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. CONCLUSIONS: While HD patients could be accurately identified using billing codes in healthcare claims data, case identification was much more problematic for patients receiving PD.
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 "PDpatient" or "HDpatient." 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 PDpatients and HDpatients, 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 PDpatients 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 HDpatients 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. CONCLUSIONS: While HDpatients could be accurately identified using billing codes in healthcare claims data, case identification was much more problematic for patients receiving PD.
Authors: Wolfgang C Winkelmayer; Robert J Glynn; Murray A Mittleman; Raisa Levin; Joseph S Pliskin; Jerry Avorn Journal: J Am Soc Nephrol Date: 2002-09 Impact factor: 10.121
Authors: Courtney S Streur; Nicholas M Moloci; Kate H Kraft; Aruna V Sarma; Vahakn B Shahinian; John M Hollingsworth Journal: J Urol Date: 2020-07-27 Impact factor: 7.450
Authors: Sangtaeck Lim; Gaurav Gangoli; Erica Adams; Robert Hyde; Michael S Broder; Eunice Chang; Sheila R Reddy; Marian H Tarbox; Tanya Bentley; Liza Ovington; Walt Danker Journal: Inquiry Date: 2019 Jan-Dec Impact factor: 1.730
Authors: Chi D Chu; Neil R Powe; Charles E McCulloch; Deidra C Crews; Yun Han; Jennifer L Bragg-Gresham; Rajiv Saran; Alain Koyama; Nilka R Burrows; Delphine S Tuot Journal: JAMA Netw Open Date: 2021-09-01
Authors: Manon J M van Oosten; Richard M Brohet; Susan J J Logtenberg; Anneke Kramer; Lambert D Dikkeschei; Marc H Hemmelder; Henk J G Bilo; Kitty J Jager; Vianda S Stel Journal: Clin Kidney J Date: 2020-11-09