Adverse Events of Immune Checkpoint Inhibitors Therapy for Urologic Cancer Patients in Clinical Trials: A Collaborative Systematic Review and Meta-analysis
Bibliographic record
Abstract
CONTEXT: Therapies based on immune checkpoint inhibitors (ICIs) are transforming the treatment landscape of urologic oncology. Nevertheless, an exhaustive overview of the toxicity spectrum of these novel therapies has yet to be provided. OBJECTIVE: To comprehensively investigate the incidence and profile of ICI therapy-related adverse events (AEs) across urologic cancers. EVIDENCE ACQUISITION: We searched for all clinical trials investigating the role of ICI therapy published between January 2010 and September 2021. Studies involving urologic cancers with reported overall incidence or tabulated data of treatment-related AEs (trAEs) or immune-related AEs (irAEs) were included. A systematic review and meta-analysis was performed after protocol registration in PROSPERO (CRD42021276435). EVIDENCE SYNTHESIS: We identified 2638 records, of which 92 studies (including 22942 participants) met the inclusion criteria. The pooled overall incidence was 81.6% (95% confidence interval [CI] 78.0-84.7) for any-grade trAEs and 29.3% (95% CI 24.9-34.1) for grade ≥3 trAEs. The pooled overall incidence was 34.3% (95% CI 28.5-40.7) for any-grade irAEs and 10.2% (95%CI 8.2-12.7) for grade ≥3 irAEs. On a multivariable analysis, cancer type, therapy combination, clinical settings (perioperative vs advanced/metastatic), and drug exposure were independently associated with the occurrence of trAEs or irAEs. The overall rate of treatment-related mortality was 0.94% (140 of 14 899 participants), with pneumonitis (9.3%), pneumonia (7.9%), and respiratory failure (7.1%) being the most common causes. Immune-related mortality occurred in 0.26% (28 of 10 723) patients, with pneumonitis (35.7%), hepatic failure (10.7%), and hepatitis (7.1%) being most common. CONCLUSIONS: Our study provides a comprehensive overview of ICI-associated AEs in urologic cancer patients. The spectrum and incidence of AEs vary across cancer types, ICI types, clinical settings, and therapy combinations. These findings provide important guidance to clinicians in counseling and management of patients with urologic cancers. PATIENT SUMMARY: A high proportion of patients experience immune checkpoint inhibitor-associated toxicity. Physician and patient education is critical for early recognition and proper management.
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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 | 0.048 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.049 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".