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. Author manuscript; available in PMC: 2025 Jun 1.
Published in final edited form as: Crit Care Med. 2024 Feb 26;52(6):951–962. doi: 10.1097/CCM.0000000000006237

Towards equitable kidney function estimation in critical care practice. Guidance from the Society of Critical Care Medicine’s Diversity, Equity and Inclusion in Renal Clinical Practice Task Force

Todd A Miano 1, Erin F Barreto 2, Molly McNett 3, Niels Martin 4, Ankit Sakhuja 5, Adair Andrews 6, Rajit K Basu 7, Enyo Ama Ablordeppey 8
PMCID: PMC11098700  NIHMSID: NIHMS1964091  PMID: 38407240

Abstract

Objective:

Accurate glomerular filtration rate (GFR) assessment is essential in critically ill patients. GFR is often estimated using creatinine-based equations, which require surrogates for muscle mass such as age and sex. Race has also been included in GFR equations, based on the assumption that Black individuals have genetically determined higher muscle mass. However, race-based GFR estimation has been questioned with the recognition that race is a poor surrogate for genetic ancestry, and racial health disparities are driven largely by socioeconomic factors. The American Society of Nephrology and the National Kidney Foundation (ASN/NKF) recommend widespread adoption of new “race-free” creatinine equations, and increased use of cystatin C as a race-agnostic GFR biomarker.

Data sources:

Literature review and expert consensus.

Study Selection:

English language publications evaluating GFR assessment and racial disparities.

Data extraction:

We provide an overview of the ASN/NKF recommendations. We then apply an Implementation Science methodology to identify facilitators and barriers to implementation of the ASN/NKF recommendations into critical care settings; and identify evidence-based implementation strategies. Lastly, we highlight research priorities for advancing GFR estimation in critically ill patients.

Data synthesis:

Implementation of the new creatinine-based GFR equation is facilitated by low cost and relative ease of incorporation into electronic health records. The key barrier to implementation is a lack of direct evidence in critically ill patients. Additional barriers to implementing cystatin C based GFR estimation include higher cost and lack of test availability in most laboratories. Further, cystatin C concentrations are influenced by inflammation, which complicates interpretation.

Conclusions:

The lack of direct evidence in critically ill patients is a key barrier to broad implementation of newly developed “race-free” GFR equations. Additional research evaluating GFR equations in critically ill patients and novel approaches to dynamic kidney function estimation is required to advance equitable GFR assessment in this vulnerable population.

Keywords: kidney function estimation, race, critical care medicine, implementation science, creatinine, cystatin C

Summary:

Implementation of new “race-free” equations for kidney function estimation will need to consider the complex web of factors influencing kidney function estimation in critical care. Additional research is required to advance equitable GFR estimation in this vulnerable population.

Introduction

Race and racism have a long and complicated history in the United States (U.S.) healthcare system (1,2). Racial disparities in healthcare are well documented, with persons of color often experiencing bias from healthcare workers, having less access to the healthcare system, and being at higher risk for adverse outcomes across a range of acute and chronic diseases (3–7). Such disparities extend to critical illness, with Black patients having a higher risk of sepsis, acute kidney injury (AKI), and intensive care unit mortality (8).

Racial differences have historically been understood to represent differences in genetic ancestry and attendant biology relevant to disease pathophysiology (1). This biological interpretation of race suggested racial healthcare disparities were driven by differences in biology, which led to race being included in many diagnostic and treatment algorithms (1). However, substantial evidence now demonstrates race is a poor surrogate for underlying genetic differences between groups (9), with genetic diversity having equal or greater magnitude within “racial” categories compared to across categories (10,11). In addition, numerous studies have described the important contribution of socioeconomic factors to existing health disparities (3,12). Taken together, this suggests racial disparities in health outcomes are not due to biologic differences, but rather the lived experiences of persons exposed to racism (12). The recognition of race as a social construct rather than a biological construct challenges the rationale for using race in treatment decisions. At the forefront of this discussion has been the debate over including race in the estimation of kidney function via the glomerular filtration rate (GFR) (13).

