Comparison of pan-immune-inflammation value with other inflammation markers of long-term survival after ST-segment elevation myocardial infarction

CYP2C19 Poor Metabolizer Status and High System Inflammation Response Index are Independent Risk Factors for Premature Myocardial Infarction: A Hospital-Based Retrospective Study

Objective

Atherosclerosis (AS) is a sustained chronic vascular inflammatory response caused by lipid metabolism disorders and immune response disorders and is the main cause of premature (men ≤ 55 years old, women ≤ 65 years old) myocardial infarction (PMI). Cytochrome P450 2C19 (CYP2C19) (related to vascular function and lipid metabolism) and peripheral immune cell levels and plays an important role in the course of AS. The association CYP2C19 polymorphisms, comprehensive immunoinflammatory indices with PMI susceptibility is unclear.

Methods

This study included 485 PMI patients, and 639 age-matched non-PMI individuals as controls, from January 2019 to March 2024. The relationship between CYP2C19 polymorphisms, peripheral immunoinflammatory indices (pan-immune inflammation value (PIV), systemic immune inflammation index (SII), and system inflammation response index (SIRI)) and PMI risk were analyzed.

Results

The inflammatory indices levels in PMI patients were higher than those in controls (all p<0.05). The frequencies of the CYP2C19 *1/*2 and *2/*2 genotypes were higher, while the frequency of the *1/*1 genotype was lower in the PMI patients than those in controls. The cut-off values of TC, TG, LDL-C, PIV, SII, and SIRI were 5.065, 1.305, 2.805, 410.485, 869.645, and 1.495 for distinguishing PMI, respectively. Logistic regression analysis showed that male (odds ratio (OR): 1.607, 95% confidence interval (CI): 1.134-2.277, p=0.008), history of smoking (OR: 7.108, 95% CI: 4.351-11.614, p<0.001), diabetes mellitus (OR: 4.906, 95% CI: 3.333-7.223, p<0.001), CYP2C19 poor metabolizer (PM) (*2/*2, *2/*3, and *3/*3) (OR: 2.147, 95% CI: 1.279-3.603, p=0.004), and high TG (≥1.305 vs <1.305, OR: 2.598, 95% CI: 1.864-3.623, p<0.001) and SIRI level (≥1.495 vs <1.495, OR: 2.495, 95% CI: 1.432-4.349, p=0.001) were independent risk factors for PMI.

Conclusion

CYP2C19 PM phenotype, high SIRI level (≥1.495) and TG level (≥1.305), male, history of smoking, and diabetes mellitus were independently associated with PMI susceptibility.

Prognostic value of pan immune-inflammation value in patients undergoing unprotected left main coronary artery stenting

Aim: This study aimed long-term prognostic factors for percutaneous coronary intervention (PCI) in left main coronary artery (LMCA) disease, focusing on inflammatory parameters like Pan Immune-Inflammation Value (PIV) and Systemic Immune-Inflammation Index (SII).Materials & methods: This retrospective, observational study involved 121 patients receiving unprotected LMCA PCI.The study's primary end point was MACE, including all-cause death, repeat revascularization and myocardial reinfarction.Results: The PIV and SII scores emerged as independent predictors of MACE occurrence in patients undergoing PCI for unprotected LMCA disease (p:0.045,p:0.048;respectively).In addition, age, ejection fraction, creatinine, shock presentation and the residual syntax score were identified as significant independent predictors of MACE in both models.Conclusion: This study showed a significant, independent correlation between PIV, SII and MACE in patients undergoing PCI for LMCA disease, with PIV showing slightly better predictive capability than SII.

Modification, validation and comparison of Naples prognostic score to determine in-hospital mortality in ST-segment elevation myocardial infarction

AIM

The relationship between inflammatory status and poor outcomes in acute coronary syndromes is a significant area of current research. This study investigates the association between in-hospital mortality and the modified Naples prognostic score (mNPS) as well as other inflammatory biomarkers in STEMI patients.

METHODS

This single-centre, cross-sectional study included 2576 consecutive STEMI patients who underwent primary percutaneous coronary intervention between January 2022 and November 2023. Participants were randomly divided into derivation and validation cohorts in a 6:4 ratio. The following inflammatory indices were calculated: pan-immune-inflammation value (PIV), systemic immune-inflammation-index (SII), systemic inflammation-response index (SIRI) and conventional NPS. The mNPS was derived by integrating hs-CRP into the conventional NPS. The performance of these indices in determining in-hospital mortality was assessed using regression, calibration, discrimination, reclassification and decision curve analyses.

RESULTS

Inflammatory biomarkers, including PIV, SII, SIRI, NPS and mNPS, were significantly higher in patients who died during in-hospital follow-up compared to those discharged alive in both the derivation and validation cohorts. Multivariable logistic regression analyses were performed separately for the derivation and validation cohorts. In the derivation cohort, mNPS was associated with in-hospital mortality (aOR = 1.490, p < .001). Similarly, in the validation cohort, mNPS was associated with in-hospital mortality (aOR = 2.023, p < .001). mNPS demonstrated better discriminative and reclassification power than other inflammatory markers (p < .05 for all). Additionally, regression models incorporating mNPS were well-calibrated and showed net clinical benefit in both cohorts.

CONCLUSION

mNPS may be a stronger predictor of in-hospital mortality in STEMI patients compared to the conventional scheme and other inflammatory indices.

Inflammatory and Metabolic Predictors of Mortality in Pulmonary Thromboembolism: A Focus on the Triglyceride-Glucose Index and Pan-Immune Inflammation Value

Objectives: We aimed to evaluate the importance of metabolic and inflammatory markers, specifically the Triglyceride-Glucose Index (TGI) and pan-immune inflammation value (PIV), in predicting mortality among patients diagnosed with pulmonary thromboembolism (PTE). Materials and Methods: A total of 450 patients diagnosed with PTE between December 2018 and December 2023 were included in his study. The diagnosis of PTE was confirmed by clinical presentation, laboratory tests, and imaging studies such as computed tomography pulmonary angiography (CTPA). Data were obtained from medical records, including demographic information, medical history, laboratory results, and clinical outcomes. Results: In terms of age, non-survivors were older on average (66.1 ± 11.8 years) compared to survivors (58.3 ± 12.4 years) (p = 0.03). In terms of gender, 55% of non-survivors and 45% of survivors were male (p = 0.111). Non-survivors had higher BMIs (28.3 ± 5.1) than survivors (25.7 ± 4.5) (p = 0.04). In terms of hypertension, 40% of non-survivors and 30% of survivors had hypertension (p = 0.041). In terms of diabetes, 35% of those who did not survive and 20% of those who survived had diabetes (p = 0.001). In terms of smoking, 25% of non-survivors and 15% of survivors smoke (p = 0.022). In terms of TGI, non-survivors had higher TGI values (12.1 ± 1.5) than survivors (5.9 ± 1.2) (p < 0.001). In terms of PIV, non-survivors had significantly higher PIV (878.2 ± 85.4) than survivors (254.5 ± 61.1) (p < 0.001). The risk factors found to be significantly associated with differentiation in the multiple logistic regression analysis included age, BMI, TGI, and PIV (p = 0.005, p = 0.002, p = 0.013, and 0.022, respectively). As a result, according to ROC analysis for patients who are non-survivors, age, BMI, TGI, and PIV were significant prognostic factors. The cut-off points for these values were >60, >27, >10, and >500, respectively. Conclusions: the TGI and PIV are strong markers for predicting mortality in PTE patients. The independent predictive value of age and BMI further demonstrates their role in risk stratification. We think that high TGI values and PIVs reflect underlying metabolic and inflammatory disorders that may contribute to worse outcomes in these patients.

