Hussein Nebal S Abu, Pradhan Pratiksha, Haug Fredrik Willumsen, Moukheiber Dana, Moukheiber Lama, Moukheiber Mira, Moukheiber Sulaiman, Weishaupt Luca Leon, Ellen Jacob G, D'Couto Helen, Williams Ishan C, Celi Leo Anthony, Matos João, Struja Tristan
Yale University.
Massachusetts Institute of Technology.
Res Sq. 2025 Feb 6:rs.3.rs-5836145. doi: 10.21203/rs.3.rs-5836145/v1.
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.
健康不平等可能由性别、语言能力和种族等人口统计学因素驱动。这些差异可能通过检测可能性表现出来，进而可能使人工智能模型产生偏差。本研究的目的是评估重症监护病房（ICU）中血清乳酸测量值的差异。
利用多参数智能监测数据库第四版（MIMIC-IV，2008 - 2019年），我们确定了符合脓毒症-3标准的成年人。排除标准为ICU住院时间<1天、种族不明、年龄<18岁以及再次住院。采用靶向最大似然估计分析，我们评估了第1天进行乳酸测量的可能性。对于在第1天进行测量的患者，我们评估了后续读数的预测因素。
我们研究了15601名患者（19.5%为少数族裔、42.4%为女性、10.0%英语水平有限）。在调整混杂因素后，黑人患者在第1天接受乳酸测量的可能性略高（优势比1.19，95%置信区间（CI）1.06 - 1.34），但其他少数族裔群体并非如此。各种族的后续测量频率相似，但女性的发病率比（IRR）较低，为0.94（95% CI 0.90 - 0.98）。有趣的是，择期入院且有私人保险的患者重复进行血清乳酸测量的频率也较高（IRR分别为1.70，95% CI 1.61 - 1.81和1.07，95% CI 1.02 - 1.12）。
我们发现，脓毒症患者中，除黑人患者略有增加、女性频率降低外，不同人口统计学特征患者在乳酸测量可能性方面没有差异。在对患者预后进行建模时，生物标志物监测的差异可能是数据偏差的一个来源，因此在每次分析中都应予以考虑。