Race in GFR estimation

GFR, the standard measure of kidney function, is most often estimated using equations based on the serum concentration of creatinine, an endogenous biomarker eliminated primarily via glomerular filtration (14). Creatinine is released into the blood as a byproduct of muscle metabolism. Consequently, using creatinine to assess GFR requires consideration of patient muscle mass. Because direct measurement of muscle mass is infeasible in routine patient care, clinicians rely on surrogates for muscle mass such as age, sex, and nutritional status (14). Contemporary GFR equations also include a race adjustment, where black individuals are assigned higher estimated GFR than non-black individuals with the same age, sex, and creatinine concentration (Figure 1) (15,16). For example, the 2009 Chronic Kidney Disease Epidemiology Collaborating equation (CKD-EPI-2009) assigns a 16% higher GFR to black individuals (15). Race adjustments are based on the observation that black individuals have higher measured GFR compared to non-black individuals with the same age, sex, and creatinine value (17), and the assumption that black individuals have higher muscle mass on average (13,14). Over the past two decades, race-based GFR equations have been widely disseminated within medical practice, with >90% of U.S. medical laboratories automatically reporting estimated GFR alongside serum creatinine (18).

Figure 1: Nonrenal determinants of creatinine and the role of race.

Figure 1:

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Race has previously been understood to be a key factor in creatinine concentrations, independently or mediated biologically through altered creatinine production due to muscle mass differences across races. Race (black or non-black) was therefore included as a factor in the modeling used to develop creatinine-based equations that predicted measured GFR (panel A). Recently, it has become clear that race is a social construct rather than a biological one, with an unclear or weak association with genetic ancestry. Rather than a biological association, there is increasing concern that the relationship between race and both racism and social determinants of health (SDOH) may have impacted previously observed associations with kidney function and kidney disease (panel B).

However, a growing number of clinicians and researchers question the appropriateness of race-adjusted GFR. Critics highlight the paucity of evidence supporting higher muscle mass in Blacks (13,19) and have argued systematic assignment of higher GFR to Blacks may worsen disparities in black individuals, including delayed diagnosis of chronic kidney disease and lower rates of kidney transplantation (13,20). In addition, health equity researchers suggest race-based GFR estimation normalizes racial differences, thereby advancing racial stigmatization and bias (21). As the debate over race-based GFR estimation intensified, some health systems began reporting “race-free” GFR by simply omitting the race term from GFR equations (19,22). While this practice might address concerns regarding earlier CKD diagnosis and greater access to transplantation, it risks meaningful underestimation of GFR for black patients, which could lead to inadequate drug dosing and reduced eligibility for treatment with medications that are renally eliminated (23).

In response to these concerns, new GFR equations have been developed and the American Society of Nephrology and the National Kidney Foundation (ASN/NKF) recently provided consensus recommendations on race and GFR estimation (22). Their key recommendations included 1) widespread implementation of newly validated creatinine-based GFR equations which omit race as a variable; and 2) increased use of GFR equations based on cystatin C (Cys-C), an alternative endogenous marker of GFR not affected by changes in muscle mass (Table 1) (14). These recommendations are likely to have far reaching consequences given the essential and ubiquitous role of GFR assessment in medicine.

Table 1.

ASN/NKF recommendations on Reassessing the Inclusion of Race in GFR estimation

Recommendation Associated equations
1.) The ASN/NKF panel recommends for US adults (>85% of whom have normal kidney function) that the CKD-EPI equation that was developed without the use of a race variable be implemented immediately, including in all laboratories.
2021 CKD-EPI Creatinine Equationa

eGFR = 142 × min(Scr/κ, 1)α × max(Scr/κ, 1)−1.200 × 0.9938Age × 1.012 [if female]
2.) The ASN/NKF panel recommends national efforts to facilitate increased, routine, and timely use of Cys-C, especially to confirm eGFR in adults who are at risk for or have chronic kidney disease.
2012 CKD-EPI Cys-C equationb

eGFR = 133 × min(Scys/0.8, 1)−0.499 × max (Scys/0.8, 1)−1.328 × 0.996Age × 0.932 [if female]

