S1149 Complication Rates of Liver Biopsies in Patients with Non-Alcoholic Fatty Liver Disease
Bibliographic record
Abstract
Introduction: Non-alcoholic fatty liver disease (NAFLD) is becoming the most common cause of liver disease as well as the most common indication for liver transplantation in Canada. Treatment options are limited as there are currently no approved medications to treat NAFLD. There are many molecules in development and prospects for a treatment in the near future. As therapeutic options for the treatment of NAFLD expand, there will be a large number of patients who may undergo liver biopsy to risk stratify those patients at highest risk of progression of their fibrosis. Accordingly, it is important to validate the safety of liver biopsy in this patient population. Methods: A retrospective chart review was performed on all patients with NAFLD who underwent outpatient elective percutaneous and transjugular liver biopsies at a tertiary care hospital from 2010 to 2020. We excluded inpatient biopsies, surgical biopsies, allograft biopsies, and targeted liver biopsies. We collected gender, age, and pre-biopsy platelets/INR/PTT. For each biopsy reviewed, we recorded the route used to obtain the biopsy (percutaneous or transjugular), indication for the liver biopsy, and the results of the biopsy. We recorded all complications up to one week post-procedure. Results: There were 582 biopsies reviewed in total, 540 (92.8%) percutaneous and 42 (7.2%) transjugular. The mean age was 53.1 (±11.2). There was an even proportion of males to females (291 each). The mean fibrosis stage was 1.9 (±1.4), platelet count was 223.9 (±83.7), INR 1 (±0.1), and PTT 31 (±3.9). There were no mortalities related to liver biopsy observed in our study period. Major complications occurred in 8 out of 582 biopsies (1.4%). Bleeding accounted for 6 of the major complications observed, while infection and pneumoperitoneum each occurred once. We did not identify any statistically significant associations between fibrosis stage, age, sex, platelet count, INR and major complication rates. Conclusion: This is the first study to evaluate the complication of outpatient liver biopsies in patients with NAFLD. We found that major complications occurred in 1.4% of all biopsies. There were no mortalities observed in our study. Our results are consistent with previous studies examining complication rates in heterogenous populations and support the overall safety of liver biopsies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".