Prognostic value of pan-immune-inflammation value in colorectal cancer patients: A systematic review and meta-analysis

Pan-immune-inflammation value as a prognostic biomarker for colon cancer and its variation by primary tumor location

BACKGROUND

A growing body of research indicates significant differences between left-sided colon cancers (LCC) and right-sided colon cancers (RCC). Pan-immune-inflammation value (PIV) is a systemic immune response marker that can predict the prognosis of patients with colon cancer. However, the specific distinction between PIV of LCC and RCC remains unclear.

AIM

To investigate the prognostic and clinical significance of PIV in LCC and RCC patients.

METHODS

This multicenter retrospective cohort study included 1510 patients with colon cancer, comprising 801 with LCC and 709 with RCC. We used generalized lifting regression analysis to evaluate the relative impact of PIV on disease-free survival (DFS) in these patients. Kaplan-Meier analysis, as well as univariate and multivariate analyses, were used to examine the risk factors for DFS. The correlation between PIV and the clinical characteristics was statistically analyzed in these patients.

RESULTS

A total of 1510 patients {872 female patients (58%); median age 63 years [interquartile ranges (IQR): 54-71]; patients with LCC 801 (53%); median follow-up 44.17 months (IQR 29.67-62.32)} were identified. PIV was significantly higher in patients with RCC [median (IQR): 214.34 (121.78-386.72) vs 175.87 (111.92-286.84), P < 0.001]. After propensity score matching, no difference in PIV was observed between patients with LCC and RCC [median (IQR): 182.42 (111.88-297.65) vs 189.45 (109.44-316.02); P = 0.987]. PIV thresholds for DFS were 227.84 in LCC and 145.99 in RCC. High PIV (> 227.84) was associated with worse DFS in LCC [PIV-high: Adjusted hazard ratio (aHR) = 2.39; 95% confidence interval: 1.70-3.38; P < 0.001] but not in RCC (PIV-high: aHR = 0.72; 95% confidence interval: 0.48-1.08; P = 0.114).

CONCLUSION

These findings suggest that PIV may predict recurrence in patients with LCC but not RCC, underscoring the importance of tumor location when using PIV as a colon cancer biomarker.

The Baseline Pan-Immune‑Inflammation Value (PIV) and PILE in Predicting Clinical Outcomes and Therapeutic Response for Primary Central Nervous System Lymphoma

Purpose

To investigate the prognostic significance of pan-immune-inflammation value (PIV) and PILE score (based on PIV, lactate dehydrogenase (LDH), and Eastern Cooperative Oncology Group Performance Status (ECOG PS)) in patients with primary central nervous system lymphoma (PCNSL).

Patients and Methods

A total of 109 patients were enrolled. PIV was calculated as follows: (neutrophil count × platelet count × monocyte count)/lymphocyte count. The PILE score was incorporated based on PIV, LDH levels, and ECOG PS. The Kaplan-Meier curves and Cox hazards regression models were applied for survival analyses. The relationship between PIV, PILE, and therapeutic response was examined.

Results

Baseline high PIV was significantly associated with worse overall survival (OS) in univariate (HR 3.990, 95% CI 1.778-8.954, p < 0.001) and multivariate (HR 3.047, 95% CI 1.175-7.897, p = 0.022) analyses. High PIV was also associated with worse progression-free survival (PFS) in univariate (HR 2.121, 95% CI 1.075-4.186, p = 0.030) but not significant in multivariate analyses. PIV outperformed other systemic inflammation parameters. The patients in the high PILE group (PILE score 2-3) had worse OS (p = 0.008) and PFS (p < 0.001) compared to the low PILE group (PILE score 0-1). PILE was independently associated with therapeutic response to initial treatment (OR 0.17, 95% CI 0.05-0.46; p < 0.001).

Conclusion

High PIV and PILE were correlated with worse clinical outcomes in PCNSL patients, indicating that PIV and PILE might be a powerful predictor of prognosis and a potential predictive indicator for therapeutic response in PCNSL.

Pan-Immune-Inflammatory Value is Superior to Other Inflammatory Indicators in Predicting Inpatient Major Adverse Cardiovascular Events and Severe Coronary Artery Stenosis after Percutaneous Coronary Intervention in STEMI Patients

Background

The inflammatory response to atherosclerosis is a process that leads to coronary artery disease. Pan-immune-inflammation value (PIV) has emerged as a new and simple biomarker of inflammation. However, studies on the predictive power of PIV for major adverse cardiovascular events (MACE) or the degree of coronary artery stenosis are scarce. We aimed to explore the predictive ability of PIV for MACE and the degree of coronary artery stenosis in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI) during hospitalization.

Methods

This study included 542 patients who were diagnosed with STEMI and who underwent PCI between 2016 and 2023 and whose PIV and other inflammatory markers were measured. Using univariate and multivariate logistic regression analysis, risk variables for MACE following PCI and severe coronary stenosis during hospitalization were assessed to create receiver operating characteristic (ROC) curves and determine the best thresholds for inflammatory markers. Spearman correlation analysis was used to evaluate the correlation of PIV and other inflammatory markers with the Gensini score (GS).