2021 CKD-EPI Creatinine-Cys-C equationc

eGFRcr-cys = 135 × min(Scr/κ, 1)α × max(Scr/κ, 1)−0.544 × min(Scys/0.8, 1)−0.323 × max(Scys/0.8, 1)−0.778 × 0.9961Age × 0.963 [if female]
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US= United States; CKD-EPI= Chronic Kidney Disease Epidemiology collaboration; eGFR (estimated glomerular filtration rate) = mL/min/1.73 m2; Scr = standardized serum creatinine in mg/dL; Scys (standardized serum Cys-C) = mg/l;

a: κ = 0.7 (females) or 0.9 (males); α = −0.241 (female) or −0.302 (male); min(Scr/κ, 1) is the minimum of Scr/κ or 1.0; max(Scr/κ, 1) is the maximum of Scr/κ or 1.0;

b: min = indicates the minimum of Scys/0.8 or 1; max = indicates the maximum of Scys/0.8 or 1;

c: κ = 0.7 (females) or 0.9 (males); α = −0.219 (female) or −0.144 (male); min(Scr/κ, 1) is the minimum of Scr/κ or 1.0; max(Scr/κ, 1) is the maximum of Scr/κ or 1.0;

Implications for Critical Care

Accurate assessment of GFR has major implications for patient care in critically ill patients (24). GFR assessment is essential for drug dosing, as nearly two-thirds of medications used in hospitalized patients are renally-eliminated (25,26). Inaccurate GFR assessment also increases the risk for drug toxicity from overdosing; and treatment failure from underdosing, especially in patients treated with antibiotics or chemotherapy (23,27). In addition, evaluation of the risk versus benefit of treatment with nephrotoxins requires accurate GFR assessment. This latter role is especially important, given that one in four drugs used in hospitalized patients is potentially nephrotoxic (25,26). Other clinical decisions based on GFR include eligibility for organ transplantation (20) and consideration for mechanical circulatory support (28). Lastly, GFR assessment is important for the conduct of clinical trials, where baseline kidney function is frequently a criterion for enrollment (29). In trials of AKI therapeutics, GFR assessment plays a role in AKI phenotyping (30) and the evaluation of renal recovery as a trial outcome (29).

The gold standard of GFR assessment is direct measurement, wherein exogenous markers are administered (e.g. iohexol) and their clearance precisely quantified (14). However, the technical complexity, turnaround time, and cost make routine GFR measurement impractical (14). Alternatively, timed urine collections can be used for measurement of creatinine clearance (14). Although easier to implement, the accuracy of such measurements is inconsistent (31). For these reasons, GFR estimation equations are widely used to guide treatment decisions in critically ill patients, despite well described limitations (31,32). In addition, most health systems automatically report estimated GFR with serum creatinine in the electronic health record (EHR) (18). Consequently, as healthcare systems move towards implementation of the ASN/NKF recommendations, the critical care community must consider the implications of using the new race-free GFR equations for patient care and be prepared to address key challenges with implementation. In this paper, we follow an implementation science approach to evaluate factors influencing implementation of the ASN/NKF recommendations in critical care.

Methods

The Society of Critical Care Medicine convened a multidisciplinary Task Force to identify determinants of adoption of the ASN/NKF recommendations into critical care practice, and to identify potential strategies for individuals and organizations who choose to pursue implementation. This charge was based on the recognition of two key factors: 1) the national reckoning with race in healthcare (1,13) and the rapid uptake of the ASN/NKF recommendations at the health system level; and 2) the lack of guidance on how the recommendations might be implemented in critically ill populations. The Task Force included intensivists, pharmacists, and nurses with expertise in kidney function assessment, drug dosing, and implementation science. In a series of virtual meetings, task force members reviewed the evidence supporting the ASN/NKF recommendations and implications for critical care medicine. Task Force members with implementation science expertise guided deliberations.

