Browsing by Subject "adverse events"

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    Infectious Complications of Ventricular Assist Device Use in Children in the US: Data from the Pediatric Interagency Registry for Mechanical Circulatory Support (Pedimacs)
    (Elsevier, 2017) Auerbach, Scott R.; Richmond, Marc E.; Schumacher, Kurt R.; Lopez-Colon, Dalia; Mitchell, Max B.; Turrentine, M. W.; Cantor, Ryan S.; Niebler, Robert A.; Eghtesady, Pirooz; Surgery, School of Medicine
    Background Infections are frequent in pediatric ventricular assist device (VAD) patients. In this study we aimed to describe infections in durable VAD patients reported to Pedimacs. Methods Durable VAD data from the Pedimacs registry (September 19, 2012 to December 31, 2015) were analyzed. Infections were described with standard descriptive statistics, Kaplan–Meier analysis and competing outcomes analysis. Results There were 248 implants in 222 patients, with a mean age and a median follow-up of 11 ± 6.4 years and 2.4 patient-months (<1 day to 2.6 years), respectively. Device types were pulsatile flow (PF) in 91 (41%) patients and continuous flow (CF) in 131 (59%) patients. PF patients were younger (4 ± 4 vs 14 ± 4 years; p < 0.0001) and were more likely to have congenital heart disease (25% vs 12%; p = 0.03), prior surgery (53% vs 26%; p < 0.0001) and prior extracorporeal membrane oxygenation (24% vs 7%; p = 0.0003). Infection accounted for 17% (96 of 564) of the reported adverse events (AEs). A non-device infection was most common (51%), followed by sepsis (24%), external pump component infection (20%) and internal pump component infection (5%). Most infections were bacterial (73%) and required intravenous therapy only (77%). The risk of infection in the constant phase was higher in patients with a history of prior infection and in patients with a history of a non-infectious major AEs. Survival was lower after infection only in CF patients (p = 0.008). Conclusions Infection was the most common AE after pediatric VAD implantation. Non-device infections were most common. The best predictor of a future infection was a past infection. CF patients have higher risk of death after an infection.
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    Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models
    (Wiley, 2017) Chiang, Chien-Wei; Zhang, Pengyue; Wang, Xueying; Wang, Lei; Zhang, Shijun; Ning, Xia; Shen, Li; Quinney, Sara K.; Li, Lang; Medical and Molecular Genetics, School of Medicine
    Polypharmacy increases the risk of drug-drug interactions (DDI's). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDI's among thirty frequent drugs. Multi-drug combinations that increased risk of myopathy were identified in the FDA Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro in vivo extrapolation. Twenty-eight 3-way and 43 4-way DDI's had significant myopathy risk in both databases and predicted increases in the area under the concentration time curve ratio (AUCR) >2-fold. The HD-DDI of omeprazole, fluconazole and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increase risk of myopathy (LFDR<0.005); the AUCR of omeprazole in this combination was 9.35.The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDI's.