The association between pan-immune-inflammation value and chronic obstructive pulmonary disease: data from NHANES 1999-2018

Background

The pan-immune-inflammation value (PIV) is an emerging biomarker quantitatively reflecting the systemic immune-inflammatory status. The predictive value of PIV has been well-established across various clinical settings. However, its role in chronic obstructive pulmonary disease (COPD) remains unclear and necessitates further investigation.

Methods

Data from NHANES 1999-2018 were filtered. Logistic regression analyses were used to assess the correlation between COPD prevalence and PIV in all participants. COX regression analyses and Kaplan-Meier survival curves were used to investigate the relationship between COPD all-cause mortality and PIV in COPD patients. Restricted cubic spline (RCS) analyses and piecewise linear regression analyses were additionally employed to explore the correlation between PIV and COPD. Subgroup analyses were performed to further clarify the effects of other covariates on the associations. Sensitivity analyses were employed to assess the robustness of the results.

Results

A total of 28,485 participants aged 40 years and older were recruited for this study. After fully adjusting for covariates, higher PIV levels were independently associated with increased COPD prevalence (OR = 1.67; 95% CI: 1.39-2.01) and all-cause mortality (HR = 2.04; 95% CI: 1.41-2.95). The COPD prevalence curve exhibited an inflection point at Log10-PIV of 2.24, showing no significant correlation on the left side (OR = 0.86; 95% CI: 0.45-1.64) but a positive correlation on the right side (OR = 2.00; 95% CI: 1.57-2.55). The COPD all-cause mortality curve displayed an inflection point at Log10-PIV of 2.38, indicating a negative correlation on the left side (HR = 0.23; 95% CI: 0.12-0.44) and a positive correlation on the right side (HR = 4.12; 95% CI: 2.62-6.48). Subgroup analyses with interaction tests showed that the strength of the correlation between PIV and COPD prevalence was influenced by race, smoking status, and BMI (all p for interaction <0.05). The relationship between PIV and COPD all-cause mortality was unaffected by any covariates (all p for interaction >0.05).

Conclusion

Elevated PIV levels are associated with increased COPD prevalence. COPD patients with either elevated or reduced PIV levels experience higher all-cause mortality. Further large-scale, longitudinal studies are required to corroborate these findings.

Leukocyte Indices as Markers of Inflammation and Predictors of Outcome in Heart Failure with Preserved Ejection Fraction

Background: The pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF) is suggested to be influenced by inflammation. Leukocyte indices, including the neutrophil-lymphocyte ratio (NLR), the monocyte-lymphocyte ratio (MLR), and the pan-immune inflammation value (PIV), can be utilized as biomarkers of systemic inflammation. Their prognostic utility is yet to be fully understood. Methods: Between December 2010 and May 2023, patients presenting to a tertiary referral center for HFpEF were included into a prospective registry. The association of the NLR, MLR, and PIV with the composite endpoint of all-cause mortality and HF-related hospitalization was tested utilizing Cox regression analysis. Results: In total, 479 patients (median 74.3, interquartile range (IQR): 69.22-78.3 years, 27.8% male) were included. Patients were observed for 43 (IQR: 11-70) months, during which a total of 267 (55.7%) patients met the primary endpoint. In a univariate Cox regression analysis, an above-the-median NLR implied a hazard ratio (HR) of 1.76 (95%-confidence interval (CI): 1.38-2.24, p < 0.001), an MLR of 1.46 (95%-CI: 1.14-1.86, p = 0.003), and a PIV of 1.67, 95%-CI: 1.30-2.13, p < 0.001) for the composite endpoint. After adjustment in a step-wise model, the NLR (HR: 1.81, 95%-CI: 1.22-2.69, p = 0.003), the MLR (HR: 1.57, 95%-CI: 1.06-2.34, p = 0.026), and the PIV (HR: 1.64, 95%-CI: 1.10-2.46, p = 0.015) remained significantly associated with the combined endpoint. Conclusions: The NLR, the MLR, and the PIV are simple biomarkers independently associated with outcomes in patients with HFpEF.

Construction of a metabolism-malnutrition-inflammation prognostic risk score in patients with heart failure with preserved ejection fraction: a machine learning based Lasso-Cox model

BACKGROUND

Metabolic disorder, malnutrition and inflammation are involved and interplayed in the mechanisms of heart failure with preserved ejection fraction (HFpEF). We aimed to construct a Metabolism-malnutrition-inflammation score (MIS) to predict the risk of death in patients with HFpEF.

METHODS

We included patients diagnosed with HFpEF and without infective or systemic disease. 20 biomarkers were filtered by the Least absolute shrinkage and selection operator (Lasso)-Cox regression. 1000 times bootstrapping datasets were generated to select biomarkers that appeared above 95% frequency in repetitions to construct the MIS.

RESULTS

Among 1083 patients diagnosed with HFpEF, 342 patients (31.6%) died during a median follow-up period of 2.5 years. The MIS was finally constructed based on 6 biomarkers, they were albumin (ALB), red blood cell distribution width-standard deviation (RDW-SD), high-sensitivity C-reactive protein (hs-CRP), lymphocytes, triiodothyronine (T3) and uric acid (UA). Incorporating MIS into the basic predictive model significantly increased both discrimination (∆C-index = 0.034, 95% CI 0.013-0.050) and reclassification (IDI, 6.6%, 95% CI 4.0%-9.5%; NRI, 22.2% 95% CI 14.4%-30.2%) in predicting all-cause mortality. In the time-dependent receiver operating characteristic (ROC) analysis, the mean area under the curve (AUC) for the MIS was 0.778, 0.782 and 0.772 at 1, 3, and 5 years after discharge in the cross-validation sets. The MIS was independently associated with all-cause mortality (hazard ratio: 1.98, 95% CI [1.70-2.31], P < 0.001).

CONCLUSIONS

A risk score derived from 6 commonly used inflammatory, nutritional, thyroid and uric acid metabolic biomarkers can effectively identify high-risk patients with HFpEF, providing potential individualized management strategies for patients with HFpEF.

