Potential source of bias in AI models: Lactate measurement in the ICU as a template

TL;DR

This study explores how differences in lactate testing in ICU patients may lead to bias in AI models, especially among Black patients and women.

Contribution

The study identifies lactate measurement variation as a potential source of bias in AI models for ICU patients with sepsis.

Findings

Black patients had a higher likelihood of receiving lactate measurements on day 1 in the ICU.

Women had a lower frequency of subsequent lactate measurements compared to men.

Patients with private insurance and elective admission had more frequent lactate measurements.

Abstract

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. The goal of this study is to evaluate variation in serum lactate measurements in the Intensive Care Unit (ICU).

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 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.

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). Interestingly, 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).

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. Variation in biomarker monitoring can be a source of data bias when modeling patient outcomes, and thus should be accounted for in every analysis.

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