Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes

Choosing to End African American Health Disparities in Patients With Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is three times more common and its manifestations are more severe in African American women compared to women of other races. It is not clear whether this is due to genetic differences or factors related to the physical or social environments, differences in health care, or a combination of these factors. Health disparities in patients with SLE between African American patients and persons of other races have been reported since the 1960s and are correlated with measures of lower socioeconomic status. Risk factors for these disparities have been demonstrated, but whether their mitigation improves outcomes for African American patients has not been tested except in self-efficacy. In 2002, the first true US population-based study of patients with SLE with death certificate records was conducted, which demonstrated a wide disparity between the number of African American women and White women dying from SLE. Five years ago, another study showed that SLE mortality rates in the United States had improved but that the African American patient mortality disparity persisted. Between 2014 and 2021, one study demonstrated racism's deleterious effects in patients with SLE. Racism may have been the unmeasured confounder, the proverbial "elephant in the room"-unnamed and unstudied. The etymology of "risk factor" has evolved from environmental risk factors to social determinants to now include structural injustice/structural racism. Racism in the United States has a centuries-long existence and is deeply ingrained in US society, making its detection and resolution difficult. However, racism being man made means Man can choose to change the it. Health disparities in patients with SLE should be addressed by viewing health care as a basic human right. We offer a conceptual framework and goals for both individual and national actions.

Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients

Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.

The Impact of Health Care Algorithms on Racial and Ethnic Disparities : A Systematic Review

BACKGROUND

There is increasing concern for the potential impact of health care algorithms on racial and ethnic disparities.

PURPOSE

To examine the evidence on how health care algorithms and associated mitigation strategies affect racial and ethnic disparities.

DATA SOURCES

Several databases were searched for relevant studies published from 1 January 2011 to 30 September 2023.

STUDY SELECTION

Using predefined criteria and dual review, studies were screened and selected to determine: 1) the effect of algorithms on racial and ethnic disparities in health and health care outcomes and 2) the effect of strategies or approaches to mitigate racial and ethnic bias in the development, validation, dissemination, and implementation of algorithms.

DATA EXTRACTION

Outcomes of interest (that is, access to health care, quality of care, and health outcomes) were extracted with risk-of-bias assessment using the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool and adapted CARE-CPM (Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models) equity extension.

DATA SYNTHESIS

Sixty-three studies (51 modeling, 4 retrospective, 2 prospective, 5 prepost studies, and 1 randomized controlled trial) were included. Heterogenous evidence on algorithms was found to: a) reduce disparities (for example, the revised kidney allocation system), b) perpetuate or exacerbate disparities (for example, severity-of-illness scores applied to critical care resource allocation), and/or c) have no statistically significant effect on select outcomes (for example, the HEART Pathway [history, electrocardiogram, age, risk factors, and troponin]). To mitigate disparities, 7 strategies were identified: removing an input variable, replacing a variable, adding race, adding a non-race-based variable, changing the racial and ethnic composition of the population used in model development, creating separate thresholds for subpopulations, and modifying algorithmic analytic techniques.

LIMITATION

Results are mostly based on modeling studies and may be highly context-specific.

CONCLUSION

Algorithms can mitigate, perpetuate, and exacerbate racial and ethnic disparities, regardless of the explicit use of race and ethnicity, but evidence is heterogeneous. Intentionality and implementation of the algorithm can impact the effect on disparities, and there may be tradeoffs in outcomes.

PRIMARY FUNDING SOURCE

Agency for Healthcare Quality and Research.

Seven Strategies to Integrate Equity within Translational Research in Neurology

The rapidly accelerating translation of biomedical advances is leading to revolutionary therapies that are often inaccessible to historically marginalized populations. We identified and synthesized recent guidelines and statements to propose 7 strategies to integrate equity within translational research in neurology: (1) learn history; (2) learn about upstream forces; (3) diversify and liberate; (4) change narratives and adopt best communication practices; (5) study social drivers of health and lived experiences; (6) leverage health technologies; and (7) build, sustain, and lead culturally humble teams. We propose that equity should be a major goal of translational research, equally important as safety and efficacy. ANN NEUROL 2024;95:432-441.

Performance of the pooled cohort equations in cancer survivors: the Atherosclerosis Risk in Communities study

BACKGROUND

Cancer survivors may have elevated atherosclerotic cardiovascular disease (ASCVD) risk. Therefore, we tested how accurately the American College of Cardiology/American Heart Association 2013 pooled cohort equations (PCEs) predict 10-year ASCVD risk in cancer survivors.

OBJECTIVES

To estimate the calibration and discrimination of the PCEs in cancer survivors compared to non-cancer participants in the Atherosclerosis Risk in Communities (ARIC) study.

METHODS

We evaluated the PCEs' performance among 1244 cancer survivors and 3849 cancer-free participants who were free of ASCVD at the start of follow-up. Each cancer survivor was incidence-density matched with up to five controls by age, race, sex, and study center. Follow-up began at the first study visit at least 1 year after the diagnosis date of the cancer survivor and finished at the ASCVD event, death, or end of follow-up. Calibration and discrimination were assessed and compared between cancer survivors and cancer-free participants.

