Conclusions
Using NLP algorithms to identify terminology associated with “poor glucose control” in clinical notes, we evaluated the demographic factors, markers of glycemia, and maternal–neonatal outcomes associated with health care professional descriptions of “poor glucose control” in pregnancy. “Poor glucose control” and “uncontrolled diabetes” were the most frequently used terms, and physicians used these descriptions more than other healthcare professionals. Individuals classified with “poor glucose control” were more likely to be birthing individuals of color, use public insurance, or require insulin therapy. These individuals gave birth a week earlier, and their neonates were more likely to be LGA and admitted to the NICU. While individuals categorized by healthcare professionals as having “poor glucose control” had worse outcomes in several domains, we identified that this clinical classification was often made in the absence of any objective data suggestive of hyperglycemia or even in the presence of normal glucose and A1c values.
The correlation between elevated maternal glucose levels and poor perinatal clinical outcomes is well documented.17 To reduce the risk of some of these adverse events, such as stillbirth, early birth has been recommended for individuals with “poor glucose control.”4 5 However, there are no established criteria for categorizing individuals as having “poor glucose control” necessitating early intervention. Thus, healthcare professionals rely on their clinical judgment to guide management, such as the timing of birth recommendations.
While clinical judgment is an essential component of care, for many individuals categorized by healthcare professionals as having “poor glucose control,” no objective markers of glucose levels were identified in the EHR. Individuals classified as having “poor glucose control” had increased complications not solely related to GA at birth, such as LGA status and shoulder dystocia, suggesting clinical judgment does accurately identify individuals at increased risk of some complication. However, we also identified individuals categorized as having “poor glucose control” who had normal A1c within 3 months of birth and did not experience increased complications. This suggests that some individuals may be incorrectly identified as having “poor glucose control” despite having normal objective markers of glucose.
No guidelines currently identify a glycemic marker to guide early birth recommendations. In our study, we selected blood glucose from self-blood glucose monitoring or continuous glucose monitoring data and A1c as indicators of maternal glycemia, as these are the commonly used markers and easily accessible in the EHR. Notably, no consensus guideline on elevated self-monitored glucose levels representing “poor glucose control” exists. However, a threshold greater than 50% of readings above goal was used based on our health system’s recommendations for initiating or adjusting insulin in pregnancy. We identified that only 53% of individuals categorized as having “poor glucose control” had any reported glucose values documented in the EHR (from clinical notes or scanned documents) and that only 37% of those individuals with documented glucose had >50% of values above the target range. Surprisingly, 77% of individuals without “poor glucose” categorization who had clinically documented glucose values had more than 50% of values above the target range. This suggests that “poor glucose control” categorization was not necessarily equitably related to reported glucose levels.
A1c has been recommended by the American Diabetes Association (ADA) as an adjunct measure of glycemia for individuals with pre-existing diabetes.18 A1c measurements tend to decrease throughout gestation due to increases in red blood cell turnover and may be difficult to interpret in the presence of maternal anemia.19–21 However, several studies have identified A1c elevation at term and increasing A1c levels throughout pregnancy as associated with increased risk of perinatal complications.22–24 In our study, only 53% of individuals categorized as having “poor glucose control” and 31% without “poor glucose control” had any A1c documented within 3 months of birth. While the median A1c was higher in the group categorized with “poor glucose” control, over 60% had an A1c≤6.5% (normal range). Additionally, 8.9% in the group without “poor glucose control” language had an A1c>6.5%.
Our study further revealed that many individuals categorized with “poor glucose control” were those of marginalized populations (ie, black, those requiring public insurance, non-English speakers). This categorization was made at increased rates in individuals of color compared with white individuals despite no objective differences in glucose values. This indicates that some clinicians may be classifying marginalized individuals with “suboptimal glycemic control” without supportive evidence of hyperglycemia. Racial differences in A1c have been reported early in pregnancy. However, both non-Hispanic black and white birthing individuals were equally able to achieve A1c targets≤6.5% despite increases in the Social Vulnerability Index among black individuals.25 It is well established that black birthing individuals and those on public insurance are more likely to experience preterm birth.26–28 While the etiology for this is multifactorial, the categorization of black, brown, and low-income birthing individuals with “poor glucose control” could potentially further perpetuate poor neonatal health outcomes, contributing to long-lasting health burdens on marginalized birthing individuals and their families.
Based on increased rates of LGA status and shoulder dystocia in those categorized as having “poor glucose control,” we suspect that some individuals were correctly identified by clinicians as having elevated glucose levels. However, many individuals were categorized by obstetricians as having “poor glucose control” despite the presence of normal objective markers of glycemia. Thus, a potential risk of this designation is unnecessary late preterm/early term birth, resulting in increased NICU admission, which can increase costs of care and adverse outcomes unnecessarily.10 29–31
Strengths and limitations
Several strengths of this study are noted. This study includes NLP algorithms and manual verification of clinical contexts to systematically identify the use of “poor glucose control language” and its variants in EHR documentation. This was done in the context of a large community-academic health system with over 10 000 births per year across seven hospitals spanning urban, suburban, and rural settings with care provided by multiple provider types in multiple practice settings, enhancing generalizability. Of note, within our system, maternal–fetal medicine specialists and endocrinologists primarily play a consultative role with most individuals remaining within the care of their primary obstetrics clinician. Although there is potential for variation in care patterns within our system, a system-wide ACOG-aligned and ADA-aligned protocol is in place for the care of individuals with diabetes in pregnancy.
There are several limitations to this study. Given the observational design, we can only identify associations between “poor glucose control” language use and individual characteristics and clinical outcomes, not causation. Further, due to limitations of data availability (including prepregnancy BMI and Area Deprivation Index), we were not able to control for all factors that may contribute to clinician language use. Additionally, language was only identified if found within clinical notes of the EHR; thus, we may have missed some individuals if language was only used in scanned outpatient documents. However, because of this potential concern, we did not quantify outcomes by the number of documented events; rather, we looked at whether any documentation was included in the EHR in any phase of pregnancy care. Additionally, while our EHR contains notes, laboratory results, electronic messages, and scanned records, and we made every effort to review these documents when assessing reported glucose levels, it is possible that we did not identify all documented glucose values. However, we believe the potential impact of this on reported findings is small. Finally, while we made efforts to identify objective markers clinicians may have used to inform their assessment of glucose levels, we cannot fully assess whether bias may have directly contributed to differential use of this terminology among marginalized groups.
In summary, pregnant individuals of color, those on public insurance, and those speaking a language other than English or Spanish are more likely to be categorized as having “poor glycemic control.” While some individuals classified as having “poor glucose control” did have glucose levels above goal, a number were identified as having “poor glucose control” without any supporting objective data. Additional clinical guidelines are needed from ACOG and other leading organizations to more clearly outline the degree of hyperglycemia and other clinical findings to support recommendations for early birth. Clearer guidelines may support less biased clinical decision-making regarding the timing of birth among individuals with diabetes and decrease potential complications.
Leave a Reply