Population Pharmacokinetic Analysis of Dexmedetomidine in Children using Real World Data from Electronic Health Records and Remnant Specimens

2021 ◽  
Author(s):  
Nathan T. James ◽  
Joseph H. Breeyear ◽  
Richard Caprioli ◽  
Todd Edwards ◽  
Brian Hachey ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Population Pharmacokinetic Analysis ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

Assessing function of electronic health records for real-world data generation

2018 ◽  
Vol 24 (3) ◽  
pp. 95-98 ◽  
Author(s):  
Daphne Guinn ◽  
Erin E Wilhelm ◽  
Grazyna Lieberman ◽  
Sean Khozin
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Data Generation ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health

scholarly journals Use of Real-World Data for the Research, Development, and Evaluation of Oncology Precision Medicines

2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jan R.R. Lewis ◽  
Ian Kerridge ◽  
Wendy Lipworth
Keyword(s):  
Electronic Health Records ◽  
Real World ◽  
Late Phase ◽  
Data Sets ◽  
Patient Identification ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health ◽  
New Treatments

Although randomized controlled trials remain the scientific ideal for determining the efficacy and safety of new treatments, they are sometimes insufficient to address the evidentiary requirements of regulators and payers. This is particularly the case when it comes to precision medicines because trials are often small, deliver incomplete insights into outcomes of most interest to policymakers (eg, overall survival), and may fail to address other complex diagnostic and treatment-related questions. Additional methods, both experimental and observational, are increasingly being used to fill critical evidentiary gaps. A number of modified early- and late-phase trial designs have been proposed to better support earlier biomarker validation, patient identification, and selection for regulatory studies, but there is still a need for confirmatory evidence from real-world data sources. These data are usually provided through observational, postapproval, phase IIIB and IV studies, which rely heavily on registries and other electronic data sets—most notably data from electronic health records. It is, therefore, crucial to understand what ethical, practical, and scientific challenges are raised by the use of electronic health records to generate evidence about precision medicines.

An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication

Nature Cancer ◽  
2021 ◽  
Vol 2 (7) ◽  
pp. 709-722 ◽  
Author(s):  
Olivier Morin ◽  
Martin Vallières ◽  
Steve Braunstein ◽  
Jorge Barrios Ginart ◽  
Taman Upadhaya ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Records ◽  
Real World ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Electronic Health ◽  
Pan Cancer

scholarly journals Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review

2020 ◽  
Author(s):  
James R Rogers ◽  
Junghwan Lee ◽  
Ziheng Zhou ◽  
Ying Kuen Cheung ◽  
George Hripcsak ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Scoping Review ◽  
Real World ◽  
Administrative Claims ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Trial Process

Abstract Objective Real-world data (RWD), defined as routinely collected healthcare data, can be a potential catalyst for addressing challenges faced in clinical trials. We performed a scoping review of database-specific RWD applications within clinical trial contexts, synthesizing prominent uses and themes. Materials and Methods Querying 3 biomedical literature databases, research articles using electronic health records, administrative claims databases, or clinical registries either within a clinical trial or in tandem with methodology related to clinical trials were included. Articles were required to use at least 1 US RWD source. All abstract screening, full-text screening, and data extraction was performed by 1 reviewer. Two reviewers independently verified all decisions. Results Of 2020 screened articles, 89 qualified: 59 articles used electronic health records, 29 used administrative claims, and 26 used registries. Our synthesis was driven by the general life cycle of a clinical trial, culminating into 3 major themes: trial process tasks (51 articles); dissemination strategies (6); and generalizability assessments (34). Despite a diverse set of diseases studied, <10% of trials using RWD for trial process tasks evaluated medications or procedures (5/51). All articles highlighted data-related challenges, such as missing values. Discussion Database-specific RWD have been occasionally leveraged for various clinical trial tasks. We observed underuse of RWD within conducted medication or procedure trials, though it is subject to the confounder of implicit report of RWD use. Conclusion Enhanced incorporation of RWD should be further explored for medication or procedure trials, including better understanding of how to handle related data quality issues to facilitate RWD use.

scholarly journals Population Pharmacokinetic Analysis of Dexmedetomidine in Children using Real-World-Data Obtained from Electronic Health Records and Remnant Clinic Samples

2021 ◽  
Author(s):  
Nathan T James ◽  
Sara L Van Driest ◽  
Prince J Kannankeril ◽  
Leena Choi
Keyword(s):  
Real World ◽  
Volume Of Distribution ◽  
Pharmacokinetic Analysis ◽  
Population Pharmacokinetic ◽  
Real World Data ◽  
World Data ◽  
Fixed Weight ◽  
Population Pk ◽  
Study Population ◽  
The Impact

Dexmedetomidine is commonly used as part of intraoperative anesthetic management and for sedation and pain control after surgery in children. Dexmedetomidine infusion dose is typically given on a fixed weight basis with titration to achieve sedation goals while avoiding potential toxicities. Pharmacokinetic (PK) studies are useful for accurate prediction of the individual dose required to achieve sedation and analgesia goals without toxicity, but lack of PK data is a challenge in precision dosing for pediatric populations. In this study, population PK models were developed using a nonlinear mixed-effects modeling approach and used to explore the relationship between PK profile and clinical, demographic, and genotype covariates. A simulation study was used to demonstrate the impact of important covariates on concentration using a fixed weight dosing scheme. Our final study population included data from 354 patients age 0 to 22 years (median age 16 months). In the final two-compartment model with fixed allometric weight scaling we found significant effects of both age and UGT2B10 genotype. The population PK parameter estimates (95% confidence interval) for a standard 70 kg weight were clearance 22.3 (18.3 - 27.3) L/hr, central compartment volume of distribution 133 (112 - 157) L, intercompartmental clearance 24.1 (19.4 - 29.9) L/hr, peripheral compartment volume of distribution 5230 (3310 - 8260) L. Our study provides support for the feasibility of using real-world data obtained from EHRs and remnant samples to perform population PK analysis for groups of patients where traditional PK studies are challenging to perform. Inclusion of UGT2B10 genotype in the model significantly improved the model fit, but the effects were not large enough to impact clinical dosing.

scholarly journals Comparing SDTM and FHIR® for Real World Data from Electronic Health Records for Clinical Trial Submissions

2021 ◽  
Author(s):  
Sarah Riepenhausen ◽  
Cornelia Mertens ◽  
Martin Dugas
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Real World Data ◽  
Health Records ◽  
World Data ◽  
Data Submission

Real world data for use in clinical trials is promising. We compared the SDTM for clinical trial data submission with FHIR® for routine documentation. After categorization of variables by relevance, clinically relevant SDTM items were mapped to FHIR®. About 30% in both were seen as clinically relevant. The majority of these SDTM items were mappable to FHIR® Observation resource.

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