Results

Compared with the systemic inflammatory index (SII), platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR), the PIV may have greater predictive value in terms of the occurrence of MACE and the degree of coronary stenosis after PCI in hospitalized STEMI patients. The correlation between the PIV and GS was strong.

Conclusions

PIV was superior to the SII, PLR, and NLR in predicting inpatient prognosis and severe coronary stenosis after PCI for STEMI patients.

Comparison of the ability of newly inflammatory markers to predict complicated appendicitis

Introduction

Acute appendicitis (AA) is the predominant condition responsible for acute abdominal pain across all age demographics. The purpose of this research is to determine if the hemoglobin, albumin, lymphocyte, and platelet (HALP) and modified HALP (m-HALP) scores differ between complicated and uncomplicated appendicitis in patients diagnosed with AA who have applied to the emergency department (ED). Additionally, this study aims to investigate whether HALP and m-HALP scores are superior to other biomarkers.

Materials and methods

The retrospective analysis included adult patients, aged eighteen or older, who were diagnosed with AA, and sought treatment at the ED of a tertiary hospital. Patients were divided into two groups: complicated appendicitis (CA) and uncomplicated appendicitis (UCA). The cut-off in diagnostic value measurements was determined using the receiver operating characteristic analysis.

Results

A total of 436 patients (CA: 126, UCA: 310) were included. Neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-albumin ratio, systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and pan-immune inflammation value (PIV) were found to have acceptable diagnostic power in CA detection (area under the curve [AUC]: 0.735-0.783). In detecting UCA, HALP and m-HALP were of fair diagnostic power (AUC: 0.64, 0.68, respectively).

Conclusions

In this study, we found that although PIV, SIRI, SII, and NLR had acceptable diagnostic values in distinguishing CA and UCA, HALP and m-HALP had fair diagnostic values.

Improving colorectal cancer screening programs

In this editorial we comment on the article by Agatsuma et al published in the World Journal of Gastroenterology. They suggest policies for more effective colorectal screening. Screening is the main policy that has led to lower mortality rates in later years among the population that was eligible for screening. Colonoscopy is the gold standard tool for screening and has preventive effects by removing precancerous or early malignant polyps. However, colonoscopy is an invasive process, and fecal tests such as the current hemoglobin immunodetection were developed, followed by endoscopy, as the general tool for population screening, avoiding logistical and economic problems. Even so, participation and adherence rates are low. Different screening options are being developed with the idea that if people could choose between the ones that best suit them, participation in population-based screening programs would increase. Blood tests, such as a recent one that detects cell-free DNA shed by tumors called circulating tumor DNA, showed a similar accuracy rate to stool tests for cancer, but were less sensitive for advanced precancerous lesions. At the time when the crosstalk between the immune system and cancer was being established as a new hallmark of cancer, novel immune system-related biomarkers and information on patients' immune parameters, such as cell counts of different immune populations, were studied for the early detection of colorectal cancer, since they could be effective in asymptomatic people, appearing earlier in the adenoma-carcinoma development compared to the presence of fecal blood. sCD26, for example, detected 80.37% of advanced adenomas. To reach as many eligible people as possible, starting at an earlier age than current programs, the direction could be to apply tests based on blood, urine or salivary fluid to samples taken during routine visits to the primary health system.

Pan‑immune‑inflammation value as a novel prognostic biomarker in nasopharyngeal carcinoma

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.

Predictive value of pretreatment circulating inflammatory response markers in the neoadjuvant treatment of breast cancer: meta-analysis

BACKGROUND

Systemic inflammatory response markers have been found to have a prognostic role in several cancers, but their value in predicting the response to neoadjuvant chemotherapy in breast cancer is uncertain. A systematic review and meta-analysis of the literature was carried out to investigate this.

METHODS

A systematic search of electronic databases was conducted to identify studies that explored the predictive value of circulating systemic inflammatory response markers in patients with breast cancer before commencing neoadjuvant therapy. A meta-analysis was undertaken for each inflammatory marker where three or more studies reported pCR rates in relation to the inflammatory marker. Outcome data are reported as ORs and 95% confidence intervals.

RESULTS

A total of 49 studies were included, of which 42 were suitable for meta-analysis. A lower pretreatment neutrophil-to-lymphocyte ratio was associated with an increased pCR rate (pooled OR 1.66 (95% c.i. 1.32 to 2.09); P < 0.001). A lower white cell count (OR 1.96 (95% c.i. 1.29 to 2.97); P = 0.002) and a lower monocyte count (OR 3.20 (95% c.i. 1.71 to 5.97); P < 0.001) were also associated with a pCR. A higher lymphocyte count was associated with an increased pCR rate (OR 0.44 (95% c.i. 0.30 to 0.64); P < 0.001).

CONCLUSION

The present study found the pretreatment neutrophil-to-lymphocyte ratio, white cell count, lymphocyte count, and monocyte count of value in the prediction of a pCR in the neoadjuvant treatment of breast cancer. Further research is required to determine their value in specific breast cancer subtypes and to establish optimal cut-off values, before their adoption in clinical practice.

