An Easy-to-Use Nomogram Based on SII and SIRI to Predict in-Hospital Mortality Risk in Elderly Patients with Acute Myocardial Infarction

Predicting In-Hospital Mortality in Myocardial Infarction: A Nomogram-Based Retrospective Analysis of the MIMIC-IV Database

Background

Despite significant advancements in early reperfusion therapy and pharmacological treatment, which have reduced mortality rates after myocardial infarction in recent decades, the in-hospital mortality rate remains high due to factors such as rapid disease progression, comorbid conditions, and potential complications. We aimed to develop and validate a predictive model for in-hospital mortality in myocardial infarction patients.

Methods

LASSO regression analysis, univariate analysis, and multivariate logistic analysis were used to construct the nomogram in the training set, followed by model comparison, internal validation, and sensitivity analysis.

Results

The analysis comprised 4688 patients in total. The population of patients was randomly assigned to the training set (n = 3512) and validation set (n = 1176). According to the results of LASSO regression analysis and other results, our nomogram contained a total of 10 independent variables related to patient death, including age, respiratory rate, blood glucose, lactate, PTT, BUN, cerebrovascular disease, chronic lung disease, mild liver disease, and metastatic solid cancer. Moreover, the web calculator and nomogram performed exceptionally well at predicting in-hospital death in myocardial infarction patients. The AUC for the training and validation sets' respective prediction models was 0.869 (95% CI: 0.849-0.889) and 0.846 (95% CI: 0.807-0.875) (p<0.01). Compared to the Sequential Organ Failure Assessment (SOFA), the nomogram showed greater discrimination in the training and validation sets, and the calibration plots demonstrated an adequate fit for the nomogram in predicting the risk of in-hospital mortality in both groups. The decision curve analysis (DCA) of the nomogram demonstrated a higher net benefit in the training and validation sets and in terms of clinical usefulness than the SOFA.

Conclusion

We developed a useful nomogram model and developed a nomogram-based web calculator to predict in-hospital mortality in myocardial infarction patients, which will support doctors in patient counseling and logical diagnosis and therapy.

Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis

BACKGROUND

Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, the predictive accuracy of these models remains controversial.

OBJECTIVE

This study aimed to review the accuracy of ML in predicting in-hospital mortality risk in MI patients and to provide evidence-based advices for the development or updating of clinical tools.

METHODS

PubMed, Embase, Cochrane, and Web of Science databases were searched, up to June 4, 2024. PROBAST and ChAMAI checklist are utilized to assess the risk of bias in the included studies. Since the included studies constructed models based on severely unbalanced datasets, subgroup analyses were conducted by the type of dataset (balanced data, unbalanced data, model type).

RESULTS

This meta-analysis included 32 studies. In the validation set, the pooled C-index, sensitivity, and specificity of prediction models based on balanced data were 0.83 (95 % CI: 0.795-0.866), 0.81 (95 % CI: 0.79-0.84), and 0.82 (95 % CI: 0.78-0.86), respectively. In the validation set, the pooled C-index, sensitivity, and specificity of ML models based on imbalanced data were 0.815 (95 % CI: 0.789-0.842), 0.66 (95 % CI: 0.60-0.72), and 0.84 (95 % CI: 0.83-0.85), respectively.

CONCLUSIONS

ML models such as LR, SVM, and RF exhibit high sensitivity and specificity in predicting in-hospital mortality in MI patients. However, their sensitivity is not superior to well-established scoring tools. Mitigating the impact of imbalanced data on ML models remains challenging.

Development of peripheral biomarker-based prognostic nomograms for short-term and long-term survival in immune checkpoint inhibitor-associated myocarditis

Background

Immune checkpoint inhibitor-associated myocarditis (ICI myocarditis) is a rare but highly fatal immune-related adverse reaction. This study aimed to develop nomogram prognostic models for both short-term and long-term survival outcomes in patients with ICI myocarditis based on key biomarkers in peripheral blood.

Methods

In this single-center retrospective study, we included 90 patients with ICI myocarditis at the Fourth Hospital of Hebei Medical University. Critical peripheral biomarkers associated with 40-day and 1-year overall survival (OS) were identified. Two prognostic models were developed and evaluation of the models were performed with receiver operating characteristic (ROC) curves, C-index, calibration curves, and decision curve analysis (DCA).