RESULTS

Cancer survivors had higher PCE-predicted risk, at 26.1%, compared with 23.1% for cancer-free participants. There were 110 ASCVD events in cancer survivors and 332 ASCVD events in cancer-free participants. The PCEs overestimated ASCVD risk in cancer survivors and cancer-free participants by 45.6% and 47.4%, respectively, with poor discrimination in both groups (C-statistic for cancer survivors = 0.623; for cancer-free participants, C = 0.671).

CONCLUSIONS

The PCEs overestimated ASCVD risk in all participants. The performance of the PCEs was similar in cancer survivors and cancer-free participants.

IMPLICATIONS FOR CANCER SURVIVORS

Our findings suggest that ASCVD risk prediction tools tailored to survivors of adult cancers may not be needed.

The use of race terms in epigenetics research: considerations moving forward

The field of environmental epigenetics is uniquely suited to investigate biologic mechanisms that have the potential to link stressors to health disparities. However, it is common practice in basic epigenetic research to treat race as a covariable in large data analyses in a way that can perpetuate harmful biases without providing any biologic insight. In this article, we i) propose that epigenetic researchers open a dialogue about how and why race is employed in study designs and think critically about how this might perpetuate harmful biases; ii) call for interdisciplinary conversation and collaboration between epigeneticists and social scientists to promote the collection of more detailed social metrics, particularly institutional and structural metrics such as levels of discrimination that could improve our understanding of individual health outcomes; iii) encourage the development of standards and practices that promote full transparency about data collection methods, particularly with regard to race; and iv) encourage the field of epigenetics to continue to investigate how social structures contribute to biological health disparities, with a particular focus on the influence that structural racism may have in driving these health disparities.

Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation

Data-driven clinical prediction algorithms are used widely by clinicians. Understanding what factors can impact the performance and fairness of data-driven algorithms is an important step towards achieving equitable healthcare. To investigate the impact of modeling choices on the algorithmic performance and fairness, we make use of a case study to build a prediction algorithm for estimating glomerular filtration rate (GFR) based on the patient's electronic health record (EHR). We compare three distinct approaches for estimating GFR: CKD-EPI equations, epidemiological models, and EHR-based models. For epidemiological models and EHR-based models, four machine learning models of varying computational complexity (i.e., linear regression, support vector machine, random forest regression, and neural network) were compared. Performance metrics included root mean squared error (RMSE), median difference, and the proportion of GFR estimates within 30% of the measured GFR value (P30). Differential performance between non-African American and African American group was used to assess algorithmic fairness with respect to race. Our study showed that the variable race had a negligible effect on error, accuracy, and differential performance. Furthermore, including more relevant clinical features (e.g., common comorbidities of chronic kidney disease) and using more complex machine learning models, namely random forest regression, significantly lowered the estimation error of GFR. However, the difference in performance between African American and non-African American patients did not decrease, where the estimation error for African American patients remained consistently higher than non-African American patients, indicating that more objective patient characteristics should be discovered and included to improve algorithm performance.

Factors Associated with Pre-Research Recruitment in Autism and Related Developmental Disorders

PURPOSE

Access to research programs and increased diversity in research enrollment may be key to improving diverse populations' health and healthcare outcomes. To facilitate research recruitment, a Research Registry ("Registry"), a pre-recruitment database, was developed at an urban tertiary Autism Center ("Autism Center"). In this study, we examined whether disparities in research participation occur in the pre-research recruitment (pre-recruitment) stage.

METHODS

We compared demographic factors of patients seen at the Autism Center (but not enrolled in the Registry) vs. patients enrolled in the Registry. We also examined whether demographic factors differ among the Registry participants who were enrolled in the Registry by signing an informed consent form (ICF) vs. by returning a research interest form (RIF).

RESULTS

A total of 18,522 patients (including 1092 patients in the Registry with 403 patients via ICF and 689 patients via RIF) were included in this study. English as the primary language, White race, Non-Hispanic ethnicity, and younger age at their first clinic encounter were associated with the Registry. In the Registry sample, the RIF group had a higher proportion of non-English as a primary language, Medicaid insurance, longer distance from the Autism Center, and lower median household income (based on their ZIP code) than the ICF group.

CONCLUSIONS

This study suggests that disparities may have existed in the pre-research recruitment stage. To achieve equity in both clinical and research advancements in autism and related developmental disorders, further efforts are needed to equitably disseminate research opportunities to patients of diverse backgrounds.

Race and Ethnicity and Primary Language in Emergency Department Triage

Importance

Emergency department (ED) triage substantially affects how long patients wait for care but triage scoring relies on few objective criteria. Prior studies suggest that Black and Hispanic patients receive unequal triage scores, paralleled by disparities in the depth of physician evaluations.

Objectives

To examine whether racial disparities in triage scores and physician evaluations are present across a multicenter network of academic and community hospitals and evaluate whether patients who do not speak English face similar disparities.

Design, Setting, and Participants

This was a cross-sectional, multicenter study examining adults presenting between February 28, 2019, and January 1, 2023, across the Mass General Brigham Integrated Health Care System, encompassing 7 EDs: 2 urban academic hospitals and 5 community hospitals. Analysis included all patients presenting with 1 of 5 common chief symptoms.

Exposures

Emergency department nurse-led triage and physician evaluation.

Main Outcomes and Measures

Average Triage Emergency Severity Index [ESI] score and average visit work relative value units [wRVUs] were compared across symptoms and between individual minority racial and ethnic groups and White patients.

