Introduction Early birth is often recommended for “poorly controlled” diabetes; however, no guidelines define the glycemic threshold that necessitates delivery. We use natural language processing (NLP) of electronic health records to identify individuals described by healthcare professionals as having “poor glucose control” and to examine the factors and outcomes associated with this categorization Research design and methods We completed a retrospective cohort study of pregnant individuals with pre-existing and gestational diabetes mellitus from 2018 to 2019. NLP identified prespecified terms indicating “poor glucose control” in clinical notes, and a cohort analysis compared those with and without “poor glucose control” language. Clinical characteristics, objective glucose measures, and neonatal and maternal outcomes were statistically compared. Results 1433 individuals met inclusion criteria, and 143 (10%) were described as having “poor glycemic control.” After adjusting for diabetes type, pregnant individuals of color (adjusted OR (aOR) 2.4, 95% CI 1.63 to 3.57, p<0.001), individuals on public insurance (aOR 3.22, 95% CI 2.2 to 4.74, p<0.001), and non-English/non-Spanish speaking individuals (aOR 2.07, 95% CI 1.22 to 3.4, p=0.005) had higher odds of being categorized as having “poor glucose control” than control groups. This designation was often applied in the absence of objective markers of glycemia. While some individuals categorized with “poor glucose control” experienced earlier births and higher rates of neonatal complications, these differences were less pronounced when comparing individuals with A1c≤6.5%. Conclusions Pregnant individuals of color, those on public insurance, and non-English/non-Spanish speakers are more likely to be categorized as having “poor glycemic control.” Little objective data supported this categorization.
Gwan et al. (Tue,) studied this question.