{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:16:46Z","timestamp":1766269006951,"version":"3.48.0"},"reference-count":47,"publisher":"BMJ","issue":"3","license":[{"start":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T00:00:00Z","timestamp":1646524800000},"content-version":"unspecified","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["BMJ Open"],"accepted":{"date-parts":[[2022,2,1]]},"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Emergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality of care during ECS in terms of the arrival-to-delivery interval.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>A retrospective cohort study. ECS were classified by urgency using emergency categories one\/two and three (delivery within 30 and 60 min). In total, 92 predictor variables were included in the analysis and grouped as follows: \u2018Maternal objective\u2019, \u2018Maternal psychological\u2019, \u2018Fetal factors\u2019, \u2018ECS Indication\u2019, \u2018Emergency category\u2019, \u2018Type of anaesthesia\u2019, \u2018Team member qualifications and experience\u2019 and \u2018Procedural\u2019. Data was analysed with a linear regression model using elastic net regularisation and jackknife technique to improve generalisability. The relative influence of the predictors, percentage significant predictor weight (PSPW) was calculated for each predictor to visualise the main determinants of arrival-to-delivery interval.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting and participants<\/jats:title>\n                    <jats:p>Patient records for mothers undergoing ECS between 2010 and 2017, Nordsj\u00e6llands Hospital, Capital Region of Denmark.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Primary outcome measures<\/jats:title>\n                    <jats:p>Arrival-to-delivery interval during ECS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing \u2018Team member qualifications and experience\u2019 was the most important predictor of arrival-to-delivery interval in all ECS emergency categories (PSPW 25.9% for ECS category one\/two; PSPW 35.5% for ECS category three). In ECS category one\/two the \u2018Indication for ECS\u2019 was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was \u2018Maternal objective predictors\u2019 (PSPW 24.2%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study provides empirical evidence for the importance of team member qualifications and experience relative to other predictors of arrival-to-delivery during ECS. Machine learning provides a promising method for expanding our current knowledge about the relative importance of different factors in predicting outcomes of complex obstetric events.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bmjopen-2021-049046","type":"journal-article","created":{"date-parts":[[2022,3,7]],"date-time":"2022-03-07T10:45:43Z","timestamp":1646649943000},"page":"e049046","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study"],"prefix":"10.1136","volume":"12","author":[{"given":"Betina Ristorp","family":"Andersen","sequence":"first","affiliation":[{"name":"Department of Gynecology and Obstetrics, Nordsj\u00e6llands Hospital & Department of Clinical Medicine, University of Copenhagen, Hillerod, Capital Region, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ida","family":"Ammitzb\u00f8ll","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics, Nordsj\u00e6llands Hospital & Department of Clinical Medicine, University of Copenhagen, Hillerod, Capital Region, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesper","family":"Hinrich","sequence":"additional","affiliation":[{"name":"Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sune","family":"Lehmann","sequence":"additional","affiliation":[{"name":"Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charlotte Vibeke","family":"Ringsted","sequence":"additional","affiliation":[{"name":"Faculty of Health, Aarhus Universitet, Aarhus, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4149-5663","authenticated-orcid":false,"given":"Ellen Christine Leth","family":"L\u00f8kkegaard","sequence":"additional","affiliation":[{"name":"Department of Gynecology and Obstetrics, Nordsj\u00e6llands Hospital & Department of Clinical Medicine, University of Copenhagen, Hillerod, Capital Region, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin G","family":"Tolsgaard","sequence":"additional","affiliation":[{"name":"Copenhagen Academy of Medical Education and Simulation, Rigshospitalet, Kobenhavn, Capital Region, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"239","published-online":{"date-parts":[[2022,3,7]]},"reference":[{"key":"2025121400114526000_12.3.e049046.1","unstructured":"Knight M , Nair M , Tuffnell D , eds. on behalf of MBRRACE-UK. 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