson P, Philippe HJ, Bréart G, Cabrol D. National survey on the use of induced labor by obstetricians. Study Group on Induced Labor. J Gynecol Obstet Biol Reprod (Paris) 1999;28:319–29.

        • Bel S.
        • Gaudineau A.
        • Zorgnotti L.
        • Sananes N.
        • Fritz G.
        • Langer B.
        Survey on cervical ripening practices in France.
        Gynecol Obstet Fertil. 2014; 42: 301-305https://doi.org/10.1016/j.gyobfe.2013.11.002
      3. 2005 - Collège National des Gynécologues et Obstétriciens.pdf n.d.

        • Wing D.A.
        • Brown R.
        • Plante L.A.
        • Miller H.
        • Rugarn O.
        • Powers B.L.
        Misoprostol vaginal insert and time to vaginal delivery: a randomized controlled trial.
        Obstet Gynecol. 2013; 122: 201-209https://doi.org/10.1097/AOG.0b013e31829a2dd6
        • Namaky D.D.
        • Franzese J.M.
        • Eschenbacher M.A.
        Timing of induction of labor and association with nighttime delivery: a retrospective cohort.
        J Perinatol. 2015; 35: 1011-1014https://doi.org/10.1038/jp.2015.135
        • Shetty A.
        • Burt R.
        • Rice P.
        • Templeton A.
        Women’s perceptions, expectations and satisfaction with induced labour–a questionnaire-based study.
        Eur J Obstet Gynecol Reprod Biol. 2005; 123: 56-61https://doi.org/10.1016/j.ejogrb.2005.03.004
        • Wu Y.W.
        • Pham T.N.
        • Danielsen B.
        • Towner D.
        • Smith L.
        • Johnston S.C.
        Nighttime delivery and risk of neonatal encephalopathy.
        Am J Obstet Gynecol. 2011; 204: 37.e1-37.e6https://doi.org/10.1016/j.ajog.2010.09.022
        • Moaddab A.
        • Davidson C.M.
        • Sangi-Haghpeykar H.
        • Dildy G.A.
        • Belfort M.A.
        • Clark S.L.
        59: Association between day and month of delivery and maternal-fetal mortality: weekend effect and july phenomenon in current obstetric practice.
        Am J Obstet Gynecol. 2017; 216: S42https://doi.org/10.1016/j.ajog.2016.11.944
        • Levast F.
        • Legendre G.
        • Hachem H.E.
        • Saulnier P.
        • Descamps P.
        • Gillard P.
        • et al.
        A mathematical model to predict mean time to delivery following cervical ripening with dinoprostone vaginal insert.
        Sci Rep. 2019; 9https://doi.org/10.1038/s41598-019-46101-2
      4. de Graaf JP, Ravelli ACJ, Visser GHA, Hukkelhoven C, Tong WH, Bonsel GJ, et al. Increased adverse perinatal outcome of hospital delivery at night. BJOG 2010;117:1098–107. https://doi.org/10.1111/j.1471-0528.2010.02611.x.

        • Bakker J.J.
        • van der Goes B.Y.
        • Pel M.
        • Mol B.W.J.
        • van der Post J.A.
        Morning versus evening induction of labour for improving outcomes.
        Cochrane Database Systematic Rev, John Wiley & Sons, Ltd. 2013; https://doi.org/10.1002/14651858.CD007707.pub2
        • Winkler J.K.
        • Sies K.
        • Fink C.
        • Toberer F.
        • Enk A.
        • Deinlein T.
        • et al.
        Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.
        Eur J Cancer. 2020; 127: 21-29https://doi.org/10.1016/j.ejca.2019.11.020
        • Yala A.
        • Lehman C.
        • Schuster T.
        • Portnoi T.
        • Barzilay R.
        A deep learning mammography-based model for improved breast cancer risk prediction.
        Radiology. 2019; 292: 60-66https://doi.org/10.1148/radiol.2019182716
        • McKinney S.M.
        • Sieniek M.
        • Godbole V.
        • Godwin J.
        • Antropova N.
        • Ashrafian H.
        • et al.
        International evaluation of an AI system for breast cancer screening.
        Nature. 2020; 577: 89-94https://doi.org/10.1038/s41586-019-1799-6
      5. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation | Breast Cancer Research | Full Text n.d. https://breast-cancer-research.biomedcentral.com/articles/10.1186/s13058-017-0852-3 (accessed May 24, 2020).

