The main finding of this study is that, in adult veterans with diabetes, ICD-9-CM diagnosis codes in VA and Medicare administrative records fail to identify the large majority of individuals with comorbid CKD, when CKD is narrowly defined by eGFR criterion as Stage 3, 4, or 5 CKD. Despite each individual having had, at minimum, two opportunities to diagnose chronic kidney disease within a year, a comprehensive search of all VA and Medicare administrative records revealed that only 20.2 percent of individuals with low eGFR received a code specifically indicating chronic renal failure (Group A code). However, if present, a Group A code indicated that the individual had Stage 3, 4, or 5 CKD with 99.4 percent specificity. Extending the algorithm to 17 additional ICD-9-CM codes for chronic kidney disorders that may not necessarily be associated with low eGFR (Groups B and C) increased sensitivity to 38.7 percent, while specificity declined to 95.5 percent. Extending the algorithm to include individuals with Group D codes, indicating acute renal failure and disease, increased the sensitivity to 40.1 percent, while specificity declined to 95.2 percent. Because underlying CKD is a risk factor for acute renal failure, it is not surprising that including diagnosis codes for acute renal failure in the algorithm identified additional individuals with CKD (Mangoset al. 1995
; McCullough et al. 1997
; Gruberg et al. 2001
). Finally, adding 37 renal-related codes (Groups E–G) for kidney disorders that may, or may not, result in chronically low eGFR increased the sensitivity of the coding algorithm to a maximum value of 42.4 percent, but at a cost of further decline in specificity to 93.2 percent.
If a hospitalized patient is recognized and coded for renal disease, VA hospitals are slightly more accurate than Medicare-reimbursed hospitals in assigning Group A codes, indicating chronic renal failure, to patients with low eGFR. While less likely to assign a Group A code to an individual with low eGFR, VA hospitals were also less likely to assign a Group A code to individuals with eGFR greater than 59 ml/min/1.73 m2
. For a medical coder to assign any of the Group A codes for chronic renal failure to a patient's discharge abstract, the word “failure” must be included in the description of chronic kidney disease within the medical record (AHA 2002
). Terminology such as “renal insufficiency, chronic kidney disease, or diabetic nephropathy” cannot be coded as any of the Group A codes. The slightly lower coding accuracy of chronic kidney disease by Medicare providers, combined with the finding that 87 percent of individuals with chronic kidney disease were dually enrolled in Medicare and the VA system suggests that the coding gap for CKD includes providers reimbursed by Medicare as well as the VA health care system. The fact that individuals in the study sample had at least two serum creatinine tests 3–12 months apart supports the inference that the lack of CKD coding was not due simply to lack of patient encounters with the medical care system.
Inaccuracy in coding CKD in hospitalized patients may compromise the validity of risk adjustment systems that depend on case mix of the patient population. The presence of comorbid CKD greatly increases the risk of adverse outcomes in patients with myocardial infarction or cardiovascular procedures (Miettinen et al. 1996
; Conlon et al. 1999
; Gruberg et al. 2001
; Mann et al. 2001
; Shlipak et al. 2002
; Wright et al. 2002
; Callahan et al. 2003
). If individuals with underlying CKD in these patient groups are not correctly identified, risk adjustment may lead to biased outcome assessments. The Work Group of K/DOQI has proposed that the stage of CKD should be included routinely with each renal-related ICD-9-CM code to facilitate the use of administrative databases for epidemiological and outcomes surveillance (NKF 2002
An important secondary finding of this study is that the prevalence of CKD, narrowly defined as Stage 3, 4, or 5 CKD, is quite high (31.2 percent) among the population of veterans with diabetes. By excluding individuals with nonexistent or unstable patterns of eGFR from the sample, we attempted to minimize misclassification in order to maximize the validity of our findings regarding the sensitivity and specificity of renal-related ICD-9-CM codes. However, our methods may have introduced selection bias, in that we excluded veterans who had no serum creatinine tests, or veterans without sequential serum creatinine values. If patients with CKD are more likely to get sequential serum creatinine testing than patients without CKD, the sample for our study could have been enriched with patients with CKD. However, even taking the extreme position that none of the veterans with diabetes that we initially excluded from our study sample had CKD, a minimum of 14.7 percent (83,338/566,509) of the national VA population of patients with diabetes have CKD based solely on low eGFR criterion.
