Cargando…

1545. Development of a Linear Mixed-Effect Pharmacodynamic Model to Quantify the Effects of Frequently Prescribed Antimicrobials on QT Interval Prolongation in Hospitalized Patients

BACKGROUND: Torsades de pointes is a life-threatening ventricular tachycardia associated with prolongation of the QT interval. Many diseases and medications have been implicated as potentially prolonging the QT interval, but little data exists regarding the means of quantifying this risk. The aim of...

Descripción completa

Detalles Bibliográficos
Autores principales: Farkas, Andras, Woods, Krystina L, Ciummo, Francesco, Shah, Ami, Sassine, Joseph, Olivo Freites, Christian, Daroczi, Gergely, Yassin, Arsheena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6808832/
http://dx.doi.org/10.1093/ofid/ofz360.1409
Descripción
Sumario:BACKGROUND: Torsades de pointes is a life-threatening ventricular tachycardia associated with prolongation of the QT interval. Many diseases and medications have been implicated as potentially prolonging the QT interval, but little data exists regarding the means of quantifying this risk. The aim of this study was to describe the impact of commonly used antimicrobials on the QT interval in hospitalized patients. METHODS: Demographic, diseases, laboratory, medication administration history and ECG recording data were collected from the electronic records of adult patients admitted, from July 2018 to December 2018, to two urban hospitals. A model for the QT interval comprised of four sub-models: gender, heart rate, circadian rhythm, and the drug and disease effects. Fixed and random effects with between occasion variability were estimated for the parameters. A Bayesian approach using the NUTS in STAN was used via the brms package in the R® software. RESULTS: Data from 1,353 patients were used with baseline characteristics shown in Table 1. Observed vs. predicted plots based on the training (Figure 1A) and validation data set (Figure 1B) showed a great fit. The parameters for QT(c0), α, gender, and circadian rhythm were accurately identified (Table 2). Similarly, the model correctly described the expected impact of acute or chronic diseases on the QT interval. Uncertainty interval estimates (Figure 2) show that patients treated with fluconazole and levofloxacin are likely to present with a QT interval [mean (95% CI) of 6.84 (0.22,21.45) and 5.05 (0.15, 16.70), respectively], that is > 5 ms longer vs. no treatment, the minimum cutoff that should evoke further risk assessment of QT interval prolongation. CONCLUSION: The model developed correctly describes the impact baseline risk factors have on the QT interval. Point estimates of QT interval prolongation show that patients treated with fluconazole and levofloxacin may be at considerable risk; while those treated with azithromycin or ciprofloxacin are more likely to be at an insignificant risk for QT interval prolongation during hospital admission. Further workup to quantify the impact of concomitant treatment with these and other at-risk medications is underway. [Image: see text] [Image: see text] [Image: see text] [Image: see text] DISCLOSURES: All authors: No reported disclosures.