Trial reportJournal of diabetes science and technology2013
Technical challenges and clinical outcomes of using a closed-loop glycemic control system in the hospital.
Trial report in Journal of diabetes science and technology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed, 10 citations in OpenAlex.
- Feasibility of fully automated closed-loop glucose control using continuous subcutaneous glucose measurements in critical illness: a randomized controlled trial.Critical care (London, England) · 2013Trial
- Managing hyperglycemia during the COVID-19 pandemic: Improving outcomes using new technologies in intensive care.SAGE open medicine · 2020Review
- Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas.Critical care (London, England) · 2018Review
- Critical Care Management of Stress-Induced Hyperglycemia.Current diabetes reports · 2018Review
- The Hypoglycaemia-Hyperglycaemia Minimizer System in the Management of Type 1 Diabetes.European endocrinology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
According to large randomized trials, results suggest that maintaining normoglycemia postoperatively through tight glycemic control (TGC) and intensive insulin therapy (IIT) can improve surgical outcomes as well as reduce mortality and morbidity in critically ill patients. However, trials examining the effects of TGC have had conflicting results. Systematic reviews and meta-analyses have also led to differing conclusions. The main reason these clinical trials and meta-analyses show negative results for TGC is the high incidence of hypoglycemia induced by IIT. This could not be prevented because there is no reliable technique that can avoid this condition during IIT. The development of accurate, continuous blood glucose monitoring devices and closed-loop systems for computer-assisted blood glucose control in the intensive care unit (ICU) will probably help avoid hypoglycemia in these situations. The STG closed-loop glycemic control system was introduced to our department to be used and evaluated for strict serum glucose control with no hypoglycemic episodes during IIT in the surgical ICU, to reduce the workload of ICU nurses, and to decrease incidents related to the management of blood glucose levels according to manual conventional venous infusion insulin therapy. The goal of our team was to use the STG closed-loop glycemic control system for perioperative TGC in surgical patients to solve the complications of IIT and reduce risk of hypoglycemia. The challenge at our hospital demonstrated that the STG closed-loop glycemic control system can be expected to achieve TGC with no occurrence of hypoglycemia induced by IIT after surgery.
Indexed as
Identifiers
What OpenQuestion holds
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.