BACKGROUND: End-stage renal disease (ESRD) patients are prescribed numerous medications. The United States Renal Data System (USRDS) reported on medication prescribing patterns in 1998. Since then, several new medications, treatment guidelines and recommendations have been introduced. The objective was to analyse and compare haemodialysis (HD) patient medication prescribing patterns between the Dialysis Clinic, Inc. (DCI) database and the USRDS report. METHODS: Point-prevalent (01/01/03) medication use data from the DCI national database was obtained. Data collected included patient demographics, reason for and duration of ESRD, and medication listed on profile. All medications were classified similar to the USRDS and by where taken (clinic vs home). Medication prescribing patterns were compared between DCI and USRDS databases. Comparisons between age groups (<65 and >or=65 years) and diabetic status [diabetes mellitus (DM) vs non-DM] were made. RESULTS: There were 128 477 medication orders categorized in 10 474 patients. DCI patient demographics were similar to present USRDS patients except for fewer Hispanics (P<0.001). Patients were prescribed 12.3+/-5.0 (median 12) different medications (2.6+/-1.4 clinic medications and 10.0+/-4.5 home medications). This is higher than reported by USRDS (median 9 medications). Patient age did not influence number of medications used (P = 0.54). DM patients are prescribed more medications than non-DM (13.3+/-5.0 DM vs 11.6+/-4.8 non-DM; P<0.00001). All medication class prescribing patterns were markedly different. CONCLUSION: The data suggest that medication prescribing patterns in HD patients have changed. The audit identified appropriate and questionable prescribing patterns. Various prescribing patterns identified areas for improvement in care (e.g. increased use of aspirin, beta-blockers and hyperlipidaemia medications) and areas requiring further investigation (e.g. high use of anti-acid, benzodiazepine and non-aluminum/non-calcium phosphate-binding medications).
BACKGROUND: End-stage renal disease (ESRD) patients are prescribed numerous medications. The United States Renal Data System (USRDS) reported on medication prescribing patterns in 1998. Since then, several new medications, treatment guidelines and recommendations have been introduced. The objective was to analyse and compare haemodialysis (HD) patient medication prescribing patterns between the Dialysis Clinic, Inc. (DCI) database and the USRDS report. METHODS: Point-prevalent (01/01/03) medication use data from the DCI national database was obtained. Data collected included patient demographics, reason for and duration of ESRD, and medication listed on profile. All medications were classified similar to the USRDS and by where taken (clinic vs home). Medication prescribing patterns were compared between DCI and USRDS databases. Comparisons between age groups (<65 and >or=65 years) and diabetic status [diabetes mellitus (DM) vs non-DM] were made. RESULTS: There were 128 477 medication orders categorized in 10 474 patients. DCI patient demographics were similar to present USRDS patients except for fewer Hispanics (P<0.001). Patients were prescribed 12.3+/-5.0 (median 12) different medications (2.6+/-1.4 clinic medications and 10.0+/-4.5 home medications). This is higher than reported by USRDS (median 9 medications). Patient age did not influence number of medications used (P = 0.54). DMpatients are prescribed more medications than non-DM (13.3+/-5.0 DM vs 11.6+/-4.8 non-DM; P<0.00001). All medication class prescribing patterns were markedly different. CONCLUSION: The data suggest that medication prescribing patterns in HDpatients have changed. The audit identified appropriate and questionable prescribing patterns. Various prescribing patterns identified areas for improvement in care (e.g. increased use of aspirin, beta-blockers and hyperlipidaemia medications) and areas requiring further investigation (e.g. high use of anti-acid, benzodiazepine and non-aluminum/non-calcium phosphate-binding medications).
Authors: Rachel B Fissell; Angelo Karaboyas; Brian A Bieber; Ananda Sen; Yun Li; Antonio A Lopes; Takashi Akiba; Jürgen Bommer; Jean Ethier; Michel Jadoul; Ronald L Pisoni; Bruce M Robinson; Francesca Tentori Journal: Hemodial Int Date: 2015-05-14 Impact factor: 1.812