UNASSIGNED: The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work.
UNASSIGNED: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated.
UNASSIGNED: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis.
UNASSIGNED: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.

Pan-Immune-Inflammation Value (PIV) has recently received more attention as a novel indicator of inflammation. We aimed to evaluate the association between PIV and prognosis in septic patients. Data were extracted from the Medical Information Mart for Intensive Care IV database. The primary and secondary outcomes were 28-day and 90-day mortality. The association between PIV and outcomes was assessed by Kaplan-Meier curves, Cox regression analysis, restricted cubic spline curves and subgroup analysis. A total of 11,331 septic patients were included. Kaplan-Meier curves showed that septic patients with higher PIV had lower 28-day survival rate. In multivariable Cox regression analysis, log2-PIV was positively associated with the risk of 28-day mortality [HR (95% CI) 1.06 (1.03, 1.09), P < 0.001]. The relationship between log2-PIV and 28-day mortality was non-linear with a predicted inflection point at 8. To the right of the inflection point, high log2-PIV was associated with an increased 28-day mortality risk [HR (95% CI) 1.13 (1.09, 1.18), P < 0.001]. However, to the left of this point, this association was non-significant [HR (95% CI) 1.01 (0.94, 1.08), P = 0.791]. Similar results were found for 90-day mortality. Our study showed a non-linear relationship between PIV and 28-day and 90-day mortality risk in septic patients.

UNASSIGNED: The prognostic value of an effective biomarker, pan-immune-inflammation value (PIV), for head and neck squamous cell carcinoma (HNSCC) patients after radical surgery or chemoradiotherapy has not been well explored. This study aimed to construct and validate nomograms based on PIV to predict survival outcomes of HNSCC patients.
UNASSIGNED: A total of 161 HNSCC patients who underwent radical surgery were enrolled retrospectively for development cohort. The cutoff of PIV was determined using the maximally selected rank statistics method. Multivariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to develop two nomograms (Model A and Model B) that predict disease-free survival (DFS). The concordance index, receiver operating characteristic curves, calibration curves, and decision curve analysis were used to evaluate the nomograms. A cohort composed of 50 patients who received radiotherapy or chemoradiotherapy (RT/CRT) alone was applied for generality testing of PIV and nomograms.
UNASSIGNED: Patients with higher PIV (≥123.3) experienced a worse DFS (HR, 5.01; 95% CI, 3.25-7.72; p<0.0001) and overall survival (OS) (HR, 5.23; 95% CI, 3.34-8.18; p<0.0001) compared to patients with lower PIV (<123.3) in the development cohort. Predictors of Model A included age, TNM stage, neutrophil-to-lymphocyte ratio (NLR), and PIV, and that of Model B included TNM stage, lymphocyte-to-monocyte ratio (LMR), and PIV. In comparison with TNM stage alone, the two nomograms demonstrated good calibration and discrimination and showed satisfactory clinical utility in internal validation. The generality testing results showed that higher PIV was also associated with worse survival outcomes in the RT/CRT cohort and the possibility that the two nomograms may have a universal applicability for patients with different treatments.
UNASSIGNED: The nomograms based on PIV, a simple but useful indicator, can provide prognosis prediction of individual HNSCC patients after radical surgery and may be broadly applicated for patients after RT/CRT alone.