Results

There were 249 829 visits (149 861 female [60%], American Indian or Alaska Native 0.2%, Asian 3.3%, Black 11.8%, Hispanic 18.8%, Native Hawaiian or Other Pacific Islander <0.1%, White 60.8%, and patients identifying as Other race or ethnicity 5.1%). Median age was 48 (IQR, 29-66) years. White patients had more acute ESI scores than Hispanic or Other patients across all symptoms (eg, chest pain: Hispanic, 2.68 [95% CI, 2.67-2.69]; White, 2.55 [95% CI, 2.55-2.56]; Other, 2.66 [95% CI, 2.64-2.68]; P < .001) and Black patients across most symptoms (nausea/vomiting: Black, 2.97 [95% CI, 2.96-2.99]; White: 2.90 [95% CI, 2.89-2.91]; P < .001). These differences were reversed for wRVUs (chest pain: Black, 4.32 [95% CI, 4.25-4.39]; Hispanic, 4.13 [95% CI, 4.08-4.18]; White 3.55 [95% CI, 3.52-3.58]; Other 3.96 [95% CI, 3.84-4.08]; P < .001). Similar patterns were seen for patients whose primary language was not English.

Conclusions and Relevance

In this cross-sectional study, patients who identified as Black, Hispanic, and Other race and ethnicity were assigned less acute ESI scores than their White peers despite having received more involved physician workups, suggesting some degree of mistriage. Clinical decision support systems might reduce these disparities but would require careful calibration to avoid replicating bias.

Evaluating equity in performance of an electronic health record-based 6-month mortality risk model to trigger palliative care consultation: a retrospective model validation analysis

OBJECTIVE

Evaluate predictive performance of an electronic health record (EHR)-based, inpatient 6-month mortality risk model developed to trigger palliative care consultation among patient groups stratified by age, race, ethnicity, insurance and socioeconomic status (SES), which may vary due to social forces (eg, racism) that shape health, healthcare and health data.

DESIGN

Retrospective evaluation of prediction model.

SETTING

Three urban hospitals within a single health system.

PARTICIPANTS

All patients ≥18 years admitted between 1 January and 31 December 2017, excluding observation, obstetric, rehabilitation and hospice (n=58 464 encounters, 41 327 patients).

MAIN OUTCOME MEASURES

General performance metrics (c-statistic, integrated calibration index (ICI), Brier Score) and additional measures relevant to health equity (accuracy, false positive rate (FPR), false negative rate (FNR)).

RESULTS

For black versus non-Hispanic white patients, the model's accuracy was higher (0.051, 95% CI 0.044 to 0.059), FPR lower (-0.060, 95% CI -0.067 to -0.052) and FNR higher (0.049, 95% CI 0.023 to 0.078). A similar pattern was observed among patients who were Hispanic, younger, with Medicaid/missing insurance, or living in low SES zip codes. No consistent differences emerged in c-statistic, ICI or Brier Score. Younger age had the second-largest effect size in the mortality prediction model, and there were large standardised group differences in age (eg, 0.32 for non-Hispanic white versus black patients), suggesting age may contribute to systematic differences in the predicted probabilities between groups.

CONCLUSIONS

An EHR-based mortality risk model was less likely to identify some marginalised patients as potentially benefiting from palliative care, with younger age pinpointed as a possible mechanism. Evaluating predictive performance is a critical preliminary step in addressing algorithmic inequities in healthcare, which must also include evaluating clinical impact, and governance and regulatory structures for oversight, monitoring and accountability.

Demonstrating Trustworthiness to Patients in Data-Driven Health Care

Patient data is used to drive an ecosystem of advanced digital tools in health care, like predictive models or artificial intelligence-based decision support. Patients themselves, however, receive little information about these technologies or how they affect their care. This raises important questions about patient trust and continued engagement in a health care system that extracts their data but does not treat them as key stakeholders. This essay explores these tensions and provides steps forward for health systems as they design advanced health information-technology (IT) policies and practices. It centers patients, their concerns, and the ways they perceive trustworthiness to reframe advanced health IT in service of patient interests.

Health equity: A concept analysis

BACKGROUND

Although health equity is critically important for healthcare delivery, there are inconsistencies in its definitions or lack of definitions.

PURPOSE

Develop a comprehensive understanding of health equity to guide nursing practice and healthcare policy.

METHOD

Walker and Avant's concept analysis method was used to establish defining attributes, antecedents, consequences, and empirical referents of health equity.

FINDINGS

Health equity defining attributes are grounded in ethical principles, the absence of unfair and avoidable differences, and fair and just opportunities to attain a person's full health potential. Health equity antecedents are categorized into environmental; financial or economic; law, politics, and policy; societal and structural; research; and digital and technology.

DISCUSSION

Health equity's antecedents are useful to distinguish health disparities from health outcomes resulting from individual preferences. To achieve health equity, organizations need to focus on addressing the antecedents.

