An Electronic CKD Phenotype: A Step Forward in Improving Kidney Care

As ESKD costs continue to rise, there is an intensified focus on improving care at earlier stages of the disease. Early identification of CKD could allow for the implementation of strategies to slow kidney disease progression, focus on patient education, increase access to preemptive transplantation and home dialysis modalities, and support patients who choose conservative management. Identifying patients with CKD across a health care system is a critical first step toward achieving the vision of improved kidney care.

Because CKD is defined on the basis of laboratory values, there has been great excitement about using automated algorithms with discrete data to identify persons with CKD in the electronic health record (EHR), particularly because EHR penetration rose in hospitals from 9% in 2008 to 80.5% in 2015 (1,2). However, much of the potential in EHRs for kidney disease population health management and research remains untapped, in part because there is currently no standardized way for health systems to identify CKD, ESKD, and kidney transplant populations in the EHR. In this issue of CJASN, results from Norton et al. (3) and the National Kidney Disease Education Program (NKDEP) CKD e-Phenotype working group come at a pivotal time. The Department of Health and Human Services announced a comprehensive kidney care strategy called Advancing American Kidney Health on July 10, 2019, and a large component of this strategy is new CKD-focused payment models. Thus, the ability to readily identify patients with CKD within an entire health system is crucial to implement and evaluate targeted interventions for CKD care delivery.

The authors performed an analysis of EHR data from five health care organizations. The NKDEP CKD e-Phenotype working group generated a consensus definition of CKD in the EHR termed an “e-phenotype.” The CKD e-phenotype was defined as follows: “most recent eGFR <60 ml/min per 1.73 m², with at least one value <60 ml/min per 1.73 m² more than 90 days prior and/or proteinuria presenting as a UACR ≥30 mg/g in the most recent test with at least one positive value more than 90 days prior” based on the Kidney Disease Improving Global Outcomes definition of CKD. For patients missing urine albumin-to-creatinine ratio (UACR) values, a corresponding urine protein result on urinalysis of trace or above was used. The authors used this definition to query the EHR in the five health care organizations and define and stage the CKD population. Manual chart validation was conducted in four of the five sites to ensure that the query was executed accurately. Of 207 charts analyzed, 202 were correctly categorized by CKD stage, suggesting a diagnostic accuracy of 98%. The authors also identified transplant and dialysis recipients using diagnostic and procedure codes, which were less specific on manual chart review (89% for dialysis and 91% for transplant).

Extracting EHR data is laborious, and clinicians and health systems may lack informatics expertise and resources to extract EHR data systematically. We applaud this important effort from the working group as an important advancement in identifying patients with CKD. Several strengths of the study should be noted. The study group successfully implemented the e-phenotype across multiple sites using different EHR systems, which is promising for the generalizability to other health organizations. A detailed description of the pragmatic components of the study, including the team members involved in querying the EHR, time requirements, Logical Observation Identifiers Names and Codes laboratory codes, International Classification of Diseases Ninth/Tenth Edition (ICD-9/10) diagnosis codes, and Current Procedural Terminology procedure codes, will be useful to those implementing this CKD e-phenotype. The authors compared the laboratory values obtained by the e-phenotype query with those abstracted by chart review. They also incorporated a method of using urine dipstick proteinuria in the absence of UACR measurements, which improves the sensitivity of the e-phenotype to detect CKD given that <7% of patients had a UACR checked.

Several factors serve to potentially limit the ability of the NKDEP e-phenotype to accurately identify CKD status across a population. The e-phenotype inherently relies on data in the EHR collected during routine clinical care, and therefore, patients with CKD with limited access to care or who do not have laboratory results available will be missed. Only about 55% of health system patients had an eGFR value, and the sensitivity of the e-phenotype in identifying CKD may vary by characteristics that influence testing frequency. Furthermore, a large proportion of patients with a most recent eGFR of <60 ml/min per 1.73 m² did not have prior eGFR measurements, and therefore, they could not be classified by the e-phenotype, which requires two measures. Given the fragmentation in our health care system, CKD diagnoses could be missed in patients who use different health systems without interoperable EHRs.