Results

A total of 24 patients (26.7%) succumbed within 40 days, while 40 patients (44.4%) died within one year. Cardiac troponin-I (cTnI), N-terminal pro-brain natriuretic peptide (NTBNP) and lactic dehydrogenase-to-albumin ratio (LAR) were identified as critical prognostic factors for 40-day OS in patients with ICI myocarditis and utilized to develop a nomogram model. The model demonstrates an area under the curve (AUC) of 0.867 [95% confidence interval (CI): 0.774-0.960] and a C-index of 0.824. Another predictive model for the 1-year OS was developed based on cTnI, NTBNP, LAR and systemic inflammatory response index (SIRI) with an AUC of 0.765 (95% CI: 0.664-0.866) and a C index of 0.742. The calibration curve demonstrates that both models exhibit strong consistency. The results of the DCA further indicate that both nomograms possess substantial clinical utility.

Conclusions

These two prediction models will enable clinicians to more effectively utilize readily available peripheral blood biomarkers for the convenient and efficient identification of high-risk patients with poor prognoses, thereby facilitating early intervention.

Association Between Systemic Immune-Inflammation Index and Outcomes of Acute Myocardial Infarction: A Systemic Review and Meta-Analysis

Objective: To assess the link between systemic immune-inflammation index (SII) and risk of major adverse cardiovascular events (MACE), contrast-induced nephropathy (CIN), and overall mortality in patients with acute myocardial infarction (AMI). Patients and Methods: Electronic search of PubMed, EMBASE, Web of Science, and Scopus databases was done for observational studies with the data on the association of SII and outcomes, such as MACE, and CIN in adult (≥18 y) patients with AMI. A random-effects model was used, and the pooled effect sizes were expressed as relative risk (RR) with corresponding 95% confidence intervals (CI). Subgroup analysis was conducted on the basis of the type of AMI (ST elevation myocardial infarction and non-ST elevation myocardial infarction), sample size (≥500 and <500), and study design. GRADE assessment was used to evaluate the certainty of the evidence. Results: The analysis included 23 studies. Most studies were conducted in China (n = 13), followed by Turkey (n = 10). Majority of the studies (n = 20) had a retrospective cohort design. Patients with high SII had increased risk of MACE (RR 2.95, 95% CI: 1.25, 6.99; n = 5, I2 = 97.5%), overall mortality (RR 2.59, 95% CI: 1.64, 4.07; n = 6, I2 = 58.0%), and CIN (RR 4.58, 95% CI: 3.44, 6.10; n = 4, I2 = 0.0%), compared with patients with lower SII. Egger's test detected publication bias for MACE (p = 0.047) and overall mortality (p = 0.012) but not for CIN. These associations remained valid in subgroup analysis. Conclusion: Findings suggest that higher SII in patients with AMI is associated with increased risks of MACE, CIN, and overall mortality. This underscores SII's potential as a prognostic marker in AMI.

The Complex Inflammatory and Nutritional Indices to Predict Prognostic Risk for Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

PURPOSE

To investigate the role of the systemic inflammatory response index (SIRI) and high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels in predicting the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI).

PATIENTS AND METHODS

Overall, 1377 patients with ACS who underwent PCI between January 2016 and December 2018 were consecutively enrolled. The patients were divided into MACEs (n = 60) and non-MACEs (n = 1317) groups. The study endpoints were MACEs, including cardiac-related mortality and rehospitalization for severe heart failure (HF), myocardial infarction (MI), and in-stent restenosis.

RESULTS

Both groups showed significant differences in the patients with age > 65 years, history of HF, acute MI, cardiogenic shock, left ventricular ejection fraction < 40%, SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609. The Kaplan-Meier curve showed that the low SIRI group had higher cumulative survival than the high SIRI group. Additionally, the univariate and multivariate Cox proportional hazards model demonstrated that SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609 were independent risk factors for patients with ACS undergoing PCI. Restricted cubic spline models were generated to visualize the relationship between SIRI, SIRI/HDL-C, and SIRI × LDL-C and the prognostic risk.

CONCLUSION

SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609 were all independent prognostic risk factors in patients with ACS undergoing PCI, which may be useful markers for assessment for long prognosis.

