Technology for predicting abnormal labor

Abstract

BACKGROUND: Abnormal labor is the most common complication of labor. It occurs in 10-15% cases of labor and is an indication for operative delivery in a third of all cases. Until now, there are no effective ways to predict abnormal labor. Meanwhile, the use of high-tech forecasting methods is not available for a wide range of obstetric institutions in the Russian Federation.

AIM: The aim of this study was to create a technology for predicting abnormal labor, based on generally available methods of laboratory and instrumental research.

MATERIALS AND METHODS: Based on the data collected in the Regional Clinical Hospital Perinatal Center, Chita, Russia in 2018-2021, the retrospective analysis of 200 cases of labor was carried out. The total sample was divided into four study groups: 100 women with normal labor activity (group 1), 30 women with uterine inertia (group 2), 30 women with incoordinate uterine activity (group 3), and 50 women with excessive uterine activity (group 4). The groups were comparable in terms of age, anthropometric parameters and extragenital pathology. All women on the eve of labor (1-2 days) underwent general clinical and ultrasound examination. Statistical processing of the results was carried out using the IBM SPSS Statistics version 25.0 software.

RESULTS: The technology for predicting abnormal labor is implemented based on a multilayer perceptron, with the percentage of incorrect predictions being 21.3%. The structure of the trained neural network included nine input neurons: labor parity, gestational age, leukocyte count, erythrocyte sedimentation rate, total protein concentration, amniotic fluid index, biparietal size, as well as fetal head and abdomen circumference.

CONCLUSIONS: An integrated approach based on generally available laboratory and instrumental research methods, such as complete blood count and biochemical blood test, as well as ultrasound examination, on the eve of labor allows for predicting the abnormal labor development with an accuracy of up to 70%. The use of this technology in clinical practice will help, in the future, not only to prevent abnormal labor, but also to reduce the incidence of adverse obstetric and perinatal outcomes.

Keywords

abnormal labor / incoordinate uterine activity / uterine inertia / excessive uterine activity

References

[1] Akusherstvo: uchebnik. Ed. Radzinsky VE, Fuks AM. Moscow: GEOTAR-Media; 2016. (In Russ.)

[2] Акушерство: учебник / ред. В.Е. Радзинский, А.М. Фукс. Москва: ГЭОТАР-Медиа, 2016.

[3] Sandstrom A, Altman M, Cnattingius S, et al. Durations of second stage of labor and pushing, and adverse neonatal outcomes: a population-based cohort study. J Perinatol. 2017;37(3):236−242. DOI: 10.1038/jp.2016.214

[4] Sandstrom A., Altman M., Cnattingius S. et al. Durations of second stage of labor and pushing, and adverse neonatal outcomes: a population-based cohort study // J. Perinatol. 2017. Vol. 37. No. 3. P. 236−242. DOI: 10.1038/jp.2016.214

[5] Pestrikova T, Yurasova E, Yurasov I. Krovotecheniya vo vremya beremennosti i rodov. Moscow: Palmarium Academic Publishing; 2012. (In Russ.)

[6] Пестрикова Т., Юрасова Е., Юрасов И. Кровотечения во время беременности и родов. Mосква: Palmarium Academic Publishing, 2012.

[7] Baskett TF, Kalder JeA, Sabaratnam A. Operativnoe akusherstvo Manro Kerra. Moscow: Rid Jelsiver; 2015. (In Russ.)

[8] Баскетт Т.Ф., Калдер Э.А., Сабаратнам А. Оперативное акушерство Манро Керра. Москва: Рид Элсивер, 2015.

[9] Chernukha EA. Rodovoi blok: rukovodstvo dlya vrachei. Moscow: Triada-X; 2005.

[10] Чернуха Е.А. Родовой блок: руководство для врачей. Москва: Триада-Х, 2005.

[11] Abramchenko VV. Rodovaja dejatel’nost’ i ee reguljacija. Saint Petersburg: ALBI-SPb; 2006. (In Russ.)

[12] Абрамченко В.В. Родовая деятельность и ее регуляция. Санкт-Петербург: ЭЛБИ-СПб, 2006.

[13] Zefirova TP, Zhelezova ME, Yagovkina NE. Anomalii rodovoy deyatel’nosti: mekhanizmy formirovaniya i faktory riska. Prakticheskaya meditsina. 2010;43(4):44−48. (In Russ.)

