Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

Betina Ristorp Andersen; Ida Ammitzbøll; Jesper Hinrich; Sune Lehmann; Charlotte Vibeke Ringsted; Ellen Christine Leth Løkkegaard; Martin G. Tolsgaard

doi:10.1136/bmjopen-2021-049046

Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

Betina Ristorp Andersen, Ida Ammitzbøll, Jesper Hinrich, Sune Lehmann, Charlotte Vibeke Ringsted, Ellen Christine Leth Løkkegaard^*, Martin G. Tolsgaard

^*Corresponding author af dette arbejde

Dekanatet, Health

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Abstract

OBJECTIVES: 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. DESIGN: 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: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. 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. SETTING AND PARTICIPANTS: Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark. PRIMARY OUTCOME MEASURES: Arrival-to-delivery interval during ECS. RESULTS: Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' 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 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%). CONCLUSION: 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.

Originalsprog	Engelsk
Artikelnummer	e049046
Tidsskrift	BMJ Open
Vol/bind	12
Nummer	3
Antal sider	8
ISSN	2044-6055
DOI	https://doi.org/10.1136/bmjopen-2021-049046
Status	Udgivet - mar. 2022

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10.1136/bmjopen-2021-049046Licens: CC BY-NC

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title = "Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study",

abstract = "OBJECTIVES: 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. DESIGN: 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: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. 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. SETTING AND PARTICIPANTS: Patient records for mothers undergoing ECS between 2010 and 2017, Nordsj{\ae}llands Hospital, Capital Region of Denmark. PRIMARY OUTCOME MEASURES: Arrival-to-delivery interval during ECS. RESULTS: Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' 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 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%). CONCLUSION: 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.",

keywords = "adult surgery, fetal medicine, maternal medicine",

author = "Andersen, {Betina Ristorp} and Ida Ammitzb{\o}ll and Jesper Hinrich and Sune Lehmann and Ringsted, {Charlotte Vibeke} and L{\o}kkegaard, {Ellen Christine Leth} and Tolsgaard, {Martin G.}",

note = "Publisher Copyright: {\textcopyright} Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.",

year = "2022",

month = mar,

doi = "10.1136/bmjopen-2021-049046",

language = "English",

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Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study. / Andersen, Betina Ristorp; Ammitzbøll, Ida; Hinrich, Jesper et al.
I: BMJ Open, Bind 12, Nr. 3, e049046, 03.2022.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

TY - JOUR

T1 - Using machine learning to identify quality-of-care predictors for emergency caesarean sections

T2 - a retrospective cohort study

AU - Andersen, Betina Ristorp

AU - Ammitzbøll, Ida

AU - Hinrich, Jesper

AU - Lehmann, Sune

AU - Ringsted, Charlotte Vibeke

AU - Løkkegaard, Ellen Christine Leth

AU - Tolsgaard, Martin G.

PY - 2022/3

Y1 - 2022/3

N2 - OBJECTIVES: 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. DESIGN: 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: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. 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. SETTING AND PARTICIPANTS: Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark. PRIMARY OUTCOME MEASURES: Arrival-to-delivery interval during ECS. RESULTS: Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' 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 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%). CONCLUSION: 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.

AB - OBJECTIVES: 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. DESIGN: 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: 'Maternal objective', 'Maternal psychological', 'Fetal factors', 'ECS Indication', 'Emergency category', 'Type of anaesthesia', 'Team member qualifications and experience' and 'Procedural'. 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. SETTING AND PARTICIPANTS: Patient records for mothers undergoing ECS between 2010 and 2017, Nordsjællands Hospital, Capital Region of Denmark. PRIMARY OUTCOME MEASURES: Arrival-to-delivery interval during ECS. RESULTS: Data was obtained from 2409 patient records for women undergoing ECS. The group of predictors representing 'Team member qualifications and experience' 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 'Indication for ECS' was the second most important predictor group (PSPW 24.9%). In ECS category three, the second most important predictor group was 'Maternal objective predictors' (PSPW 24.2%). CONCLUSION: 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.

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KW - fetal medicine

KW - maternal medicine

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JO - BMJ Open

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Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

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