Association of the systemic immune-inflammation index with clinical outcomes in acute myocardial infarction patients with hypertension

BACKGROUND

A new indicator of immunological and inflammatory condition, the Systemic Immunoinflammatory Index (SII), has been linked to a bad prognosis in a number of disorders.

METHODS

Two thousand three hundred seventeen ICU patients were admitted with hypertension and acute myocardial infarction (AMI). Patients were grouped according to their baseline SII tertile number into Q1, Q2, and Q3 groups. The main outcomes were death from all causes at 30 days, 365 days, cardiogenic shock, and congestive heart failure.

RESULTS

The case fatality rate increases with increasing SII. The correlation between SII and 30-day all-cause mortality [hazard ratio (HR) 1.765, 95% confidence interval (CI) 1.330-2.343 (Q3 versus Q1 group)], 365-day all-cause mortality [HR 2.713, 95% CI 2.250-3.272 (Q3 versus Q1 group), HR 1.603, 95% CI 1.312-1.959 (Q3 vs. Q1 group)], congestive heart failure [odds ratio (OR) 1.255, 95% CI 1.006-1.565 (Q2 vs. Q1 group), OR 1.565, 95% CI 1.220-2.009 (Q3 vs. Q1 group)] and cardiogenic shock [OR 1.930. 95% CI 1.271-2.974 (Q2 vs. Q1 group)] were all validated. According to subgroup analysis, individuals who had chosen to have CABG surgery had a stronger correlation between SII and a worse outcome. According to Kaplan-Meier (K-M) survival curves, patients in the Q3 group with SII had the highest rates of morbidity and death. The RCS curves demonstrated an essentially linear connection between SII and 30 days, 365 days, and congestive heart failure even after controlling for covariates.

CONCLUSIONS

SII was substantially correlated with 30-day all-cause mortality, 365-day all-cause mortality, in-hospital congestive heart failure, and cardiogenic shock in patients who had both hypertension and acute myocardial infarction. In individuals with acute myocardial infarction and hypertension, a greater SII would be regarded as an independent risk factor for a higher death rate.

Associations of Inflammatory Markers With Neurological Dysfunction and Prognosis in Patients With Progressive Stroke

OBJECTIVE

This study aimed to explore the associations between inflammatory markers and the severity of early neurological dysfunction and prognosis in patients with progressive stroke (PS) and evaluated the predictive value of inflammatory markers for PS.

METHODS

Among 711 acute ischemic stroke (AIS) patients, 210 patients with PS and 501 patients without PS were included. Six inflammatory markers, including neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV), were measured and compared between two groups. Correlation analysis was used to analyze the correlation between inflammatory markers and early neurological dysfunction in patients with PS. Univariate and multivariate regression analyses were applied to screen the factors for the prognosis of PS patients. The receiver operating characteristic (ROC) curve was utilized to evaluate the predictive value for the prognosis of PS patients.

RESULTS

Elevated levels of NLR, LMR, SII, and PIV were observed in PS patients. Correlation analysis revealed positive correlations between NLR, PLR, SII, SIRI, PIV, and early neurological deficits, while LMR showed a negative correlation in PS patients. Multivariate analysis identified LMR and the National Institutes of Health Stroke Score (NIHSS) as independent risk factors for poor outcome of PS patients. The predictive value of LMR alone was limited (AUC = 0.59), but combining it with NIHSS improved predictive accuracy (AUC = 0.73) (p < 0.05).

CONCLUSION

These findings suggest that inflammatory markers, particularly LMR, should be considered in PS management, and their combination with NIHSS enhances outcome prediction.

Prognostic value of inflammatory markers for all-cause mortality in patients with acute myocardial infarction in the coronary care unit: a retrospective study based on MIMIC-IV database

Background

Acute myocardial infarction (AMI) is prevalent and perilous, leading to mortality and disability in the coronary care unit (CCU). This paper was to verify the correlation of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR), and systemic inflammation response index (SIRI) with all-cause mortality for AMI patients in the CCU.