Associations between pan-immune-inflammation value and abdominal aortic calcification: a cross-sectional study

Background

Abdominal aortic calcification (AAC) pathogenesis is intricately linked with inflammation. The pan-immune-inflammation value (PIV) emerges as a potential biomarker, offering reflection into systemic inflammatory states and assisting in the prognosis of diverse diseases. This research aimed to explore the association between PIV and AAC.

Methods

Employing data from the National Health and Nutrition Examination Survey (NHANES), this cross-sectional analysis harnessed weighted multivariable regression models to ascertain the relationship between PIV and AAC. Trend tests probed the evolving relationship among PIV quartiles and AAC. The study also incorporated subgroup analysis and interaction tests to determine associations within specific subpopulations. Additionally, the least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were used for characteristics selection to construct prediction model. Nomograms were used for visualization. The receiver operator characteristic (ROC) curve, calibration plot and decision curve analysis were applied for evaluate the predictive performance.

Results

From the cohort of 3,047 participants, a distinct positive correlation was observed between PIV and AAC. Subsequent to full adjustments, a 100-unit increment in PIV linked to an elevation of 0.055 points in the AAC score (β=0.055, 95% CI: 0.014-0.095). Categorizing PIV into quartiles revealed an ascending trend: as PIV quartiles increased, AAC scores surged (β values in Quartile 2, Quartile 3, and Quartile 4: 0.122, 0.437, and 0.658 respectively; P for trend <0.001). Concurrently, a marked rise in SAAC prevalence was noted (OR values for Quartile 2, Quartile 3, and Quartile 4: 1.635, 1.842, and 2.572 respectively; P for trend <0.01). Individuals aged 60 or above and those with a history of diabetes exhibited a heightened association. After characteristic selection, models for predicting AAC and SAAC were constructed respectively. The AUC of AAC model was 0.74 (95%CI=0.71-0.77) and the AUC of SAAC model was 0.84 (95%CI=0.80-0.87). According to the results of calibration plots and DCA, two models showed high accuracy and clinical benefit.

Conclusion

The research findings illuminate the potential correlation between elevated PIV and AAC presence. Our models indicate the potential utility of PIV combined with other simple predictors in the assessment and management of individuals with AAC.

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.

Association of immune inflammatory biomarkers with pathological complete response and clinical prognosis in young breast cancer patients undergoing neoadjuvant chemotherapy

Background

The persistence of inflammatory stimulus has a tight relationship with the development of age-related diseases, ultimately resulting in a gradual escalation in the prevalence of tumors, but this phenomenon is rare in young cancer patients. Breast cancer arising in young women is characterized by larger tumor diameters and more aggressive subtypes, so neoadjuvant chemotherapy (NACT) can be especially appropriate for this population. Immune inflammatory biomarkers have been reportedly linked to the prognosis of some malignant tumor types, with varying results. In this study, we investigated the possible predictive value of blood-based markers in young breast cancer patients undergoing NACT, in addition to the association between the clinicopathological features and prognosis.

Methods

From December 2011 to October 2018, a total of 215 young breast cancer patients referred to Harbin Medical University Cancer Hospital received NACT and surgery were registered in this retrospective study. The pretreatment complete blood counts were used to calculate the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and pan-immune-inflammation value (PIV).

Results

NLR, PLR, MLR, and PIV optimal cut-off values were 1.55, 130.66, 0.24, and 243.19, as determined by receiver operating characteristic analysis. Multivariate analysis revealed that PIV, HR status, HER-2 status, and Ki-67 index were all independent predictive factors for pathological complete response. Subgroup analysis revealed that young breast cancer patients in the population characterized by low PIV and HR negative group were more likely to get pCR (P=0.001). The five-year overall survival (OS) rate was 87.9%, and Cox regression models identified PIV as independently related to OS.

Conclusion

In the present study, the pretreatment PIV was found to be a useful prognostic indicator for pCR and long-term survival in young breast cancer patients undergoing NACT. High immune and inflammation levels, MLR and PIV were connected to poor clinical prognosis in young breast cancer patients. PIV is a promising biomarker to guide strategic decisions in treating young breast cancer.

Pan-immune-inflammation value as a prognostic tool for overall survival and disease-free survival in non-metastatic muscle-invasive bladder cancer

PURPOSE

To evaluate the association between preoperative pan-immune inflammation value (PIV) and overall survival (OS) and disease-free survival (DFS) in patients who underwent radical cystectomy for non-metastatic muscle-invasive bladder cancer.

METHODS

A total of 119 patients with non-metastatic muscle-invasive bladder cancer who underwent radical cystectomy at our institution between January 2014 and January 2022 were included in this retrospective study. PIV was calculated using the formula (monocyte count × neutrophil count × platelet count)/lymphocyte count. Ideal cut-off values for PIV were determined using ROC curve analysis. Kaplan-Meier analysis was used to evaluate the impact of PIV on survival outcomes.

RESULTS

The mean age of patients was 65 ± 14 years, and the mean follow-up duration was 36 months. The ideal cutoff value for PIV was determined to be 406.29, and a PIV above this value was associated with poorer OS (p < 0.001) (73 months vs. 21 months) and DFS (p = 0.002) (35 months vs 19 months). Higher neutrophil-to-lymphocyte ratio (NLR) values were also associated with poorer OS (p < 0.001) and DFS (p < 0.001), with similar effectiveness to PIV. PIV was found to be significantly more effective than platelet-to-lymphocyte ratio (PLR) and systemic immune inflammation index (SII) in predicting DFS.

