Trial reportDiabetes care2013
LOGIC-insulin algorithm-guided versus nurse-directed blood glucose control during critical illness: the LOGIC-1 single-center, randomized, controlled clinical trial.
Trial report in Diabetes care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 34 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.
LOGIC-Insulin Computerized Algorithm-guided Versus Nurse-directed Blood Glucose Control in Critically Ill Patients: the LOGIC-1 Randomized Controlled Trial
LOGIC-Insulin Computerized Algorithm-guided Versus Nurse-directed Blood Glucose Control in Critically Ill Patients: the LOGIC-2 Multicentre Randomized Controlled Trial
Who cites it
34 citing papers in PubMed, 90 citations in OpenAlex.
- Software-guided versus nurse-directed blood glucose control in critically ill patients: the LOGIC-2 multicenter randomized controlled clinical trial.Critical care (London, England) · 2017Trial
- Computer-determined dosage of insulin in the management of neonatal hyperglycaemia (HINT2): protocol of a randomised controlled trial.BMJ open · 2017Trial
- 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
- Individualising blood glucose control in ICU patients: evidence and practical implementations.Critical care (London, England) · 2026Review
- Algorithm-guided insulin therapy in hospitalized patients: current evidence, implementation challenges, and future perspectives.Reviews in endocrine & metabolic disorders · 2026Review
- Diabetes Management Strategies in Intensive Care Settings.The Medical clinics of North America · 2026Review
- Review Article - Diabetes Technology in the Hospital: An Update.Current diabetes reports · 2024Review
- What, why and how to monitor blood glucose in critically ill patients.World journal of diabetes · 2023Review
- Article
- Fifteen-minute Frequency of Glucose Measurements and the Use of Threshold Alarms: Impact on Mitigating Dysglycemia in Critically Ill Patients.Journal of diabetes science and technology · 2021Article
- Insulin sensitivity in critically ill patients: are women more insulin resistant?Annals of intensive care · 2021Article
- Managing hyperglycemia during the COVID-19 pandemic: Improving outcomes using new technologies in intensive care.SAGE open medicine · 2020Review
- Effect of informatization-based blood glucose team management on the control of hyperglycaemia in noncritical care units.PloS one · 2020Article
- Interpretation of Retrospective BG Measurements.Journal of diabetes science and technology · 2018Article
- Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas.Critical care (London, England) · 2018Review
- Hypoglycemia Prevention by Algorithm Design During Intravenous Insulin Infusion.Current diabetes reports · 2018Review
- Critical Care Management of Stress-Induced Hyperglycemia.Current diabetes reports · 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
- Nutrition delivery of a model-based ICU glycaemic control system.Annals of intensive care · 2018Article
- Hypoglycemia Reduction Strategies in the ICU.Current diabetes reports · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTight blood glucose control (TGC) in critically ill patients is difficult and labor intensive, resulting in poor efficacy of glycemic control and increased hypoglycemia rate. The LOGIC-Insulin computerized algorithm has been developed to assist nurses in titrating insulin to maintain blood glucose levels at 80-110 mg/dL (normoglycemia) and to avoid severe hypoglycemia (<40 mg/dL). The objective was to validate clinically LOGIC-Insulin relative to TGC by experienced nurses. RESEARCH DESIGN AND
methodsThe investigator-initiated LOGIC-1 study was a prospective, parallel-group, randomized, controlled clinical trial in a single tertiary referral center. A heterogeneous mix of 300 critically ill patients were randomized, by concealed computer allocation, to either nurse-directed glycemic control (Nurse-C) or algorithm-guided glycemic control (LOGIC-C). Glycemic penalty index (GPI), a measure that penalizes both hypoglycemic and hyperglycemic deviations from normoglycemia, was the efficacy outcome measure, and incidence of severe hypoglycemia (<40 mg/dL) was the safety outcome measure.
resultsBaseline characteristics of 151 Nurse-C patients and 149 LOGIC-C patients and study times did not differ. The GPI decreased from 12.4 (interquartile range 8.2-18.5) in Nurse-C to 9.8 (6.0-14.5) in LOGIC-C (P < 0.0001). The proportion of study time in target range was 68.6 ± 16.7% for LOGIC-C patients versus 60.1 ± 18.8% for Nurse-C patients (P = 0.00016). The proportion of severe hypoglycemic events was decreased in the LOGIC-C group (Nurse-C 0.13%, LOGIC-C 0%; P = 0.015) but not when considered as a proportion of patients (Nurse-C 3.3%, LOGIC-C 0%; P = 0.060). Sampling interval was 2.2 ± 0.4 h in the LOGIC-C group versus 2.5 ± 0.5 h in the Nurse-C group (P < 0.0001).
conclusionsCompared with expert nurses, LOGIC-Insulin improved efficacy of TGC without increasing rate of hypoglycemia.
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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.