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Related Concept Videos

Hyperglycemia01:29

Hyperglycemia

Hyperglycemia is an abnormally high blood glucose level. It is diagnosed by fasting glucose ≥126 mg/dL, 2-hour oral glucose tolerance test (or OGTT) ≥200 mg/dL, random glucose ≥200 mg/dL with symptoms, or HbA1c ≥6.5%. However, HbA1c results may be unreliable in certain conditions, such as anemia or hemoglobinopathies, and the diagnosis should be confirmed unless classic symptoms are present. Postprandial hyperglycemia is typically considered significant when glucose levels exceed 180 mg/dL two...
Hyperosmolar Hyperglycemic State01:21

Hyperosmolar Hyperglycemic State

Hyperosmolar Hyperglycemic State, or HHS, is a serious and life-threatening complication of type 2 diabetes mellitus. It is characterized by three main features: severe hyperglycemia, profound dehydration, and elevated serum osmolality, all occurring without significant ketoacidosis.HHS typically develops in older adults or individuals with limited access to fluids. This may result from illness, cognitive impairment, or medications such as diuretics or corticosteroids. These factors reduce...
Diabetic Ketoacidosis l: Introduction01:25

Diabetic Ketoacidosis l: Introduction

DefinitionDiabetic ketoacidosis (DKA) is an acute, life-threatening complication of diabetes mellitus, characterized by a triad of hyperglycemia (blood glucose >250 mg/dL), ketonemia or ketonuria, and metabolic acidosis (arterial pH <7.30 and serum bicarbonate <18 mEq/L). It results from insulin deficiency combined with elevated levels of counterregulatory hormones—glucagon, catecholamines, cortisol, and growth hormone—leading to increased lipolysis, hepatic ketone production, and...

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Related Experiment Video

Updated: Jun 19, 2026

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

A benchmark data set for model-based glycemic control in critical care.

J Geoffrey Chase1, Aaron LeCompte, Geoffrey M Shaw

  • 1University of Canterbury, Centre for Bio-Engineering, Department of Mechanical Engineering, Christchurch, New Zealand. geoff.chase@canterbury.ac.nz

Journal of Diabetes Science and Technology
|November 4, 2009
PubMed
Summary

This study introduces a benchmark dataset for critical care glycemic control, using data from 20 intensive care unit (ICU) patients. This resource facilitates the development and comparison of model-based control strategies for hyperglycemia management.

Keywords:
clinical resultscontrolcritical careglucose variabilityhyperglycemiamodel basedmortality

Related Experiment Videos

Last Updated: Jun 19, 2026

Improving IV Insulin Administration in a Community Hospital
12:08

Improving IV Insulin Administration in a Community Hospital

Published on: June 11, 2012

Area of Science:

  • Critical care medicine
  • Biomedical engineering
  • Data science

Background:

  • Hyperglycemia is common in critical care settings, necessitating effective glucose control strategies.
  • While tight glycemic control can improve outcomes, optimal methods and patient selection remain unclear.
  • Model-based approaches offer patient-specific control and insights but lack standardized benchmarks for comparison.

Purpose of the Study:

  • To establish a benchmark dataset for critical care glycemic control in a medical intensive care unit (ICU).
  • To facilitate the development and comparison of model-based glycemic control methods.
  • To provide a common reference for analyzing control strategies using real clinical data.

Main Methods:

  • Utilized data from 20 post-pilot patients with a length of stay (LoS) > 5 days from the Christchurch ICU.
  • The dataset includes insulin and nutrition inputs, blood glucose measurements, and mortality outcomes.
  • Data were sourced from the SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies.

Main Results:

  • The dataset comprises 6372 total patient hours and 4182 blood glucose measurements (average ~1.5 hours apart).
  • Patient and glycemic control data align with the overall SPRINT cohort and the LoS > 5-day subgroup.
  • Mortality outcomes (15%) in the dataset are consistent with SPRINT results for this patient group.

Conclusions:

  • A benchmark dataset enables the development of patient-specific, adaptable model-based glycemic control solutions.
  • This resource aids groups with limited clinical data in developing and validating control protocols.
  • It provides a standardized platform for comparing diverse glycemic control strategies on a virtual cohort derived from real patient data.