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 for this work

Research output: Contribution to journalJournal articleResearchpeer-review

1 Downloads (Pure)

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.

Original languageEnglish
Article numbere049046
JournalBMJ Open
Volume12
Issue number3
Number of pages8
DOIs
Publication statusPublished - 7 Mar 2022

Keywords

  • Adult surgery
  • Fetal medicine
  • Maternal medicine

Fingerprint

Dive into the research topics of 'Using machine learning to identify quality-of-care predictors for emergency caesarean sections: A retrospective cohort study'. Together they form a unique fingerprint.

Cite this