Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes
Author
Periyathambi, NishanthiParkhi, Durga
Ghebremichael-Weldeselassie, Yonas
Patel, Vinod
Sukumar, Nithya
Siddharthan, Rahul
Narlikar, Leelavati
Saravanan, Ponnusamy
Affiliation
University of Warwick; George Eliot Hospital; The Open University, Milton Keynes; The Institute of Mathematical Sciences, Chennai, India; Homi Bhabha National Institute, Mumbai, India; CSIR-National Chemical Laboratory, Pune, India.Publication date
2022-03-07
Metadata
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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.Citation
Periyathambi N, Parkhi D, Ghebremichael-Weldeselassie Y, Patel V, Sukumar N, Siddharthan R, Narlikar L, Saravanan P. Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. PLoS One. 2022 Mar 7;17(3):e0264648. doi: 10.1371/journal.pone.0264648. PMID: 35255105; PMCID: PMC8901061.Type
ArticlePMID
35255105Journal
PLoS ONEPublisher
Public Library of Scienceae974a485f413a2113503eed53cd6c53
10.1371/journal.pone.0264648