Implementation Science uses theories, models, and frameworks to explain and inform implementation of evidence-based practices. Common examples include determinant frameworks, process models, and evaluation frameworks (33). Because our goal was to identify potential facilitators (factors promoting implementation) or barriers (factors impeding implementation) that may influence uptake of the ASN/NKF recommendations, the task force chose the Consolidated Framework for Implementation Research (CFIR) (34), a widely used determinant framework, as the organizing principle for our qualitative deliberations. CFIR categorizes factors influencing implementation across five domains: (1) Intervention Characteristics – design features of the intervention (2) Outer Settings – external political and network influences, (3) Inner Settings – site-specific political and organizational influences, (4) Process – plans and procedures for implementation, and (5) Individual Characteristics –experiences and characteristics of people involved in implementation (34,35).

Taskforce members independently identified and ranked barriers and facilitators to implementation, followed by group discussion. Once a list of facilitators and barriers was constructed, assessment of priorities and identification of potential implementation strategies was informed by Lewin’s change theory to suggest organizational level prioritization (36). Finally, taskforce members used the CFIR - Expert Recommendations for Implementing Change (ERIC) matching tool to identify implementation strategies aligned with identified determinants (37, 38). These strategies can be tailored at sites based on local resources and needs, in conjunction with an implementation process model.

RESULTS

Factors influencing implementation of the new race-free creatinine-based GFR equation

Race-free GFR estimating equations

In response to growing concerns regarding the use of race in GFR estimation, the CKD-EPI investigators developed a new creatinine-based GFR estimation equation that does not include a race variable (CKD-EPI-2021, see Table 1) (39). Compared to the CKD-EPI-2009 equation, this new equation on average assigns a lower GFR for black patients (~ 5ml/min/1.73m2) and a higher GFR to non-black patients (~5 ml/min/1.73m2) (22,39). After review of 26 possible approaches to removing race from GFR estimation, the ASN/NKF panel recommended immediate implementation of the CKD-EPI-2021 equation in all U.S. laboratories (22).

Considerations for implementation in critical care populations

When evaluating implementation determinants, the Task Force identified more facilitators to routine implementation of the CKD-EPI-2021 equation than barriers (Table 2). Specifically, factors such as existing external policies, adaptability, low cost, relative ease of updating EHR systems, and enthusiasm for moving away from race-based GFR estimation within the healthcare community, were viewed as likely facilitators of implementation.

Table 2:

Barriers, facilitators, and strategies to implementation of the new creatinine based GFR equation

Factor Role CFIR domain, (construct)a Rationale Implementation Strategiesb
Increased focus on disparities in critical care Facilitator Outer setting, (Cosmopolitanism) Growing data indicates race contributes to disparities in care Disseminate data
Simple equation, IT implementation Facilitator Intervention characteristics, (Adaptability) Already developed equation, implementation is at the IT level, avoids barriers at individual level Early IT involvement and buy-in
Equation supported by societal guidelines Facilitator Outer setting, (External Policies) Endorsement/societal support from experts in the field. Disseminate and increase awareness of societal endorsement
No associated cost Facilitator Intervention characteristics, (Cost) Simple equation change that has no large impact in workflow, cost, integration Awareness; Early IT involvement and buy-in; advertise success
Lack of data supporting creatinine based GFR equation in critical care Barrier Intervention characteristics, (Evidence Strength and Quality) Equation only validated in outpatient populations; outcomes to be assessed in critical care are unclear. Conduct educational meetings, identify champions, share knowledge locally, conduct local consensus discussions, inform local opinion leaders, identify early adopters, develop educational materials, develop academic partnerships, conduct educational outreach visits, distribute educational materials,
GFR equations for critical care Barrier Inner setting, (Structural Characteristics) Current GFR equation used inconsistently in critical care. May be built into EHR systems for drug dosing, or other methods may be used. Identify champions, share knowledge locally, build a coalition, promote adaptability, identify early adopters, conduct cyclical small tests of change
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a- implementation domain and related construct(s) applicable to each factor as specified in the Consolidated Framework For Implementation Research determinant framework (34);

b- potential implementation strategies as identified by the CFIR - Expert Recommendations for Implementing Change (ERIC) matching tool (38); GFR- glomerular filtration rate; IT- information technology; EHR- electronic health record