[14] Зефирова Т.П., Железова М.Е., Яговкина Н.Е. Аномалии родовой деятельности: механизмы формирования и факторы риска // Практическая медицина. 2010. T. 43. № 4. С. 44−48.

[15] Okazanie meditsinskoy pomoshchi pri odnoplodnykh rodakh v zatylochnom predlezhanii (bez oslozhneniy) i v poslerodovom periode. Klinicheskie rekomendatsii (protokol lecheniya) No. 15-4/10/2-3185, utverzhdennye Ministerstvom zdravookhraneniya Rossiyskoy Federatsii 06.05.2014. (In Russ.). [cited 6 Sept 2021]. Available from: http://base.garant.ru/36976959/

[16] Оказание медицинской помощи при одноплодных родах в затылочном предлежании (без осложнений) и в послеродовом периоде. Клинические рекомендации (протокол лечения) № 15-4/10/2-3185, утвержденные Министерством здравоохранения Российской Федерации 6 мая 2014 г. [дата обращения 06.09.2021]. Доступ по ссылке: http://base.garant.ru/36976959/

[17] Merc Je. Ul’trazvukovaja diagnostika v akusherstve i ginekologii. Vol. 1: Akusherstvo. Moscow: MEDpress-inform; 2011. (In Russ.)

[18] Мерц Э. Ультразвуковая диагностика в акушерстве и гинекологии. Т. 1: Акушерство. Москва: МЕДпресс-информ, 2011.

[19] International Committee of Medical Journal Editors. Uniform requirements for manuscripts submitted to biomedical journals: writing and editing for biomedical publication, 2011. [cited 3 Jun 2021]. Available from: https://www.icjme.org

[20] International Committee of Medical Journal Editors. Uniform requirements for manuscripts submitted to biomedical journals: writing and editing for biomedical publication, 2011. [дата обращения 03.06.2021]. Доступ по ссылке: https://www.icjme.org

[21] Lang TA, Altman DG. Statistical analyses and methods in the published literature: The SAMPL guidelines. Medical Writing. 2016;25(3):31−36. DOI: 10.18243/eon/2016.9.7.4

[22] Lang T.A., Altman D.G. Statistical analyses and methods in the published literature: The SAMPL guidelines // Medical Writing. 2016. Vol. 25. No. 3. P. 31−36. DOI: 10.18243/eon/2016.9.7.4

[23] Mc Guane JT, Grlj L, Peek MJ. Obesity, gestational diabetes and macrosomia are associated with increasing rates of early-term induction of labour at The Canberra Hospital. Aust NZJ Obstet Gynaecol. 2019;59(2):215−220. DOI: 10.1111/ajo.12820

[24] Mc Guane J.T., Grlj L., Peek M.J. Obesity, gestational diabetes and macrosomia are associated with increasing rates of early-term induction of labour at The Canberra Hospital // Aust. NZJ Obstet. Gynaecol. 2019. Vol. 59. No. 2. P. 215−220. DOI: 10.1111/ajo.12820

[25] Ailamazyan JeK, Tarasova MA, Baranov VS, et al. Akusherstvo: uchebnik. Moscow: GEOTAR-Media; 2019. (In Russ.)

[26] Айламазян Э.К., Тарасова М.А., Баранов В.С. и др. Акушерство: учебник. Москва: ГЭОТАР-Медиа, 2019.

[27] Romero R, Dey SK, Fisher SJ. Preterm labor: one syndrome, many causes. Science. 2014;345(6198):760−765. DOI: 10.1126/science.1251816

[28] Romero R., Dey S.K., Fisher S.J. Preterm labor: one syndrome, many causes // Science. 2014. Vol. 345 (6198). P. 760−765. DOI: 10.1126/science.1251816

[29] Katz D, Beilin Y. Disorders of coagulation in pregnancy. Br J Anaesth. 2015;115(Suppl 2):ii75−88. DOI: 10.1093/bja/aev374

[30] Katz D., Beilin Y. Disorders of coagulation in pregnancy // Br. J. Anaesth. 2015. Vol. 115. Suppl. 2. P. ii75−88. DOI: 10.1093/bja/aev374

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