Use of Electronic Health Record Data to Evaluate the Impact of Race on 30-Day Mortality in Patients Admitted to the Intensive Care Unit

Linking Sepsis with chronic arterial hypertension, diabetes mellitus, and socioeconomic factors in the United States: A scoping review

RATIONALE

Sepsis is a syndrome of life-threatening organ dysfunction caused by a dysregulated host immune response to infection. Social risk factors including location and poverty are associated with sepsis-related disparities. Understanding the social and biological phenotypes linked with the incidence of sepsis is warranted to identify the most at-risk populations. We aim to examine how factors in disadvantage influence health disparities related to sepsis.

METHODS

A scoping review was performed for English-language articles published in the United States from 1990 to 2022 on PubMed, Web of Science, and Scopus. Of the 2064 articles found, 139 met eligibility criteria and were included for review.

RESULTS

There is consistency across the literature of disproportionately higher rates of sepsis incidence, mortality, readmissions, and associated complications, in neighborhoods with socioeconomic disadvantage and significant poverty. Chronic arterial hypertension and diabetes mellitus also occur more frequently in the same geographic distribution as sepsis, suggesting a potential shared pathophysiology.

CONCLUSIONS

The distribution of chronic arterial hypertension, diabetes mellitus, social risk factors associated with socioeconomic disadvantage, and sepsis incidence, are clustered in specific geographical areas and linked by endothelial dysfunction. Such population factors can be utilized to create equitable interventions aimed at mitigating sepsis incidence and sepsis-related disparities.

Racial Disparities in ICU Outcomes: A Systematic Review

OBJECTIVES

Racial disparities in the United States healthcare system are well described across a variety of clinical settings. The ICU is a clinical environment with a higher acuity and mortality rate, potentially compounding the impact of disparities on patients. We sought to systematically analyze the literature to assess the prevalence of racial disparities in the ICU.

DATA SOURCES

We conducted a comprehensive search of PubMed/MEDLINE, Scopus, CINAHL, and the Cochrane Library.

STUDY SELECTION

We identified articles that evaluated racial differences on outcomes among ICU patients in the United States. Two authors independently screened and selected articles for inclusion.

DATA EXTRACTION

We dual-extracted study characteristics and outcomes that assessed for disparities in care (e.g., in-hospital mortality, ICU length of stay). Studies were assessed for bias using the Newcastle-Ottawa Scale.

DATA SYNTHESIS

Of 1,325 articles screened, 25 articles were included (n = 751,796 patients). Studies demonstrated race-based differences in outcomes, including higher mortality rates for Black patients when compared with White patients. However, when controlling for confounding variables, such as severity of illness and hospital type, mortality differences based on race were no longer observed. Additionally, results revealed that Black patients experienced greater financial impacts during an ICU admission, were less likely to receive early tracheostomy, and were less likely to receive timely antibiotics than White patients. Many studies also observed differences in patients' end-of-life care, including lower rates on the quality of dying, less advanced care planning, and higher intensity of interventions at the end of life for Black patients.

CONCLUSIONS

This systematic review found significant differences in the care and outcomes among ICU patients of different races. Mortality differences were largely explained by accompanying demographic and patient factors, highlighting the effect of structural inequalities on racial differences in mortality in the ICU. This systematic review provides evidence that structural inequalities in care persist in the ICU, which contribute to racial disparities in care. Future research should evaluate interventions to address inequality in the ICU.

The Problem of Fairness in Synthetic Healthcare Data

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must "fairly" represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets.