Article Text

Original research
Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study
  1. Betina Ristorp Andersen1,
  2. Ida Ammitzbøll1,
  3. Jesper Hinrich2,
  4. Sune Lehmann2,
  5. Charlotte Vibeke Ringsted3,
  6. Ellen Christine Leth Løkkegaard1,
  7. Martin G Tolsgaard4
  1. 1Department of Gynecology and Obstetrics, Nordsjællands Hospital & Department of Clinical Medicine, University of Copenhagen, Hillerod, Capital Region, Denmark
  2. 2Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
  3. 3Faculty of Health, Aarhus Universitet, Aarhus, Denmark
  4. 4Copenhagen Academy of Medical Education and Simulation, Rigshospitalet, Kobenhavn, Capital Region, Denmark
  1. Correspondence to Professor Ellen Christine Leth Løkkegaard; Ellen.Christine.Leth.Loekkegaard{at}regionh.dk

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.

  • maternal medicine
  • fetal medicine
  • adult surgery

Data availability statement

No data are available.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Data availability statement

No data are available.

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Footnotes

  • Contributors BRA contributed to the conception and design of the work, and to data acquisition and interpretation, data analysis and drafted the paper. IA contributed to data acquisition and interpretation and assisted in drafting the paper. MGT and ECLL contributed to the conception and design of the work and to data acquisition and interpretation, data analysis and assisted in drafting the paper. JH and SL contributed to data analysis and interpretation and assisted in the drafting of the paper. CVR contributed to the conception and design of the work and assisted in drafting the paper. All authors contributed to the critical revision of the paper and approved the final manuscript for publication. All authors have agreed to be accountable for all aspects of the work. BRA acts as a gaurantor of the study.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.