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Related Concept Videos

One-Compartment Model: IV Infusion01:09

One-Compartment Model: IV Infusion

Intravenous (IV) infusion is often utilized when continuous and controlled drug delivery is necessary, such as during surgery or in the treatment of chronic diseases. This method offers numerous advantages, including immediate drug action, precise control over dosage, and bypassing the first-pass metabolism.
The one-compartment model for IV infusion uses mathematical equations to describe the rate of change in drug quantity in the body. At steady-state or infusion equilibrium, the drug input...
Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations01:15

Determination of Multiple Dosing Parameters: Steady-State, Minimum and Maximum Concentrations

Gentamicin, an aminoglycoside antibiotic, is commonly administered via intermittent intravenous infusion to treat severe infections. An intermittent one-hour infusion of gentamicin, administered at eight-hour intervals, allows for precise control of plasma drug concentrations, minimizing toxicity while ensuring therapeutic efficacy. Pharmacokinetic principles govern the dynamics of plasma concentrations and can be mathematically described using specific equations.The plasma drug concentration...
Two-Compartment Open Model: IV Bolus Administration01:18

Two-Compartment Open Model: IV Bolus Administration

The two-compartment model for intravenous (IV) bolus administration illustrates drug distribution in the body, subdividing it into central and peripheral compartments. This model operates on the concept of two-compartment kinetics. The drug's plasma concentration shows a bi-exponential decline following IV bolus administration, signaling the presence of two disposition processes: distribution and elimination.
The disparity between drug input and the sum of drug transfer rates between...
Two-Compartment Open Model: IV Infusion01:15

Two-Compartment Open Model: IV Infusion

A two-compartment model is a vital tool in pharmacokinetics, providing an essential understanding of drug behavior, especially for those administered via zero-order intravenous infusion. This model outlines two compartments: the central compartment, where elimination occurs, and the peripheral compartment.
The model illustrates the decrease in plasma drug concentration from the central compartment with a specific equation. It shows that under steady-state conditions, the drug's input rate...
Drug Accumulation During Multiple Dosing: Intermittent IV Infusions01:24

Drug Accumulation During Multiple Dosing: Intermittent IV Infusions

Intermittent intravenous (IV) infusion is a method of drug administration where medications are delivered over short infusion periods followed by intervals of no drug delivery. This approach helps to prevent sustained high drug concentrations in the bloodstream, reducing the risk of adverse effects associated with prolonged exposure. Unlike continuous infusion, steady-state concentrations may not be achieved during a single dosing cycle but can be reached through repeated...
IV Infusion to Oral Dosing: Conversion Methods01:28

IV Infusion to Oral Dosing: Conversion Methods

The development of extended-release formulations has facilitated the transition from intravenous to oral medication, offering a more convenient and patient-friendly approach to drug administration. This transition, however, requires careful management to ensure that therapeutic drug levels are maintained, preserving efficacy and avoiding adverse effects. Understanding pharmacokinetic principles and dosage calculations is critical during this process.Pharmacokinetics of the...

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Related Experiment Video

Updated: Jun 16, 2026

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Intermediary variables and algorithm parameters for an electronic algorithm for intravenous insulin infusion.

Susan S Braithwaite1, Hemant Godara, Julie Song

  • 1University of Illinois Chicago, Chicago, Illinois 60202, USA. braith@uic.edu

Journal of Diabetes Science and Technology
|February 11, 2010
PubMed
Summary

This study presents a new algorithm for intravenous insulin infusion that accurately estimates insulin needs before blood glucose levels normalize. The algorithm improves safety and allows for personalized glycemic targets, reducing variability.

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Hyperinsulinemic-euglycemic Clamps in Conscious, Unrestrained Mice
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Last Updated: Jun 16, 2026

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Combined Intravital Microscopy and Contrast-enhanced Ultrasonography of the Mouse Hindlimb to Study Insulin-induced Vasodilation and Muscle Perfusion
08:22

Combined Intravital Microscopy and Contrast-enhanced Ultrasonography of the Mouse Hindlimb to Study Insulin-induced Vasodilation and Muscle Perfusion

Published on: March 20, 2017

Hyperinsulinemic-euglycemic Clamps in Conscious, Unrestrained Mice
11:10

Hyperinsulinemic-euglycemic Clamps in Conscious, Unrestrained Mice

Published on: November 16, 2011

Area of Science:

  • Biomedical Engineering
  • Computational Physiology
  • Endocrinology

Background:

  • Intravenous insulin infusion algorithms often use a two-step process involving maintenance rate (MR) estimation and next infusion rate (IR(next)) calculation.
  • Current methods may not accurately estimate MR before achieving euglycemia, impacting the precision of insulin delivery.
  • Blood glucose (BG) dynamics and patient-specific factors are crucial for effective insulin therapy management.

Purpose of the Study:

  • To develop and validate a novel algorithm for intravenous insulin infusion that estimates the maintenance rate (MR) prior to achieving euglycemia.
  • To compare the performance of the new algorithm's estimated infusion rate (IR(next)) against historically assigned rates.
  • To propose practical recommendations for computerizing the advanced insulin infusion algorithm.

Main Methods:

  • Computed a "maintenance rate cross step next estimate" (MR(csne)) using historical hyperglycemic data and previous insulin infusion rates (IR(previous)) and BG changes.
  • Compared MR(csne) with the mean IR on historical stable intervals (MR(true)), an estimate of the biologic MR.
  • Mathematically developed an expanded theory of the algorithm and compared hypothetically calculated MR(csne)-dependent IR(next) with historically assigned IR(next).

Main Results:

  • MR(csne) and MR(true) were strongly correlated (R² = 0.88), despite median differences (2.7 vs. 3.2 units/h).
  • Historically assigned and MR(csne)-dependent IR(next) during hyperglycemia were also correlated (R² = 0.87), with median differences (4.0 vs. 4.6 units/h).
  • The algorithm establishes a fundamental equation relating IR, MR, and BG rate of change variables, incorporating unique natural parameters.

Conclusions:

  • The described algorithm effectively estimates MR before euglycemia and computes MR-dependent IR(next) values.
  • Key design features address glycemic variability and enhance safety by mitigating hypoglycemia risk.
  • The method allows for specifying patient-condition-specific glycemic targets, promoting tailored insulin therapy.