Cargando…

Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea

This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random...

Descripción completa

Detalles Bibliográficos
Autores principales: Wie, Jeong Ha, Lee, Se Jin, Choi, Sae Kyung, Jo, Yun Sung, Hwang, Han Sung, Park, Mi Hye, Kim, Yeon Hee, Shin, Jae Eun, Kil, Ki Cheol, Kim, Su Mi, Choi, Bong Suk, Hong, Hanul, Seol, Hyun-Joo, Won, Hye-Sung, Ko, Hyun Sun, Na, Sunghun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033083/
https://www.ncbi.nlm.nih.gov/pubmed/35455095
http://dx.doi.org/10.3390/life12040604
_version_ 1784692803223158784
author Wie, Jeong Ha
Lee, Se Jin
Choi, Sae Kyung
Jo, Yun Sung
Hwang, Han Sung
Park, Mi Hye
Kim, Yeon Hee
Shin, Jae Eun
Kil, Ki Cheol
Kim, Su Mi
Choi, Bong Suk
Hong, Hanul
Seol, Hyun-Joo
Won, Hye-Sung
Ko, Hyun Sun
Na, Sunghun
author_facet Wie, Jeong Ha
Lee, Se Jin
Choi, Sae Kyung
Jo, Yun Sung
Hwang, Han Sung
Park, Mi Hye
Kim, Yeon Hee
Shin, Jae Eun
Kil, Ki Cheol
Kim, Su Mi
Choi, Bong Suk
Hong, Hanul
Seol, Hyun-Joo
Won, Hye-Sung
Ko, Hyun Sun
Na, Sunghun
author_sort Wie, Jeong Ha
collection PubMed
description This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth. A total of 6549 term nulliparous women was included in the analysis, and the emergent CS rate was 16.1%. The C-statistics values for KNN, Voting, XGBoost, Stacking, gradient boosting, random forest, LGBM, logistic regression, and SVM were 0.6, 0.69, 0.64, 0.59, 0.66, 0.68, 0.68, 0.7, and 0.69, respectively. The logistic regression model showed the best predictive performance with an accuracy of 0.78. The machine learning model identified nine significant variables of maternal age, height, weight at pre-pregnancy, pregnancy-associated hypertension, gestational age, and fetal sonographic findings. The C-statistic value for the logistic regression machine learning model in the external validation set (1391 term nulliparous women) was 0.69, with an overall accuracy of 0.68, a specificity of 0.83, and a sensitivity of 0.41. Machine learning algorithms with clinical and sonographic parameters at near term could be useful tools to predict individual risk of emergent CS during active labor in nulliparous women.
format Online
Article
Text
id pubmed-9033083
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-90330832022-04-23 Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea Wie, Jeong Ha Lee, Se Jin Choi, Sae Kyung Jo, Yun Sung Hwang, Han Sung Park, Mi Hye Kim, Yeon Hee Shin, Jae Eun Kil, Ki Cheol Kim, Su Mi Choi, Bong Suk Hong, Hanul Seol, Hyun-Joo Won, Hye-Sung Ko, Hyun Sun Na, Sunghun Life (Basel) Article This study was a multicenter retrospective cohort study of term nulliparous women who underwent labor, and was conducted to develop an automated machine learning model for prediction of emergent cesarean section (CS) before onset of labor. Nine machine learning methods of logistic regression, random forest, Support Vector Machine (SVM), gradient boosting, extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), k-nearest neighbors (KNN), Voting, and Stacking were applied and compared for prediction of emergent CS during active labor. External validation was performed using a nationwide multicenter dataset for Korean fetal growth. A total of 6549 term nulliparous women was included in the analysis, and the emergent CS rate was 16.1%. The C-statistics values for KNN, Voting, XGBoost, Stacking, gradient boosting, random forest, LGBM, logistic regression, and SVM were 0.6, 0.69, 0.64, 0.59, 0.66, 0.68, 0.68, 0.7, and 0.69, respectively. The logistic regression model showed the best predictive performance with an accuracy of 0.78. The machine learning model identified nine significant variables of maternal age, height, weight at pre-pregnancy, pregnancy-associated hypertension, gestational age, and fetal sonographic findings. The C-statistic value for the logistic regression machine learning model in the external validation set (1391 term nulliparous women) was 0.69, with an overall accuracy of 0.68, a specificity of 0.83, and a sensitivity of 0.41. Machine learning algorithms with clinical and sonographic parameters at near term could be useful tools to predict individual risk of emergent CS during active labor in nulliparous women. MDPI 2022-04-18 /pmc/articles/PMC9033083/ /pubmed/35455095 http://dx.doi.org/10.3390/life12040604 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wie, Jeong Ha
Lee, Se Jin
Choi, Sae Kyung
Jo, Yun Sung
Hwang, Han Sung
Park, Mi Hye
Kim, Yeon Hee
Shin, Jae Eun
Kil, Ki Cheol
Kim, Su Mi
Choi, Bong Suk
Hong, Hanul
Seol, Hyun-Joo
Won, Hye-Sung
Ko, Hyun Sun
Na, Sunghun
Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title_full Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title_fullStr Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title_full_unstemmed Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title_short Prediction of Emergency Cesarean Section Using Machine Learning Methods: Development and External Validation of a Nationwide Multicenter Dataset in Republic of Korea
title_sort prediction of emergency cesarean section using machine learning methods: development and external validation of a nationwide multicenter dataset in republic of korea
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9033083/
https://www.ncbi.nlm.nih.gov/pubmed/35455095
http://dx.doi.org/10.3390/life12040604
work_keys_str_mv AT wiejeongha predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT leesejin predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT choisaekyung predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT joyunsung predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT hwanghansung predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT parkmihye predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT kimyeonhee predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT shinjaeeun predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT kilkicheol predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT kimsumi predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT choibongsuk predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT honghanul predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT seolhyunjoo predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT wonhyesung predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT kohyunsun predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea
AT nasunghun predictionofemergencycesareansectionusingmachinelearningmethodsdevelopmentandexternalvalidationofanationwidemulticenterdatasetinrepublicofkorea