CKD is increasingly recognized as a common comorbid condition in selected patient populations. CKD is present in 60 percent of diabetic and nondiabetic patients discharged from hospitalizations for acute myocardial infarction (Langston et al. 2003
). The prevalence of CKD was 13 percent among U.S. adults with type 2 diabetes in the population-based sample from the Third National Health and Nutrition Examination Survey (NHANES III), where CKD was defined as Stage 3, 4 or 5 CKD based on eGFR (Kramer et al. 2003
). Although the mean age of our sample of veterans was older (mean age of 65 versus 61 years for NHANES III) and predominantly male, the age and sex standardized prevalence of CKD remained high in our study sample, at 29 percent. The lower prevalence of CKD in NHANES III, compared with our study, may possibly be explained by a lower percent of individuals in NHANES III who had diabetes of long enough duration to develop kidney disease because (1) NHANES III excluded individuals with type 1 diabetes, and (2) NHANES III included individuals with previously undiagnosed type 2 diabetes.
The importance of validating methods to identify patients with CKD is highlighted by contrasting our study results with an earlier study that estimated the prevalence of CKD in veterans with diabetes using ICD-9-CM codes. Young et al. (2004)
found the prevalence of CKD in the VA diabetic population to be 6 percent, which is much lower than the prevalence of CKD in our study. Therefore, case identification using only renal-related diagnosis codes in administrative records greatly underestimates the true prevalence of disease.
The high prevalence of CKD in our study sample underscores the need for more research on the quality of care and outcomes of kidney disease within populations of patients with diabetes. The failure to code the majority of CKD cases in patients with diabetes may indicate under-diagnosis. The insensitivity of serum creatinine tests to indicate CKD in older patients is evident in our data, as 43 percent of the patients with Stage 3 CKD had an index creatinine value less than 1.5 mg/dl (data not shown). To aid recognition of CKD in individual patients, the Work Group of K/DOQI has advocated routine laboratory reporting of eGFR with serum creatinine tests (NKF 2002
Our study has limitations. If veterans with CKD are more likely to get tested for serum creatinine, our study sample may be enriched with individuals with CKD. Such a selection bias could inflate our estimates of the prevalence of CKD in the veteran population, but should not affect our conclusions regarding the insensitivity of diagnosis codes to identify individuals with CKD. Because our data did not include values for urine protein, we were unable to classify individuals with proteinuria and eGFR greater than 59 ml/min/1.73 m2 as having CKD. Therefore, our study may overestimate the sensitivity of diagnosis codes to identify individuals with CKD, while underestimating the specificity. Individuals covered by health care plans in addition to Medicare fee-for-service and VA during the year of the study may have had missing claims, possibly diminishing our ability to find encounters with a renal-related code. Our study sample has a high proportion of older, male patients. Age or gender bias in coding renal diagnoses might alter the sensitivity and specificity of our algorithm to identify younger individuals, or women with diabetes and CKD. However, the concomitant use of Medicare and VA claims records extends the generalizability of our diagnosis code algorithm to male individuals enrolled in either system.
In summary, CKD is a common comorbidity in older, adult patients with diabetes. The ICD-9-CM diagnosis codes in administrative records are an insensitive, although reasonably specific marker for individuals with comorbid CKD. Research focused on health care quality and outcomes in diabetes, or case-mix methodology that adjusts for excess risk imposed by CKD, should use eGFR rather than diagnosis codes to identify individuals with CKD.