Evaluating equitable care in the ICU:Creating a causal inference template to assess the impact of life-sustaining interventions across racial and ethnic groups

Authors

Tristan Struja, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical University Clinic, Kantonsspital Aarau, Aarau, Switzerland. Electronic address: tstruja@mit.edu.
João Matos, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Faculty of Engineering of University of Porto, Porto, Portugal. Electronic address: jcmatos@mit.edu.
Barbara Lam, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. Electronic address: barbaradlam@gmail.com.
Yiren Cao, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: irene.yiren.cao@gmail.com.
Xiaoli Liu, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Center for Artificial Intelligence in Medicine, The General Hospital of PLA, Beijing, China. Electronic address: xiaoliliubuaa@gmail.com.
Ziyue Chan, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore. Electronic address: ziyue.chen3@gmail.com.
Yugang Jia, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: yugang@verily.com.
Christopher M. Sauer, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Hematology and Stem Cell Transplantation, University Hospital Essen, Germany; Institute for Artificial Intelligence in Medicine, University Hospital Essen, Germany. Electronic address: christopher.sauer@uk-essen.de.
Helen D'Couto, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. Electronic address: hdcouto@bidmc.harvard.edu.
Irene Dankwa-Mullan, Milken Institute School of Public Health, The George Washington University, Washington DC, USA. Electronic address: idankwamullan@gmail.com.
Leo Anthony Celi, Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address: lceli@mit.edu.
Andre Kurepa Waschka, Mercer University, Macon, GA, USA; Elon University, Elon NC, USA. Electronic address: waschka_ak@mercer.edu.

Document Type

Journal Article

Publication Date

3-30-2025

Journal

Heart & lung : the journal of critical care

Volume

72

DOI

10.1016/j.hrtlng.2025.03.011

Keywords

MIMIC-IV; TMLE; causal inference; critical care; sepsis; target trial

Abstract

BACKGROUND: Variability in the provision of intensive care unit (ICU)-interventions may lead to disparities between socially defined racial-ethnic groups. OBJECTIVES: We aimed to study the use of invasive mechanical ventilation (IMV), renal replacement therapy (RRT), and vasopressor agents (VP) to identify disparities in outcomes across race-ethnicity in patients with sepsis. METHODS: Retrospective analysis of treatment effect with a target trial design with treatment assignment within the first 24 h in MIMIC-IV (2008-2019) using targeted maximum likelihood estimation. Of 76,943 ICU stays in MIMIC-IV, 32,971 adult stays fulfilled sepsis-3 criteria. Primary outcome was in-hospital mortality. Secondary outcomes were hospital-free days, and occurrence of nosocomial infection stratified by predicted mortality probability and self-reported race-ethnicity. Average treatment effects by treatment type and race-ethnicity, Racial-ethnic group (REG) or White group (WG), were estimated. RESULTS: Of 19,419 admissions that met inclusion criteria, median age was 68 years, 57.4 % were women, 82 % were White, and mortality was 18.2 %. There was no difference in mortality benefit associated with the administration of IMV, RRT, or VP between the REG and the WG. There was also no difference in hospital-free days or nosocomial infections. These findings are unchanged with different eligibility periods. CONCLUSION: There were no differences in the treatment outcomes from three life-sustaining interventions in the ICU according to race-ethnicity. While there was no discernable harm from the treatments across mortality risk, on average there was also no measurable benefit. These findings highlight the need for research to better understand the risk-benefit ratio of interventions in the ICU.

APA Citation

Struja, Tristan; Matos, João; Lam, Barbara; Cao, Yiren; Liu, Xiaoli; Chan, Ziyue; Jia, Yugang; Sauer, Christopher M.; D'Couto, Helen; Dankwa-Mullan, Irene; Celi, Leo Anthony; and Waschka, Andre Kurepa, "Evaluating equitable care in the ICU:Creating a causal inference template to assess the impact of life-sustaining interventions across racial and ethnic groups" (2025). GW Authored Works. Paper 6740.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/6740

Department

Health Policy and Management