Hyperprogressive disease in patients suffering from solid malignancies treated by immune checkpoint inhibitors: A systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Hyperprogressive disease (HPD) is a paradoxically rapid disease progression during or shortly after antitumor treatment, especially immune checkpoint inhibitors (ICIs). Various diagnosis criteria of HPD cause heterogeneous incidence rates in different clinical research, and there is no consensus on potential risk factors associated with HPD occurrence. Hence, we aimed to summarize incidence of HPD in ICI treatment for solid tumors. Clinicopathological factors associated with HPD are also analyzed. Methods Clinical studies about HPD during/after ICI treatment of solid malignancies are included. Pubmed, Embase, and Cochrane library were searched for eligible studies published before October 7. The Newcastle–Ottawa scale was used to assess the quality of the included studies. Random effect and fixed effect models were, respectively, used for pooling incidence of HPD and analysis of risk factors for HPD. Heterogeneity, subgroup analysis, and publication bias were also analyzed. All meta-analysis was performed via R software (y -40v4.0.2). Results Forty-one studies with 6009 patients were included. The pooled incidence of HPD was 13.2% (95% CI, 11.2%–15.4%). Head and neck cancer (HNC) had the highest incidence of HPD (18.06%), and melanoma had the lowest (9.9%). Tumor types (P = .0248) and gender ratio (P = .0116) are sources of heterogeneity of pooled incidence of HPD. For five clinicopathological factors associated with HPD, only programmed cell death protein 1 ligand 1 (PD-L1) positivity was a preventive factor (odds ratio = 0.61, P <.05). High lactate dehydrogenase (LDH) level (OR = 1.51, P = .01), metastatic sites >2 (OR = 2.38, P <.0001), Eastern Cooperative Oncology Group Performance Score ≥2 (OR = 1.47, P = .02), and liver metastasis (OR = 3.06, P <.0001) indicate higher risk of HPD. Conclusions The pooled incidence of HPD was less than 15%, and HNC had the highest incidence of HPD. LDH and PD-L1 are remarkable biomarkers for prediction of HPD in future medical practice.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".