Anticipated barriers to implementation include structural characteristics of healthcare organizations, and most importantly, the lack of evidence to support the new equation in critically ill patients. Like previous equations, CKD-EPI-2021 was developed in outpatients with stable kidney function. Thus, it is unclear how this new equation performs in critically ill patients, who often have rapidly changing kidney function, variable kidney function reserve, altered nutritional status, and deconditioning – factors that generally limit GFR equation accuracy. In addition, changes in creatinine can be delayed for up to 48-hour lag from the onset of kidney damage, complicating the use of any creatinine-based GFR equation in patients with AKI (24). While a small number of studies suggest the CKD-EPI-2009 equation may be less biased compared to alternative equations (e.g., the Cockroft-Gault creatinine clearance equation), the accuracy of all kidney function equations is consistently low in critically ill patients (31,32). Considering these limitations, the Task Force universally agreed that GFR estimation equations perform poorly in the critically ill and should be interpreted cautiously when making treatment decisions. Insofar as GFR estimation equations continue to be used in critical care and for automated GFR reporting in the EHR, it is nevertheless essential for critical care clinicians to be aware of the new equation and its relative performance compared to currently used GFR equations. In most scenarios, GFR estimates provided by the new race-free GFR equation will lead to the same clinical decision compared to prior race-based equations (e.g., drug dosage adjustment or device eligibility) (22). However, for patients with kidney function near decision thresholds, even small differences in estimated GFR could lead to discrepant clinical recommendations across GFR equations. Table 2 summarizes additional anticipated facilitators and barriers, as well as recommended implementation strategies. For example, the barrier of ‘inconsistent use of race-free GFR equation in critical care’ can be mitigated by identifying champions to promote its use and sharing knowledge locally.

Factors influencing increased use of Cys-C for GFR estimation

Cys-C as a race-free GFR biomarker

Cys-C is a low molecular weight protein produced by all nucleated cells, freely filtered in the glomerulus, and then reabsorbed and catabolized by proximal tubular cells (14). Because Cys-C is produced by nearly all cells in the body, concentrations are not influenced by changes in muscle mass (14). This, combined with a shorter half-life compared to creatinine (14) make it an attractive GFR biomarker. The CKD-EPI group validated a Cys-C based GFR estimation equation in 2012 (40). The equation showed similar accuracy compared to creatinine, and improved accuracy when both Cys-C and creatinine were included in the equation. Notably, the equation based on Cys-C alone does not include a race variable, and in 2021 the CKD-EPI group developed a new equation including both Cys-C and creatinine that does not include a race variable (39). This new equation based on both Cys-C and creatinine was more accurate than the new race-free creatinine equation (CKD-EPI-2021) and led to smaller differences between Black and non-Black participants than equations using either creatinine or Cys-C alone. Based on these findings, the ASN/NKF panel recommended expanding use of Cys-C based GFR equations (Table 1).

Evidence to support Cys-C as a kidney biomarker in critically ill populations

As an AKI biomarker, Cys-C has been shown to detect AKI earlier than creatinine (41). In addition, Cys-C based GFR estimates may have utility for drug dosing and monitoring, particularly in patients with creatinine confounders such as frailty, malnutrition, or altered muscle mass. In a systematic review of 3455 patients (1168 of whom were critically ill) across 16 different medications, Cys-C based GFR predicted drug clearance as well or better than creatinine based GFR estimates (42). Despite these promising results, few studies have translated such pharmacokinetic associations to practical bedside application (43).