CONCLUSION

Preoperative PIV may serve as an independent prognostic factor for OS in patients who undergo radical cystectomy with non-metastatic muscle-invasive bladder cancer. A high PIV value was associated with poorer survival outcomes. Prospective multicenter studies are needed to further validate the relationship between PIV and histopathological features of bladder cancer.

The prognostic importance of the pan-immune-inflammation value in patients with septic shock

INTRODUCTION

The purpose of this study was to determine whether the pan-immune-inflammation value (PIV), a novel biomarker combining neutrophil platelet, monocyte, and lymphocyte counts, some of the most widespread indicators of systemic inflammation, can predict mortality and prognosis in patients admitted to the intensive care unit (ICU) with septic shock.

METHOD

This prospective study was performed with 82 patients aged 18 or over admitted to a tertiary ICU with diagnoses of septic shock. Patients with hematological disease and neutropenia were excluded. PIV was calculated with the formula [neutrophil count (103/μL) × platelet count (103/μL) × monocyte count (103/μL)]/lymphocyte count (103/μL).

RESULTS

Median age, presence of hypertension, Acute Physiology and Chronic Health Evaluation II (APACHE II) levels, and neutrophil, monocyte, and platelet counts were lower in the low-PIV group than in the high-PIV group (p < 0.05). The highest area under ROC curve (AUC) was determined for Sequential Organ Failure Assessment (SOFA) (0.94 (0.89 - 0.99)), followed by Glasgow Coma Scale (GCS) (0.81 (0.70 - 0.91)), APACHE II (0.80 (0.69 - 0.91)) and lactate (0.77 (0.67 - 0.88)). Median survival was longer in the low-PIV group than in the high-PIV group (28 (15.25 - 40.76) vs 16 (9.46 - 22.55) days, respectively, p < 0.05). The univariate Cox proportional hazards (CPH) model showed that high PIV (HR = 2.13 (1.03-4.38)), low GCS (HR = 3.31 (1.34 - 8.15)), high SOFA (HR = 9.41 (2.86 - 30.95)), high APACHE II (HR = 3.08 (1.47 - 6.45)), high lactate (HR = 6.56 (2.73 - 15.75)), and high procalcitonin (PCT) (HR = 2.73 (1.11 - 6.69)) values were associated with a decreased survival time among ICU patients (p < 0.05). The multivariate CPH model showed the age-adjusted risk estimates for these six laboratory parameters. High lactate (HR = 7.97 (2.19 - 29.08)) and high SOFA scores (HR = 4.85 (1.22 - 19.32)) were significantly associated with shorter survival in ICU patients (p < 0.05).

CONCLUSION

The findings of this research suggest that PIV could predict the longer survival in patients with septic shock. Despite PIV score's capability to show inflammation, it is not significantly associated with mortality in the multivariate analysis.

A Novel Prognostic Model Using Pan-Immune-Inflammation Value and Programmed Death Ligand 1 in Patients with Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma Receiving Immune Checkpoint Inhibitors: A Retrospective Multicenter Analysis

BACKGROUND

Little is known regarding the prognostication of the Pan-Immune-Inflammation Value (PIV) in patients with recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC).

OBJECTIVES

This study aimed to investigate the prognostic role of PIV in patients with R/M HNSCC receiving immune checkpoint inhibitors (ICI).

PATIENTS AND METHODS

Patients who were diagnosed to have R/M HNSCC and treated with ICI were reviewed retrospectively. The cutoff value of PIV was set at the median. Patients were stratified into high PIV and low PIV. Kaplan-Meier curves were estimated for progression-free survival (PFS) and overall survival (OS).

RESULTS

A total of 192 patients were included in our study for oncologic outcomes evaluation. For the total population, the median PFS was 5.5 months and OS was 18.2 months. After stratification by PIV, median PFS was 11.7 months in the low PIV and 2.8 months in the high PIV groups (p < 0.001). The median OS was 21.8 months in the low PIV and 11.5 months in the high PIV groups (p < 0.001). Multivariate analysis demonstrated that PIV and PD-L1 were independent predictors associated with survival. A prognostic model using both PIV and PD-L1 was constructed. The median PFS was 12.2, 6.4, and 3.0 months for patients with risk scores of 0, 1, and 2, respectively (p < 0.001). The median OS was 23.7, 18.1, and 11.4 months for patients with risk scores of 0, 1, and 2, respectively (p < 0.001).

CONCLUSIONS

PIV is a prognostic biomarker in patients with R/M HNSCC treated with ICI. A prognostic model using PIV and PD-L1 could provide outcome prediction and risk stratification.

