Long-term albumin treatment for decompensated cirrhosis in Italy: A propensity score-matched, retrospective, real-world chart analysis
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: International guidelines recommend short-term albumin in specific acute conditions related to decompensated cirrhosis. Recent data from the large-scale ANSWER trial suggest long-term albumin (LTA) can be beneficial in selected patients. This study compared clinical outcomes in patients with decompensated cirrhosis in Italy, treated with LTA plus standard of care (SOC; LTA cohort) versus SOC alone (non-LTA cohort). MATERIALS AND METHODS: A retrospective chart analysis assessed patients with decompensated cirrhosis and ascites, receiving LTA (regular albumin, ≥40 g per infusion per week) plus SOC (albumin administered for acute complications) versus SOC alone. Propensity score matching was used to balance the cohorts. The primary endpoint was the incidence of therapeutic paracentesis. RESULTS: Overall, 311 charts were screened; 125 matched pairs in the LTA and non-LTA cohorts were analyzed. The incidence per patient per year of therapeutic paracentesis procedures was significantly reduced in the LTA cohort versus the non-LTA cohort (-47.8 %; p < 0.001). The incidence per patient per year of refractory ascites (-44.2 %; p = 0.018), spontaneous bacterial peritonitis (-52.7 %; p = 0.009), and hepatorenal syndrome (-62.6 %; p = 0.003), as well as duration of hospitalization per patient per year for cirrhosis-related complications (-35.0 %; p = 0.015), were also significantly reduced in the LTA cohort versus the non-LTA cohort. There were no significant differences between cohorts in the incidence per patient per year of hospital admissions for cirrhosis-related complications (-24.6 %; p = 0.050) and hepatic encephalopathy (-13.1 %; p = 0.605). CONCLUSIONS: This real-world study provides evidence that LTA can improve the care of patients with decompensated cirrhosis and may reduce related healthcare burden.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".