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.

Background: Contrast-induced nephropathy (CIN) is one of the most important complications after invasive cardiovascular procedures. Considering the pivotal role of inflammation in CIN development, the use of peripheral blood-based indexes may be an easily available biomarker to predict CIN risk. Therefore, in the present study, we evaluated the association between the pan-immune-inflammation value (PIV) and the risk of CIN. Patients and Methods: A total of 1343 patients undergoing coronary angiography (CAG) were included. The PIV was calculated with the following equation: (neutrophil count × platelet count × monocyte count)/lymphocyte count. Multivariable regression analyses were used to determine the association between clinical and laboratory parameters and CIN development. Results: The median age of the cohort was 58 (IQR 50-67), and 48.2% of the patients were female. CIN developed in 202 patients (15%) in follow-up. In multivariate analyses, older age (OR: 1.015, 95% CI: 1.002-1.028, p = 0.020) and higher PIV levels (OR: 1.016, 95% CI: 1.004-1.028, p = 0.008) were associated with a higher CIN risk, while the use of antiplatelet agents was associated with a lower risk of CIN (OR: 0.670, 95% CI: 0.475-0.945, p = 0.022). Conclusions: We demonstrated that the risk of CIN was significantly higher in patients with higher PIV and older patients in a large cohort of patients undergoing CAG for stable ischemic heart disease. If supported with prospective evidence, PIV levels could be used as a minimally invasive reflector of CIN.

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.

In this study, the correlation between pan-immune-inflammation value (PIV) and coronary collateral circulation (CCC) in patients with chronic coronary syndrome (CCS) was analyzed. The study included 663 patients with CCS who underwent coronary angiography and had coronary stenosis of ≥95% in at least one major coronary vessel. The participants were divided into two groups: good CCC (Rentrop score 2-3) and poor CCC (Rentrop score 0-1). PIV score was calculated as monocyte x platelet x neutrophil/lymphocyte count. When the patient groups who developed good and poor CCC were compared, neutrophil/lymphocyte ratio (NLR) (P < .001), C-reactive protein (CRP) levels, CRP/albumin ratio (CAR) (P < .001), systemic immune-inflammation index (SII) (P < .001), and PIV (P < .001) were higher in patients with poor CCC. In multivariate logistic regression analysis, age, SII, NLR, CRP, CAR, and PIV were found to be independent predictors of poor CCC (P < .001, for all). Receiver operating characteristic (ROC) analysis demonstrated that a cut-off value of 442.2 for PIV predicted poor CCC slightly better compared to other markers, with 76.8% sensitivity and 70.1% specificity (area under ROC curve = 0.808 (95% CI: 0.764-0.851), P < .001). These findings suggest that PIV can be used as an independent predictor of CCC development.

BACKGROUND: The recently used pan-immune-inflammation value (PIV) has not been adequately studied as a predictive marker for mortality in immunosuppressed patients. The aim of this study was to evaluate the usefulness of baseline PIV level as a predictor of 30-day mortality in solid organ transplant (SOT) recipients with gram negative bloodstream infections (GN-BSI).
METHODS: This retrospective, cross-sectional study was conducted between January 1, 2019, and December 31, 2022, in 1104 SOT recipients. During the study period, 118 GN-BSI were recorded in 113 patients. Clinical, epidemiological, and laboratory data were collected, and mortality rates (30-day and all-cause) were recorded.
RESULTS: The 113 recipients had a median age of 50 years [interquartile range (IQR) 37.5-61.5 years] with a male predominance (n = 72, 63.7%). The three most common microorganisms were as follows: 46 isolates (38.9%) of Escherichia coli, 41 (34.7%) of Klebsiella pneumoniae, and 12 (10.2%) of Acinetobacter baumannii. In 44.9% and 35.6% of the isolates, production of extended-spectrum beta-lactamases and carbapenem resistance were detected, respectively. The incidence of carbapenem-resistant GN-BSI was higher in liver recipients than in renal recipients (n = 27, 69.2% vs n = 13, 17.6%, p < 0.001). All-cause and 30-day mortality rates after GN-BSI were 26.5% (n = 30), and 16.8% (n = 19), respectively. In the group with GN-BSI-related 30-day mortality, the median PIV level was significantly lower (327.3, IQR 64.8-795.4 vs. 1049.6, IQR 338.6-2177.1; p = 0.002). The binary logistic regression analysis identified low PIV level [hazard ratio (HR) = 0.93, 95% confidence interval (CI) 0.86-0.99; p = 0.04], and increased age (HR = 1.05, 95% CI 1.01-1.09; p = 0.002) as factors associated with 30-day mortality. The receiver operating characteristic analysis revealed that PIV could determine the GN-BSI-related 30-day mortality with area under curve (AUC): 0.723, 95% CI 0.597-0.848, p = 0.0005.
CONCLUSIONS: PIV is a simple and inexpensive biomarker that can be used to estimate mortality in immunosuppressed patients, but the results need to be interpreted carefully.

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.

UNASSIGNED: Type 2 diabetes mellitus (DM) is a recognized independent risk factor for both chronic coronary syndrome (CCS) and its complication, acute coronary syndrome (ACS). Patients with DM and prediabetes (preDM) face an increased ACS risk. Inflammation plays a significant role in the pathogenesis of both CCS and ACS. This study delves into novel inflammatory markers, such as the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI, also known as SIIRI or PIV), to explore their relationship with ACS and CCS in patients that have been or have not been diagnosed with DM or preDM.
UNASSIGNED: This study included data of 493 patients with chest pain undergoing coronary angiography. They were categorized into four groups: 1) without DM/preDM and with CCS; 2) with both DM/preDM and CCS; 3) without DM/preDM and with ACS, 4) with both DM/preDM and ACS. Standard methods of statistical analysis were used to reveal possible differences between groups and to find the most influential ACS risk factors in groups with DM/preDM and without DM/preDM.
UNASSIGNED: The analysis showed no significant differences in SII, SIRI, or AISI between the respective patient groups. A logistic regression analysis generated a model incorporating SII, high-density lipoprotein, and low-density lipoprotein levels as the influential ACS risk factors for patients with DM/preDM. The model demonstrated 71.0% accuracy, 37.0% sensitivity, and 89.4% specificity.
UNASSIGNED: The findings suggest that the aforementioned inflammatory markers may have potential for distinguishing DM/preDM patients at higher risk of ACS at a low financial cost. However, further comprehensive and well-designed research is required to validate their clinical utility.
People with type 2 diabetes (DM) and prediabetes (preDM) have a higher risk of heart problems. These include chronic coronary syndrome (CCS) and acute coronary syndrome (ACS). Inflammation is a key element in these issues. We looked at 493 patients with chest pain. We divided them into groups based on diabetes status (DM/preDM vs no diabetes) and heart conditions (ACS and CCS). We explored new markers related to inflammation. These include the systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) that all can be calculated from simple blood tests. We found no differences in these markers between groups. To understand ACS risk factors better, we used statistical analysis. The model found key factors for patients with DM/preDM: SII, LDL, and low-density lipoprotein levels. It was accurate (71.0%), but sensitivity was 37.0%, and specificity was 89.4%. These markers could be helpful in identifying DM/preDM patients at risk of ACS with low cost tests. We need more research to confirm their use in real-life medical settings.