ArticleAnnals of intensive care2011
Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?
Article in Annals of intensive care, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Insulin resistance in early vs late nutrition and complications of sirs in neurosurgical intensive care unit (ICU).Medical archives (Sarajevo, Bosnia and Herzegovina) · 2015Trial
- Pilot study of a model-based approach to blood glucose control in very-low-birthweight neonates.BMC pediatrics · 2012Trial
- Decision support for optimized blood glucose control and nutrition in a neurotrauma intensive care unit: preliminary results of clinical advice and prediction accuracy of the Glucosafe system.Journal of clinical monitoring and computing · 2012Trial
- Data entry errors and design for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Trial
- Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance.Journal of diabetes science and technology · 2022Article
- Insulin sensitivity in critically ill patients: are women more insulin resistant?Annals of intensive care · 2021Article
- Optimising mechanical ventilation through model-based methods and automation.Annual reviews in control · 2019Review
- Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas.Critical care (London, England) · 2018Review
- Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them.Biomedical engineering online · 2018Review
- Autoregressive Modeling of Drift and Random Error to Characterize a Continuous Intravascular Glucose Monitoring Sensor.Journal of diabetes science and technology · 2018Article
- Continuous Glucose Monitoring Measures Can Be Used for Glycemic Control in the ICU: An In-Silico Study.Journal of diabetes science and technology · 2018Article
- Traversing the valley of glycemic control despair.Critical care (London, England) · 2017Article
- In-Silico Trials for Glucose Control in Hospitalized Patients with Type 2 Diabetes.Journal of Korean medical science · 2016Article
- Article
- Evaluation of a model-based hemodynamic monitoring method in a porcine study of septic shock.Computational and mathematical methods in medicine · 2013Article
- Physiological relevance and performance of a minimal lung model: an experimental study in healthy and acute respiratory distress syndrome model piglets.BMC pulmonary medicine · 2012Article
- Variability of insulin sensitivity during the first 4 days of critical illness: implications for tight glycemic control.Annals of intensive care · 2012Article
- Interface design and human factors considerations for model-based tight glycemic control in critical care.Journal of diabetes science and technology · 2012Article
- Monitoring in the intensive care.Critical care research and practice · 2012Article
- Stochastic targeted (STAR) glycemic control: design, safety, and performance.Journal of diabetes science and technology · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Critically ill patients are highly variable in their response to care and treatment. This variability and the search for improved outcomes have led to a significant increase in the use of protocolized care to reduce variability in care. However, protocolized care does not address the variability of outcome due to inter- and intra-patient variability, both in physiological state, and the response to disease and treatment. This lack of patient-specificity defines the opportunity for patient-specific approaches to diagnosis, care, and patient management, which are complementary to, and fit within, protocolized approaches.Computational models of human physiology offer the potential, with clinical data, to create patient-specific models that capture a patient's physiological status. Such models can provide new insights into patient condition by turning a series of sometimes confusing clinical data into a clear physiological picture. More directly, they can track patient-specific conditions and thus provide new means of diagnosis and opportunities for optimising therapy.This article presents the concept of model-based therapeutics, the use of computational models in clinical medicine and critical care in specific, as well as its potential clinical advantages, in a format designed for the clinical perspective. The review is presented in terms of a series of questions and answers. These aspects directly address questions concerning what makes a model, how it is made patient-specific, what it can be used for, its limitations and, importantly, what constitutes sufficient validation.To provide a concrete foundation, the concepts are presented broadly, but the details are given in terms of a specific case example. Specifically, tight glycemic control (TGC) is an area where inter- and intra-patient variability can dominate the quality of care control and care received from any given protocol. The overall review clearly shows the concept and significant clinical potential of using computational models in critical care medicine.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.