Efficacy and Safety of Hydrocortisone, Ascorbic Acid, and Thiamine Combination Therapy for the Management of Sepsis and Septic Shock: A Systematic Review and Meta-Analysis of Randomised Controlled Trials
Bibliographic record
Abstract
INTRODUCTION: This systematic review aimed to assess the efficacy and safety of hydrocortisone, ascorbic acid, and thiamine (HAT) combination therapy in patients with sepsis and septic shock. METHODS: We conducted a database search in MEDLINE, Embase, CENTRAL, Web of Science, and CNKI for randomised controlled trials (RCTs) comparing HAT against placebo/standard of care or against hydrocortisone in sepsis/septic shock patients. Outcomes included mortality, ICU/hospital length of stay (LOS), vasopressor durations, mechanical ventilation durations, change in SOFA at 72 h, and adverse events. RCT results were pooled in random-effects meta-analyses. Quality of evidence was assessed using GRADE. RESULTS: Fifteen RCTs (N = 2,594) were included. At 72 h, HAT reduced SOFA scores from baseline (mean difference [MD] -1.16, 95% confidence interval [CI]: -1.58 to -0.74, I2 = 0%) compared to placebo/SoC, based on moderate quality of evidence. HAT also reduced the duration of vasopressor use (MD -18.80 h, 95% CI: -23.67 to -13.93, I2 = 64%) compared to placebo/SoC, based on moderate quality of evidence. HAT increased hospital LOS (MD 2.05 days, 95% CI: 0.15-3.95, I2 = 57%) compared to placebo/SoC, based on very low quality of evidence. HAT did not increase incidence of adverse events compared to placebo/SoC. CONCLUSIONS: HAT appears beneficial in reducing vasopressor use and improving organ function in sepsis/septic shock patients. However, its advantages over hydrocortisone alone remain unclear. Future research should use hydrocortisone comparators and distinguish between sepsis-specific and comorbidity- or care-withdrawal-related mortality.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.028 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".