Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models
Abstract Objective To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. Methods A retrosp...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-025-07283-y |
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| author | Sughashini Murugesu Kristofer Linton-Reid Emily Braun Jennifer Barcroft Nina Cooper Margaret Pikovsky Alex Novak Nina Parker Catriona Stalder Maya Al-Memar Srdjan Saso Eric O. Aboagye Tom Bourne |
| author_facet | Sughashini Murugesu Kristofer Linton-Reid Emily Braun Jennifer Barcroft Nina Cooper Margaret Pikovsky Alex Novak Nina Parker Catriona Stalder Maya Al-Memar Srdjan Saso Eric O. Aboagye Tom Bourne |
| author_sort | Sughashini Murugesu |
| collection | DOAJ |
| description | Abstract Objective To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. Methods A retrospective, multi-site study of patients opting for expectant or medical management of miscarriage was undertaken. A total of 1075 patients across two hospital early pregnancy units were eligible for inclusion. Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. The area under the receiver operating characteristic curve (AUC) scores were the metrics used to demonstrate the performance of the best performing model and feature selection combination for the training, validation and external data set for expectant and medical management separately. Results Parameters were in the majority well matched across training, validation and external test sets. The respective optimum training, validation and external test set AUC scores were as follows in the expectant management cohort: 0.72 (95% CI 0.67,0.77), 0.63 (95% CI 0.53,0.73) and 0.70 (95% CI 0.60,0.79) (Logistic Regression in combination with Least Absolute Shrinkage and Selection Operator (LASSO)). The AUC scores in the medical management cohort were 0.64 (95% CI 0.56,0.72), 0.62 (95% CI 0.45,0.77) and 0.71 (95% CI 0.58,0.83) (Logistic Regression in combination with Recursive Feature Elimination (RFE)). Conclusions Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage. |
| format | Article |
| id | doaj-art-210abf9b46a14cdf984cd88943ff720e |
| institution | Kabale University |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-210abf9b46a14cdf984cd88943ff720e2025-08-20T04:01:47ZengBMCBMC Pregnancy and Childbirth1471-23932025-02-0125111710.1186/s12884-025-07283-yPredicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction modelsSughashini Murugesu0Kristofer Linton-Reid1Emily Braun2Jennifer Barcroft3Nina Cooper4Margaret Pikovsky5Alex Novak6Nina Parker7Catriona Stalder8Maya Al-Memar9Srdjan Saso10Eric O. Aboagye11Tom Bourne12Queen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeQueen Charlotte’s and Chelsea Hospital, Imperial CollegeAbstract Objective To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. Methods A retrospective, multi-site study of patients opting for expectant or medical management of miscarriage was undertaken. A total of 1075 patients across two hospital early pregnancy units were eligible for inclusion. Data pre-processing derived 14 features for predictive modelling. A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. The area under the receiver operating characteristic curve (AUC) scores were the metrics used to demonstrate the performance of the best performing model and feature selection combination for the training, validation and external data set for expectant and medical management separately. Results Parameters were in the majority well matched across training, validation and external test sets. The respective optimum training, validation and external test set AUC scores were as follows in the expectant management cohort: 0.72 (95% CI 0.67,0.77), 0.63 (95% CI 0.53,0.73) and 0.70 (95% CI 0.60,0.79) (Logistic Regression in combination with Least Absolute Shrinkage and Selection Operator (LASSO)). The AUC scores in the medical management cohort were 0.64 (95% CI 0.56,0.72), 0.62 (95% CI 0.45,0.77) and 0.71 (95% CI 0.58,0.83) (Logistic Regression in combination with Recursive Feature Elimination (RFE)). Conclusions Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage.https://doi.org/10.1186/s12884-025-07283-yMiscarriageExpectant managementMedical managementMachine learning |
| spellingShingle | Sughashini Murugesu Kristofer Linton-Reid Emily Braun Jennifer Barcroft Nina Cooper Margaret Pikovsky Alex Novak Nina Parker Catriona Stalder Maya Al-Memar Srdjan Saso Eric O. Aboagye Tom Bourne Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models BMC Pregnancy and Childbirth Miscarriage Expectant management Medical management Machine learning |
| title | Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| title_full | Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| title_fullStr | Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| title_full_unstemmed | Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| title_short | Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| title_sort | predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models |
| topic | Miscarriage Expectant management Medical management Machine learning |
| url | https://doi.org/10.1186/s12884-025-07283-y |
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