Alterations in novel inflammatory biomarkers during perioperative cardiovascular surgeries involving cardiopulmonary bypass: a retrospective propensity score matching study

Background

Cardiopulmonary bypass (CPB) triggers a strong inflammatory response in cardiovascular surgery patients during the perioperative period. This article mainly focuses on the perioperative application of novel inflammatory biomarkers in cardiovascular surgeries involving CPB.

Methods

Patients were divided into a CPB group and a non-CPB group according to whether they underwent CPB during cardiovascular surgery. Novel inflammatory biomarkers and clinical results were recorded. The neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), platelet × neutrophil/lymphocyte ratio (SII), and monocyte × platelet × neutrophil/lymphocyte ratio (PIV) were calculated. The primary outcomes were perioperative prognosis between the CPB and non-CPB groups. The secondary outcomes included perioperative alterations of novel inflammatory biomarkers in the CPB group and predictive values of novel inflammatory biomarkers for postoperative infection and acute kidney injury.

Results

A total of 332 patients were initially included in the study. Before propensity score matching (PSM), there were 96 patients in the CPB group and 236 patients in the non-CPB group. After PSM, both groups included 58 patients each. Compared with the non-CPB group, the CPB group had a higher proportion of intraoperative transfusion of blood products (63.79% vs. 6.90%, P < 0.001), specifically for red blood cells (58.62% vs. 3.45%, P < 0.001) and plasma (41.38% vs. 1.72, P < 0.001), exhibited a higher drainage fluid volume within 24 h [380 (200-550) ml vs. 200 (24-330) ml, P = 0.002], and required longer durations of mechanical ventilation [14.3 (6.6-21.3) h vs. 5.75 (4.08-10.1) h, P < 0.001] and ICU stay [48.78 (44.92-89.38) h vs. 27.16 (21.67-46.25) h, P < 0.001]. After surgery, NLR [14.00 (9.93-23.08) vs. 11.55 (7.38-17.38), P = 0.043] was higher in the CPB group, while the PIV, PLR, and SII in the CPB group were lower than those in the non-CPB group on the first day after surgery.

Conclusions

Cardiovascular surgeries involving CPB exhibit a poorer prognosis compared to non-CPB procedures. Novel inflammatory biomarkers, including PLR, PIV, and SII, may offer valuable insights into the degree of postoperative inflammation, with NLR emerging as a potentially reliable prognostic indicator.

Predictive Value of the Naples Prognostic Score for Cardiovascular Outcomes in Patients With Chronic Kidney Disease Receiving Percutaneous Coronary Intervention

The Naples prognostic score (NPS) is a novel multidimensional inflammatory and nutritional assessment system in cancer patients. However, its significance in patients with chronic kidney disease (CKD) after percutaneous coronary intervention (PCI) remains unclear. The study has a single-center, retrospective design and included 631 patients with CKD who underwent index PCI between 2019 and 2022. All participants were divided into 2 groups according to the NPS (Low-risk group: n = 209; High-risk group: n = 422) and followed up until November 2022. The primary endpoint was Major Adverse Cardiac Events (MACE). NPS predicted MACE events better than other scores, besides, high-risk NPS with severe renal dysfunction (RD) group (MODEL 2) had superior MACE diagnostic efficiency than NPS high-risk group lonely. (NPS: AUC: 0.605, P < .001; MODEL 2: AUC: 0.624, P < .001, respectively). Kaplan-Meier survival analysis of two groups showed that high-risk group had higher incidence of MACE (P < .001). Meanwhile, high-risk group had higher MACE events [adjusted Hazard Ratio (aHR) 2.013, 95% CI 1.294, 3.132; P = .002]. NPS is an independent prognostic factor for CKD patients undergoing index PCI before operation whose predictive value for survival prognosis is better than other nutritional and inflammatory indicators. Compared with low NPS, patients with high NPS have a relatively poor prognosis.

Effects of antiretroviral therapy on glycemic and inflammatory indices in people living with HIV (PLWH)

BACKGROUND

This study explores the relationship between different ART therapy based on NRTIs, and inflammatory markers, along with fasting blood glucose levels in treatment-naïve people living with HIV (PLWH).

METHODS

We retrospectively analyzed the variations in fasting blood glucose and inflammatory markers and their relationship with different ART regimens in 497 treatment-naïve PLWH at the ART clinic of Zhongnan Hospital of Wuhan University from June 2018 to March 2022.

RESULTS

From baseline to 24 months, fasting blood glucose, systemic immune-inflammation index (SII), pan-immune-inflammation value (PIV) and lymphocyte-to-monocyte ratio (LMR) in PLWH receiving ART increased, while neutrophillymphocyte ratio (NLR) decreased (p < .05). In the NNRTIs group, fasting blood glucose, SII, PIV and LMR were higher than before (p < .05). In the INSTIs group, fasting blood glucose and LMR increased (p < .05), while NLR was lower (p < .05). Compared to the INSTIs, fasting blood glucose in the NNRTIs group was higher at 12 and 24 months (p < .05). At 24 months, both NLR and SII were higher in the NNRTIs group than in the INSTIs group (p < .05).

CONCLUSIONS

Despite the virus suppression, fasting blood glucose and certain inflammatory markers in PLWH can gradually increase. Compared to NNRTIs, the INSTIs regimen was associated with favorable alterations in the levels of glucose and inflammatory markers.

Association of the panimmune-inflammatory value (PIV) with all-cause and cardiovascular mortality in maintenance hemodialysis patients: a propensity score matching retrospective study

PURPOSE

The panimmune-inflammatory value (PIV) is a novel inflammatory indicator. However, its role in maintenance hemodialysis (MHD) remains unclear. Our goal was to explore the predictive value of PIV for cardiovascular and all-cause mortality in MHD patients.

METHODS

In this retrospective cohort study, 507 patients receiving MHD between November 2017 and December 2022 were enrolled. The PIV value was calculated as follows: neutrophil count × monocyte count × platelet count/lymphocyte count. Patients were divided into two groups on the basis of the median PIV. Propensity score matching (PSM) was used to adjust for imbalances in baseline information between groups. Kaplan‒Meier curves, Cox regression, the Fine‒Gray competing risk model, and restricted cubic spline (RCS) curves were used to analyze the relationship between PIV and mortality.

RESULTS

By the end of follow-up, 126 deaths had occurred, 91 of which were due to cardiovascular disease. The Kaplan‒Meier curves demonstrated that MHD patients with higher PIV levels had a poorer prognosis for all-cause death (p = 0.019). PIV levels were linked to all-cause death in multivariate Cox proportional risk regression (HR = 1.76; 95% CI 1.14, 2.72; p = 0.011). The Fine‒Gray model revealed a greater cumulative incidence of cardiovascular death in the higher PIV group (p = 0.035). PIV levels were linked to cardiovascular mortality in the Fine‒Gray competing risk model (HR = 2.06; 95% CI 1.25, 3.42; p = 0.005). The RCS revealed a nonlinear relationship between PIV and mortality risk (p < 0.05). Using 63 years of age as the threshold, we observed a multiplicative interaction effect between age and PIV for all-cause mortality (p = 0.006).