Retaining Race in Chronic Kidney Disease Diagnosis and Treatment

The best overall measure of kidney function is glomerular filtration rate (GFR) as commonly estimated from serum creatinine concentrations (eGFRcr) using formulas that correct for the higher average creatinine concentrations in Blacks. After two decades of use, these formulas have come under scrutiny for estimating GFR differently in Blacks and non-Blacks. Discussions of whether to include race (Black vs. non-Black) in the calculation of eGFRcr fail to acknowledge that the original race-based eGFRcr provided the same CKD treatment recommendations for Blacks and non-Blacks based on directly (exogenously) measured GFR. Nevertheless, the National Kidney Foundation and the American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease removed race in CKD treatment guidelines and pushed for the immediate adoption of a race-free eGFRcr formula by physicians and clinical laboratories. This formula is projected to negate CKD in 5.51 million White and other non-Black adults and reclassify CKD to less severe stages in another 4.59 million non-Blacks, in order to expand treatment eligibility to 434,000 Blacks not previously diagnosed and to 584,000 Blacks previously diagnosed with less severe CKD. This review examines: 1) the validity of the arguments for removing the original race correction, and 2) the performance of the proposed replacement formula. Excluding race in the derivation of eGFRcr changed the statistical bias from +3.7 to -3.6 ml/min/1.73m2 in Blacks and from +0.5 to +3.9 in non-Blacks, i.e., promoting CKD diagnosis in Blacks at the cost of restricting diagnosis in non-Blacks. By doing so, the revised eGFRcr greatly exaggerates the purported racial disparity in CKD burden. Claims that the revised formulas identify heretofore undiagnosed CKD in Blacks are not supported when studies that used kidney failure replacement therapy and mortality are interpreted as proxies for baseline CKD. Alternatively, a race-stratified eGFRcr (i.e., separate equations for Blacks and non-Blacks) would provide the least biased eGFRcr for both Blacks and non-Blacks and the best medical treatment for all patients.

An Anti-Racism and Equity Initiative Improves Residency Educational Conferences

Background

Graduate medical education curricula may reinforce systemic inequities and bias, thus contributing to health disparities. Curricular interventions and evaluation measures are needed to increase trainee awareness of bias and known inequities in health care.

Objective

This study sought to improve the content of core noontime internal medicine residency educational conferences by implementing the Department of Medicine Anti-Racism and Equity (DARE) educational initiative.

Methods

DARE best practices were developed from available anti-racism and equity educational materials. Volunteer trainees and faculty in the department of medicine of a large urban academic medical center were recruited and underwent an hourlong training to utilize DARE best practices to coach faculty on improving the anti-racist and equity content of educational conferences. DARE coaches then met with faculty to review the planned 2021-2022 academic year (AY) lectures and facilitate alignment with DARE best practices. A rubric was created from DARE practices and utilized to compare pre-intervention (AY21) and post-intervention (AY22) conferences.

Results

Using the DARE best practices while coaching increased the anti-racism and equity content from AY21 to AY22 (total rubric score mean [SD] 0.16 [1.19] to 1.38 [1.39]; P=.001; possible scores -4 to +5), with 75% (21 of 28) of AY22 conferences showing improvement. This included increased diversity of photographs, discussion of the racial or ethnic makeup of research study participants, appropriate use of race in case vignettes, and discussion of the impact of racism or bias on health disparities.

Conclusions

Training coaches to implement DARE best practices improved the anti-racism and equity content of existing noontime internal medicine residency educational conferences.

Practical Strategies to Overcome Racial Bias in Nursing

Race has long been used to classify and oppress or provide privilege to groups of people. Despite race being a construct created by White Europeans to justify colonialism and the inhumane enslavement of Africans, race is still used in health care 400 years later. Similarly, race-based clinical algorithms are used today to justify deferential treatment of minoritized people, which often drives racial inequities in health outcomes. In this commentary, we provide an overview of race and discuss its relevance in health care and nursing practice. We provide recommendations for nurses to challenge their own biases and beliefs related to race and to be advocates for their clients by interrogating the unjust practices that drive inequities so that we may progress toward health equity.

Social Determinants of Health and Their Impact on the Black Race Coefficient in Serum Creatinine-Based Estimation of GFR: Secondary Analysis of MDRD and CRIC Studies

BACKGROUND

The cause for differences in serum creatinine between Black and non-Black individuals incorporated into prior GFR-estimating equations is not understood. We explored whether social determinants of health can account for this difference.

METHODS

We conducted a secondary analysis of baseline data of the Modification of Diet in Renal Disease and Chronic Renal Insufficiency Cohort studies ( N =1628 and 1423, respectively). Data in both study cohorts were stratified by race (Black versus non-Black). We first evaluated the extent to which the coefficient of Black race in estimating GFR from creatinine is explained by correlations of race with social determinants of health and non-GFR determinants of creatinine. Second, we evaluated whether the difference between race groups in adjusted mean creatinine can be explained by social determinants of health and non-GFR determinants of creatinine.

RESULTS

In models regressing measured GFR on creatinine, age, sex, and race, the coefficient for Black race was 21% (95% confidence interval, 0.176 to 0.245) in Modification of Diet in Renal Disease and 13% (95% confidence interval, 0.097 to 0.155) in the Chronic Renal Insufficiency Cohort and was not attenuated by the addition of social determinants of health, alone or in combination. In both studies, the coefficient for Black race was larger at lower versus higher income levels. In models, regressing creatinine on measured GFR, age, and sex, mean creatinine was higher in Black versus non-Black participants in both studies, with no effect of social determinants of health.

CONCLUSIONS

Adjustment for selected social determinants of health did not influence the relationship between Black race and creatinine-based estimated GFR.

