Overall survival by baseline and on-treatment systemic immune-inflammation index in patients with advanced cancer receiving immune checkpoint inhibitors: a large single-centre cohort study
Bibliographic record
Abstract
Abstract The Systemic Immune-Inflammation Index (SIII; neutrophils/lymphocytes × platelets) is a low-cost biomarker proposed to predict outcomes with immune checkpoint inhibitors (ICIs). This study evaluated associations of baseline and early on-treatment changes in SIII with overall survival (OS) for common ICI regimens. Patients with advanced cancer treated with ICIs at a UK centre were categorized by baseline SIII (above vs. below the median) and by changes at 3–6 weeks (increase/decrease). OS was analysed using Kaplan–Meier estimates. Adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs) were calculated using multivariable Cox regression. Among 2578 patients included, 1514 deaths occurred over a median follow-up of 2.6 years. Common regimens included pembrolizumab or atezolizumab with (15.9%) or without chemotherapy (13.9%) for NSCLC, and nivolumab plus ipilimumab for melanoma (12.6%). Lower baseline SIII was associated with improved OS (28.1 vs. 11.1 months; aHR 0.56, 0.50–0.62), with a weaker association observed in those receiving ICI-targeted therapy combinations. An on-treatment increase in SIII was linked to reduced OS (16.8 vs. 21.5 months; aHR 1.33, 1.18–1.49). Patients with low baseline SIII and an on-treatment decline had the longest OS (33.2 months), whereas those with high baseline SIII and an on-treatment increase had the shortest (8.2 months; aHR 2.88, 2.41–3.44; interaction between baseline and on-treatment SIII P < 0.001). SIII is a low-cost, readily available biomarker. Both baseline SIII levels and on-treatment changes in SIII are significantly associated with OS. SIII may help identify patients who could benefit from closer monitoring or treatment adjustments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".