CONCLUSION

In MHD patients, PIV is an independent hazard factor for cardiovascular-related mortality and all-cause mortality.

Association between the pan-immune-inflammation value and coronary collateral circulation in chronic total coronary occlusive patients

BACKGROUND

Inflammation and immunity play important roles in the formation of coronary collateral circulation (CCC). The pan-immune-inflammation value (PIV) is a novel marker for evaluating systemic inflammation and immunity. The study aimed to investigate the association between the PIV and CCC formation in patients with chronic total occlusion (CTO).

METHODS

This retrospective study enrolled 1150 patients who were diagnosed with CTO through coronary angiographic (CAG) examinations from January 2013 to December 2021 in China. The Cohen-Rentrop criteria were used to catagorize CCC formation: good CCC formation (Rentrop grade 2-3) and poor CCC formation group (Rentrop grade 0-1). Based on the tertiles of the PIV, all patients were classified into three groups as follows: P1 group, PIV ≤ 237.56; P2 group, 237.56< PIV ≤ 575.18; and P3 group, PIV > 575.18.

RESULTS

A significant relationship between the PIV and the formation of CCC was observed in our study. Utilizing multivariate logistic regression and adjusting for confounding factors, the PIV emerged as an independent risk factor for poor CCC formation. Notably, the restricted cubic splines revealed a dose-response relationship between the PIV and risk of poor CCC formation. In terms of predictive accuracy, the area under the ROC curve (AUC) for PIV in anticipating poor CCC formation was 0.618 (95% CI: 0.584-0.651, P < 0.001). Furthermore, the net reclassification index (NRI) and integrated discrimination index (IDI) for PIV, concerning the prediction of poor CCC formation, were found to be 0.272 (95% CI: 0.142-0.352, P < 0.001) and 0.051 (95% CI: 0.037-0.065, P < 0.001), respectively. It's noteworthy that both the NRI and IDI values were higher for PIV compared to other inflammatory biomarkers, suggesting its superiority in predictive capacity.

CONCLUSIONS

PIV was associated with the formation of CCC. Notably, PIV exhibited potential as a predictor for poor CCC formation and showcased superior predictive performance compared to other complete blood count-based inflammatory biomarkers.

The Pan-Immune Inflammation Value at Admission Predicts Postoperative in-hospital Mortality in Patients with Acute Type A Aortic Dissection

Purpose

The inflammatory response of the body is intimately linked to the quick onset and high in-hospital mortality of Acute Type A Aortic Dissection (ATAAD). The purpose of the study was to examine the connection between in-hospital mortality in patients with ATAAD upon admission and the Pan-Immune-Inflammation Value (PIV).

Patients and Methods

308 patients who were diagnosed with ATAAD between September 2018 and October 2021 at Fujian Provincial Center for Cardiovascular Medicine had their clinical data retrospectively examined. PIV was assessed at the time of study population admission, with in-hospital mortality serving as the main outcome measure. Patients were divided into two groups, the high PIV group (PIV > 1807.704) and the low PIV group (PIV < 1807.704), based on the PIV ROC curve and the best threshold of the Youden index. The clinical results of the two groups were then compared.

Results

Among ATAAD patients, postoperative in-hospital mortality was higher in the high PIV group (54.7% vs 10.6%, P < 0.001), and the high PIV group had significantly higher rates of postoperative acute kidney injury, acute liver insufficiency, and gastrointestinal hemorrhage (P < 0.05). Additionally, the high PIV group's ICU stays lasted longer than the low PIV group's (P < 0.05). The results of multifactorial logistic regression analysis, which controlled for other variables, indicated that the mechanical ventilation time (OR = 1.860, 95% CI: 1.437, 2.408; P < 0.001), the high PIV group (> 1807.704) (OR = 1.939, 95% CI: 1.257, 2.990; P = 0.003), the cardiopulmonary bypass time (OR = 1.011, 95% CI: 1.004, 1.018; P = 0.002), and the white blood cell count (OR = 1.188, 95% CI: 1.054, 1.340; P = 0.005) were independent risk factors for postoperative in-hospital mortality in ATAAD patients.

Conclusion

Postoperative death in ATAAD patients was independently predicted by high PIV levels at admission. Patients should be informed about their preoperative inflammatory status and actively participate in prompt clinical decision-making and treatment.

Association between admission pan-immune-inflammation value and short-term mortality in septic patients: a retrospective cohort study

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.

Association Between Systemic Immune-Inflammation Index and Adverse Outcomes in Patients With Acute Coronary Syndrome: A Meta-Analysis

The systemic immune-inflammation index (SII) has been developed for the risk prognostication of acute coronary syndrome (ACS) patients. This meta-analysis aimed to assess the value of SII for predicting adverse outcomes in ACS patients. Relevant studies were identified by searching the PubMed, Web of Science, and Embase databases. Studies that investigated the association of SII with all-cause mortality or major adverse cardiovascular events (MACEs) in ACS patients were eligible. The short-term outcomes were defined as adverse events occurring during the hospital and within 30 days. 11 studies with 16,596 patients were eligible for analysis. A random effect meta-analysis indicated that a higher SII level significantly predicted short-term death (hazard ratios [HR] 2.60; 95% confidence intervals [CI] 1.29-5.25) and long-term all-cause mortality (HR 2.40; 95% CI 1.25-4.59), even after adjusting for traditional confounding factors. Additionally, a higher SII level was also significantly associated with an increased risk of short-term MACEs (HR 1.61; 95% CI 1.28-2.03) and long-term MACEs (HR 2.43; 95% CI 1.74-3.40). These findings suggest that SII level has a strong prognostic value for both MACEs and all-cause mortality in patients with ACS. Determining the SII level has the potential to improve risk prognostication in ACS patients.

The Importance of Pan-Immune Inflammation Value (PIV) in Predicting Coronary Collateral Circulation in Stable Coronary Artery Patients

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.

Can the pan-immune-inflammation value predict gram negative bloodstream infection-related 30-day mortality in solid organ transplant patients?

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.

Could Pan-Immune-Inflammation Value be a Marker for the Diagnosis of Coronary Slow Flow Phenomenon?