Materialized Oppression in Medical Tools and Technologies

It is well-known that racism is encoded into the social practices and institutions of medicine. Less well-known is that racism is encoded into the material artifacts of medicine. We argue that many medical devices are not merely biased, but materialize oppression. An oppressive device exhibits a harmful bias that reflects and perpetuates unjust power relations. Using pulse oximeters and spirometers as case studies, we show how medical devices can materialize oppression along various axes of social difference, including race, gender, class, and ability. Our account uses political philosophy and cognitive science to give a theoretical basis for understanding materialized oppression, explaining how artifacts encode and carry oppressive ideas from the past to the present and future. Oppressive medical devices present a moral aggregation problem. To remedy this problem, we suggest redundantly layered solutions that are coordinated to disrupt reciprocal causal connections between the attitudes, practices, and artifacts of oppressive systems.

Clinical Decision Support for Precision Dosing: Opportunities for Enhanced Equity and Inclusion in Health Care

Precision dosing aims to tailor doses to individual patients with the goal of improving treatment efficacy and avoiding toxicity. Clinical decision support software (CDSS) plays a crucial role in mediating this process, translating knowledge derived from clinical trials and real-world data (RWD) into actionable insights for clinicians to use at the point of care. However, not all patient populations are proportionally represented in clinical trials and other data sources that inform CDSS tools, limiting the applicability of these tools for underrepresented populations. Here, we review some of the limitations of existing CDSS tools and discuss methods for overcoming these gaps. We discuss considerations for study design and modeling to create more inclusive CDSS, particularly with an eye toward better incorporation of biological indicators in place of race, ethnicity, or sex. We also review inclusive practices for collection of these demographic data, during both study design and in software user interface design. Because of the role CDSS plays in both recording routine clinical care data and disseminating knowledge derived from data, CDSS presents a promising opportunity to continuously improve precision dosing algorithms using RWD to better reflect the diversity of patient populations.

The Impact of the COVID-19 Pandemic on Kidney Transplant Candidate Waitlist Status across Demographic and Geographic Groups: A National Analysis of UNOS STAR Data

The primary goal of this retrospective study is to understand how the COVID-19 pandemic differentially impacted transplant status across race, sex, age, primary insurance, and geographic regions by examining which candidates: (i) remained on the waitlist, (ii) received transplants, or (iii) were removed from the waitlist due to severe sickness or death on a national level. Methods: The trend analysis aggregated by monthly transplant data from 1 December 2019 to 31 May 2021 (18 months) at the transplant center level. Ten variables about every transplant candidate were extracted from UNOS standard transplant analysis and research (STAR) data and analyzed. Characteristics of demographical groups were analyzed bivariately using t-test or Mann-Whitney U test for continuous variables and using Chi-sq/Fishers exact tests for categorical variables. Results: The trend analysis with the study period of 18 months included 31,336 transplants across 327 transplant centers. Patients experienced a longer waiting time when their registration centers in a county where high numbers of COVID-19 deaths were observed (SHR < 0.9999, p < 0.01). White candidates had a more significant transplant rate reduction than minority candidates (-32.19% vs. -20.15%) while minority candidates were found to have a higher waitlist removal rate than White candidates (9.23% vs. 9.45%). Compared to minority patients, White candidates' sub-distribution hazard ratio of the transplant waiting time was reduced by 55% during the pandemic period. Candidates in the Northwest United States had a more significant reduction in the transplant rate and a greater increase in the removal rate during the pandemic period. Conclusions: Based on this study, waitlist status and disposition varied significantly based on patient sociodemographic factors. During the pandemic period, minority patients, those with public insurance, older patients, and those in counties with high numbers of COVID-19 deaths experienced longer wait times. In contrast, older, White, male, Medicare, and high CPRA patients had a statistically significant higher risk of waitlist removal due to severe sickness or death. The results of this study should be considered carefully as we approach a reopening world post-COVID-19, and further studies should be conducted to elucidate the relationship between transplant candidate sociodemographic status and medical outcomes during this era.

National Projections for Clinical Implications of Race-Free Creatinine-Based GFR Estimating Equations

BACKGROUND

The National Kidney Foundation and American Society of Nephrology Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease recently recommended a new race-free creatinine-based equation for eGFR. The effect on recommended clinical care across race and ethnicity groups is unknown.

METHODS

We analyzed nationally representative cross-sectional questionnaires and medical examinations from 44,360 participants collected between 2001 and 2018 by the National Health and Nutrition Examination Survey. We quantified the number and proportion of Black, White, Hispanic, and Asian/Other adults with guideline-recommended changes in care.

RESULTS

The new equation, if applied nationally, could assign new CKD diagnoses to 434,000 (95% confidence interval [CI], 350,000 to 517,000) Black adults, reclassify 584,000 (95% CI, 508,000 to 667,000) to more advanced stages of CKD, restrict kidney donation eligibility for 246,000 (95% CI, 189,000 to 303,000), expand nephrologist referrals for 41,800 (95% CI, 19,800 to 63,800), and reduce medication dosing for 222,000 (95% CI, 169,000 to 275,000). Among non-Black adults, these changes may undo CKD diagnoses for 5.51 million (95% CI, 4.86 million to 6.16 million), reclassify 4.59 million (95% CI, 4.28 million to 4.92 million) to less advanced stages of CKD, expand kidney donation eligibility for 3.96 million (95% CI, 3.46 million to 4.46 million), reverse nephrologist referral for 75,800 (95% CI, 35,400 to 116,000), and reverse medication dose reductions for 1.47 million (95% CI, 1.22 million to 1.73 million). The racial and ethnic mix of the populations used to develop eGFR equations has a substantial effect on potential care changes.

