Intensive insulin therapy and mortality among critically ill patients: a meta-analysis including NICE-SUGAR study data
Bibliographic record
Abstract
BACKGROUND: Hyperglycemia is associated with increased mortality in critically ill patients. Randomized trials of intensive insulin therapy have reported inconsistent effects on mortality and increased rates of severe hypoglycemia. We conducted a meta-analysis to update the totality of evidence regarding the influence of intensive insulin therapy compared with conventional insulin therapy on mortality and severe hypoglycemia in the intensive care unit (ICU). METHODS: We conducted searches of electronic databases, abstracts from scientific conferences and bibliographies of relevant articles. We included published randomized controlled trials conducted in the ICU that directly compared intensive insulin therapy with conventional glucose management and that documented mortality. We included in our meta-analysis the data from the recent NICE-SUGAR (Normoglycemia in Intensive Care Evaluation - Survival Using Glucose Algorithm Regulation) study. RESULTS: We included 26 trials involving a total of 13 567 patients in our meta-analysis. Among the 26 trials that reported mortality, the pooled relative risk (RR) of death with intensive insulin therapy compared with conventional therapy was 0.93 (95% confidence interval [CI] 0.83-1.04). Among the 14 trials that reported hypoglycemia, the pooled RR with intensive insulin therapy was 6.0 (95% CI 4.5-8.0). The ICU setting was a contributing factor, with patients in surgical ICUs appearing to benefit from intensive insulin therapy (RR 0.63, 95% CI 0.44-0.91); patients in the other ICU settings did not (medical ICU: RR 1.0, 95% CI 0.78-1.28; mixed ICU: RR 0.99, 95% CI 0.86-1.12). The different targets of intensive insulin therapy (glucose level < or = 6.1 mmol/L v. < or = 8.3 mmol/L) did not influence either mortality or risk of hypoglycemia. INTERPRETATION: Intensive insulin therapy significantly increased the risk of hypoglycemia and conferred no overall mortality benefit among critically ill patients. However, this therapy may be beneficial to patients admitted to a surgical ICU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".