Inflammation plays a key role in the pathogenesis of the coronary slow flow phenomenon (CSFP). The newly developed inflammatory marker, pan-immune-inflammation value (PIV), is associated with adverse cardiovascular events. This study investigated the predictive value of PIV for diagnosing CSFP in comparison to other inflammation-based markers. A total of 214 patients, 109 in the CSFP group and 105 in the normal coronary flow (NCF) group, were retrospectively included in the study. Coronary flow was calculated using the Thrombolysis in Myocardial Infarction frame count method. In addition to PIV, other inflammatory markers such as neutrophil-lymphocyte ratio, platelet-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were calculated for the patients. The average age of patients was 50.3 ± 8.4, with a male ratio of 55.1%. Compared to the NCF group, patients in the CSFP group had higher levels of hyperlipidemia, glucose, triglyceride, NLR, PLR, SII, and PIV, while their high-density lipoprotein cholesterol (HDL-C), was lower (p < 0.05). Logistic regression analysis demonstrated that HDL-C, glucose, triglyceride, and PIV were independent predictor factors for CSFP (p < 0.05). PIV is a strong and independent predictor factor for CSFP and superior in predicting CSFP compared to other inflammatory markers.

Correlations Between Acute Coronary Syndrome and Novel Inflammatory Markers (Systemic Immune-Inflammation Index, Systemic Inflammation Response Index, and Aggregate Index of Systemic Inflammation) in Patients with and without Diabetes or Prediabetes

Purpose

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.

Patients and Methods

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.

Results

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.

Conclusion

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.

Relationship between pan-immune- inflammation value and in major cardiovascular and cerebrovascular events in stable coronary artery disease patients undergoing on-pump coronary artery bypass graft surgery

BACKGROUND

In this study, we aimed to evaluate the association of pan-immune-inflammation value (PIV) with major cardiovascular and cerebrovascular events (MACCE) in stable coronary artery disease patients undergoing on-pump coronary artery bypass graft (CABG) surgery.

METHODS

We retrospectively analyzed data from 527 patients who underwent on-pump CABG surgery for stable coronary artery disease between June 2015 and December 2020. Patients were categorized into two groups based on MACCE development. PIV levels were calculated from blood samples taken on admission. PIV was calculated as [neutrophil count (×103/µL)×platelet count (×103/µL))×monocyte count (×103/µL)]/lymphocyte count (×103/µL). The primary endpoint was long-term major cardiovascular and cerebrovascular events (MACCE) at a median follow-up of 4.6 years.

RESULTS

Of the included patients, 103 (19.5%) developed MACCE. PIV was higher in patients with MACCE compared to those without (470.8 [295.3-606.8] vs. 269.8 [184.3-386.4], p < 0.001). Multivariate analysis showed a significant positive association between PIV and MACCE (HR: 1.326, 95%CI:1.212-1452, p < 0.001). The cut-off value for the PIV in the estimation of MACCE was 368.28 ( AUC: 0.726 with 69% sensitivity, 71% specificity, p < 0.001).

CONCLUSION

This study shows a significant link between high PIV levels and MACCE in stable coronary artery disease patients undergoing on-pump CABG surgery. Our findings suggest that PIV may be a valuable, routinely available, and inexpensive marker for identifying patients at increased risk of MACCE.

Preinterventional pan-immune-inflammation value as a tool to predict postcontrast acute kidney injury among acute coronary syndrome patients implanted drug-eluting stents: a retrospective observational study

We evaluated the value of pan-immune-inflammation value (PIV) in predicting the risk for postcontrast acute kidney injury (PCAKI), an important complication following percutaneous coronary intervention (PCI) in acute coronary syndrome (ACS) patients. Medical records of 839 ACS patients underwent PCI between June 2019 and December 2022 were retrospectively analyzed. Patients were divided into two groups: PCAKI (-) and PCAKI (+). PCAKI was defined as a ≥ 0.5 mg/dL and/or a ≥ 25% increase in serum creatinine within 72 h after PCI. The PIV was computed as [neutrophils × platelets × monocytes]÷lymphocytes. The mean age was 60.7 ± 12.9 years. PCAKI was detected in 105 (12.51%) patients. PIV was higher in the PCAKI (+) group compared to PCAKI (-) group (median 1150, interquartile range [IQR] 663-2021 vs median 366, IQR 238-527, p < 0.001). Receiver operating characteristic curve analysis showed that the best cutoff of PIV for predicting PCAKI was 576 with 81% sensitivity and 80% specificity. PIV was superior to neutrophil-lymphocyte ratio and platelet-lymphocyte ratio for the prediction of PCAKI (area under curve:0.894, 0.849 and 0.817, respectively, p < 0.001 for all). A high PIV was independently correlated with PCAKI (≤576 vs. >576, odds ratio [OR] 12.484, 95%confidence interval [CI] 4.853-32.118, p < 0.001) together with older age (OR 1.058, p = 0.009), female gender (OR 4.374, p = 0.005), active smoking (OR 0.193, p = 0.012), left ventricular ejection fraction (OR 0.954, p = 0.021), creatinine (OR 10.120, p < 0.001), hemoglobin (OR 0.759, p = 0.019) and c-reactive protein (OR 1.121, p = 0.002). In conclusion, a high PIV seems to be an easily assessable tool that can be used in clinical practice for predicting the risk of PCAKI in ACS patients implanted drug-eluting stents.

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 between pan-immune-inflammation value and coronary slow flow phenomenon in patients with angiographically normal coronary arteries

INTRODUCTION

Coronary slow flow phenomenon (CSFP) is characterized by the delayed contrast filling of terminal vessels of coronary arteries in the presence of normal or nearly normal epicardial coronary arteries. Given that inflammation plays a role in cardiovascular disorders, including CSFP, using peripheral blood-derived compound prognostic indexes could be a feasible way to predict the presence of CSFP. Therefore, in the present study, we evaluated the association between pan-immune-inflammation value (PIV) and the CSFP.

METHODS

This single-center, retrospective study was composed of 612 patients aged over 18 years who underwent CAG for suspected stable ischemic heart disease. The association of clinical and laboratory parameters with the CSFP was evaluated with univariate and multivariate analyses.

RESULTS

The median age of the patients was 54 (IQR 46-63) and 61.3% of the patients were male. The 12.6% (84/612) of the patients had CSFP, while the coronary flow was normal in the remaining 87.4% of patients. The PIV levels had moderate success for the prediction of the CSFP (AUC: 0.675, 95% CI: 0.615-0.735, p < 0.001). In multivariate analyses, male gender (OR: 4.858, 95% CI: 2.851-8.277, p < 0.001), presence of diabetes (OR: 2.672, 95% CI: 1.396-5.113, p = 0.003), lower HDL-C values (OR: 2.120, 95% CI: 1.286-3.496, p = 0.003), and higher PIV levels (OR: 2.527, 95% CI: 1.519-4.203, p < 0.001) were associated with a higher risk of CSFP.

CONCLUSION

We demonstrated that a higher risk of CSFP in patients with PIV levels. If supported by prospective evidence, PIV levels could be used as a minimally invasive reflector of CSFP.

