Impacts of center and clinical factors in antihypertensive medication use after kidney transplantation.
Farrukh M KoraishyHala YamoutAbhijit S NaikZidong ZhangMark A SchnitzlerRosemary OusephNgan N LamVikas R DharnidharkaDavid A AxelrodGregory P HessDorry L SegevBertram L KasiskeKrista L LentinePublished in: Clinical transplantation (2020)
Hypertension guidelines recommend calcium channel blockers (CCBs), thiazide diuretics, and angiotensin-converting-enzyme inhibitors/angiotensin receptor blockers (ACEi/ARBs) as first-line agents to treat hypertension. Hypertension is common among kidney transplant (KTx) recipients, but data are limited regarding patterns of antihypertensive medication (AHM) use in this population. We examined a novel database that links national registry data for adult KTx recipients (age > 18 years) with AHM fill records from a pharmaceutical claims warehouse (2007-2016) to describe use and correlates of AHM use during months 7-12 post-transplant. For patients filling AHMs, individual agents used included: dihydropyridine (DHP) CCBs, 55.6%; beta-blockers (BBs), 52.8%; diuretics, 30.0%; ACEi/ARBs, 21.1%; non-DHP CCBs, 3.0%; and others, 20.1%. Both BB and ACEi/ARB use were significantly lower in the time period following the 2014 Eighth Joint National Committee (JNC-8) guidelines (2014-2016), compared with an earlier period (2007-2013). The median odds ratios generated from case-factor adjusted models supported variation in use of ACEi/ARBs (1.51) and BBs (1.55) across transplant centers. Contrary to hypertension guidelines for the general population, KTx recipients are prescribed relatively more BBs and fewer ACEi/ARBs. The clinical impact of this AHM prescribing pattern warrants further study.
Keyphrases
- angiotensin converting enzyme
- blood pressure
- angiotensin ii
- hypertensive patients
- clinical practice
- end stage renal disease
- quality improvement
- electronic health record
- primary care
- kidney transplantation
- newly diagnosed
- healthcare
- chronic kidney disease
- adverse drug
- ejection fraction
- prognostic factors
- big data
- emergency department
- young adults
- machine learning
- growth factor
- data analysis
- childhood cancer
- artificial intelligence