Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes.
Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes.
复制标题
机器学习预测妊娠糖尿病后，对产后葡萄糖筛查的非纳入和随后的2型糖尿病风险。
DOI：
10.1371/journal.pone.0264648
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发表时间：
2022
期刊：
影响因子：
3.7
通讯作者：
Saravanan P
中科院分区：
文献类型：
--
作者：
Periyathambi N;Parkhi D;Ghebremichael-Weldeselassie Y;Patel V;Sukumar N;Siddharthan R;Narlikar L;Saravanan P
本研究的目的是使用机器学习算法确定妊娠期糖尿病（GDM）妊娠后未参加产后立即血糖检测的相关因素。一项回顾性队列研究，对所有GDM女性（n = 607）进行产后血糖检测，检测时间为2016年1月至2019年12月，地点为英国乔治艾略特医院NHS信托基金。65%的妇女参加了产后血糖检测。2型糖尿病的诊断率为2.8%，21.6%的患者在分娩后6-13周时患有持续性血供异常。那些没有参加产后血糖测试的人似乎更年轻，多产，肥胖，并在怀孕期间继续吸烟。在产前口服葡萄糖耐量试验中，他们也有较高的空腹血糖。我们的机器学习算法预测产后葡萄糖不出勤，受试者工作特征曲线下面积为0.72。该模型可以达到70%的灵敏度和66%的特异性，风险评分阈值为0.46。共有233名（38.4%）妇女在分娩后的头两年内至少参加了一次随后的葡萄糖检测，24%的妇女患有血脂异常。与参加产后血糖测试的妇女相比，那些没有参加的妇女有更高的2型糖尿病转化率（2.5% vs 11.4%; p = 0.005）。GDM后的产后筛查仍然很差。没有参加产后筛查的妇女在产后两年内似乎有更高的代谢风险和更高的2型糖尿病转化率。机器学习模型可以使用简单的产前因素预测不太可能参加产后血糖测试的女性。对这些妇女加强个性化教育可能会改善产后葡萄糖筛查。
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. 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. 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). 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.
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影响因子：
15.8
作者：
Daly B;Toulis KA;Thomas N;Gokhale K;Martin J;Webber J;Keerthy D;Jolly K;Saravanan P;Nirantharakumar K
通讯作者：
Nirantharakumar K
影响因子：
3.6
作者：
Vickers, Andrew J.;Elkin, Elena B.
通讯作者：
Elkin, Elena B.
影响因子：
2.9
作者：
Van Ryswyk, E. M.;Middleton, P. F.;Crowther, C. A.
通讯作者：
Crowther, C. A.
影响因子：
16.2
作者：
Lawrence JM;Black MH;Hsu JW;Chen W;Sacks DA
通讯作者：
Sacks DA
影响因子：
5
作者：
Arnold, William D.;Kupfer, Kenneth;San George, Richard C.
通讯作者：
San George, Richard C.