The Role of Pan-Immune-Inflammation Value in Determining the Severity of Coronary Artery Disease in NSTEMI Patients

BACKGROUND

Even though medication and interventional therapy have improved the death rate for non-ST elevation myocardial infarction (NSTEMI) patients, these patients still have a substantial residual risk of cardiovascular events. Early identification of high-risk individuals is critical for improving prognosis, especially in this patient group. The focus of recent research has switched to finding new related indicators that can help distinguish high-risk patients. For this purpose, we examined the relationship between the pan-immune-inflammation value (PIV) and the severity of coronary artery disease (CAD) defined by the SYNTAX score (SxS) in NSTEMI patients.

METHODS

Based on the SxS, CAD patients were split into three groups. To evaluate the risk variables of CAD, multivariate logistic analysis was employed.

RESULTS

The PIV (odds ratio: 1.003; 95% CI: 1.001-1.005; p = 0.005) was found to be an independent predictor of a high SxS in the multivariate logistic regression analysis. Additionally, there was a positive association between the PIV and SxS (r: 0.68; p < 0.001). The PIV predicted the severe coronary lesion in the receiver-operating characteristic curve analysis with a sensitivity of 91% and specificity of 81.1%, using an appropriate cutoff value of 568.2.

CONCLUSIONS

In patients with non-STEMI, the PIV, a cheap and easily measured laboratory variable, was substantially correlated with a high SxS and the severity of CAD.

Association of systemic inflammatory response index with ST segment elevation myocardial infarction and degree of coronary stenosis: a cross-sectional study

BACKGROUND

Systemic Inflammatory Response Index (SIRI), a composite inflammatory marker encompassing neutrophils, monocytes, and lymphocytes, has been recognized as a reliable marker of systemic inflammation. This article undertakes an analysis of clinical data from ST-segment Elevation Myocardial Infarction (STEMI) patients, aiming to comprehensively assess the relationship between SIRI, STEMI, and the degree of coronary stenosis.

METHODS

The study involved 1809 patients diagnosed with STEMI between the years 2020 and 2023. Univariate and multivariate logistic regression analyses were conducted to evaluate the risk factors for STEMI. Receiver operating characteristic (ROC) curves were generated to determine the predictive power of SIRI and neutrophil-to-lymphocyte ratio (NLR). Spearman correlation analysis was performed to assess the correlation between SIRI, NLR, and the Gensini score (GS).

RESULTS

Multivariate logistic regression analysis showed that the SIRI was the independent risk factor for STEMI (adjusted odds ratio (OR) in the highest quartile = 24.96, 95% confidence interval (CI) = 15.32-40.66, P < 0.001). In addition, there is a high correlation between SIRI and GS (β:28.54, 95% CI: 24.63-32.46, P < 0.001). The ROC curve analysis was performed to evaluate the predictive ability of SIRI and NLR for STEMI patients. The area under the curve (AUC) for SIRI was 0.789. The AUC for NLR was 0.754. Regarding the prediction of STEMI in different gender groups, the AUC for SIRI in the male group was 0.771. The AUC for SIRI in the female group was 0.807. Spearman correlation analysis showed that SIRI exhibited a stronger correlation with GS, while NLR was lower (SIRI: r = 0.350, P < 0.001) (NLR: r = 0.313, P < 0.001).

CONCLUSION

The study reveals a strong correlation between the SIRI and STEMI as well as the degree of coronary artery stenosis. In comparison to NLR, SIRI shows potential in predicting acute myocardial infarction and the severity of coronary artery stenosis. Additionally, SIRI exhibits a stronger predictive capability for female STEMI patients compared to males.

Pan-Immune-Inflammation Value Is Independently Correlated to Impaired Coronary Flow After Primary Percutaneous Coronary Intervention in Patients With ST-Segment Elevation Myocardial Infarction

Immune-inflammatory biomarkers have been shown to be correlated with impaired coronary flow (ICF) in ST-segment elevation myocardial infarction. In this study, we assessed the relation between a novel comprehensive biomarker, pan-immune-inflammation value (PIV), and ICF after primary percutaneous coronary intervention (pPCI) in ST-segment elevation myocardial infarction. A total of 687 patients who underwent pPCI between 2019 and 2023 were retrospectively analyzed. Blood samples were collected at admission. PIV and other inflammation parameters were compared. PIV was calculated as (neutrophil count × platelet count × monocyte count)/lymphocyte count. Postprocedural coronary flow was assessed by thrombolysis in myocardial infarction (TIMI) classification. Patients were divided into 2 groups: a group with ICF defined as postprocedural TIMI 0 to 2 and a group with normal coronary flow defined as postprocedural TIMI flow grade of 3. The mean age was 61 ± 12 years, and 22.4% of the patients were women. Compared with the normal coronary flow group (median 492, interquartile range 275 to 931), the ICF group (median 1,540, interquartile range 834 to 2,909) showed significantly increased PIV (p <0.001). The optimal cutoff for the PIV was 804, as determined by receiver operating characteristic curve. The incidence of ICF was 17.0% in all patients, 6.4% in low-PIV group (<804), and 34.2% in high-PIV group (≥804). Multivariate analyses revealed that a baseline PIV ≥804 was independently associated with post-pPCI ICF (odds ratio 5.226, p <0.001). PIV was superior to neutrophil/lymphocyte ratio and platelet/lymphocyte ratio in determining ICF. In conclusion, a high-PIV was significantly associated with an increased risk of ICF after pPCI. Moreover, PIV was a better indicator of ICF than were other inflammatory markers.

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.

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.

Comparison of the value of various complex indexes of blood cell types and lipid levels in coronary heart disease

Background

Inflammation and lipid infiltration play crucial roles in the development of atherosclerosis. This study aimed to investigate the association between various complex indexes of blood cell types and lipid levels with the severity of coronary artery stenosis and their predictive value in coronary heart disease (CHD).

Methods

The retrospective study was conducted on 3,201 patients who underwent coronary angiography at the Department of Zhongnan Hospital of Wuhan University. The patients were divided into two groups: CHD group and non-CHD group. The CHD group was further classified into three subgroups (mild, moderate, severe) based on the tertiles of their Gensini score or SYNTAX score I. Various complex indexes of blood cell types and lipid levels were compared between the groups.

Results

It revealed a positive correlation between all complex indexes and the severity of coronary artery stenosis. The systemic inflammation-response index/high-density lipoprotein cholesterol count (SIRI/HDL) exhibited the strongest correlation with both severity scores (Gensini score: r = 0.257, P < 0.001; SYNTAX score I: r = 0.171, P < 0.001). The monocyte to high-density lipoprotein cholesterol ratio (MHR) was identified as a stronger independent risk factor for CHD. However, SIRI/HDL had higher diagnostic efficacy for CHD (sensitivity 66.7%, specificity 60.4%, area under curve 0.680, 95% CI: 0.658-0.701). Notably, the pan-immune-inflammation value multiplied by low-density lipoprotein cholesterol count (PIV × LDL) exhibited the highest sensitivity of 85.2%.

