Impact of rifaximin use in infections and mortality in patients with decompensated cirrhosis and hepatic encephalopathy
Bibliographic record
Abstract
Introduction: Infections in patients with cirrhosis are associated with high morbidity and mortality. Rifaximin is an antibiotic used to treat and prevent hepatic encephalopathy (HE); however, it has been suggested that it may play a crucial role in reducing infections in these populations. Aim: To evaluate the role of rifaximin in preventing frequent cirrhosis-related infections [spontaneous bacterial peritonitis, pneumonia, urinary tract infection (UTI), and bacteremia], Clostridioides difficile infection, and all-cause mortality, as well as determining adverse effects and adherence to the drug. Methods: A retrospective cohort study was conducted on decompensated cirrhotic patients with history of HE between January 2017 and November 2022 at a university center. Patients with cirrhosis, regardless of their etiology and severity, were included in the study, encompassing both hospitalized and outpatient cases. The statistical analysis included adjusted general linear models, Poisson regressions, and propensity score matching. Results: We included 153 patients. The mean age in the cohort was 60.2 ± 12.3 years and 67 (43.8%) were women. The main cause of cirrhosis was metabolic dysfunction-associated steatotic liver disease 52 (38%), and the median Model of End-Stage Liver Disease sodium was 16.5 (7–32). In the cohort, 65 (45%) patients used rifaximin. The mean follow-up was 32 months. Eighty-five patients with infectious events were recorded, and a total of 164 infectious events were registered. The main infectious events were UTIs (62, 37.8%) and pneumonia (38, 23.2%). The use of rifaximin was associated with lower infection rates, displaying an incidence rate ratio (IRR) of 0.64 [95% confidence interval (CI) (0.47–0.89); p = 0.008]. However, no discernible impact on mortality outcome was observed [IRR 1.9, 95% CI (0.9–4.0); p = 0.09]. There were no reported adverse effects, and no patient discontinued the therapy due to adverse effects. Conclusion: The use of rifaximin significantly reduces infections in patients with cirrhosis and HE. Despite rifaximin was associated with a decreased all-cause mortality, this impact was not statistically significant in the adjusted analysis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".