Evaluating Risk of ESRD in the Urban Poor

Abstract

The capacity of risk prediction to guide management of CKD in underserved health settings is unknown. We conducted a retrospective cohort study of 28,779 adults with nondialysis-requiring CKD who received health care in two large safety net health systems during 1996-2009 and were followed for ESRD through September of 2011. We developed and evaluated the performance of ESRD risk prediction models using recently proposed criteria designed to inform population health approaches to disease management: proportion of cases followed and proportion that needs to be followed. Overall, 1730 persons progressed to ESRD during follow-up (median follow-up=6.6 years). ESRD risk for time frames up to 5 years was highly concentrated among relatively few individuals. A predictive model using five common variables (age, sex, race, eGFR, and dipstick proteinuria) performed similarly to more complex models incorporating extensive sociodemographic and clinical data. Using this model, 80% of individuals who eventually developed ESRD were among the 5% of cohort members at the highest estimated risk for ESRD at 1 year. Similarly, a program that followed 8% and 13% of individuals at the highest ESRD risk would have included 80% of those who eventually progressed to ESRD at 3 and 5 years, respectively. In this underserved health setting, a simple five-variable model accurately predicts most cases of ESRD that develop within 5 years. Applying risk prediction using a population health approach may improve CKD surveillance and management of vulnerable groups by directing resources to a small subpopulation at highest risk for progressing to ESRD. In the United States, CKD has been estimated to affect nearly 26 million Americans, is the ninth leading cause of death, and costs the federal government over $56 billion annually. 1,2 Currently, over one third of incident patients with ESRD are enrolled in Medicaid (the United States joint federal and state health insurance program for the poor) or uninsured at ESRD onset. 1 Despite the disproportionate burden of CKD among socially disadvantaged groups, relatively little progress has been made in reducing socioeconomic disparities in the incidence and treatment of ESRD over the past two decades.In many instances, CKD can be slowed or prevented. Interventions, such as BP lowering, use of renin-angiotensin system inhibitors, and avoidance of nephrotoxins, are effective in slowing CKD progression if initiated in earlier stages. [3][4][5][6][7] However, these interventions seem to be underutilized in patients at all stages of CKD from socially disadvantaged groups. 8,9 Moreover, uninsured persons and Medicaid enrollees are suboptimally prepared for ESRD, as evidenced by worse biochemical abnormalities at ESRD onset, lower prevalence of permanent vascular access at dialysis initiation, and marked delays in accessing a kidney transplant compared with counterparts with Medicare or private health insurance. [10][11][12][13] A major contributor to socioeconomic disparities in ESRD incidence and CKD...

Patterns of Healthcare Encounters Experienced by Patients With Chronic Kidney Disease

“…Chronic kidney disease (CKD) is a national and international public health problem characterised by a slow loss of kidney function over time, and between 23 and 26 million people in the United States have CKD (Coresh et al ; Levey et al ). Commonly recognised risk factors for the development and progression of CKD include diabetes, hypertension, cardiovascular disease, older age, higher body mass index and being African American (Maziarz et al ; United States Renal Data System [USRDS] ). Preserving kidney function and slowing progression of CKD require self‐management behaviours that include adherence to pharmacological therapy, avoidance of non‐steroidal anti‐inflammatory drugs (NSAIDS), diet modifications, glycemic control, regular exercise, blood pressure control and tobacco avoidance (Tuot et al ).…”

Risk prediction to inform surveillance of chronic kidney disease in the US Healthcare Safety Net: a cohort study

“…Complementary studies in large study cohorts have yielded additional risk predictive models for ESRD which are intended for use by clinicians to estimate individual patient- rather than population-level risk [ 25 , 26 ]. Using criteria (proportion of cases followed and proportion needed to follow) designed to inform population-level disease assessment, we recently observed that a model incorporating age, race, sex, eGFR and dipstick proteinuria adequately predicted progression to ESRD among vulnerable persons with moderate or severe CKD who were identified through systematic review of the EHR [ 18 ]. Our current study findings extend and leverage our prior work, by placing the proportion of cases followed (PCF) and proportion of the population needed to be followed (PNF) more squarely in the context of how clinical care is actually delivered for this patient population.…”

“…Historically, low individual-level provider and patient awareness of CKD have reinforced the need to optimize multi-level strategies (at the community, organization, practice, and patient levels) to help assess and manage CKD [ 18 , 37 – 41 ]. Our study findings demonstrate the potential usefulness of clinical data from the EHR to provide reliable information for CKD care assessment at the level of the organization and at the level of a disease- or clinic-based practice which might readily generalize to other chronic diseases.…”

“…Services are rendered irrespective of a patient’s ability to pay and further include a wide range of interpreter services to facilitate care for each system’s diverse patient population. Specific details of the health systems have been previously described [ 18 ].…”

“…Model 1 included eGFR, dipstick proteinuria, and the “residual” associations with age, sex, and race-ethnicity. To this model, we added health insurance coverage, comorbidities (diabetes mellitus, CVD, hypertension, substance abuse and chronic viral disease) ( model 2 ) and additional laboratory variables (serum albumin, calcium, cholesterol and hemoglobin) ( model 3 ) [ 18 ].…”