Cys-C may also be useful for monitoring drugs that inhibit creatinine transport in the kidney. Although creatinine is predominantly eliminated via glomerular filtration, it also is excreted via tubular secretion, which accounts for 10–40% of total creatinine clearance (44). Drugs that are substrates or inhibitors of these transporters (e.g., trimethoprim) may increase creatinine concentrations independent of changes to underlying kidney health, so called ‘pseudo-nephrotoxicity’ (45,46). Cys-C monitoring may be especially valuable in these scenarios, as it is not subject to renal tubular secretion (14,46). This was recently demonstrated in a study that examined whether effects on creatinine secretion may be responsible for the association between combined vancomycin and piperacillin-tazobactam treatment and creatinine-defined AKI (46).

Considerations for implementation in critical care populations

Although Cys-C may have advantages as a GFR biomarker in critically ill patients, the Task Force agreed that implementation of Cys-C based GFR equations poses a greater challenge compared to the new race-free creatinine-based equation. First, Cys-C is more expensive than creatinine (reagent cost in the U.S. for Cys-C vs creatinine is approximately $4 vs $0.20 per test, respectively) (47). While this additional cost may be negligible for individual patients, broad implementation could translate to hundreds of thousands of extra tests per year, with substantially increased associated assay costs. Such costs would likely function as a barrier at the health-system level. Ideally, health-systems evaluating the comparative costs of Cys-C vs. creatinine for GFR monitoring would consider aspects beyond assay costs, such as the potential for improved outcomes with the broader Cys-C use. However, Task Force members are unaware of any cost-effectiveness analyses addressing this point. If such evidence becomes available, it could act as an important facilitator of implementation.

Second, Cys-C is not readily available in most U.S. laboratories, being commonly processed as a “send out” test with a 2–3-day turn-around time, obviously limiting acute clinical utility in the dynamic critically ill population. Third, Cys-C concentrations are affected by non-renal factors that are common in critically ill patients, including inflammation, corticosteroids, obesity, and cancer (14, 48). Inflammation and corticosteroids can cause increased Cys-C concentrations, which could lead to falsely low GFR estimates. These factors may contribute to notable discrepancies in GFR estimates provided by Cys-C vs. creatinine-based equations. One study showed that Cys-C based GFR estimates led to lower dose recommendations in 38% of patients compared to creatinine-based estimates (49). To the extent that such estimates are falsely low due to non-renal factors in critically ill patients, the use of Cys-C equations could create significant risk of harm from underdosing (e.g., underdosing antibiotics in severe infection). These challenges are compounded by the fact that most clinicians have limited training and experience with using Cys-C, limiting the clinical acumen needed to interpret Cys-C values in the presence of dynamic organ function and confounding non-renal factors.

Table 3 summarizes additional anticipated facilitators and barriers, as well as recommended implementation strategies (35). Common strategies identified by the Task Force include: (1) ‘Identify and prepare champions’, (2) ‘Conduct local needs assessment’, (3) ‘Conduct educational meetings’, (4) ‘Inform local opinion leaders’, (5) ‘Build a coalition’, (6) ‘Conduct small clinical test of change’, and (7) ‘Develop resource sharing agreements’. These strategies can be tailored to specific organizations and units, depending on existing resources and structures.

Table 3:

Barriers, facilitators, and strategies to implementation of Cys-C

Factor Role CFIR domain, (construct)a Rationale Implementation Strategiesb
Numerical value similar to creatinine Facilitator Inner setting, (Compatibility) While the unit of measure is different (mg/L for Cys-C and mg/dL for creatinine) the actual normal values are similar Disseminate, educate stakeholders
Congruence with eGFR calculations Facilitator Intervention characteristics, (Adaptability) If Cys-C is integrated with eGFR, can be interpreted similar to creatinine Disseminate, educate stakeholders
Rapid Cys-C measurement methods Barrier Intervention characteristics (trialability)