Methods

Adult patients diagnosed with AMI and admitted to CCU were selected from the MIMIC-IV database. Various clinical and laboratory data were extracted. Logistic regression models were employed to determine the correlation between NLR and in-hospital mortality, 30-day mortality, and 90-day mortality. Confounding factors were adjusted to validate the result robustness. Restricted cubic spline (RCS) curves were adopted to analyze the potential correlation between NLR and all-cause mortality. Meanwhile, the area under the receiver operating characteristic (ROC) curve (AUC) was utilized to compare the prediction ability of NLR, SII, PLR, and SIRI in all-cause mortality. Subsequently, subgroup analyses of gender and comorbidities were performed.

Results

1,386 AMI patients in the CCU were enrolled. The NLR was non-linearly and positively associated with in-hospital mortality [Q4: OR (95%CI) 2.61; (1.261-5.626), p = 0.012], 30-day mortality [Q4: OR (95%CI) 2.005; (1.048-3.925); p = 0.038], 90-day mortality [Q4: OR (95%CI) 2.191; (1.235-3.948); p = 0.008] with Q1 as the reference group. The NLR had the highest AUC for in-hospital mortality, 30-day mortality, and 90-day mortality among four inflammatory markers (NLR, SII, PLR, SIRI). Stratified analyses based on gender and comorbidities showed that the risk of death was significantly increased in male and female patients, with or without diabetes, without cerebral infarction, chronic obstructive pulmonary disease, liver disease, and renal disease in the Q4 group when compared to the Q1 group.

Conclusions

NLR is nonlinearly and positively associated with all-cause mortality of AMI patients in the CCU. The predictive ability of NLR in in-hospital mortality, 30-day mortality, and 90-day mortality is superior to that of SII, PLR, and SIRI.

Prognostic implications of system inflammation response index in atrial fibrillation patients with type 2 diabetes mellitus

Systemic inflammation plays a crucial role in the pathogenesis and prognosis of diabetes and cardiovascular diseases. System inflammation response index (SIRI), is an emerging biomarker designed to assess the extent of systemic inflammation. We aimed to delineate the prognostic significance of SIRI in patients with both AF and type 2 diabetes mellitus (T2DM). Utilizing the Medical Information Mart for Intensive Care IV (MIMIC-IV) (v2.2) repository, subjects divided into three groups based on the SIRI index. The primary endpoint of our study was all-cause mortality during hospitalization, with one-year mortality serving as the secondary endpoint. A cohort of 2054 AF and T2DM patients participated. COX regression analysis revealed elevated SIRI levels as an independent risk factor for both in-hospital and 1-year mortality. 192 patients died during hospitalization, and 265 died during the follow-up of 1 year. When treating the SIRI as a continuous variable, a higher SIRI was significantly associated with increased all-cause mortality both in-hospital [hazard ratio (HR) 1.015, 95%CI 1.010-1.020, P = 0.015] and 1-year (HR 1. 016, 95%CI 1.008-1.015, P = 0.012). Additionally, compared to patients with the lowest tertiles of SIRI, those with the highest tertiles of SIRI possessed significantly higher all-cause mortality both in-hospital and 1-year after multivariable adjustment, and this relationship remained pronounced in AF and T2DM patients [in-hospital mortality (HR: 1.863, 95% CI 1.189-2.918, P = 0.007); one-year mortality (HR: 2.143, 95% CI 1.621-2.831, P < 0.001)]. Our RCS analyses indicated a pronounced linear association between SIRI and mortality in T2DM (p-value for non-linear < 0.001). In AF patients with T2DM, high SIRI is an independent predictor of poor survival and may be helpful for patient's risk stratification.

The mediating role of insulin resistance in the association between inflammatory score and MAFLD: NHANES 2017-2018

BACKGROUND

The association between inflammatory score, insulin resistance (IR), and metabolic-associated fatty liver disease (MAFLD) is inconclusive.

OBJECTIVE

The objective of this study was to examine the relationship between the inflammatory score and MAFLD and investigate the potential mediating effect of IR (evaluated by triglyceride-glucose index) in this association.

METHODS

Calculating inflammatory score was performed based on white blood cells and high-sensitivity C-reactive protein. The association between the inflammatory score and MAFLD was evaluated based on the weighted multifactor logistic regression model. Restricted cubic splines (RCS) were used to visualize the dose-response relationship between the inflammatory score and MAFLD. We also conducted a mediation analysis to assess the extent to which IR mediates this association.