Pretreatment Pan-Immune-Inflammation Value (PIV) in Predicting Therapeutic Response and Clinical Outcomes of Neoadjuvant Immunochemotherapy for Esophageal Squamous Cell Carcinoma

PURPOSE

The pan-immune-inflammation value (PIV), which reflects the balance between the host immune and inflammatory status, is a readily available index for evaluating cancer outcomes. Until now, however, no study has demonstrated the clinical response of PIV to neoadjuvant immunochemotherapy (NICT) in esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 218 patients with ESCC who underwent NICT. The relationship between PIV and therapeutic response (pathological complete response [PCR]) and clinical outcomes (overall survival [OS] and disease-free survival [DFS]) was examined. Cox proportional, hazard-regression analyses and the Kaplan-Meier method were used for survival analyses. Recursive partitioning analysis (RPA) was used to establish a novel risk stratification model.

RESULTS

Sixty-six patients (30.3%) achieved PCR after NICT. Using PCR as the endpoint of interest, patients were compared in groups based on the optimal threshold. PIV was closely related to PCR (odds ratio [OR] 0.311, 95% confidence interval [CI] 0.140-0.690, P = 0.004). Compared with patients in the low PIV cohort, patients with high PIV had worse 3-year OS (58.7% vs. 83.6%, P < 0.001) and DFS (51.9% vs. 79.1%, P < 0.001). PIV was an independent predictor of OS (hazard ratio [HR] 2.364, 95% CI 1.183-4.724, P = 0.015) and DFS (HR 1.729, 95% CI 1.026-2.913, P = 0.040). Three risk groups with varied DFS and OS were staged by using an RPA method, and the prognostication accuracy was considerably improved.

CONCLUSIONS

Pretreatment PIV can predict the therapeutic efficacy of NICT for ESCC. Because of better prognostic stratification, pretreatment PIV is a novel, sensitive, and effective indicator in ESCC receiving NICT. The prognostic results of PIV need to be verified in additional prospective studies.

Comparison of Machine Learning and Logic Regression Algorithms for Predicting Lymph Node Metastasis in Patients with Gastric Cancer: A two-Center Study

OBJECTIVES

This two-center study aimed to establish a model for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning (ML) and logistic regression (LR) algorithms, and to evaluate its predictive performance in clinical practice.

METHODS

Data of a total of 369 patients who underwent radical gastrectomy in the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) from March 2016 to November 2019 were collected and retrospectively analyzed as the training group. In addition, data of 123 patients who underwent radical gastrectomy in the Department of General Surgery of Jining First People's Hospital (Jining, China) were collected and analyzed as the verification group. Besides, 7 ML and logistic models were developed, including decision tree, random forest, support vector machine (SVM), gradient boosting machine (GBM), naive Bayes, neural network, and LR, in order to evaluate the occurrence of lymph node metastasis in patients with gastric cancer. The ML model was established following 10 cross-validation iterations within the training dataset, and subsequently, each model was assessed using the test dataset. The model's performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.

RESULTS

Compared with the traditional logistic model, among the 7 ML algorithms, except for SVM, the other models exhibited higher accuracy and reliability, and the influences of various risk factors on the model were more intuitive.

CONCLUSION

For the prediction of lymph node metastasis in gastric cancer patients, the ML algorithm outperformed traditional LR, and the GBM algorithm exhibited the most robust predictive capability.

Association between a four-parameter inflammatory index and all-cause mortality in critical ill patients with non-traumatic subarachnoid hemorrhage: a retrospective analysis of the MIMIC-IV database (2012-2019)

Background

Non-traumatic subarachnoid hemorrhage (SAH), primarily due to the rupture of intracranial aneurysms, contributes significantly to the global stroke population. A novel biomarker, pan-immune-inflammation value (PIV) or called the aggregate index of systemic inflammation (AISI), linked to progression-free survival and overall survival in non-small-cell lung cancer and mortality in Coronavirus Disease 2019 (COVID-19) patients, has surfaced recently. Its role in non-traumatic SAH patients, however, remains under-researched. This study aims to determine the relationship between PIV and all-cause mortality in non-traumatic SAH patients.

Methods

A retrospective analysis was conducted using data from the Medical Information Mart for Intensive Care (MIMIC-IV) database to examine the association between PIV and all-cause mortality in critically ill patients with non-traumatic SAH. PIV measurements were collected at Intensive Care Unit (ICU) admission, and several mortality measures were examined. To control for potential confounding effects, a 1:1 propensity score matching (PSM) method was applied. The optimal PIV cutoff value was identified as 1362.45 using X-tile software that is often used to calculate the optimal cut-off values in survival analysis and continuous data of medical or epidemiological research. The relationship between PIV and short- and long-term all-cause mortality was analyzed using a multivariate Cox proportional hazard regression model and Kaplan-Meier (K-M) survival curve analysis. Interaction and subgroup analyses were also carried out.