Conclusion

All complex indexes which we investigated exhibited positive correlations with the severity of coronary artery stenosis. SIRI/HDL demonstrated higher diagnostic efficiency for CHD and a significant correlation with the severity of coronary artery stenosis.

The Role of Pan-Immune-Inflammation Value in Predicting Contrast-Induced Nephropathy Development in Patients Undergoing Percutaneous Coronary Intervention Due to NSTEMI

Contrast-induced nephropathy (CIN), which can develop after procedures involving contrast agents, is a significant cause of patient morbidity and mortality. This study aims to investigate the role of pre-procedural pan-immune-inflammation value (PIV) in predicting CIN development in patients undergoing percutaneous coronary intervention (PCI) due to non-ST segment elevation myocardial infarction (NSTEMI). A total of 1006 NSTEMI patients were included in the study. CIN was defined as an increase of at least 0.5 mg/dl or 25% in serum baseline creatinine level 72 h after the procedure. Patients were divided into two groups: those with and without CIN. NSTEMI patients who developed CIN, glucose level (P = .01), platelet count (P < .01), monocyte count (P < .001), neutrophil-to-lymphocyte ratio (NLR) (P < .001), systemic immune inflammation index (SII) score (P < .001), and PIV (P < .001) were higher compared with those without CIN. In the multivariate analysis of all these parameters, the Odds ratios of PIV and SII were similar and slightly lower than NLR. Receiver operating characteristic curve analysis (ROC) showed a PIV cut-off value of 448.43 with a sensitivity of 83.1% and a specificity of 72.8% in patients with CIN. Our study demonstrated an independent relationship between PIV at admission and CIN development in NSTEMI patients.

The prognostic impact of inflammation in patients with decompensated acute heart failure, as assessed using the pan-immune inflammation value (PIV)

There is increasing evidence that composite scores based on blood counts, which are reflectors of uncontrolled inflammation in the development and progression of heart failure, can be used as prognostic biomarkers in heart failure patients. The prognostic effects of pan-immune inflammation (PIV) as an independent predictor of in-hospital mortality in patients with acute heart failure (AHF) were evaluated based on this evidence. The data of 640 consecutive patients hospitalized for New York Heart Association (NYHA) class 2-3-4 AHF with reduced ejection fraction were analyzed and 565 patients were included after exclusion. The primary outcome was in hospital all-cause death. Secondary outcomes were defined as the following in-hospital events: Acute kidney injury (AKI), malignant arrhythmias, acute renal failure (ARF) and stroke. The PIV was computed using hemogram parameters such as lymphocytes, neutrophils, monocytes and platelets. Patients were categorized as low or high PIV group according to the median value, which was 382.8. A total of 81 (14.3%) in-hospital deaths, 31 (5.4%) AKI, 34 (6%) malignant arrhythmias, 60 (10.6%) ARF and 11 (2%) strokes were reported. Patients with high PIV had a higher in-hospital mortality rate than patients with low PIV (OR: 1.51, 95% CI, 1.26-1.80, p < 0.001). Incorporating PIV into the full model significantly improved model performance (odds ratio X2, p < 0.001) compared to the baseline model constructed with other inflammatory markers. PIV is a potent predictor of prognosis with better performance than other well-known inflammatory markers for patients with AHF.

Inflammatory Index as a Predictor of Mortality in Elderly Patients With Intracapsular Femoral Neck Fracture

A femoral neck fracture is a major cause of mortality in the elderly population, and intracapsular femoral neck fractures (ICFNFs) are commonly treated with hemiarthroplasty. The 30-day mortality rate for elderly hip fracture patients ranges from 1.0% to 6.5%, and one-year mortality increases significantly to 37.3%. Identifying predictors of mortality in these patients is crucial for better management. Inflammatory indices, such as neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and monocyte-to-lymphocyte ratio (MLR), have gained popularity for assessing mortality risk in various diseases. Several studies have demonstrated the value of these indices in predicting mortality after hip fracture. The pan-inflammatory immune value (PIV), which combines hematological parameters, has been shown to predict mortality in cancer patients. However, its role in predicting mortality in ICFNF patients treated with hemiarthroplasty has yet to be explored. This study aimed to assess the association of PIV, SII, NLR, and MLR with 30-day and one-year mortality in ICFNF patients. We also investigated the impact of surgical delay time (≤24h, 24-48h, ≥48h) on these inflammatory indices and mortality. Data from 522 patients with ICFNF treated with hemiarthroplasty were retrospectively collected. We observed 30-day and one-year mortality rates of 5.24% and 21.2%, respectively. Age, gender, and American Society of Anesthesiologists (ASA) score were identified as significant predictors of mortality. Preoperative PIV, SII, NLR, and MLR were significant predictors in the evaluation of early mortality. However, postoperatively, only NLR on the third day (NLR3rd) demonstrated statistical significance. Stepwise logistic regression further confirmed NLR3rd as the most effective predictor for early mortality. For mortality occurring between 30 to 365 days, NLR3rd remained statistically significant, albeit with diminished sensitivity. No other inflammatory index demonstrated significant predictive power for mortality during this later period. Our findings suggest different inflammatory indices may have varying predictive abilities depending on the mortality period. We recommend considering NLR3rd as a valuable and reliable predictor for early mortality in ICFNF patients treated with hemiarthroplasty. Respiratory system disease and preoperative chronic obstructive pulmonary disease (COPD) were identified as risk factors for mortality in our study, in line with previous research. In conclusion, our study highlights the potential of specific inflammatory indexes, particularly NLR3rd, in predicting mortality in elderly patients with ICFNFs treated with hemiarthroplasty. Further research is needed to validate these findings and optimize risk assessment in orthopedic practice.

Incorporating inflammatory biomarkers into a prognostic risk score in patients with non-ischemic heart failure: a machine learning approach

Objectives

Inflammation is involved in the mechanisms of non-ischemic heart failure (NIHF). We aimed to investigate the prognostic value of 21 inflammatory biomarkers and construct a biomarker risk score to improve risk prediction for patients with NIHF.

Methods

Patients diagnosed with NIHF without infection during hospitalization were included. The primary outcome was defined as all-cause mortality and heart transplantations. We used elastic net Cox regression with cross-validation to select inflammatory biomarkers and construct the best biomarker risk score model. Discrimination, calibration, and reclassification were evaluated to assess the predictive value of the biomarker risk score.

