ArticleJournal of diabetes science and technology2008
A benchmark data set for model-based glycemic control in critical care.
Article in Journal of diabetes science and technology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Tight computerized versus conventional glucose control in the ICU: a randomized controlled trial.Intensive care medicine · 2014Trial
- Risk and reward: extending stochastic glycaemic control intervals to reduce workload.Biomedical engineering online · 2020Article
- 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
- Untangling glycaemia and mortality in critical care.Critical care (London, England) · 2017Article
- In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting.Journal of diabetes science and technology · 2016Article
- Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.Proceedings of the ... American Control Conference. American Control Conference · 2015Article
- Modeling Inpatient Glucose Management Programs on Hospital Infection Control Programs: An Infrastructural Model of Excellence.Joint Commission journal on quality and patient safety · 2015Article
- Multiplicative surrogate standard deviation: a group metric for the glycemic variability of individual hospitalized patients.Journal of diabetes science and technology · 2013Article
- Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence.Current diabetes reports · 2013Review
- Physiological modeling, tight glycemic control, and the ICU clinician: what are models and how can they affect practice?Annals of intensive care · 2011Article
- In silico evaluation of a control system and algorithm for automated insulin infusion in the ICU setting.Biomedical engineering online · 2010Article
- Continuous glucose monitors and the burden of tight glycemic control in critical care: can they cure the time cost?Journal of diabetes science and technology · 2010Article
- In silico preclinical trials: methodology and engineering guide to closed-loop control in type 1 diabetes mellitus.Journal of diabetes science and technology · 2009Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the "how" and "for whom" in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control.
methodsData from 20 of the first 150 postpilot patients treated under SPRINT are presented. All patients had longer than a 5-day length of stay (LoS) in the Christchurch ICU. The benchmark data set matches overall patient data and glycemic control results for the entire cohort and this particular LoS >5-day group. The mortality outcome (n =3, 15%) also matches SPRINT results for this patient group.
resultsData cover 20 patients and 6372 total patient hours with an average of 339.4 hours per patient. It includes insulin and nutrition inputs along with 4182 blood glucose measurements at an average of 224.3 measurements per patient, averaging a measurement approximately every 1.5 hours (16 per day). Data are available via download in a Microsoft Excel format. A series of cumulative distribution functions and tables are used to summarize data in this article.
conclusionModel-based methods can provide tighter, more adaptable "one method fits all" solutions using methods that enable patient-specific modeling and control. A benchmark data set will enable easier model and protocol development for groups lacking clinical data, as well as providing a benchmark to compare results of different protocols on a single (virtual) cohort based on real clinical data.
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