CONCLUSION

The newly recommended 2021 CKD-EPI creatinine-based eGFR equation may result in substantial changes to recommended care for US patients of all racial and ethnic groups.

Use of Race in Kidney Function Estimation: Lessons Learned and the Path Toward Health Justice

In 2020, the nephrology community formally interrogated long-standing race-based clinical algorithms used in the field, including the kidney function estimation equations. A comprehensive understanding of the history of kidney function estimation and racial essentialism is necessary to understand underpinnings of the incorporation of a Black race coefficient into prior equations. We provide a review of this history, as well as the considerations used to develop race-free equations that are a guidepost for a more equity-oriented, scientifically rigorous future for kidney function estimation and other clinical algorithms and processes in which race may be embedded as a variable.

Increased COVID-19 Infection Risk Drives Racial and Ethnic Disparities in Severe COVID-19 Outcomes

COVID-19 inequities have been well-documented. We evaluated whether higher rates of severe COVID-19 in racial and ethnic minority groups were driven by higher infection rates by evaluating if disparities remained when analyses were restricted to people with infection. We conducted a retrospective cohort study of adults insured through Kaiser Permanente (Colorado, Northwest, Washington), follow-up in March-September 2020. Laboratory results and hospitalization diagnosis codes identified individuals with COVID-19. Severe COVID-19 was defined as invasive mechanical ventilation or mortality. Self-reported race and ethnicity, demographics, and medical comorbidities were extracted from health records. Modified Poisson regression estimated adjusted relative risks (aRRs) of severe COVID-19 in full cohort and among individuals with infection. Our cohort included 1,052,774 individuals, representing diverse racial and ethnic minority groups (e.g., 68,887 Asian, 41,243 Black/African American, 93,580 Hispanic or Latino/a individuals). Among 7,399 infections, 442 individuals experienced severe COVID-19. In the full cohort, severe COVID-19 aRRs for Asian, Black/African American, and Hispanic individuals were 2.09 (95% CI: 1.36, 3.21), 2.02 (1.39, 2.93), and 2.09 (1.57, 2.78), respectively, compared to non-Hispanic Whites. In analyses restricted to individuals with COVID-19, all aRRs were near 1, except among Asian Americans (aRR 1.82 [1.23, 2.68]). These results indicate increased incidence of severe COVID-19 among Black/African American and Hispanic individuals is due to higher infection rates, not increased susceptibility to progression. COVID-19 disparities most likely result from social, not biological, factors. Future work should explore reasons for increased severe COVID-19 risk among Asian Americans. Our findings highlight the importance of equity in vaccine distribution.

Health inequities start early in life, even before birth: Why race-specific fetal and neonatal growth references disadvantage Black infants

Clinicians and researchers use published standards to assess and classify the size and growth of the fetus and newborn infant. Fetal growth is slower on average in Black fetuses as compared with White fetuses, and existing standards differ in whether they are race-specific or not. Here, we apply a health equity lens to the topic of fetal and newborn growth assessment by critically appraising two widely available growth standards. We conclude that using race-based standards is not well-justified and could perpetuate or even worsen inequities in perinatal health outcomes. We therefore recommend that neonatal and perinatal providers remove race from the assessment of fetal and newborn size.

A Framework for Antiracist Curriculum Changes in Nephrology Education

Addressing persistent racial health disparities in cases of kidney disease will first require significant investment in examining how structural racism has influenced our clinical practice and medical education. Improving how we understand and articulate race is critical for achieving this goal. This work begins with ensuring that race's mention within nephrology literature and curricular materials for medical trainees is thoroughly rooted in evidence-based rationale-not to serve as a proxy for polygenic contributions, social determinants of health, or systemic health care barriers. While many institutions are increasingly recognizing the importance of instituting such changes on behalf of the systematically marginalized patient populations who are most affected by these disparities, there is a paucity of guidance on how to critically appraise and revise decades of pathophysiological and epidemiological findings through an antiracist lens. In this article, we propose an inquiry-based framework with case-study examples to help readers recognize improper use of race within nephrology, assess personal and institutional readiness to introduce changes to said content, and generate materials that center evidence-based findings and reject harmful misinterpretations of race.

The race coefficient in glomerular filtration rate-estimating equations and its removal

PURPOSE OF REVIEW

To review new publications about the use of the race coefficient in glomerular filtration rate (GFR)-estimating equations since this topic was last reviewed a year ago in Current Opinion in Nephrology and Hypertension .

RECENT FINDINGS

Accounting for race (or genetic ancestry) does improve the performance of GFR-estimating equations when serum creatinine (SCr) is used as the filtration marker but not when cystatin C is used. The National Kidney Foundation (NKF)-American Society of Nephrology (ASN) Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease recommended immediate adoption of a new refitted SCr-based equation without race and increased use of cystatin C. This report has created consensus but the endorsed new SCr equation without race underestimates GFR in Black Americans and overestimates GFR in non-Black Americans, which may result in diminished ability to detect racial disparities.

SUMMARY

The approach recommended by the NKF-ASN Task Force represents a compromise attempting to balance a number of competing values, including racial justice, benefit of classifying more Black Americans as having (more severe) chronic kidney disease, accuracy compared with measured GFR, and financial cost. The full implications of adopting the race-free refitted CKD-EPI SCr equation are yet to be known.