Results

The study included 774 non-traumatic SAH patients. After PSM, 241 pairs of score-matched patients were generated. The Cox proportional hazard model, adjusted for potential confounders, found a high PIV (≥ 1362.45) independently associated with 90-day all-cause mortality both pre- (hazard ratio [HR]: 1.67; 95% confidence intervals (CI): 1.05-2.65; P = 0.030) and post-PSM (HR: 1.58; 95% CI: 1.14-2.67; P = 0.042). K-M survival curves revealed lower 90-day survival rates in patients with PIV ≥ 1362.45 before (31.1% vs. 16.1%%, P < 0.001) and after PSM (68.9% vs. 80.9%, P < 0.001). Similarly, elevated PIV were associated with increased risk of ICU (pre-PSM: HR: 2.10; 95% CI: 1.12-3.95; P = 0.02; post-PSM: HR: 2.33; 95% CI: 1.11-4.91; P = 0.016), in-hospital (pre-PSM: HR: 1.91; 95% CI: 1.12-3.26; P = 0.018; post-PSM: 2.06; 95% CI: 1.10-3.84; P = 0.034), 30-day (pre-PSM: HR: 1.69; 95% CI: 1.01-2.82; P = 0.045; post-PSM: 1.66; 95% CI: 1.11-2.97; P = 0.047), and 1-year (pre-PSM: HR: 1.58; 95% CI: 1.04-2.40; P = 0.032; post-PSM: 1.56; 95% CI: 1.10-2.53; P = 0.044) all-cause mortality. The K-M survival curves confirmed lower survival rates in patients with higher PIV both pre- and post PSM for ICU (pre-PSM: 18.3% vs. 8.4%, P < 0.001; post-PSM:81.7 vs. 91.3%, P < 0.001), in-hospital (pre-PSM: 25.3% vs. 12.8%, P < 0.001; post-PSM: 75.1 vs. 88.0%, P < 0.001), 30-day (pre-PSM: 24.9% vs. 11.4%, P < 0.001; post-PSM:74.7 vs. 86.3%, P < 0.001), and 1-year (pre-PSM: 36.9% vs. 20.8%, P < 0.001; P = 0.02; post-PSM: 63.1 vs. 75.1%, P < 0.001) all-cause mortality. Stratified analyses indicated that the relationship between PIV and all-cause mortality varied across different subgroups.

Conclusion

In critically ill patients suffering from non-traumatic SAH, an elevated PIV upon admission correlated with a rise in all-cause mortality at various stages, including ICU, in-hospital, the 30-day, 90-day, and 1-year mortality, solidifying its position as an independent mortality risk determinant. This study represents an attempt to bridge the current knowledge gap and to provide a more nuanced understanding of the role of inflammation-based biomarkers in non-traumatic SAH. Nevertheless, to endorse the predictive value of PIV for prognosticating outcomes in non-traumatic SAH patients, additional prospective case-control studies are deemed necessary.

Prognostic significance of the Gustave Roussy immune (GRIm) score in cancer patients: a meta-analysis

BACKGROUND

The prognostic value of the Gustave Roussy immune (GRIm) score in cancer patients has been widely reported but remains inconsistent. The aim of this study is to systematically investigate the relationship between the GRIm score and survival outcomes in cancer patients.

METHODS

Relevant literature was identified using electronic databases including Web of Science, PubMed, and Embase from the inception to March 2023. The primary endpoints were long-term oncological outcomes. Subgroup analysis and sensitivity analysis were conducted during the meta-analysis.

RESULTS

Fifteen studies (20 cohorts) including 4997 cancer patients were enrolled. The combined results revealed that patients in the high GRIm group had a deteriorated overall survival (HR = 2.07 95%CI: 1.73-2.48; p < 0.0001; I2 = 62%) and progression-free survival (HR = 1.42; 95%CI: 1.22-1.66; p < 0.0001; I2 = 36%). The prognostic values of GRIm on overall survival and progression-free survival were observed across various tumour types and tumour stages. Sensitivity analysis supported the stability and reliability of the above results.

CONCLUSION

Our evidence suggested that the GRIm score could be a valuable prognostic marker in cancer patients, which can be used by clinicians to stratify patients and formulate individualized treatment plans.

Prognostic significance of the pretreatment pan-immune-inflammation value in cancer patients: an updated meta-analysis of 30 studies

Background

The pan-immune-inflammation value (PIV) has been reported as a promising prognostic biomarker in multiple cancers but still remains inconclusive. The objective of this study is to systematically investigate the association of the pretreatment PIV with survival outcomes in cancer patients, based on available literature.

Methods

Online databases including PubMed, Embase and the Web of Science were thoroughly searched for studies evaluating the prognostic role of the pretreatment PIV in cancers from the inception to June 2023. Hazard ratios (HRs) with 95% confidence intervals (CIs) were always assessed using a random-effects model. Statistical analyses were performed using Stata 12.0.

Results

Thirty studies were finally included after comprehensively study searching. In total, 8,799 cancer patients were enrolled in this meta-analysis. The pooled results demonstrated that patients in the high PIV group had a significantly poorer overall survival (HR = 2.07; 95%CI: 1.77-2.41; I2 = 73.0%) and progression-free survival (HR = 1.83; 95%CI: 1.37-2.45; I2 = 98.2%) than patients in the low PIV group. The prognostic significance of the PIV score on overall survival and progression-free survival was observed across various geographical regions, tumor stages and treatment strategies. Sensitivity analyses supported the stability of the above combined results.

Conclusion

This meta-analysis demonstrated that the pretreatment PIV could be a non-invasive and efficacious prognostic biomarker for cancer patients.

Clinical utility of preoperative pan-immune-inflammation value (PIV) for prognostication in patients with esophageal squamous cell carcinoma

BACKGROUND

Several researches have shown that pan-immune-inflammation value (PIV) is related to cancer prognosis in recent years. In esophageal squamous cell carcinoma (ESCC), nevertheless, the prognostic impact of PIV remains unclear. The present study sought to investigate the prognostic impact of preoperative PIV in ESCC with radical resection.

