Potential source of bias in AI models: lactate measurement in the ICU in sepsis patients as a template

Abstract

Objective: Health inequities may be driven by demographics such as sex, language proficiency, and race-ethnicity. These disparities may manifest through likelihood of testing, which in turn can bias artificial intelligence models. We aimed to evaluate variation in serum lactate measurements in the intensive care unit (ICU) in sepsis. Methods: Utilizing MIMIC-IV (2008-2019), we identified adults fulfilling sepsis-3 criteria. Exclusion criteria were ICU stay < 1-day, unknown race-ethnicity, < 18 years of age, and recurrent ICU-stays. Employing targeted maximum likelihood estimation analysis, we assessed the likelihood of a lactate measurement on day 1. For patients with a measurement on day 1, we evaluated the predictors of subsequent readings. Results: We studied 15,601 patients (19.5% racial-ethnic minority, 42.4% female, and 10.0% limited English proficiency). After adjusting for confounders, Black patients had a slightly higher likelihood of receiving a lactate measurement on day 1 [odds ratio 1.19, 95% confidence interval (CI) 1.06-1.34], but not the other minority groups. Subsequent frequency was similar across race-ethnicities, but women had a lower incidence rate ratio (IRR) 0.94 (95% CI 0.90-0.98). Patients with elective admission and private insurance also had a higher frequency of repeated serum lactate measurements (IRR 1.70, 95% CI 1.61-1.81 and 1.07, 95% CI, 1.02-1.12, respectively). Conclusion: We found no disparities in the likelihood of a lactate measurement among patients with sepsis across demographics, except for a small increase for Black patients, and a reduced frequency for women. Subsequent analyses should account for the variation in biomarker monitoring being present in MIMIC-IV.

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