Inner setting, (Available resources)		 Valid estimation of GFR with Cys-C requires assay traceable to a reference standard, which is not widely available. Limited resources for rapid testing available on site at most institutions. Consequently, few technicians familiar with testing procedures. Inability to trial methods without equipment. Identify champions, share local knowledge, identify early adopters, build a coalition, conduct small cyclical test of change, promote adaptability, tailor strategies, change physical structure and equipment, access new funding, fund and contract for clinical innovation, develop resource sharing agreements, use an implementation advisor, provide ongoing training
Moderate data for Cys-C in critical care Barrier Intervention characteristics, (Evidence Strength and Quality) Equation only validated in outpatient populations; outcomes to be assessed in critical care are unclear. Some studies demonstrate benefits of Cys-C, but may be affected by non-renal factors. Insufficient data present for widespread buy-in Conduct educational meetings, identify champions, share knowledge locally, conduct local consensus discussions, inform local opinion leaders, identify early adopters, develop educational materials, develop academic partnerships, conduct educational outreach visits, distribute educational materials
Cys-C not required in pharmacokinetic evaluations Barrier Outer setting, (External policies) No regulatory support for drug dosing algorithms to be developed with non-creatinine biomarkers Identify champions, build a coalition to inform opinion leaders to advocate for updated regulatory requirements
Broad set of local stakeholders, extensive planning, other priorities Barrier Inner setting, (Structural characteristics)

Inner setting, (Leadership engagement)		 Need full buy-in from leadership teams across multiple departments; initial costs associated with the practice change at the systems level. Simultaneously need buy-in from front line staff/stakeholders and designation of champions involved in practice change across departments. Conduct local consensus decisions, alter incentive structures, conduct local needs assessment, increase demand, mandate the change, inform local opinion leaders, conduct educational meetings, build a coalition, develop a formal implementation blueprint, develop formal quality monitoring system
EHR infrastructure Barrier Inner setting, (Structural characteristics)

Process, (Planning)		 EHR built to support creatinine-based tools, not Cys-C values. Logistical barriers associated with change Identify champions, share local knowledge, identify early adopters, build a coalition, change physical structure and equipment,
Cost of Cys-C Barrier Intervention characteristics, (Cost)

Inner setting, (Relative priority)

Process, (Engaging Stakeholders)		 Cys-C considerably more expensive compared to creatinine. There may be unintended negative consequences related to resources allocation to implement Access new funding, alter incentives, involve executive boards, fund and contract for clinical innovation, develop resource sharing agreements, use other payment schemes
Clinician Knowledge of Cys-C Barrier Characteristics of individuals, (Knowledge and Beliefs; self-efficacy)

Intervention characteristics, (Complexity)		 Clinicians may have limited knowledge/training/experience with Cys-C, variable beliefs regarding efficacy. Low self-efficacy without frequent use and trialability. Identify champions, inform local opinion leaders, share local knowledge, conduct educational meetings, identify early adopters, conduct local needs assessment, educational outreach visits, conduct ongoing training, develop educational materials, model and simulate change, make training dynamic, shadow other experts
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a- implementation domain and related construct(s) applicable to each factor as specified in the Consolidated Framework For Implementation Research determinant framework (34);

b- potential implementation strategies as identified by the CFIR - Expert Recommendations for Implementing Change (ERIC) matching tool (38); GFR- glomerular filtration rate; EHR- electronic health record

Research priorities

Although the development of race-free GFR equations represents an important step towards equitable kidney function estimation in the general population, significant challenges remain in the critically ill. All available GFR equations were designed for patients with stable kidney function, limiting their applicability in critically ill patients, especially in the setting of AKI, where GFR often changes substantially over hours to days (50,51). In addition, interpretation of endogenous kidney function markers is complicated by rapidly changing volume status and, in the case of creatinine, muscle wasting that occurs over the course of an intensive care unit admission (52). No GFR equations have been developed specifically for the critically ill population, and the existing literature evaluating currently available GFR equations in this group is limited by small sample sizes and heterogenous study designs. We believe that achievement of equitable GFR estimation in critically ill patients requires substantial additional research focused specifically on this vulnerable population. Key research priorities (Table 4) include the development of critically ill specific GFR equations, the evaluation of dynamic kidney function estimation equations (53), and evaluation of novel approaches to real-time GFR measurement (54).