METHODS

The data of 294 ESCC patients who received radical resection were retrospectively analyzed. Based on analyzing the non-linear relationship between PIV and cancer-specific survival (CSS), the optimal cutoff value for PIV was calculated by the restricted cubic spline (RCS) model. Cox proportional hazards regression was carried out to identify the prognostic factors. A risk stratification model was established by recursive partitioning analysis (RPA). The performance of the RPA-based model was assessed by the decision curve analysis (DCA) and receiver operating characteristic (ROC).

RESULTS

The RCS visualized the non-linear relationship between PIV and CSS (P < 0.0001). Then patients were then divided into high and low groups based on the optimal threshold of 308.2. The 5-year CSS (17.7 % vs. 48.3 %, P < 0.001) was significantly worse in patients with high PIV than those in the low group. Subgroup analyses confirmed that patients with low PIV also achieved better 5-year survival at different pathological tumor node metastasis (pTNM) stages (pTNM I: P = 0.022; pTNM II: P = 0.001; pTNM III: P = 0.011). PIV served as an independent prognostic factor of CSS (hazard ratio = 1.983, P < 0.001). A new staging involving three risk groups with significantly different CSS was developed using RPA algorithms based on pTNM and PIV. Compared with the pTNM classification, the RPA-based model exhibited significantly superior performance for prognostication.

CONCLUSION

The present study confirmed the prognostic impact of PIV in ESCC who treated with radical resection. PIV was associated with the tumor stage and prognosis, which might be useful in the preoperative assessment of ESCC.

Pan-Immune-Inflammation Index as a Biomarker for Rheumatoid Arthritis Progression and Diagnosis

Introduction Rheumatoid arthritis (RA) is a chronic autoimmune condition that causes systemic inflammation and affects multiple joints. It is characterized by joint warmth, swelling, pain, and the formation of invasive synovial tissue known as pannus, which contributes to cartilage and bone degradation. Pan-immune-inflammation value (PIV), a marker derived from complete blood count parameters, has shown promise in predicting prognosis in various cancer types and pediatric conditions associated with immune abnormalities. This study aims to explore the relationship between RA, characterized by chronic inflammation and immune system involvement, and PIV, potentially shedding light on novel insights into RA's clinical implications. Methods One hundred four participants, including 64 RA patients (both newly diagnosed and established cases) and 40 healthy controls, were included in the study. Exclusion criteria for RA patients included acute infection, cancer, diabetes, or chronic illness, while control participants were excluded for inflammatory disorders, active infection, diabetes, or malignancy. We assessed disease severity using Disease Activity Score 28 (DAS 28) and obtained complete blood count values, including neutrophil, lymphocyte, platelet, monocyte, and red cell distribution width. C-reactive peptide (CRP) and erythrocyte sedimentation rate were also added. Statistical analyses included correlation assessments, t-tests, Mann-Whitney U tests, and multivariate linear regression. A multiclass receiver operating characteristic analysis determined optimal PIV cut-off values for distinguishing control, remission, and active RA groups, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and odds ratios calculated. Results This study comprised a cohort of 104 participants, with a median age of 43.5±17.5. The Remission group was significantly younger than the Control group (p=0.006) but not compared to the Active RA group (p=0.393). CRP levels were significantly higher in the Active RA group (p<0.001). Neutrophil counts were highest in the Active RA group (p<0.001), as were monocyte counts. Lymphocyte counts were significantly lower in the Active RA group (p<0.001). There were no significant differences in sedimentation rate, hemoglobin, platelet count, and mean platelet volume. PIV was significantly elevated in the Active RA group (p<0.001) and higher in the Remission group than in the Control group (p=0.001). A PIV value of 353.48 exhibited 71.4% sensitivity, 86.2% specificity, 86.2% PPV, 71.4% NPV, and 78.13% test accuracy for distinguishing active rheumatoid arthritis (p<0.001). A PIV value exceeding 353.48 substantially increased the likelihood of a patient belonging to the active rheumatoid arthritis group, with a 14.62-fold higher probability. Furthermore, the study explored the relationship between clinical and laboratory variables and disease activity in RA patients, finding significant differences in PIV among DAS groups (p=0.025). Conclusions The PIV offers a notable advantage as its constituent parameters are routinely assessed in rheumatoid arthritis and involve cost-effective and straightforward tests. We demonstrated that PIV serves as a valuable marker for distinguishing between remission and active RA when compared to healthy individuals. Additionally, it proved to be an effective tool for assessing disease activity in patients with active rheumatoid arthritis.

The Prognostic Value of the Novel Global Immune-Nutrition-Inflammation Index (GINI) in Stage IIIC Non-Small Cell Lung Cancer Patients Treated with Concurrent Chemoradiotherapy

BACKGROUND

We sought to determine the prognostic value of the newly developed Global Immune-Nutrition-Inflammation Index (GINI) in patients with stage IIIC non-small cell lung cancer (NSCLC) who underwent definitive concurrent chemoradiotherapy (CCRT).