Results

Of 1,250 patients included (median age, 53 years, 31.9% women), 436 patients (34.9%) experienced the primary outcome during a median of 2.8 years of follow-up. The final biomarker risk score included high-sensitivity C-reactive protein-to-albumin ratio (CAR) and red blood cell distribution width-standard deviation (RDW-SD), both of which were 100% selected in 1,000 times cross-validation folds. Incorporating the biomarker risk score into the best basic model improved the discrimination (ΔC-index = 0.012, 95% CI 0.003-0.018) and reclassification (IDI, 2.3%, 95% CI 0.7%-4.9%; NRI, 17.3% 95% CI 6.4%-32.3%) in risk identification. In the cross-validation sets, the mean time-dependent AUC ranged from 0.670 to 0.724 for the biomarker risk score and 0.705 to 0.804 for the basic model with a biomarker risk score, from 1 to 8 years. In multivariable Cox regression, the biomarker risk score was independently associated with the outcome in patients with NIHF (HR 1.76, 95% CI 1.49-2.08, p < 0.001, per 1 score increase).

Conclusions

An inflammatory biomarker-derived risk score significantly improved prognosis prediction and risk stratification, providing potential individualized therapeutic targets for NIHF patients.

Association of Systemic Inflammatory Response Index and Pan-Immune-Inflammation-Value with Long-Term Adverse Cardiovascular Events in ST-Segment Elevation Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention

Aim

Reducing the high morbidity and mortality of ST-segment elevation myocardial infarction (STEMI) and improving patient prognosis remains a major global challenge. This study aimed to explore whether dynamic fluctuations in biomarkers are valuable predictors of prognosis in patients with STEMI.

Methods

This study included 216 patients with STEMI. Blood routine tests were performed on admission, 12 h after percutaneous coronary intervention (PCI), and at discharge. Systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation-value (PIV) serum immune-inflammatory markers were calculated. The Cox proportional hazard model was used to assess the factors independently associated with the prognosis of STEMI. The optimal cutoff values for the inflammatory markers were calculated.

Results

Eighty-five (39.35%) of the 216 patients had major adverse cardiovascular events (MACEs) during the 1-year follow-up. Most were male (81.18%) with a median age of 64 years (interquartile, 55-69.5). Killip class ≥ II on admission (hazard ratio [HR], 1.859; 95% CI, 1.169-2.957; P = 0.009), total stent length (HR, 1.016; 95% CI, 1.003-1.029; P = 0.019), values of SIRI at 12 h after PCI (HR, 1.079; 95% CI, 1.050-1.108; P < 0.001), and the Gensini score (HR, 1.014; 95% CI, 1.007-1.022; P < 0.001) were independently associated with an increased risk of MACEs. Compared with SII, SIRI and PIV calculated at various time points and dynamically fluctuating changes, SIRI (cutoff value, 4.15; 95% CI, 0.701-0.819; P < 0.001) and PIV (cutoff value, 622.71; 95% CI, 0.674-0.796; P < 0.001) at 12 h after PCI showed the best efficacy for the prognosis of STEMI.

Conclusion

Our study provides relevant evidence to the notion that SIRI or PIV at 12 h after PCI may be more accurate and economical predictors of long-term adverse prognosis in patients with STEMI.

The pan-immune-inflammation value is associated with clinical outcomes in patients with advanced TNBC treated with first-line, platinum-based chemotherapy: an institutional retrospective analysis

Background

Advanced triple-negative breast cancer (aTNBC) has a poor prognosis; thus, there is a need to identify novel biomarkers to guide future research and improve clinical outcomes.

Objectives

We tested the prognostic ability of an emerging, complete blood count (CBC)-based inflammatory biomarker, the pan-immune-inflammation value (PIV), in patients with aTNBC treated with first-line, platinum-based chemotherapy.

Design

This was a retrospective, monocentric, observational study.

Methods

We included consecutive aTNBC patients treated with platinum-based, first-line chemotherapy at our Institution, and for whom baseline (C1) CBC data were available. We collected CBC data early on-treatment, when available. PIV was calculated as: (neutrophil count × platelet count × monocyte count)/lymphocyte count. Patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative advanced breast cancer (aBC) were included in a control, non-TNBC cohort.

Results

A total of 78 aTNBC patients were included. When evaluated as a continuous variable, PIV-C1 was associated with worse overall survival (OS; p < 0.001) and progression-free survival (PFS; p < 0.001). On the other hand, when PIV-C1 was assessed on the basis of its quantile distribution, patients with 'high PIV-C1' experienced worse OS [adjusted hazard ratio (HR): 4.46, 95% confidence interval (CI): 2.22-8.99; adjusted p < 0.001] and PFS (adjusted HR: 2.03, 95% CI: 1.08-3.80; adjusted p = 0.027) when compared to patients with 'low PIV-C1'. Higher PIV-C1 was also associated with primary resistance to chemotherapy. Similarly, a higher PIV calculated from CBC at C2D1 (PIV-C2) was associated with worse survival outcomes. We also created a PIV-based score combining information about both PIV-C1 and PIV-C2 and allowing the stratification of patients at low, intermediate, and high risk of death. No association was observed between PIV-C1 and clinical outcomes of HR+/HER2- aBC patients.

Conclusion

PIV has a promising prognostic discrimination ability in aTNBC patients treated with first-line, platinum-based chemotherapy. Both baseline and early on-treatment PIV are associated with clinical outcomes and may be exploited for creating PIV-based risk classifiers if further validated.

The relationship between the pan-immune-inflammation value and long-term prognoses in patients with hypertension: National Health and Nutrition Examination Study, 1999-2018

Background

Direct antihypertensive therapy in hypertensive patients with a high CVD risk can reduce the incidence of cardiovascular death but increase adverse cardiovascular events, so additional ways to identify hypertensive patients at high risk may be needed. Studies have shown that immunity and inflammation affect the prognoses of patients with hypertension and that the pan-immune-inflammation value (PIV) is an index to assess immunity and inflammation, but few studies have applied the PIV index to patients with hypertension.

Objective

To explore the relationship between the PIV and long-term all-cause and cardiovascular mortality in patients with hypertension.

Method

Data from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 with a mortality follow-up through December 31, 2019, were analyzed. A total of 26,781 participants were evaluated. The patients were grouped based on PIV levels as follows: T1 group (n = 8,938), T2 group (n = 8,893), and T3 group (n = 8,950). The relationship between the PIV and long-term all-cause and cardiovascular death was assessed by survival curves and Cox regression analysis based on the NHANES recommended weights.

Result

The PIV was significantly associated with long-term all-cause and cardiovascular mortality in patients with hypertension. After full adjustment, patients with higher PIV have a higher risk of all-cause [Group 3: HR: 1.37, 95% CI: 1.20-1.55, p < 0.001] and cardiovascular [Group 3: HR: 1.62, 95% CI: 1.22-2.15, p < 0.001] mortality.

Conclusion

Elevated PIV was associated with increased all-cause mortality and cardiovascular mortality in hypertensive patients.