Trastuzumab emtansine (T-DM1) is a mainstay therapy for HER2-positive metastatic breast cancer (mBC). However, identifying patients who will benefit most remains a challenge due to the lack of reliable biomarkers. The recently developed pan-immune-inflammation value (PIV), a novel immune-inflammation marker, could aid in this regard, considering the immunomodulatory effects of T-DM1. Therefore, we aimed to evaluate the association between the PIV and the efficacy of T-DM1 in patients with HER2-positive mBC. A total of 122 HER2-positive mBC patients treated with T-DM1 were included. Receiver operating characteristic (ROC) curve analyses were conducted to determine the optimal PIV threshold value for survival prediction. Kaplan-Meier survival curves and Cox regression analyses were used for univariable and multivariable survival analyses, respectively. The median age was 51 years, and 95.1% of the patients had ECOG PS 0-1. The optimal PIV cutoff value was identified as 338 in ROC analyses (AUC: 0.667, 95% CI: 0.569-0.765, p = 0.002). The multivariate analysis revealed that patients in the high-PIV group had significantly shorter OS (HR: 2.332; 95% CI: 1.408-3.861; p = 0.001) and PFS (HR: 2.423; 95% CI: 1.585-3.702; p < 0.001) than patients in the low-PIV group. Additionally, both ORR and DCR were significantly lower in the high-PIV group (36.6% vs. 61.3%, p = 0.011; 56.1% vs. 76.0%, p = 0.027). Our findings suggest that pre-treatment PIV may be a novel prognostic biomarker for HER2-positive mBC patients receiving T-DM1. A low PIV level is associated with more favorable outcomes. Future prospective studies are warranted to validate these findings and explore the potential utility of PIV in aiding treatment decisions.

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.

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.

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: 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.

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.