METHODS

This study was conducted on a cohort of 802 newly diagnosed stage IIIC NSCLC patients who underwent CCRT. The novel GINI created first here was defined as follows: GINI = [C-reactive protein × Platelets × Monocytes × Neutrophils] ÷ [Albumin × Lymphocytes]. The receiver operating characteristic (ROC) curve analysis was used to determine the optimal pre-CCRT GINI cut-off value that substantially interacts with the locoregional progression-free (LRPFS), progression-free (PFS), and overall survival (OS).

RESULTS

The optimal pre-CCRT GINI cutoff was 1562 (AUC: 76.1%; sensitivity: 72.4%; specificity: 68.2%; Youden index: 0.406). Patients presenting with a GINI ≥ 1562 had substantially shorter median LRPFS (13.3 vs. 18.4 months; p < 0.001), PFS (10.2 vs. 14.3 months; p < 0.001), and OS (19.1 vs. 37.8 months; p < 0.001) durations than those with a GINI < 1562. Results of the multivariate analysis revealed that the pre-CCRT GINI ≥ 1562 (vs. <1562), T4 tumor (vs. T3), and receiving only 1 cycle of concurrent chemotherapy (vs. 2-3 cycles) were the factors independently associated with poorer LRPS (p < 0.05 for each), PFS (p < 0.05 for each), and OS (p < 0.05 for each).

CONCLUSION

The newly developed GINI index efficiently divided the stage IIIC NSCLSC patients into two subgroups with substantially different median and long-term survival outcomes.

Effect of Microsatellite Status and Pan-Immune-Inflammation Score on Pathological Response in Patients with Clinical Stage III Stomach Cancer Treated with Perioperative Chemotherapy

Background and Objective: This study evaluated the relationship between microsatellite status (MSI) and pan-immune-inflammation score (PIV) in tumor response to neoadjuvant chemotherapy (NAC) in patients with clinical stage III gastric cancer (cStage III GC). Materials and Methods: Microsatellite instability (MSI) status was evaluated based on pathology preparations. Pan-immune-inflammation score (PIV) was obtained from pre-treatment blood tests. The relationship of both parameters with pathological complete response (pCR) was evaluated. Results: A total of 104 patients were included in this study. All the patients were stage III GC patients receiving perioperative treatment. There were 13 patients in total who achieved a pCR response. While CNS was detected in 11 of the patients who achieved a pCR, the MSI status of the other two patients was unknown. No pCR was observed in any patient with MSI-H. According to the cut-off value for PIV, 25 (24%) patients were in the PIV-low (≤53.9) group, while 79 (76%) were in the PIV-high (>53.9) group. Based on univariate analysis, a higher PIV was associated with worse outcomes for pathological response, disease recurrence, and survival (p < 0.05). Conclusions: In patients with clinically stage III GC, the presence of MSI-H may predict no benefit from perioperative treatment. Conversely, a pre-treatment PIV score using specific cut-off values may provide a positive prediction of pathological response and survival.

Prognostic significance of the systemic inflammation response index in gastrointestinal malignancy patients: a pooled analysis of 10,091 participants

Background: We performed a meta-analysis to investigate the association of the systemic inflammation response index (SIRI) with long-term survival outcomes in patients with gastrointestinal malignancy. Methods: PubMed, Web of Science and Embase were searched for relevant studies evaluating the prognostic significance of the SIRI in gastrointestinal malignancies until May 2023. Results: 30 studies with 10,091 patients were included. The pooled results identified that patients in the high SIRI group had a worse overall survival and disease-free survival, which was observed across various tumor types, tumor stages and primary treatments. Conclusion: An elevated SIRI is negatively associated with worse survival outcomes of gastrointestinal malignancy patients and can be used as a risk stratification index for gastrointestinal malignancies.

Clinical significance of the controlling nutritional status (CONUT) score in gastric cancer patients: A meta-analysis of 9,764 participants

Background

The clinical value of the controlling nutritional status (CONUT) score has been widely reported in multiple malignancies. The aim of this study is to investigate the association between the CONUT score and clinical outcomes in patients with gastric cancer.

Methods

A comprehensive literature search of electronic databases including PubMed, Embase, and Web of Science was performed up to December 2022. The primary endpoints were survival outcomes and postoperative complications. Subgroup analysis and sensitivity analysis were performed during the pooled analysis.

Results

Nineteen studies including 9,764 patients were included. The pooled results indicated that patients in the high CONUT group had a worse overall survival (HR = 1.70 95%CI: 1.54-1.87; P < 0.0001; I ² = 33%) and recurrence-free survival (HR = 1.57; 95%CI: 1.36-1.82; P < 0.0001; I ² = 30%), and a higher risk of complications (OR = 1.96; 95%CI: 1.50-2.57; P < 0.0001; I ² = 69%). In addition, a high CONUT score was significantly associated with larger tumor size, higher percentage of microvascular invasion, later TNM stage and fewer patients receiving adjuvant chemotherapy, but not with tumor differentiation.

Conclusion

Based on existing evidence, the CONUT score could act as a valuable biomarker to predict clinical outcomes in patients with gastric cancer. Clinicians could use this useful indicator to stratify patients and formulate individual treatment plans.