Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis

Association of healthy lifestyles with risk of all-cause and cause-specific mortality among individuals with metabolic dysfunction-associated steatotic liver disease: results from the DFTJ cohort

AIM

To examine the associations of healthy lifestyles with risk of all-cause and cause-specific mortality among adults with metabolic dysfunction-associated steatotic liver disease (MASLD), and whether the association was mediated by systemic immune-inflammatory biomarkers (SIIBs).

METHODS

The study included 10,347 subjects with MASLD, who were enrolled in the Dongfeng-Tongji cohort study. The healthy lifestyles referred to non-smoking, being physically active (≥7.5 metabolic equivalents-hours/week), low-risk alcohol consumption (1-14 g/day for women and 1-28 g/day for men), and optimal sleep duration (≥6 to ≤8 h/day). Cox proportional hazard models were used to examine the relationship between each lifestyle and SIIBs with the risk of all-cause and cause-specific mortality. A mediation analysis was conducted to investigate the role of SIIBs on the association between healthy lifestyles and mortality.

RESULTS

There were 418 MASLD subjects dead till the follow-up of 2018, including 259 deaths from cardiovascular disease (CVD). Compared to MASLD participants with 0-1 healthy lifestyle score (HLS), those with 3-4 HLS had the lowest risk of all-cause mortality [hazard ratio (HR), 0.46; 95% CI, (0.36-0.60)], and CVD mortality [HR (95%CI), 0.41 (0.29-0.58)]. Mediation analyses indicated that SIIBs mediated the association between healthy lifestyles and mortality, with proportions ranging from 2.5% to 6.1%.

CONCLUSIONS

These findings suggest that adherence to healthy lifestyles can significantly reduce mortality for MASLD patients, and the decreased SIIBs may partially explain the protection mechanism of healthy lifestyles.

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.

Aggregate index of systemic inflammation: A novel systemic inflammatory index for prediction of neonatal outcomes and chorioamnionitis in women with preterm premature rupture of membranes

OBJECTIVE

To determine the value of the Aggregate index of systemic inflammation (AISI) in predicting admission to neonatal intensive care unit (NICU) and chorioamnionitis.

METHODS

The present retrospective cohort study with pregnant women who were diagnosed with preterm premature rupture of membranes (PPROM) in the Department of Perinatology, Ministry of Health Ankara City Hospital between January 1, 2021, and June 1, 2023 (n = 357). The patients were categorized into subgroups: (1) cases with (n = 27) or without (n = 330) chorioamnionitis, (2) admission (n = 182) or no admission (n = 175) to NICU; (3) gestational age at birth <28 weeks or 28 weeks or longer; and (4) gestational age at birth <34 weeks or 34 weeks or longer. AISI values were compared between the subgroups, and cut-off values for AISI were determined to predict adverse outcomes.

RESULTS

AISI values were significantly higher in the admission to NICU group compared with the no admission to NICU group (707.0 vs 551.2) (P < 0.05). AISI values were also significantly higher in the chorioamnionitis group compared with those without chorioamnionitis (850.3 vs 609.4) (P < 0.05). AISI levels were significantly higher in cases delivered before 28 weeks of gestation compared with the cases delivered at 28 weeks of gestation or later (945.6 vs 604.9) (P < 0.05), and were also significantly higher in cases delivered before 34 weeks of gestation compared with the cases delivered at 34 weeks of gestation or later (715.5 vs 550.1) (P < 0.05). Optimal cut-off values of AISI were found to be 626.19 (74.1% sensitivity, 52.8% specificity), 506.09 (68.9% sensitivity and, 47.7% specificity), and 555.1 (69.8% sensitivity, 48.1% specificity) in predicting NICU admission, chorioamnionitis, and delivery before 28 weeks, respectively.

CONCLUSION

The novel inflammatory marker AISI may be used in the prediction of chorioamnionitis and NICU admission in PPROM cases.

SYNOPSIS

Aggregate index of systemic inflammation may be used as a novel marker in predicting high-risk for chorioamnionitis and neonatal intensive care unit admission in women with preterm premature rupture of membranes.

Machine Learning-Based Prediction of In-Stent Restenosis Risk Using Systemic Inflammation Aggregation Index Following Coronary Stent Placement

Introduction

Coronary artery disease (CAD) remains a significant global health challenge, with percutaneous coronary intervention (PCI) being a primary revascularization method. In-stent restenosis (ISR) post-PCI, although reduced, continues to impact patient outcomes. Inflammation and platelet activation play key roles in ISR development, emphasizing the need for accurate risk assessment tools. The systemic inflammation aggregation index (AISI) has shown promise in predicting adverse outcomes in various conditions but has not been studied in relation to ISR.

Methods

A retrospective observational study included 1712 patients post-drug-eluting stent (DES) implantation. Data collected encompassed demographics, medical history, medication use, laboratory parameters, and angiographic details. AISI, calculated from specific blood cell counts, was evaluated alongside other variables using machine learning models, including random forest, Xgboost, elastic networks, logistic regression, and multilayer perceptron. The optimal model was selected based on performance metrics and further interpreted using variable importance analysis and the SHAP method.

Results

Our study revealed that ISR occurred in 25.8% of patients, with a range of demographic and clinical factors influencing the risk of its development. The random forest model emerged as the most adept in predicting ISR, and AISI featured prominently among the top variables affecting ISR prediction. Notably, higher AISI values were positively correlated with an elevated probability of ISR occurrence. Comparative evaluation and visual analysis of model performance, the random forest model demonstrates high reliability in predicting ISR, with specific metrics including an AUC of 0.9569, accuracy of 0.911, sensitivity of 0.855, PPV of 0.81, and NPV of 0.948.

Conclusion

AISI demonstrated itself as a significant independent risk factor for ISR following DES implantation, with an escalation in AISI levels indicating a heightened risk of ISR occurrence.

Inflammatory Profile Assessment in a Highly Selected Athyreotic Population Undergoing Controlled and Standardized Hypothyroidism

Background: Hypothyroidism (hT) presents heterogeneous symptoms and findings. Evidence on this topic comes mainly from heterogeneous populations in terms of disease duration, residual thyroid function, and comorbidities. Therefore, it would be useful to assess systemic inflammation in a homogeneous hT population. The aim of this study was to investigate inflammation in a population that underwent standardized controlled hT. Methods: We recruited thyroidectomized patients diagnosed with thyroid cancer who were otherwise fit and healthy, showing hypothyroidism before I131 treatment using a standard protocol of LT4 withdrawal. The blood inflammatory indexes (BIIXs) (i.e., NLR, PLR, MLR, SII, SIRI, and AISI) were calculated using the blood tests collected just before I131 administration. Patients were divided according to sex, BMI, and thyroglobulin. The relationships between the BIIXs, age, and thyroid hormones were also investigated. Results: We included 143 patients. The median age of the sample was 43 years. The BIIX median values showed significant differences based on sex, BMI, and thyroglobulin levels (p < 0.05). No significant correlations were found between the BIIXs and age, TSH, FT4, and FT3. Conclusions: This study shows the BIIX median values of a population which underwent standardized hT. It suggests a role for some BIIXs in the evaluation of hypothyroidism in obese people and as hypothetical prognostic markers for thyroid cancer.