OBJECTIVE: Using the preoperative pan-immune-inflammation value (PIV) and the monocyte to high-density lipoprotein ratio (MHR) to reflect inflammation, immunity, and cholesterol metabolism, we aim to develop and visualize a novel nomogram model for predicting the survival outcomes in patients with colorectal cancer (CRC).
METHODS: A total of 172 patients with CRC who underwent radical resection were retrospectively analyzed. Survival analysis was conducted after patients were grouped according to the optimal cut-off values of PIV and MHR. Univariate and multivariate analyses were performed using Cox proportional hazards regression to screen the independent prognostic factors. Based on these factors, a nomogram was constructed and validated.
RESULTS: The PIV was significantly associated with tumor location (P < 0.001), tumor maximum diameter (P = 0.008), and T stage (P = 0.019). The MHR was closely related to gender (P = 0.016), tumor maximum diameter (P = 0.002), and T stage (P = 0.038). Multivariate analysis results showed that PIV (Hazard Ratio (HR) = 2.476, 95% Confidence Interval (CI) = 1.410-4.348, P = 0.002), MHR (HR = 3.803, 95%CI = 1.609-8.989, P = 0.002), CEA (HR = 1.977, 95%CI = 1.121-3.485, P = 0.019), and TNM stage (HR = 1.759, 95%CI = 1.010-3.063, P = 0.046) were independent prognostic indicators for overall survival (OS). A nomogram incorporating these variables was developed, demonstrating robust predictive accuracy for OS. The area under the curve (AUC) values of the predictive model for 1-, 2-, and 3- year are 0.791,0.768,0.811, respectively. The calibration curves for the probability of survival at 1-, 2-, and 3- year presented a high degree of credibility. Furthermore, Decision curve analysis (DCA) for the probability of survival at 1-, 2-, and 3- year demonstrate the significant clinical utility in predicting survival outcomes.
CONCLUSIONS: Preoperative PIV and MHR are independent risk factors for CRC prognosis. The novel developed nomogram demonstrates a robust predictive ability, offering substantial utility in facilitating the clinical decision-making process.

OBJECTIVE: This study aimed to further evaluate the potential value of Pan-Immune-Inflammation Value (PIV) as a prognostic marker in patients with laryngeal and pharyngeal tumors.
METHODS: A total of 545 patients with laryngeal and pharyngeal tumors who underwent surgery at Qilu Hospital of Shandong University were included. We determined the optimal cutoff of PIV and divided the patients into two groups. The relationship between PIV and clinicopathological features was explored by the chi-square test and the Mann-Whitney U test. Survival analysis and Cox regression analysis were used to evaluate the relationship between PIV and overall survival (OS) and disease-free survival (DFS). We also compared the prognostic predictive value of PIV with other inflammation-related markers. Finally, we developed a simple scoring prediction model based on several independent prognostic parameters.
RESULTS: We found that PIV was statistically associated with clinicopathological features such as tumor stage (p < 0.001), node stage (p = 0.001), postoperative chemotherapy (p = 0.026), and vascular thrombosis (p = 0.027). Survival analysis demonstrated a significant correlation between elevated PIV and reduced OS and DFS (p < 0.0001). Multivariate Cox regression analysis further confirmed PIV as a prognostic indicator (HR 2.507; 95% CI 1.343-4.681; p = 0.004), which is superior to SII, NLR, MLR and PLR. Three of the independent prognostic factors screened by multivariate Cox regression analysis were selected to be used to create a scoring system with a concordance index of 0.756.
CONCLUSIONS: Elevated PIV is associated with poor prognosis in patients with laryngeal and pharyngeal tumors, suggesting that PIV may be an important adjunctive indicator for assessing patient prognosis.
UNASSIGNED: Registration number: KYLL-202307-001, date: July 2023.

UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
Key findingsHigh pan-immune-inflammation value (PIV) is an independent indicator of unfavorable recurrence-free survival (RFS) and overall survival (OS) for early-stage hepatocellular carcinoma (HCC) patients who received curative radiofrequency ablation (RFA).Adjuvant anti-angiogenesis target therapy plus immune checkpoint inhibitor (AA-ICI) treatment showed added benefit in OS for the high-risk patients defined by a nomogram risk model based on PIV, tumor number and BCLC staging.What is known and what is new?Inflammation and impaired host immunity are associated with carcinogenesis and progression of HCC. Increasing evidences showed that immune-inflammatory biomarkers (IIBs) had prognostic roles in early-stage HCC patients who received RFA. However, prognostic potential of PIV has not been determined in this setting.Herein, high PIV was first reported to be an independent risk factor of poor RFS and OS in early-stage HCC patients treated by curative RFA and helped to discriminate patients between low- and high-risk groups. Adjuvant AA-ICI treatment following RFA was beneficial to OS of patients in the high-risk group.What is the implication, and what should change now?For early-stage HCC with high-risk factors (high PIV, multiple tumor foci and more advanced BCLC stage), intensive follow-up and adjuvant systemic therapy following curative RFA were warranted.