RESULTS

Among the 1090 participants, 563 were ultimately diagnosed with MAFLD. Multivariate logistic regression results indicated a close positive association between inflammatory score and MAFLD (odds ratio = 1.235, 95% confidence interval 1.069-1.427, p = .007). The RCS results indicated a linear dose-response relationship between the inflammatory score and the risk of MAFLD after adjusting for potential confounding factors. Furthermore, the mediation analysis results showed that IR partially mediated the association between the inflammatory score and MAFLD (percent mediation = 33%).

CONCLUSION

Our research results indicate that the inflammatory score is positively associated with the risk of MAFLD, and IR plays a partial mediating effect in this association.

The association between triglyceride-glucose index and its combination with systemic inflammation indicators and all-cause and cardiovascular mortality in the general US population: NHANES 1999-2018

BACKGROUND

The correlation between the triglyceride-glucose (TyG) index and mortality in the general population remains controversial, with inconsistent conclusions emerging from different studies.

OBJECTIVE

This study aims to investigate whether there is an association between the TyG index and mortality in the general population in the United States, and to explore whether a new index combining the TyG index with systemic inflammation indicators can better predict all-cause and cardiovascular mortality risks in the general population than using the TyG index alone.

METHODS

Calculate the systemic inflammation indicators and TyG index for each participant based on their complete blood count, as well as their triglyceride and glucose levels in a fasting state. TyG-inflammation indices were obtained by multiplying the TyG index with systemic inflammation indicators (TyG-NLR, TyG-MLR, TyG-lgPLR, TyG-lgSII, and TyG-SIRI). Based on the weighted Cox proportional hazards model, assess whether the TyG and TyG-Inflammation indices are associated with mortality risk in the general population. Restricted cubic splines (RCS) are used to clarify the dose-response relationship between the TyG and TyG-Inflammation indices and mortality, and to visualize the results. Time-dependent receiver operating characteristic (ROC) curves are used to evaluate the accuracy of the TyG and TyG-Inflammation indices in predicting adverse outcomes.

RESULTS

This study included 17,118 participants. Over a median follow-up period of 125 months, 2595 patients died. The TyG index was not found to be related to mortality after adjusting for potentially confounding factors. However, the TyG-inflammation indices in the highest quartile (Q4), except for TyG-lgPLR, were significantly associated with both all-cause and cardiovascular mortality, compared to those in the lowest quartile (Q1). Among them, TyG-MLR and TyG-lgSII showed the strongest correlations with all-cause mortality and cardiovascular mortality. Specifically, compared to their respective lowest quartiles (Q1), participants in the highest quartile (Q4) of TyG-MLR had a 48% increased risk of all-cause mortality (95% CI: 1.23-1.77, P for trend < 0.0001), while participants in the highest quartile (Q4) of TyG-lgSII had a 92% increased risk of cardiovascular mortality (95% CI: 1.31-2.81, P for trend < 0.001). Time-dependent ROC curve analysis showed that the TyG-MLR had the highest accuracy in predicting long-term mortality outcomes.

CONCLUSIONS

The TyG-Inflammation indices constructed based on TyG and systemic inflammation indicators are closely related to mortality in the general population and can better predict the risk of adverse outcomes. However, no association between TyG and mortality in the general population was found.

Screening significance of systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) in coronary heart disease of symptomatic youth

BACKGROUND

The incidence of coronary heart disease (CHD) in youth is rapidly increasing but difficultly recognized in the early stage.

METHODS AND RESULTS

In this retrospective study, 194 CHD patients under the age of 45 who previously experienced chest pain symptoms and 170 non-CHD patients were included and demographic data were collected. Systemic inflammation index (SII) and systemic inflammation response index (SIRI) were increased in young CHD patients (p < 001). Spearman's correlation analysis showed that both SII and SIRI were negatively correlated with HDL and positively correlated with hypertension, Gensini score, and hsTnI. Logistic regression analysis indicated that SII and SIRI were independently associated with the presence of CHD in youth with chest pain symptoms. The area under the ROC curve (AUC) of the SII model for young CHD patients was 0.805 (0.728-0.869), and the sensitivity and specificity were 0.65 and 0.823, respectively. Meanwhile, the AUC for the SIRI model was 0.812 (0.739-0.872), and the sensitivity and specificity were 0.673 and 0.8022. The calibration curves of both SII and SIRI models are in good agreement with the actual curves. And the decision curves of both models indicated their clinical practicality.

