A292 IMPACT OF SIROLIMUS PROTEINURIA FOLLOWING LIVER TRANSPLANTATION
Bibliographic record
Abstract
Abstract Background Sirolimus (Sr) is a potent immunosuppressant used in liver transplant recipients to prevent rejection in settings of calcineurin inhibitor toxicity and in transplanted hepatocellular carcinoma (HCC) patients. Sr can cause proteinuria, which can lead to poor renal function and survival. The significance of proteinuria is poorly understood, with a lack of studies assessing its risk factors and their impact on clinical outcomes. Aims We evaluated the incidence of proteinuria and its impact on clinical outcomes among liver transplant (LT) recipients who were Sr users compared with non-sirolimus (nonSr) users. Methods We analyzed patients with their first LT between 2001 and 2020. Data were gathered from Organ Transplant Tracking records and chart reviews. Sr users received Sr for at least 6 consecutive months in the first year post-LT. We studied demographics, pre-LT comorbidities, and immunosuppression use. We evaluated the development of proteinuria, renal dysfunction, and new comorbidities including features of metabolic syndrome and cardiovascular disease. Data on post-LT infections, graft rejection, and patient survival were collected. Results We analyzed 359 Sr and 762 non-Sr users (73.5% vs. 65% male). The average ages of Sr and non-Sr users were 54.9±9 and 51.7±11.6 years, respectively (pampersand:003C0.001). Of Sr users, 40.4% had HCC (pampersand:003C0.001). Among non-Sr users, 95% were on tacrolimus and 67.8% were on mycophenolate. Higher frequency of pre-LT hypertension (HTN) was observed in Sr users (27.2% vs. 18.2%; pampersand:003C0.001). There were no differences in pre-LT chronic kidney disease (CKD), cardiovascular disease (CVD), dyslipidemia (DLD), diabetes mellitus (DM) prevalence, creatinine levels, or proteinuria. Sr users had a higher incidence of proteinuria (13.1% vs. 7.9%, p=0.006). Protein-creatinine ratios and albumin-creatinine ratios 12 months post-LT were not significantly different in Sr and non-Sr users ([40.1±105.6 vs. 57.5±169.9 mg/g, p=0.39], and [56.2±101.4 vs. 54.6±166.8 mg/g, p=0.268], respectively). Pre-LT creatinine (OR1.5; pampersand:003C 0.001) and DM (OR2.7; pampersand:003C 0.001) were associated with an increased risk of proteinuria. Sr users had higher (pampersand:003C0.001) incidence of post-LT CVD (24% vs. 14.8%), DM (20.4% vs. 12.6%), HTN (41.5% vs. 30.3%), and DLD (41.3% vs. 20.3%). There was no difference in post-LT CKD between Sr vs non-Sr (41% vs. 47%; p =0.119). Sr users had a lower frequency of post-LT infection (43.1% vs. 53%, p=0.007). Higher incidence of graft rejection was seen in Sr users (43.5% vs 27.8%, pampersand:003C0.001). There was no difference in median graft (15.2 ± 0.40 vs. 14.4±0.30 years; p = 0.681) or patient survival (12.5±0.44 vs. 12.5±0.33 years; p = 0.536). Conclusions Although Sr users were less likely to develop CKD post-LT, they had significantly higher rates of proteinuria, CVD, HTN, and DLD. However, no significant difference was observed in graft or patient survival rates. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".