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      Proton pump inhibitors may enhance the risk of citalopram‐ and escitalopram‐associated sudden cardiac death among patients receiving hemodialysis

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          Abstract

          Purpose

          Polypharmacy is common in the hemodialysis population and increases the likelihood that patients will be exposed to clinically significant drug–drug interactions. Concurrent use of proton pump inhibitors (PPIs) with citalopram or escitalopram may potentiate the QT‐prolonging effects of these selective serotonin reuptake inhibitors through pharmacodynamic and/or pharmacokinetic interactions.

          Methods

          We conducted a retrospective cohort study using data from the U.S. Renal Data System (2007–2017) and a new‐user design to examine the differential risk of sudden cardiac death (SCD) associated with citalopram/escitalopram initiation vs. sertraline initiation in the presence and absence of PPI use among adults receiving hemodialysis. We studied 72 559 patients:14 983 (21%) citalopram/escitalopram initiators using a PPI; 26 503 (36%) citalopram/escitalopram initiators not using a PPI;10 779 (15%) sertraline initiators using a PPI; and 20 294 (28%) sertraline initiators not using a PPI (referent). The outcome of interest was 1‐year SCD. We used inverse probability of treatment weighted survival models to estimate weighted hazard ratios (HRs) and 95% confidence intervals (CIs).

          Results

          Compared with sertraline initiators not using a PPI, citalopram/escitalopram initiators using a PPI had the numerically highest risk of SCD (HR [95% CI] = 1.31 [1.11–1.54]), followed by citalopram/escitalopram initiators not using a PPI (HR [95% CI] = 1.22 [1.06–1.41]). Sertraline initiators using a PPI had a similar risk of SCD compared with those not using a PPI (HR [95% CI] = 1.03 [0.85–1.26]).

          Conclusions

          Existing PPI use may elevate the risk of SCD associated with citalopram or escitalopram initiation among hemodialysis patients.

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          Most cited references36

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          A Proportional Hazards Model for the Subdistribution of a Competing Risk

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            Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

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              Evaluating medication effects outside of clinical trials: new-user designs.

              Wayne Ray (2003)
              Recent clinical trials demonstrating that hormone replacement therapy (HRT) does not prevent coronary heart disease in women have again raised doubts concerning observational studies. Although much of the explanation probably lies in what might be called the "healthy HRT user" effect, another contributing factor may be that most observational studies included many prevalent users: women taking HRT for some time before study follow-up began. This practice can cause two types of bias, both of which plausibly may have contributed to the discrepancy between observational and randomized studies. First, prevalent users are "survivors" of the early period of pharmacotherapy, which can introduce substantial bias if risk varies with time, just as in studies of operative procedures that enroll patients after they have survived surgery. This article provides several examples of medications for which the hazard function varies with time and thus would be subject to prevalent user bias. Second, covariates for drug users at study entry often are plausibly affected by the drug itself. Investigators often do not adjust for these factors on the causal pathway, which may introduce confounding. A new-user design eliminates these biases by restricting the analysis to persons under observation at the start of the current course of treatment. This article thus argues that such designs should be used more frequently in pharmacoepidemiology.
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                Author and article information

                Journal
                Pharmacoepidemiology and Drug Safety
                Pharmacoepidemiology and Drug
                Wiley
                1053-8569
                1099-1557
                June 2022
                March 24 2022
                June 2022
                : 31
                : 6
                : 670-679
                Affiliations
                [1 ] University of North Carolina Kidney Center, Division of Nephrology and Hypertension, Department of Medicine UNC School of Medicine Chapel Hill North Carolina USA
                [2 ] Division of Nephrology, Department of Medicine Duke University School of Medicine Durham North Carolina USA
                [3 ] Duke Clinical Research Institute Durham North Carolina USA
                [4 ] Division of Cardiology Duke University Medical Center Durham North Carolina USA
                [5 ] Department of Population Health Sciences Duke University School of Medicine Durham North Carolina USA
                [6 ] Department of Psychiatry UNC School of Medicine Chapel Hill North Carolina USA
                [7 ] Department of Epidemiology UNC Gillings School of Global Public Health Chapel Hill North Carolina USA
                [8 ] Selzman Institute for Kidney Health, Section of Nephrology Baylor College of Medicine Houston Texas USA
                [9 ] Cecil G. Sheps Center for Health Services Research University of North Carolina Chapel Hill North Carolina USA
                Article
                10.1002/pds.5428
                35285107
                d369e1ac-deb5-4a4f-bd38-dd660be39ce8
                © 2022

                http://onlinelibrary.wiley.com/termsAndConditions#vor

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