Other aspects may also decrease the performance of the NKDEP e-phenotype algorithm. Patients with recurrent AKIs may have been classified as CKD by the NKDEP CKD e-phenotype definition. Moreover, given some within-subject variability of eGFR measurements, patients with laboratory values around the threshold of eGFR of 60 or UACR of 30 may also be misclassified (4). We previously found a lower correlation between eGFR-based EHR ascertainment of CKD on manual chart validation, where the comparison was nephrologist-adjudicated CKD status, on the basis of all available eGFR measures and other information in the EHR, suggesting that laboratory-defined CKD may overestimate disease prevalence (5). The e-phenotype should also be validated across race/ethnic groups, particularly black patients, because institutions have different practices in reporting race-based eGFR estimates (6). For example, we showed that CKD classification is sensitive to race coefficients in young blacks and may misclassify high-risk persons as not having CKD (7).

We are excited to see the momentum in this field. It is clear that the Centers for Medicare and Medicaid Services and other payors are signaling to the health care ecosystem that change in CKD care is imperative. This e-phenotype is an important first step. However, we believe that its ability to affect care will be limited if it is not followed by additional work. We outline three actionable areas for additional study: (1) implementation of automated risk stratification models, (2) refinement to develop e-phenotypes with claims data, and (3) support for pragmatic trials of implementation strategies that incorporate the e-phenotype in clinical care.

The use of any identification algorithm must include additional risk stratification, because the majority of patients with CKD will not progress to ESKD. Risk stratification should include an assessment of risk for ESKD in addition to cardiovascular risk, death, and hospitalization. Risk stratification could be accomplished by adding additional markers, such as cystatin C; we have shown that a triple-marker approach improves the ability to identify persons with CKD at highest risk for complications (8). Risk stratification should also use validated formulas and could include refinement of published algorithms by incorporating social determinants of health or using natural language processing to obtain data from clinician notes (9).

Claims data using ICD-9/10 coding to detect CKD miss the majority of affected patients (10). This is problematic for payors interested in CKD population health management, because many only have access to claims data and may not have access to laboratory data. It is also a barrier for research that uses claims data, such as Medicare datasets and the Optum Clinformatics Data Mart Database. Accurate ICD-9/10 coding of CKD has the potential to improve quality measurement and CKD research. The e-phenotype could be used to partially automate ICD-9/10 coding for CKD in the medical record, increasing its accuracy for each stage of CKD. As value-based payment models are introduced for CKD stages 4 and 5, the e-phenotype could potentially be used to define CKD populations for which health systems bear financial responsibility in risk-sharing models.

Most importantly, after identifying patients with CKD using the e-phenotype, implementing evidence-based strategies and evaluating these interventions with pragmatic randomized trials will ultimately determine if these approaches improve outcomes for patients with CKD. For example, the e-phenotype could be used to identify patients for pragmatic trials of CKD clinician decision support. Incorporating behavioral economics principles, such as default ordering and incentives, could improve the ability for these support tools to change physician behavior. For patients with ESKD, the e-phenotype could be used to identify patients on dialysis and intensify care support, medication reconciliation, and coordination with primary care to reduce hospitalizations and readmissions. For transplant programs, identifying recipients and following them prospectively using the e-phenotype could identify patients lost to care and potentially reduce the risk of graft failure. Other quality improvement and population health initiatives could also benefit from EHR-based CKD identification (11). Using the e-phenotype, primary care and nephrology clinics could generate individual physician reports to assess process measures, such as angiotensin-converting enzyme inhibitor or angiotensin receptor blocker use, statin use, and BP control.

The catalyst to change the current status quo of kidney care is here. The task of the next decade will be to design and implement interventions that improve CKD care in a reliable, efficient, and scalable manner. The entire ecosystem of kidney health will benefit from efforts like those of the NKDEP group, and our community must continue the support of these important steps forward.

Disclosures

Dr. Peralta has ownership in, is Chief Medical Officer of, and has received consulting and other fees from Cricket Health, Inc. Dr. Tummalapalli has nothing to disclose.

Funding

Dr. Peralta is supported by an American Heart Association Established Investigator Award, National Institute on Aging grant R01AG046206, and National Institute of Diabetes and Digestive and Kidney Diseases grant R18DK110959. Dr. Tummalapalli is supported by the American Society of Nephrology Foundation for Kidney Research Ben J. Lipps Research Fellowship Program (Sharon Anderson Research Fellowship) and the Jonathan A. Showstack Career Advancement Award in Health Policy/Health Services Research at the University of California, San Francisco Philip R. Lee Institute for Health Policy Studies.