The pan-immune-inflammation-value (PIV) is a comprehensive biomarker that integrates different peripheral blood cell subsets. The present study aimed to evaluate the prognostic ability of PIV in patients with nasopharyngeal carcinoma (NPC) undergoing chemoradiotherapy. PIV was assessed using the following equation: (Neutrophil count × platelet count × monocyte count)/lymphocyte count. The Kaplan-Meier method and Cox hazards regression models were used for survival analyses. The optimal cut-off values for PIV and systemic immune-inflammation index (SII) were determined using receiver operating characteristic analysis to be 428.0 and 1032.7, respectively. A total of 319 patients were recruited. Patients with a low baseline PIV (≤428.0) accounted for 69.9% (n=223) and patients with a high baseline PIV (>428.0) accounted for 30.1% (n=96). Compared with patients with low PIV, patients with a high PIV had significantly worse 5-year progression-free survival [PFS; 66.8 vs. 77.1%; hazard ratio (HR), 1.97; 95% confidence interval (CI), 1.22-3.23); P=0.005] and 5-year overall survival (OS; 68.7 vs. 86.9%, HR, 2.71; 95% CI, 1.45-5.03; P=0.001). PIV was also a significant independent prognostic indicator for OS (HR, 2.19; 95% CI, 1.16-4.12; P=0.016) and PFS (HR, 1.86; 95% CI, 1.14-3.04; P=0.013) and outperformed the SII in multivariate analysis. In conclusion, the PIV was a powerful predictor of survival outcomes and outperformed the SII in patients with NPC treated with chemoradiotherapy. Prospective validation of the PIV should be performed to better stratify radical treatment of patients with NPC.

UNASSIGNED: Blood-based immune-inflammation indexes have been widely used to predict survival in a variety of cancers. In this research, we seeked to evaluate a novel immune-inflammation marker, named the pan-immune-inflammation value (PIV), in patients with nasopharyngeal carcinoma (NPC) undergoing definitive radiotherapy.
UNASSIGNED: A group of 377 patients with NPC was retrospectived analyzed. Clinical data and laboratory data were collected. Receiver operating characteristic (ROC) curve analysis was performed in order to determine the optimal PIV cut-off value. Survival curves were estimated by Kaplan-Meier method, and prognostic variables were identified using a Cox regression model. Additionally, we developed a nomogram and assessed its acuracy using the concordance index (C-index) and a calibration curve.
UNASSIGNED: The optimal PIV cut-off value was 146.24 according to ROC analysis. High PIV was related to poorer Eastern Cooperative Oncology Group Performance Status (ECOG PS) score (p = 0.017), more advanced T (p＜0.001) and clinical stages (p = 0.024). In univariate analysis, older Age, poorer ECOG PS, higher Epstein-Barr virus DNA (EBV-DNA), advanced T, N and clinical stage, and higher PIV levels were related to patients\' poorer overall survival (OS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients\' poorer progression free survival (PFS). Male sex and later T stage were associated with patients\' poorer locoregional recurrence free survival (LRRFS). Poorer ECOG PS, higher EBV-DNA, later T stage, later clinical stage, and higher PIV were associated with patients\' poorer distant metastasis free survival (DMFS). Multivariate analysis demonstrated that PIV was an independent prognostic index for OS (HR 2.231, 95 % CI 1.241-4.011, P = 0.007), PFS (HR 1.664, 95 % CI 1.003-2.760, P = 0.049), and DMFS(HR 2.081, 95 % CI 1.071-4.044, P = 0.031). Nomogram C-indexes for the nomogram of OS were 0.684, and PFS were 0.62, respectively. Survival predictions and actual survival were consistent according to the calibration curve.
UNASSIGNED: Pre-treatment PIV is a promising biomarker for predicting survival in patients with NPC.