CONCLUSION

SII and SIRI are independent risk factors for CHD in young adults, which can quickly and effectively identify CHD patients among young adults who have previously experienced chest pain symptoms.

Albumin corrected anion gap for predicting in-hospital death among patients with acute myocardial infarction: A retrospective cohort study

OBJECTIVE

To explore the relationship between Anion Gap (AG), Albumin Corrected AG (ACAG), and in-hospital mortality of Acute Myocardial Infarction (AMI) patients and develop a prediction model for predicting the mortality in AMI patients.

METHODS

This was a retrospective cohort study based on the Medical Information Mart for Intensive Care (MIMIC)-Ⅲ, MIMIC-IV, and eICU Collaborative Study Database (eICU). A total of 9767 AMI patients who were admitted to the intensive care unit were included. The authors employed univariate and multivariable cox proportional hazards analyses to investigate the association between AG, ACAG, and in-hospital mortality; p < 0.05 was considered statistically significant. A nomogram incorporating ACAG and clinical indicators was developed and validated for predicting mortality among AMI patients.

RESULTS

Both ACAG and AG exhibited a significant association with an elevated risk of in-hospital mortality in AMI patients. The C-index of ACAG (C-index = 0.606) was significantly higher than AG (C-index = 0.589). A nomogram (ACAG combined model) was developed to predict the in-hospital mortality for AMI patients. The nomogram demonstrated a good predictive performance by Area Under the Curve (AUC) of 0.763 in the training set, 0.744 and 0.681 in the external validation cohort. The C-index of the nomogram was 0.759 in the training set, 0.756 and 0.762 in the validation cohorts. Additionally, the C-index of the nomogram was obviously higher than the ACAG and age shock index in three databases.

CONCLUSION

ACAG was related to in-hospital mortality among AMI patients. The authors developed a nomogram incorporating ACAG and clinical indicators, demonstrating good performance for predicting in-hospital mortality of AMI patients.

Association of inflammatory score with all-cause and cardiovascular mortality in patients with metabolic syndrome: NHANES longitudinal cohort study

Background

Inflammatory scores are known to reflect the systemic inflammatory burden. Despite this, the association between the inflammatory score and the risk of all-cause and cardiovascular mortality in patients with metabolic syndrome (MetS) remains poorly understood. To address this gap in the literature, this study investigated this potential association between these two factors.

Methods

A total of 3401 patients with MetS from the National Health and Nutrition Examination Survey (1999-2010) were enrolled. Survival status and cause of death were obtained by linking data from the National Death Index (NDI). The inflammatory score was calculated based on the sum of the Z-scores for white blood cell (WBC) count and C-reactive protein (CRP) at baseline. The patients were divided into inflammatory score quartiles. Cox proportional hazards regression was used to determine the association between inflammatory score and mortality. Restricted cubic splines (RCS) were used to explore the dose-response relationship between inflammatory score and mortality. Stratified analyses and interaction tests were conducted according to sex, age, body mass index (BMI), alcohol consumption, smoking status, hypertension, diabetes, and stroke status.

Results

After a mean follow-up of 145.9 months, 1039 all-cause deaths and 295 cardiovascular deaths were recorded. The results of multivariate Cox regression analysis showed that compared to the lowest quartile (Q1), patients in the highest quartile (Q4) had a 1.74-fold increased risk of all-cause mortality (Model 3: HR = 1.74, 95%CI 1.30-2.32, P < 0.001) and a 1.87-fold increased risk of cardiovascular mortality (Model 3: HR = 1.87, 95%CI 1.12-3.13, P = 0.020). There was a 'J'-shaped nonlinear relationship between the inflammatory score and all-cause mortality (P for nonlinearity = 0.001), and a marginally significant 'J'-shaped relationship with cardiovascular mortality (P for nonlinearity = 0.057). The threshold points of the inflammatory score for adverse outcomes were - 0.643 and - 0.621, respectively.

Conclusion

The inflammatory score is independently associated with increased all-cause and cardiovascular mortality in patients with MetS, and risk stratification of these patients using inflammatory scores may provide specific therapeutic strategies to improve their prognosis.

