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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

601
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
601
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
296
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

268
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
268
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

564
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
564
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

357
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
357
Harmonic Mean01:09

Harmonic Mean

3.8K
The arithmetic mean is usually skewed towards the larger values in the data set. Therefore, to avoid this inherent bias towards smaller values, the harmonic mean is used.
Take the example of the speed of a car, which is the measure of the rate of distance traveled. If the vehicle traverses the same distance back-and-forth, its average speed equals the total distance traveled divided by the total time taken. However, if the car moves with varying speeds, then the arithmetic mean is more skewed...
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A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR.

Guoqian Jiang1, Richard C Kiefer1, Deepak K Sharma1

  • 1Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Studies in Health Technology and Informatics
|January 4, 2018
PubMed
Summary

Harmonizing the Observational Health Data Sciences and Informatics Common Data Model (OHDSI CDM) with Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) showed moderate agreement. The FHIR W5 Classification System aided consensus on data model harmonization.

Keywords:
Observational studyReference standardsVocabularycontrolled

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Area of Science:

  • Health Informatics
  • Clinical Data Standards
  • Interoperability

Background:

  • Standardized data models are crucial for organizing clinical research data into integrated repositories.
  • Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) is a leading standard for healthcare data exchange.
  • Existing data models require harmonization to align with emerging interoperability standards like FHIR.

Purpose of the Study:

  • To design and evaluate a consensus-driven method for harmonizing the OHDSI Common Data Model (CDM) with HL7 FHIR.
  • To assess the effectiveness of the FHIR W5 (Who, What, When, Where, Why) Classification System in this harmonization process.
  • To measure the level of agreement among data curators during the harmonization effort.

Main Methods:

  • Developed harmonization approaches using the FHIR W5 Classification System.
  • Applied these approaches to harmonize the OHDSI CDM with HL7 FHIR.
  • Assessed curator consensus using inter-rater agreement measures (kappa statistic).

Main Results:

  • Moderate agreement (kappa = 0.50) was achieved for model-level harmonization between OHDSI CDM and FHIR.
  • Fair agreement (kappa = 0.21) was observed for property-level harmonization.
  • The FHIR W5 Classification System proved useful in guiding harmonization and achieving consensus.

Conclusions:

  • The FHIR W5 Classification System is a valuable tool for developing and implementing data model harmonization strategies.
  • Achieving consensus in harmonizing complex data models like OHDSI CDM with FHIR presents challenges, particularly at the property level.
  • Further refinement of harmonization methods is needed to improve agreement and facilitate broader adoption of interoperable clinical data standards.