UNASSIGNED: Brain tumors, especially gliomas, are known for high lethality. It is currently understood that the correlations of tumors with coagulation and inflammation have been gradually revealed.
UNASSIGNED: This study aimed to explore the potential value of several reported peripheral blood parameters as comprehensively as possible, with preoperative diagnosis and identification of brain tumors (focus on gliomas).
UNASSIGNED: Patients with central nervous system tumors (craniopharyngioma, ependymoma, spinal meningioma, acoustic neuroma, brain metastases, meningioma, and glioma) or primary trigeminal neuralgia admitted to our hospital were retrospectively analyzed. The results of the routine coagulation factor test, serum albumin test, and blood cell test in peripheral blood were recorded for each group of patients on admission. Neutrophil-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), prognostic nutritional index (PNI), the systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV), and their pairings were calculated. Their ability to identify brain tumors and their correlation with glioma grade were analyzed.
UNASSIGNED: A total of 698 patients were included in this retrospective case-control study. Glioma patients had higher NLR, SII, and PIV but lower LMR. The NLR in the brain metastasis group was lower than that in the control, meningioma, and acoustic neuroma groups, but the SII and PIV were higher than those in the ependymoma group. Fibrinogen, white blood cell count, neutrophil count, NLR, SII, and PIV in the GBM group were higher than those in the control group. In all comparisons, NLR and NLR + dNLR showed the greatest accuracy, with areas under the curve (AUCs) of 0.7490 (0.6482-0.8498) and 0.7481 (0.6457-0.8505), respectively. PIV, dNLR + PIV, and LMR + PIV ranked second, with AUCs of 0.7200 (0.6551-0.7849), 0.7200 (0.6526-0.7874), 0.7204 (0.6530-0.7878) and 0.7206 (0.6536-0.7875), respectively.
UNASSIGNED: NLR, PIV, and their combinations show high sensitivity and specificity in the diagnosis of brain tumors, especially gliomas. Overall, our results provide evidence for these convenient and reliable peripheral blood markers.

OBJECTIVE: Immune and inflammatory response plays a central role in the clinical outcomes of stroke. This study is aimed to explore the clinical significance of the new inflammation index named pan-immune-inflammation value (PIV) in patients with acute ischemic stroke (AIS) after intravenous thrombolysis therapy (IVT).
METHODS: Data were collected from 717 patients who received IVT at the First Affiliated Hospital of Soochow University. Baseline data were collected before intravenous thrombolysis. Multivariate logistic regression analysis was used to assess the association between PIV and 3 months clinical outcome after intravenous thrombolysis. We also used receiver operating characteristic (ROC) curves analysis to assess the discriminative ability of PIV, platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) in predicting 3 months poor outcome.
RESULTS: Of 717 patients, 182 (25.4%) patients had poor outcomes at 3 months. Patients with 3 months of poor outcome had significantly higher PIV levels compared to those with favorable outcomes [316.32 (187.42-585.67) vs. 223.80 (131.76-394.97), p < 0.001)]. After adjusting for potential confounders, the risk of 3 months of poor outcome was significantly higher among patients whose PIV fell in the third quartile (244.21-434.49) and the fourth quartile (> 434.49) than those in the first quartile (< 139.93) (OR = 1.905, 95% CI: 1.040-3.489; OR = 2.229, 95%CI: 1.229-4.044). The area under the ROC curve of PIV to predict 3 months of poor outcome was 0.607 (95%CI: 0.560-0.654; p < 0.001). The optimal cut-off values of PIV were 283.84 (59% sensitivity and 62% specificity).
CONCLUSIONS: The higher levels of PIV were independently associated with 3 months of poor outcomes in AIS patients receiving IVT. PIV like other inflammatory factors (PLR, NLR, and SII), can also predict adverse outcomes after IVT in AIS patients.