Effectiveness of Fludrocortisone Plus Hydrocortisone versus Hydrocortisone Alone in Septic Shock: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Abstract Rationale The use of hydrocortisone in adult patients with septic shock is controversial, and the effectiveness of adding fludrocortisone to hydrocortisone remains uncertain. Objectives To assess the comparative effectiveness and safety of fludrocortisone plus hydrocortisone, hydrocortisone alone, and placebo or usual care in adults with septic shock. Methods A systematic review and a Bayesian network meta-analysis of peer-reviewed randomized trials were conducted. The primary outcome was all-cause mortality at last follow-up. Treatment effects are presented as relative risks (RRs) with 95% credible intervals (CrIs). Placebo or usual care was the reference treatment. Measurements and Main Results Among 7,553 references, we included 17 trials (7,688 patients). All-cause mortality at last follow-up was lowest with fludrocortisone plus hydrocortisone (RR, 0.85; 95% CrI, 0.72–0.99; 98.3% probability of superiority, moderate-certainty evidence), followed by hydrocortisone alone (RR, 0.97; 95% CrI, 0.87–1.07; 73.1% probability of superiority, low-certainty evidence). The comparison of fludrocortisone plus hydrocortisone versus hydrocortisone alone was based primarily on indirect evidence (only two trials with direct evidence). Fludrocortisone plus hydrocortisone was associated with a 12% lower risk of all-cause mortality compared with hydrocortisone alone (RR, 0.88; 95% CrI, 0.74–1.03; 94.2% probability of superiority, moderate-certainty evidence). Conclusions In adult patients with septic shock, fludrocortisone plus hydrocortisone was associated with lower risk of all-cause mortality at last follow-up than placebo and hydrocortisone alone. The scarcity of head-to-head trials comparing fludrocortisone plus hydrocortisone versus hydrocortisone alone led our network meta-analysis to rely primarily on indirect evidence for this comparison. Although we undertook several sensitivity analyses and assessments, these findings should be considered while also acknowledging the heterogeneity of included trials.
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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 | 0.030 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".