Periyathambi, Nishanthi, Parkhi, Durga, Ghebremichael-Weldeselassie, Yonas, Patel, Vinod, Sukumar, Nithya, Siddharthan, Rahul, Narlikar, Leelavati and Saravanan, Ponnusamy (2022) Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. PLoS One, 17 (3). e0264648. doi:10.1371/journal.pone.0264648 ISSN 1932-6203.

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Abstract

Objective
The aim of the present study was to identify the factors associated with non-attendance of immediate postpartum glucose test using a machine learning algorithm following gestational diabetes mellitus (GDM) pregnancy.

Method
A retrospective cohort study of all GDM women (n = 607) for postpartum glucose test due between January 2016 and December 2019 at the George Eliot Hospital NHS Trust, UK.

Results
Sixty-five percent of women attended postpartum glucose test. Type 2 diabetes was diagnosed in 2.8% and 21.6% had persistent dysglycaemia at 6–13 weeks post-delivery. Those who did not attend postpartum glucose test seem to be younger, multiparous, obese, and continued to smoke during pregnancy. They also had higher fasting glucose at antenatal oral glucose tolerance test. Our machine learning algorithm predicted postpartum glucose non-attendance with an area under the receiver operating characteristic curve of 0.72. The model could achieve a sensitivity of 70% with 66% specificity at a risk score threshold of 0.46. A total of 233 (38.4%) women attended subsequent glucose test at least once within the first two years of delivery and 24% had dysglycaemia. Compared to women who attended postpartum glucose test, those who did not attend had higher conversion rate to type 2 diabetes (2.5% vs 11.4%; p = 0.005).

Conclusion
Postpartum screening following GDM is still poor. Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. Enhanced, personalised education of these women may improve postpartum glucose screening.

Item Type:	Journal Article
Subjects:	R Medicine > RG Gynecology and obstetrics
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School > Health Sciences Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
Library of Congress Subject Headings (LCSH):	Diabetes in pregnancy, Machine learning, Diabetes
Journal or Publication Title:	PLoS One
Publisher:	Public Library of Science
ISSN:	1932-6203
Official Date:	7 March 2022
Dates:	Date Event 7 March 2022 Published 14 February 2022 Accepted
Volume:	17
Number:	3
Article Number:	e0264648
DOI:	10.1371/journal.pone.0264648
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	22 February 2022
Date of first compliant Open Access:	12 April 2022
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID MR/R020981/1 [MRC] Medical Research Council http://dx.doi.org/10.13039/501100000265
Related URLs:	Publisher

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