Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis
Abstract Background Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in...
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| Format: | Article |
| Language: | English |
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BMC
2024-04-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-024-06461-8 |
| _version_ | 1797199159950835712 |
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| author | Sidsel Boie Julie Glavind Pinar Bor Philip Steer Anders Hammerich Riis Bo Thiesson Niels Uldbjerg |
| author_facet | Sidsel Boie Julie Glavind Pinar Bor Philip Steer Anders Hammerich Riis Bo Thiesson Niels Uldbjerg |
| author_sort | Sidsel Boie |
| collection | DOAJ |
| description | Abstract Background Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction. Methods Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual’s predictors that result in a change of the individual’s risk output value from the population-based risk. Results Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points. Conclusion In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation. |
| first_indexed | 2024-04-24T07:11:20Z |
| format | Article |
| id | doaj.art-ed06e78580db428f94710f2c62fc70f9 |
| institution | Directory Open Access Journal |
| issn | 1471-2393 |
| language | English |
| last_indexed | 2024-04-24T07:11:20Z |
| publishDate | 2024-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj.art-ed06e78580db428f94710f2c62fc70f92024-04-21T11:33:23ZengBMCBMC Pregnancy and Childbirth1471-23932024-04-012411910.1186/s12884-024-06461-8Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysisSidsel Boie0Julie Glavind1Pinar Bor2Philip Steer3Anders Hammerich Riis4Bo Thiesson5Niels Uldbjerg6Department of Obstetrics and Gynaecology, Randers Regional HospitalDepartment of Obstetrics and Gynaecology, Aarhus University HospitalDepartment of Obstetrics and Gynaecology, Aarhus University HospitalAcademic Department of Obstetrics and Gynaecology, Chelsea and Westminster Hospital, Imperial College LondonEnversion A/SEnversion A/SDepartment of Clinical Medicine, Aarhus UniversityAbstract Background Current guidelines regarding oxytocin stimulation are not tailored to individuals as they are based on randomised controlled trials. The objective of the study was to develop an artificial intelligence (AI) model for individual prediction of the risk of caesarean delivery (CD) in women with a cervical dilatation of 6 cm after oxytocin stimulation for induced labour. The model included not only variables known when labour induction was initiated but also variables describing the course of the labour induction. Methods Secondary analysis of data from the CONDISOX randomised controlled trial of discontinued vs. continued oxytocin infusion in the active phase of induced labour. Extreme gradient boosting (XGBoost) software was used to build the prediction model. To explain the impact of the predictors, we calculated Shapley additive explanation (SHAP) values and present a summary SHAP plot. A force plot was used to explain specifics about an individual’s predictors that result in a change of the individual’s risk output value from the population-based risk. Results Among 1060 included women, 160 (15.1%) were delivered by CD. The XGBoost model found women who delivered vaginally were more likely to be parous, taller, to have a lower estimated birth weight, and to be stimulated with a lower amount of oxytocin. In 108 women (10% of 1060) the model favoured either continuation or discontinuation of oxytocin. For the remaining 90% of the women, the model found that continuation or discontinuation of oxytocin stimulation affected the risk difference of CD by less than 5% points. Conclusion In women undergoing labour induction, this AI model based on a secondary analysis of data from the CONDISOX trial may help predict the risk of CD and assist the mother and clinician in individual tailored management of oxytocin stimulation after reaching 6 cm of cervical dilation.https://doi.org/10.1186/s12884-024-06461-8OxytocinInduction of labourMode of deliveryPrediction modelArtificial intelligenceExplainable AI |
| spellingShingle | Sidsel Boie Julie Glavind Pinar Bor Philip Steer Anders Hammerich Riis Bo Thiesson Niels Uldbjerg Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis BMC Pregnancy and Childbirth Oxytocin Induction of labour Mode of delivery Prediction model Artificial intelligence Explainable AI |
| title | Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis |
| title_full | Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis |
| title_fullStr | Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis |
| title_full_unstemmed | Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis |
| title_short | Continued versus discontinued oxytocin stimulation in the active phase of labour (CONDISOX): individual management based on artificial intelligence - a secondary analysis |
| title_sort | continued versus discontinued oxytocin stimulation in the active phase of labour condisox individual management based on artificial intelligence a secondary analysis |
| topic | Oxytocin Induction of labour Mode of delivery Prediction model Artificial intelligence Explainable AI |
| url | https://doi.org/10.1186/s12884-024-06461-8 |
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