{
  "abstract": "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 categorizationResearch 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.",
  "authors": [
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Anwei Gwan"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Isai Ortiz"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Katelyn M Tessier"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Renee Mahr"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Anna Ayers Looby"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Sanjana Molleti"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Jessica Makori"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Oluwabukola Akingbola"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Sereen Nashif"
    },
    {
      "affiliations": [
        "University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "J’Mag Karbeah"
    },
    {
      "affiliations": [
        "Obstetrics, Gynecology and Women’s Health, University of Minnesota, Minneapolis, Minnesota, USA"
      ],
      "name": "Sarah A Wernimont"
    }
  ],
  "title": "Healthcare professional classification of “poor glucose control” and perinatal outcomes in pregnancies with diabetes: a retrospective cohort study",
  "uid": "1a9e6543-4740-52ac-9f78-429ff81eb275"
}