        • McVeigh T.P.
        • Kerin M.J.
        Clinical use of the Oncotype DX genomic test to guide treatment decisions for patients with invasive breast cancer.
        Breast Cancer (Dove Med Press). 2017; 9: 393-400https://doi.org/10.2147/BCTT.S109847
        • Zaninovic N.
        • Irani M.
        • Meseguer M.
        Assessment of embryo morphology and developmental dynamics by time-lapse microscopy: is there a relation to implantation and ploidy?.
        Fertil Steril. 2017; 108: 722-729https://doi.org/10.1016/j.fertnstert.2017.10.002
        • Hadlock F.P.
        • Harrist R.B.
        • Sharman R.S.
        • Deter R.L.
        • Park S.K.
        Estimation of fetal weight with the use of head, body, and femur measurements–a prospective study.
        Am J Obstet Gynecol. 1985; 151: 333-337https://doi.org/10.1016/0002-9378(85)90298-4
        • Schuchter K.
        • Hafner E.
        • Stangl G.
        • Metzenbauer M.
        • Höfinger D.
        • Philipp K.
        The first trimester “combined test” for the detection of Down syndrome pregnancies in 4939 unselected pregnancies.
        Prenat Diagn. 2002; 22: 211-215https://doi.org/10.1002/pd.288
      6. Hon EH. The electronic evaluation of the fetal heart rate. Preliminary report. 1958. Am J Obstet Gynecol 1996;175:747–8. https://doi.org/10.1053/ob.1996.v175.aob17503a00.

        • Vandenbroucke L.
        • Doyen M.
        • Le Lous M.
        • Beuchée A.
        • Loget P.
        • Carrault G.
        • et al.
        Chorioamnionitis following preterm premature rupture of membranes and fetal heart rate variability.
        PLoS ONE. 2017; 12: e0184924https://doi.org/10.1371/journal.pone.0184924
        • Beksac M.S.
        • Tanacan A.
        • Bacak H.O.
        • Leblebicioglu K.
        Computerized prediction system for the route of delivery (vaginal birth versus cesarean section).
        J Perinat Med. 2018; 46: 881-884https://doi.org/10.1515/jpm-2018-0022
        • Alberola-Rubio J.
        • Garcia-Casado J.
        • Prats-Boluda G.
        • Ye-Lin Y.
        • Desantes D.
        • Valero J.
        • et al.
        Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography?.
        Comput Methods Programs Biomed. 2017; 144: 127-133https://doi.org/10.1016/j.cmpb.2017.03.018
        • Friedman E.A.
        Primigravid labor; a graphicostatistical analysis.
        Obstet Gynecol. 1955; 6: 567-589https://doi.org/10.1097/00006250-195512000-00001
        • Gauthier T.
        • Mazeau S.
        • Dalmay F.
        • Eyraud J.-L.
        • Catalan C.
        • Marin B.
        • et al.
        Obesity and cervical ripening failure risk.
        J Matern Fetal Neonatal Med. 2012; 25: 304-307https://doi.org/10.3109/14767058.2011.575485
      7. Ducarme G, Chesnoy V, Petit L. Facteurs prédictifs d’échec d’entrée en travail par dinoprostone en cas de grossesse prolongée et de conditions locales défavorables. /data/revues/03682315/v44i1/S0368231513003335/ 2014.

        • Bostancı E.
        • Eser A.
        • Yayla Abide C.
        • Kılıccı C.
        • Kucukbas M.
        Early amniotomy after dinoprostone insert used for the induction of labor: a randomized clinical trial.
        J Matern Fetal Neonatal Med. 2018; 31: 352-356https://doi.org/10.1080/14767058.2017.1285893
        • Macones G.A.
        • Cahill A.
        • Stamilio D.M.
        • Odibo A.O.
        The efficacy of early amniotomy in nulliparous labor induction: a randomized controlled trial.
        Am J Obstet Gynecol. 2012; 207: e1-e5https://doi.org/10.1016/j.ajog.2012.08.032
        • Thorsell M.
        • Lyrenäs S.
        • Andolf E.
        • Kaijser M.
        Starting time for induction of labor and the risk for night-time delivery.
        Sexual Reprod Healthcare. 2011; 2: 113-117https://doi.org/10.1016/j.srhc.2011.05.001
        • Miller H.
        • Goetzl L.
        • Wing D.A.
        • Powers B.
        • Rugarn O.
        Optimising daytime deliveries when inducing labour using prostaglandin vaginal inserts.
        J Matern Fetal Neonatal Med. 2016; 29: 517-522https://doi.org/10.3109/14767058.2015.1011117
      8. Agrawal S, Barrington L, Bromberg C, Burge J, Gazen C, Hickey J. Machine Learning for Precipitation Nowcasting from Radar Images. ArXiv:191212132 [Cs, Stat] 2019.

      9. Accouchement normal : accompagnement de la physiologie et interventions médicales. Haute Autorité de Santé n.d. https://www.has-sante.fr/jcms/c_2820336/fr/accouchement-normal-accompagnement-de-la-physiologie-et-interventions-medicales (accessed August 6, 2020).

        • Dupont C.
        • Carayol M.
        • Le Ray C.
        • Barasinski C.
        • Beranger R.
        • Burguet A.
        • et al.
        Recommandations pour l’administration d’oxytocine au cours du travail spontané. Texte court des recommandations.
        La Revue Sage-Femme. 2017; 16: 111-118https://doi.org/10.1016/j.sagf.2016.11.006