Hidden in Plain Sight—Reconsidering the Use of Race Correction in Clinical Algorithms

Vyas, Darshali A; Eisenstein, Leo; Jones, David S.

doi:10.1097/01.ogx.0000725672.30764.f7

Cited by 48 publications

(56 citation statements)

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“…Efficient COVID-19 transmission, a relatively high infection fatality ratio [2] and underprepared health systems [3] have seen many hospitals exceed capacity [4,5]. In the context of insufficient surge capacity, these predictors, alongside ethical considerations to avoid health inequities [6,7], may save lives through early risk stratification and better resource management [8,9]. Consequently, prognostic models are needed to identify the relative importance of different prognostic factors, their impact on mortality risk and to predict the course of infection of hospitalized patients [10].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

Boroujeni

Mendoza

Stopard

et al. 2021

Infectious Disease Reports

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As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients’ laboratory values. From the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (MCHC). Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.

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Section: Introductionmentioning

confidence: 99%

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

Boroujeni

Mendoza

Stopard

et al. 2021

Infectious Disease Reports

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“…It allows OPOs and transplant centers to mask poor performance rather than incentivizing organizational diversity, identifying and standardizing optimal OPO clinical practice, and gaining cultural competency in their approach to donation among minorities. The position of CMS to reject race‐based adjustments in the OPO Final Rule is warranted and is supported by current research 1 …”

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confidence: 79%

“…While these are difficult conversations, they are necessary to begin to dismantle biases that prevent equity, diversity, and inclusion. A recent paper in the New England Journal of Medicine highlighted examples of how race is incorporated into our medical decision‐making in numerous different fields 1 . Many medical algorithms adjust for race both in outcomes and treatment strategies without true scientific basis.…”

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confidence: 99%

Organ donation in diverse communities: The pitfalls of race correction

Shah

Cummings

Gray

et al. 2021

Clinical Transplantation

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“…Reflecting this, the use of a 'race correction' in clinical algorithms has been challenged, urging researchers and clinicians to distinguish between a descriptive and a prescriptive use of race, which may worsen inequities. 30 Second, medical education must rethink how it teaches race because we do not live in a post-racial society; race is often the defining feature of a patient's identity, determining as well their life chances and lived experiences. Finally, the field must acknowledge how students feel both about how race is presented to them in their medical education and whether they feel competent to integrate race into their future clinical care provision.…”

Section: Discussionmentioning

confidence: 99%

Thinking with two brains: Student perspectives on the presentation of race in pre‐clinical medical education

et al. 2021

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Context:There is growing concern that during their education medical students come to believe that 'race' is a biological construct and that differential treatment of patients based on 'race' is clinically beneficial. How 'race' is presented to medical students may influence both their implicit biases and future clinical practices, potentially widening racial disparities in care. Methods:We conducted in-depth interviews with twenty-two pre-clinical mostly non-White medical students attending a public medical school in a major metropolitan area in the northeastern United States. Interview content focused on how medical students experience the presentation of race in medical education, use race in their learning experiences, and envision using race as physicians in future clinical encounters. Transcripts were analysed using the framework method and emergent themes were identified.Results: Participants described being most aware of the presentation of race in board-style questions and least aware of the presentation of race during lectures.They described being aware of race in problem-based learning (PBL) modules if the case revolved around a likely race-disease association. They identified imprecision in how race was presented during lectures and insufficient explanations of causes of racial disparities in health. Participants described feeling ill-prepared to obtain racial self-identification and receiving mixed messages around the utility of race in diagnosing a patient. Participants reported experiences of cognitive dissonance around the presentation of race in board-style questions and lectures.Conclusions: Critical evaluation of the presentation of and instruction around 'race' is needed to address whether it is presented as a biological vs. social construct, the level of precision of racial categorisation in curricular content, and the causes of and mechanisms behind race-disease associations. This has the potential to minimise false beliefs about race as a biological construct and the resultant negative impacts on clinical care. Future research could evaluate whether problem-based or experiential (OSCE) learning, in contrast to board-style questions and didactic lectures, are the most effective way to educate students around race in health and illness.Additionally, future research can investigate if the mission (ie social) and composition

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Hidden in Plain Sight—Reconsidering the Use of Race Correction in Clinical Algorithms

Abstract: (Abstracted from N Engl J Med 2020;383(9):874–882) The role of race and ethnicity in medicine continues to be debated. On one side, categories of race and ethnicity are seen as clinically useful because they reflect population genetics.

Cited by 48 publications

References 0 publications

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients

Organ donation in diverse communities: The pitfalls of race correction

Thinking with two brains: Student perspectives on the presentation of race in pre‐clinical medical education

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