Observational studyJournal of biomedical informatics2024
Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations.
Observational study in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- What is the ideal glucose range for a patient with sepsis in the ICU? A retrospective analysis of MIMIC-IV.BMJ open · 2026Article
- Moving beyond the empty cell: The threat of decontextualized healthcare data.PLOS digital health · 2026Review
- Differences in Arterial Blood Gas Testing by Race and Sex across 161 U.S. Hospitals in Four Electronic Health Record Databases.American journal of respiratory and critical care medicine · 2025Observational
- The Data Artifacts Glossary: a community-based repository for bias on health datasets.Journal of biomedical science · 2025Article
- Potential source of bias in AI models: lactate measurement in the ICU in sepsis patients as a template.Frontiers in medicine · 2025Article
- Economics and Equity of Large Language Models: Health Care Perspective.Journal of medical Internet research · 2024Article
- Why federated learning will do little to overcome the deeply embedded biases in clinical medicine.Intensive care medicine · 2024Article
- Association between glycemic variability and the risk of acute kidney injury in patients with traumatic brain injury: a retrospective cohort study with independent cohort analysis.Frontiers in neurologyArticle
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
objectiveHealth inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU).
methodsUsing the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency.
resultsWe studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings.
conclusionWe found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.
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