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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
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Related Experiment Video

Updated: Jun 19, 2026

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
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A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

A multiple imputation model for imputing missing physiologic data in the national trauma data bank.

Lynne Moore1, James A Hanley, Alexis F Turgeon

  • 1Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada.

Journal of the American College of Surgeons
|October 27, 2009
PubMed
Summary
This summary is machine-generated.

This study developed a multiple imputation (MI) model to address missing physiologic data in the National Trauma Data Bank (NTDB). The model improves trauma research quality by accurately simulating Glasgow Coma Scale (GCS), respiratory rate (RR), and systolic blood pressure (SBP) values.

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A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials
11:28

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Published on: May 18, 2015

Area of Science:

  • Trauma Registry Research
  • Biostatistics
  • Health Informatics

Background:

  • The National Trauma Data Bank (NTDB) suffers from incomplete physiologic data, limiting research accuracy.
  • Missing Glasgow Coma Scale (GCS), respiratory rate (RR), and systolic blood pressure (SBP) are significant issues.
  • Multiple Imputation (MI) is a potential solution for simulating missing trauma data.

Purpose of the Study:

  • To develop and validate a multiple imputation (MI) model for missing physiologic data within the NTDB.
  • To provide practical guidelines for implementing the MI model in trauma research.
  • To enhance the quality and reliability of research utilizing NTDB data.

Main Methods:

  • Utilized NTDB 7.0 data from 2005 admissions with at least one anatomic injury code.
  • Selected auxiliary variables through literature review and expert opinion for imputation.
  • Employed logistic regression to identify significant predictors for imputation, with a Bonferroni-corrected p-value <0.05.

Main Results:

  • The study analyzed 373,243 observations, with missing data for GCS (20.3%), RR (3.9%), and SBP (8.5%).
  • The MI model incorporated variables like demographics, injury severity, mechanism, and treatment details.
  • The model demonstrated strong predictive performance, with areas under the ROC curve ranging from 0.832 to 0.999.

Conclusions:

  • A robust MI model is proposed for imputing missing physiologic data in the NTDB.
  • Implementation of this model is expected to significantly improve NTDB-based research quality.
  • The developed methodology is adaptable for use in other trauma registries.