Browsing by Subject "real-world data"
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Item Open Access Association between simulated ketamine exposures and oxygen saturations in children.(International journal of pharmacokinetics, 2023-02) Commander, Sarah Jane; Gonzalez, Daniel; Kumar, Karan R; Spears, Tracy; Cohen-Wolkowiez, Michael; Zimmerman, Kanecia O; Lee, Jan Hau; Hornik, Christoph PAim
We performed a real-world data analysis to evaluate the relationship between simulated ketamine exposures and oxygen desaturation in children.Materials & methods
A previously developed population pharmacokinetic model was used to simulate exposures and evaluate target attainment, as well as the association with oxygen desaturation in children ≤17 years treated with intravenous ketamine.Results
In 2022 children, there was no significant association between simulated plasma ketamine concentrations and oxygen saturation; however, a higher cumulative area under the curve was associated with increased odds of progression to significant desaturation (<85%), though magnitude of effect was small.Conclusion
By leveraging a population pharmacokinetic model and real-world data, we confirmed there is no relationship between simulated ketamine plasma concentration and oxygen desaturation.Item Open Access Randomized Trials Versus Common Sense and Clinical Observation: JACC Review Topic of the Week.(Journal of the American College of Cardiology, 2020-08) Fanaroff, Alexander C; Califf, Robert M; Harrington, Robert A; Granger, Christopher B; McMurray, John JV; Patel, Manesh R; Bhatt, Deepak L; Windecker, Stephan; Hernandez, Adrian F; Gibson, C Michael; Alexander, John H; Lopes, Renato DConcerns about the external validity of traditional randomized clinical trials (RCTs), together with the widespread availability of real-world data and advanced data analytic tools, have led to claims that common sense and clinical observation, rather than RCTs, should be the preferred method to generate evidence to support clinical decision-making. However, over the past 4 decades, results from well-done RCTs have repeatedly contradicted practices supported by common sense and clinical observation. Common sense and clinical observation fail for several reasons: incomplete understanding of pathophysiology, biases and unmeasured confounding in observational research, and failure to understand risks and benefits of treatments within complex systems. Concerns about traditional RCT models are legitimate, but randomization remains a critical tool to understand the causal relationship between treatments and outcomes. Instead, development and promulgation of tools to apply randomization to real-world data are needed to build the best evidence base in cardiovascular medicine.