Prediction of chronic kidney disease and its progression by artificial intelligence algorithms

BACKGROUND AND OBJECTIVE

Aim of nephrologists is to delay the outcome and reduce the number of patients undergoing renal failure (RF) by applying prevention protocols and accurately monitoring chronic kidney disease (CKD) patients. General practitioners and nephrologists are involved in the first and in the late stages of the disease, respectively. Early diagnosis of CKD is an important step in preventing the progression of kidney damage. Our aim was to review publications on machine learning algorithms (MLAs) that can predict early CKD and its progression.

METHODS

We conducted a systematic review and selected 55 articles on the application of MLAs in CKD. PubMed, Medline, Scopus, Web of Science and IEEE Xplore Digital Library of the Institute of Electrical and Electronics Engineers were searched. The search terms were chronic kidney disease, artificial intelligence, data mining and machine learning algorithms.

RESULTS

MLAs use enormous numbers of predictors combining them in non-linear and highly interactive ways. This ability increases when new data is added. We observed some limitations in the publications: (i) databases were not accurately reviewed by physicians; (ii) databases did not report the ethnicity of the patients; (iii) some databases collected variables that were not important for the diagnosis and progression of CKD; (iv) no information was presented on the native kidney disease causing CKD; (v) no validation of the results in external independent cohorts was provided; and (vi) no insights were given on the MLAs that were used. Overall, there was limited collaboration among experts in electronics, computer science and physicians.

CONCLUSIONS

The application of MLAs in kidney diseases may enhance the ability of clinicians to predict CKD and RF, thus improving diagnostic assistance and providing suitable therapeutic decisions. However, it is necessary to improve the development process of MLA tools.

A Critical Look at Race-Based Practices in Rheumatology Guidelines

OBJECTIVE

To assess how race has been incorporated into rheumatology practice guidelines, including how race is defined and used in diagnostic and treatment recommendations.

METHODS

We searched race and ethnicity terms in all clinical practice guidelines from the American College of Rheumatology (ACR) and European Alliance of Associations for Rheumatology (EULAR) that were published between 2010 and 2020 and publicly available on professional society websites. Findings were summarized and assessed through standardized data abstraction forms. Key themes were identified through a thematic analysis approach.

RESULTS

A total of 23 ACR clinical practice guidelines and 42 EULAR recommendations were reviewed. In total, 16 of 65 (25%) of the guidelines used race terms in their text. No guideline clearly defined race, and race was often conflated with ethnicity and/or genetic ancestry. Reported racial categories varied substantially by guideline and often used classifications that oversimplified and excluded non-White races. Research with insufficient racial diversity was used to make race-based recommendations for Black patients that may not be generalizable. Additionally, recommendations using research on predominantly White populations reinforced data of White populations as normative and perpetuated race-based stereotypes, especially for rare diseases. Structural causes of identified racial disparities were not discussed in clinical guidelines.

CONCLUSION

There is an urgent need for standardized race reporting in rheumatology. Recommendations are provided to enhance consistency and accuracy of race and ethnicity terms, mitigate conflation of race with ethnicity or genetic ancestry, encourage a critical reanalysis of race-based diagnostic tools and treatment options, and better address the structural causes of racial disparities.

Short Report: Race and Ethnicity Misclassification in Kidney Transplantation Research

Recently, the misuse of race as a biological variable, rather than a social construct, in biomedical research has received national attention for its contributions to medical bias. In national transplant registry data, bias may arise from measurement imprecision because of the collection of provider-perceived race rather than patients’ own self-report.

Artificial intelligence in gastroenterology and hepatology: how to advance clinical practice while ensuring health equity

Artificial intelligence (AI) and machine learning (ML) systems are increasingly used in medicine to improve clinical decision-making and healthcare delivery. In gastroenterology and hepatology, studies have explored a myriad of opportunities for AI/ML applications which are already making the transition to bedside. Despite these advances, there is a risk that biases and health inequities can be introduced or exacerbated by these technologies. If unrecognised, these technologies could generate or worsen systematic racial, ethnic and sex disparities when deployed on a large scale. There are several mechanisms through which AI/ML could contribute to health inequities in gastroenterology and hepatology, including diagnosis of oesophageal cancer, management of inflammatory bowel disease (IBD), liver transplantation, colorectal cancer screening and many others. This review adapts a framework for ethical AI/ML development and application to gastroenterology and hepatology such that clinical practice is advanced while minimising bias and optimising health equity.

Clin-Star corner: What is new at the interface of geriatrics and nephrology?

Chronic kidney disease (CKD) is prevalent and burdensome among older adults in the United States. CKD affects at least 15% of the US population, and adults over 65 comprise the largest subset within this group. In this special article, we highlight key findings of three recent original investigations in nephrology and describe each study, relevance to the care of older adults, and current areas of uncertainty that warrant further investigation. Articles relate to removal of the race adjustment in the estimation of kidney function, the use of novel therapeutics to halt CKD progression and improve cardiovascular outcomes, and medication management for short-term pain control in CKD.

