Elevated glucose levels at 24 hours predict mortality in cardiogenic shock
Bibliographic record
Abstract
Abstract Background Hyperglycemia is associated with poor outcomes in critically ill patients. However, its role as a predictor of outcomes in cardiogenic shock (CS) remains unclear due to conflicting evidence in the literature. Purpose Our study aimed to assess whether blood glucose levels 24 hours after Cardiac Intensive Care Unit (CICU) admission could predict 30-day all-cause mortality in patients with CS. Methods All patients enrolled in the multicentre prospective AltShock-2 registry between March 2020 and November 2023 with recorded blood glucose levels 24 hours post-CICU admission were included. The relationship between 24-hour blood glucose levels and 30-day all-cause mortality was analyzed. Optimal 24-hour blood glucose at cut-off values for outcome prediction were identified across the cohort. Patients were categorized as follows: Group A (BGL < 140 mg/dL), Group B (BGL 140–210 mg/dL), and Group C (BGL > 210 mg/dL). Results In total, 408 patients with CS (mean age 64 ± 15 years, 76% males) were included. At 24 hours post-CICU admission, blood glucose levels were < 140 in 211 patients (52%), 140-210 in 153 (37%), and > 210 mg/dl in 44 (11%). A previous diagnosis of diabetes mellitus (DM) was more common in groups B and C (p<0.01). Elevated 24-hour blood glucose was independently associated with increased 30-day all cause mortality (p=0,04). Patients with blood glucose >210 mg/dL had significantly higher 30-day mortality (aOR 3.2, 95% CI 1.2–8.9, p=0.02) compared to lower glucose groups. Intriguing, at multivariable logistic regression analysis, adjusting for DM status, the effect remained significant (aOR 2.94, 95% CI 1.07–8.02, P = 0.03). The 24-hour glucose measurement showed higher predictive accuracy for mortality than other time points (AUC 0.6 vs. 0.5). The optimal glucose threshold for mortality prediction was 155 mg/dl (aOR 2.0, IC 1.1-4.0, p=0.03). Notably, mechanical circulatory support use in this cohort was protective (aOR 0.44, 95% CI 0.2-0.9, p=0,04), while etiology of CS had no impact on outcomes. Conclusion Elevated 24-hour blood glucose levels were independently associated with increased 30-day all-cause mortality in patients with CS, irrespective of the DM status, while baseline and peak glycemia values were not statistically significant. The optimal blood glucose cutoff for predicting mortality was 155 mg/dL. Future studies should explore whether optimizing glycemic control strategies for this high-risk cohort can directly enhance clinical outcomes.30-d survival analysis stratified by BGL24h glycemia & 30 days mortality
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".