Pan-immune-inflammation and its dynamics: predictors of survival and immune-related adverse events in patients with advanced NSCLC receiving immunotherapy

Pan-immune-inflammation value and survival in patients with breast cancer from a Peruvian reference hospital

The pan-immune-inflammation value (PIV), calculated as (neutrophil × platelet × monocyte)/lymphocyte count, may be useful for estimating survival in breast cancer patients. To determine the prognostic value of PIV for overall survival in breast cancer patients in Lima, Peru. A retrospective cohort study was conducted. 97 breast cancer patients diagnosed between January 2010 and December 2016 had their medical records analyzed. The primary dependent variable was overall survival, and the key independent variable was the PIV, divided into high (≥ 310) and low (< 310) groups. Patient data included demographics, treatment protocols and other clinical variables. Statistical analysis involved Kaplan-Meier survival curves and Cox proportional hazards modeling. Patients with a PIV ≥ 310 had significantly lower 5-year survival functions (p = 0.004). Similar significant differences in survival were observed for clinical stage III-IV (p = 0.015), hemoglobin levels < 12 mg/Dl (p = 0.007), histological grade (p = 0.019), and nuclear grade (p < 0.001); however, molecular classification did not show a significant survival difference (p = 0.371). The adjusted Hazard Ratios showed that PIV ≥ 310 was significantly associated with poor outcome (5.08, IC95%: 1.52-16.92). While clinical stage and hemoglobin levels were associated with survival in the unadjusted model. These factors did not maintain significance after adjustment. PIV is an independent predictor of reduced survival in Peruvian breast cancer patients.

Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?

PURPOSE

In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors.

METHODS

We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC.

RESULTS

The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey.

CONCLUSION

Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value  of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.

Prognostic significance of pan-immune-inflammation value in hepatocellular carcinoma treated by curative radiofrequency ablation: potential role for individualized adjuvant systemic treatment

BACKGROUND

This study aimed to explore the prognostic role of pan-immune-inflammation value (PIV) and develop a new risk model to guide individualized adjuvant systemic treatment following radiofrequency ablation (RFA) for early-stage hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Patients with early-stage HCC treated by RFA were randomly divided into training cohort A (n = 65) and testing cohort B (n = 68). Another 265 counterparts were enrolled into external validating cohort C. Various immune-inflammatory biomarkers (IIBs) were screened in cohort A. Prognostic role of PIV was evaluated and validated in cohort B and C, respectively. A nomogram risk model was built in cohort C and validated in pooled cohort D. Clinical benefits of adjuvant anti-angiogenesis therapy plus immune checkpoint inhibitor (AA-ICI) following RFA was assessed in low- and high-risk groups.

RESULTS

The cutoff point of PIV was 120. High PIV was an independent predictor of unfavorable recurrence-free survival (RFS) and overall survival (OS). RFS and OS rates of patients with high PIV were significantly lower than those with low PIV both in cohort B (PRFS=0.016, POS=0.011) and C (PRFS<0.001, POS<0.001). The nomogram model based on PIV, tumor number and BCLC staging performed well in risk stratification in external validating cohort C. Adjuvant AA-ICI treatment showed an added benefit in OS (p = 0.011) for high-risk patients.

CONCLUSIONS

PIV is a feasible independent prognostic factor for RFS and OS in early-stage HCC patients who received curative RFA. The proposed PIV-based nomogram risk model could help clinicians identify high-risk patients and tailor adjuvant systemic treatment and disease follow-up scheme.

Pan-immune-inflammation value predict delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage

Inflammatory reaction and immune dysregulation are known as components contributing to delayed cerebral ischemia (DCI) in patients with following aneurysmal subarachnoid hemorrhage (aSAH). The objective of this study was to investigate the role of pan-immune-inflammation value (PIV) as a novel comprehensive inflammatory marker in predicting the DCI development following aSAH. A total of 1028 participants with aSAH were enrolled. There were 296 patients with DCI and 732 patients without DCI. Various inflammatory markers were analyzed using peripheral blood sample obtained at admission. Receiver operating characteristic (ROC) analysis was performed to identify the optimal cutoff value of PIV for distinguishing DCI. Multivariate analysis was used to determine independent predictors for DCI. Mean PIV was significantly higher in the DCI (+) group than in the DCI (-) group (437.6 ± 214.7 vs 242.1 ± 154.7, P = 0.007). In ROC analysis, the optimal cutoff value of PIV was 356.7 for predicting DCI (area under the curve [AUC] 0.772, 95 % confidence interval [CI] 0.718-0.816; P < 0.001). Multivariate analysis showed that high Hunt-Hess grade (odds ratio [OR] 1.70, 95 % CI 1.38-2.22; P = 0.007), thick SAH (OR 1.82, 95 % CI 1.44-2.32; P = 0.005), and elevated PIV (≥356.7) (OR 1.42, 95 % CI 1.10-1.74; P = 0.013) were independent predictors of DCI after aSAH. PIV is a potent predictor of DCI in patients with aSAH. Elevated PIV is associated with more DCI development. Thus, PIV has predictive value for DCI development.