Table 4.

Research priorities for GFR assessment in critically ill patients

Research priority Description
Critically ill specific GFR equations Development and validation of GFR estimation equations in cohort studies enrolling large, representative populations of critically ill patients. Studies should employ measured GFR using exogenous markers (e.g., iohexol) as the reference gold standard, and include ICU specific factors such as severity of illness, fluid resuscitation, and vasopressor support.
Cys-C as a GFR marker Additional studies evaluating the utility of Cys-C as a marker of GFR wherein exogenously measured GFR is used as the reference gold standard. Studies exploring the value of Cys-C for predicting drug clearance (e.g., vancomycin, beta-lactam antibiotics) would be of considerable interest. Studies evaluating whether Cys-C based GFR estimation improves outcomes (reduced drug toxicity, decreased treatment failure). Cost-effectiveness analyses comparing Cys-C with creatinine.
Dynamic kidney function assessment Studies evaluating the utility of dynamic kidney function equations based on creatinine such as the Modified Jelliffe equation (55) and the kinetic GFR equation (53). Extension of these equations to incorporate Cys-C and ICU specific assumptions regarding GFR marker volume of distribution. Studies evaluating whether dynamic GFR estimation improves outcomes.
Real time GFR measurement Studies evaluating the utility of technologies for real-time, bedside GFR measurement such as transcutaneous measurement of fluorescent biomarkers (54).
Assessment of muscle mass Incorporation of muscle mass into creatinine-based equations using technologies to estimate muscle mass such as bioelectrical impedance (56).
Clinical decision support Development of clinical decision support tools to automatically report population specific GFR estimates (e.g., CKD-EPI 2021 equation estimates in stable outpatient populations and a kinetic GFR estimate in patients with AKI)
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Conclusion

The lack of direct evidence in critically ill patients is a key barrier to broad implementation of newly developed “race-free” GFR equations. Additional research evaluating GFR equations in critically ill patients and novel approaches to dynamic kidney function estimation is required to advance equitable GFR assessment in this vulnerable population.

Key Points.

Question:

What factors influence implementation of “race-free” creatinine and cystatin C-based kidney function equations?

Findings:

Implementation of race-free creatinine-based kidney function equations is facilitated by low cost and ease of incorporation into electronic health records. The key barrier to implementation is a lack of direct evidence in critically ill patients. Barriers to implementation of cystatin C equations include higher cost, lack of test availability, and limited evidence in critically ill patients.

Meaning:

Implementation of new “race-free” approaches to kidney function estimation will need to consider the complex web of factors influencing kidney function estimation practice in critical care.

Funding and Disclosures:

Supported by the Society of Critical Care Medicine. Drs. Miano, Sakhuja, and Barreto received funding from the National Institute of Health (K08DK124658 to T.A.M); (K08KD 131286 to A.S); (K23AI143882 to E.F.B). Additionally, Dr. Barreto consults for Wolters-Kluwer on topics unrelated to the content of this manuscript. Dr. Martin consults for Nestle, Astra Zeneca, and FloBio on topics unrelated to the content of this manuscript. Dr. Basu is a consultant and/or advisory board member for: BioPorto Diagnostics, Biomerieux, Potrero, and Seastar Therapeutics, receiving personal fees. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Copyright Form Disclosure:

Drs. Miano, Barreto, Sakhuja, and Basu received support for article research from the NIH. Dr. Barreto received funding from Wolters-Kluwer. Dr. Martin received funding from Astra Zeneca and Nestle. Dr. Sakhuja’s institution received funding from the National Institute of Diabetes and Digestive and Kidney Diseases (1K08DK131286–01A1). Dr. Basu received funding from Biomerieux and Bioporto. The remaining authors have disclosed that they do not have any potential conflicts of interest.

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