Diabetic Kidney Disease Is Associated With Increased Complications Following Operative Management of Ankle Fractures

Background:

Diabetes mellitus and peripheral neuropathy are established risk factors for complications in operatively treated ankle fractures. Generally, the presence of peripheral neuropathy and diabetic nephropathy have been used as independent variables in studies of diabetic ankle fracture cohorts but are typically treated as binary risk factors. Our purpose was to quantify the effects of risk factors on complication rate specific to diabetic patients undergoing ankle fracture fixation.

Methods:

We identified 617 rotational ankle fractures treated operatively at a single academic medical center from 2010 to 2019, of which 160 were identified as diabetic. Of these, 91 ankle fractures in 90 diabetic patients met criteria for retrospective review of clinical and radiographic data. Criteria included perioperative laboratory studies, including glycated hemoglobin (HbA1c) and estimated glomerular filtration rate (eGFR), as well as follow-up radiographs in the electronic record. We defined complications in this surgical cohort as deep surgical site infection, unplanned return to the operating room, and failure of fixation. Logistic regression was performed and odds ratios (ORs) calculated.

Results:

The overall complication rate was 28.6% (26/91) in this cohort. Median follow-up was 29 weeks (range: 5-520 weeks). Mean perioperative HbA1c in patients who experienced postoperative complications was 7.6% (range: 5.1%-14.2%) compared with 7.8% (range: 5.6%-13.5%) who did not ( P = .69). Diabetic patients with chronic kidney disease (eGFR <60 mL/min per body surface area) (OR 5.29, P = .006) and peripheral neuropathy (OR 4.61, P = .003) were at significantly higher risk of all complications compared with diabetic patients without these comorbidities. Of note, we did not find an association between perioperative HbA1c or body mass index and complication rate.

Conclusion:

Patients with diabetes complicated by chronic kidney disease are at significantly higher risk of complications following operative management of ankle fractures. Our study also corroborated previous reports that within this high-risk cohort, the presence of peripheral neuropathy is a significant risk factor for complications. These sequalae of diabetic disease are manifestations of microvascular disease, glycosylation of soft tissues, and impaired metabolic pathways. Identifying these risk factors in diabetic patients allows for patient-specific risk stratification, education, and management decisions of ankle fractures.

Level of Evidence:

Level III, retrospective cohort study.

Kidney Function Assessment in African American Patients: A Narrative Review for Pharmacists

Recent recognitions of longstanding societal inequity in kidney function assessments have prompted the call to eliminate race as part of the algorithm to assess estimated glomerular filtration rate (eGFR). Previous equations for eGFR estimation adopted race as part of the calculation. Incorporating race within eGFR equations results in overestimating and underestimating Black and nonblack patients, respectively. The inclusion of race is controversial. In September 2021, the National Kidney Foundation (NKF) and the American Society of Nephrology (ASN) combined task force recommended estimating the kidney function without using a race variable. The task force endorsed race-free creatinine-cystatin C equations to be more accurate than the creatinine-only equations. Before the application of NKF-ASN revised recommendations, major healthcare disparities influenced daily clinical practice. Those disparities include the delay in initiating medications that have reanl or cardio-protective effects, such as sodium-glucose cotransporter–2 inhibitors (SGLT-2i) and angiotensin-converting enzyme inhibitors (ACEIs). Clinical judgment should be employed when dose adjusting medications. Combining the eGFR with other clinical assessment tools such as urinary output, the expanded use of confirmatory tests, and the eGFR trend is suggested for a better kidney function assessment. Additionally, creatinine-cystatin C is recommended when feasible, and when institutions have the laboratory abilities.

Past and Present Policy Efforts in Achieving Racial Equity in Kidney Transplantation

Purpose of Review

Recent Findings

Summary

A scoping review of inequities in access to organ transplant in the United States

Background

Organ transplant is the preferred treatment for end-stage organ disease, yet the majority of patients with end-stage organ disease are never placed on the transplant waiting list. Limited access to the transplant waiting list combined with the scarcity of the organ pool result in over 100,000 deaths annually in the United States. Patients face unique barriers to referral and acceptance for organ transplant based on social determinants of health, and patients from disenfranchised groups suffer from disproportionately lower rates of transplantation. Our objective was to review the literature describing disparities in access to organ transplantation based on social determinants of health to integrate the existing knowledge and guide future research.

Methods

We conducted a scoping review of the literature reporting disparities in access to heart, lung, liver, pancreas and kidney transplantation based on social determinants of health (race, income, education, geography, insurance status, health literacy and engagement). Included studies were categorized based on steps along the transplant care continuum: referral for transplant, transplant evaluation and selection, living donor identification/evaluation, and waitlist outcomes.

Results

Our search generated 16,643 studies, of which 227 were included in our final review. Of these, 34 focused on disparities in referral for transplantation among patients with chronic organ disease, 82 on transplant selection processes, 50 on living donors, and 61 on waitlist management. In total, 15 studies involved the thoracic organs (heart, lung), 209 involved the abdominal organs (kidney, liver, pancreas), and three involved multiple organs. Racial and ethnic minorities, women, and patients in lower socioeconomic status groups were less likely to be referred, evaluated, and added to the waiting list for organ transplant. The quality of the data describing these disparities across the transplant literature was variable and overwhelmingly focused on kidney transplant.

Conclusions

This review contextualizes the quality of the data, identifies seminal work by organ, and reports gaps in the literature where future research on disparities in organ transplantation should focus. Future work should investigate the association of social determinants of health with access to the organ transplant waiting list, with a focus on prospective analyses that assess interventions to improve health equity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01616-x.