Risk Factors of Immune-Mediated Hepatotoxicity Induced by Immune Checkpoint Inhibitors in Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) significantly improve survival, while immune-mediated hepatotoxicity (IMH) has been reported. To evaluate the incidence and potential risk factors of IMH among cancer patients treated by ICIs, PubMed/Medline, Web of Science, Cochrane, and Embase were searched before 30 March 2024 for systematic review and meta-analysis. Odds ratios (ORs) with 95% confidence intervals (CI) were calculated. Quality assessment was completed using the Newcastle-Ottawa scale. Of 1217 articles identified, 24 consisting of 9076 patients were included, with one study being prospective and the rest retrospective. The overall incidence of any grade IMH and grade ≥ 3 secondary to ICIs was 14% and 7%, respectively. The cholestatic pattern was more prevalent than the hepatocellular and mixed patterns. The meta-analysis revealed that ICI treatment was related to reduced risk of IMH in older patients (SMD: -0.18; 95% CI: -0.33 to -0.04), individuals with higher body mass index (WMD: -2.15; 95% CI: -3.92 to -0.38), males (OR: 0.44; 95% CI: 0.27 to 0.72), and patients with lung cancer (OR: 0.58, 95%CI 0.41 to 0.83). On the other hand, patients with liver metastasis (OR: 1.80; 95% CI: 1.47 to 2.20), history of ICI treatment (OR: 3.09; 95% CI: 1.21 to 7.89), diabetes (OR: 2.19; 95% CI: 1.36 to 3.51), chronic HBV (OR: 3.06; 95% CI: 1.11 to 8.46), and concomitant use of ICIs (OR: 8.73; 95% CI: 2.41 to 31.59) increased the risk of developing IMH. This study will provide clinicians with information on potentially high-risk groups for IMH, who need to be cautiously monitored for liver function when receiving immunotherapy.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".