Can Dialysis Patients be Accurately Identified using Healthcare Claims Data?

Abstract

♦ Background: While health insurance claims data are often used to estimate the costs of renal replacement therapy in patients with end-stage renal disease (ESRD), the accuracy of methods used to identify patients receiving dialysis -especially peritoneal dialysis (PD) and hemodialysis (HD) -in these data is unknown. ♦ Methods: The study population consisted of all persons aged 18 -63 years in a large US integrated health plan with ESRD and dialysis-related billing codes (i.e., diagnosis, procedures) on healthcare encounters between January 1, 2005, and December 31, 2008. Using billing codes for all healthcare encounters within 30 days of each patient's first dialysis-related claim ("index encounter"), we attempted to designate each study subject as either a "PD patient" or "HD patient." Using alternative windows of ± 30 days, ± 90 days, and ± 180 days around the index encounter, we reviewed patients' medical records to determine the dialysis modality actually received. We calculated the positive predictive value (PPV) for each dialysis-related billing code, using information in patients' medical records as the "gold standard." ♦ Results: We identified a total of 233 patients with evidence of ESRD and receipt of dialysis in healthcare claims data. Based on examination of billing codes, 43 and 173 study subjects were designated PD patients and HD patients, respectively (14 patients had evidence of PD and HD, and modality could not be ascertained for 31 patients). The PPV of codes used to identify PD patients was low based on a ± 30-day medical record review window (34.9%), and increased with use of ± 90-day and ± 180-day windows (both 67.4%). The PPV for codes used to identify HD patients was uniformly high -86.7% based on ± 30-day review, 90.8% based on ± 90-day review, and 93.1% based on ± 180-day review.

Limited Accuracy of Administrative Data for the Identification and Classification of Adult Congenital Heart Disease

“…A recent meta-analysis of the validity of heart failure diagnoses in administrative data sets suggests that the specificity and positive predictive value of these codes are high but with low sensitivity. 7 Other studies have shown that the positive predictive value of ICD codes is highly variable [8][9][10][11] but also that the performance of these codes may be improved by the application of algorithms or the addition of multiple associated billing codes or further clinical information, such as we have tried to include. 8,12 Notably, the current investigation examined the specificity of ACHD codes but not the sensitivity, given the lack of ability to identify the total number of ACHD patients across the hospital system.…”

Comparison of Healthcare Utilization and Out-of-pocket Expenditure for Severe Disease by Health Security System: based on End-stage Renal Disease in South Korea

“…The meaning of this study is to identify the actual burden on ESRD because the cost studies in the past have been based on insurance claims data [17,[29][30][31][32][33][34]. The detailed meaning is as follows.…”

Comparison of Out-of-Pocket Expenditure and Catastrophic Health Expenditure for Severe Disease by Health Security System: based on End-stage Renal Disease in South Korea

“…The meaning of this study is to identify the actual burden on ESRD because the cost studies in the previous have been based on insurance claims data [17,[29][30][31][32][33][34]. The detailed meaning is as follows.…”