The mediation effect of Systemic Immunity Inflammation Index between urinary metals and TOFAT among adults in the NHANES dataset

Systemic Immune Inflammatory Index (SII) is a novel indicator of inflammation. However, no studies have reported the effect of SII on the association between metals and total fat (TOFAT). We aim to investigate the mediated effect of SII on the relationship between urinary metals and TOFAT in a US adult population. This cross-sectional study was conducted among adults with complete information on SII, urine metal concentrations, and TOFAT from the 2011-2018 National Health and Nutrition Examination Survey (NHANES). Multifactorial logistic regression and restricted cubic splines were used to explore the association between urine metal levels and TOFAT. Furthermore, serial mediation analyses were used to investigate the mediating effect of SII on metals and TOFAT. A total of 3324 subjects were included in this study. After adjusting for confounders, arsenic (As), cadmium (Cd), cobalt (Co), cesium (Cs), inorganic mercury (Hg), molybdenum (Mo), manganese (Mn), lead (Pb), antimony (Sb), and thallium(Tl) had negative decreased trends of odds ratios for TOFAT (all P for trend < 0.05). In the total population, we found that Cd, Co, and Tu were positively associated with SII (β = 29.70, 79.37, and 31.08), whereas As and Hg had a negative association with SII. The mediation analysis showed that SII mediated the association of Co with TOFAT, with the β of the mediating effect being 0.9% (95%CI: 0.3%, 1.6%). Our findings suggested that exposure to As, Cd, and Hg would directly decrease the level of TOFAT. However, Co would increase TOFAT, completely mediated by SII, mainly exerted in females rather than males.

Association of systemic inflammation response index with mortality risk in older patients with hip fracture: a 10-year retrospective cohort study

Objective

With a rapidly aging global population, the assessment of mortality risk following hip fracture in older adults has received increasing attention. Recently, the system inflammation response index (SIRI) has been identified as a novel prognostic marker to reflect both systemic inflammation and immune status. However, it is not yet known whether SIRI is a potential predictor of subsequent death in hip fracture patients. Therefore, this study aimed to investigate the association between SIRI and mortality in older patients with hip fracture.

Methods

A total of 1,206 older hip fracture patients undergoing surgery between January 2013 and December 2022 were consecutively derived from our longitudinal database. Patients were divided into three groups according to SIRI tertiles, calculated as neutrophil × monocyte / lymphocyte. Survival status was obtained from medical records or telephone interviews, and the study outcome was all-cause mortality after hip fracture at the longest follow-up. Multivariate Cox proportional hazard model and restricted cubic spline (RCS) regression model were used to evaluate the association between SIRI and mortality. Moreover, a series of sensitivity analyses were conducted to further validate the robustness of the association.

Results

During a median follow-up of 43.85 months, 337 patients (27.94%) died. After full adjustment, each unit increase in SIRI was significantly associated with a 2.2% increase in overall mortality (95% confidence interval [CI]: 1.001-1.042, p = 0.029). Similarly, compared with the first tertile of SIRI, the second and third tertile showed a 1.335-fold (95% CI: 1.011-1.762, p = 0.042) and 1.447-fold (95% CI, 1.093-1.917, p = 0.010) higher risk of death. Sensitivity analyses confirmed the stability of the association. Moreover, RCS analysis revealed a positive non-linear relationship between SIRI and mortality (P for nonlinearity = 0.021).

Conclusion

High SIRI level at admission was significantly and positively associated with an increased risk of death, suggesting that SIRI may be an independent predictor of mortality in older patients with hip fracture.

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.

Systemic Immune-Inflammation Index and Systemic Inflammatory Response Index as Predictors of Mortality in ST-Elevation Myocardial Infarction

(1) Background: The systemic inflammatory response index (SIRI; neutrophil count × monocyte/lymphocyte count), and the systemic immune-inflammation index (SII; platelet count × neutrophil count/lymphocyte count) are recently proposed biomarkers to assess the immune and inflammatory status. However, data on SIRI and SII are still relatively lacking and do not definitively and exhaustively define their role as predictors of an adverse prognosis in acute myocardial infarction (AMI). The aim of the present study was to evaluate SII and SIRI determinants as well as to assess SIRI and SII prognostic power in ST-elevation myocardial infarction (STEMI). (2) Methods: A total of 105 STEMI patients (74 males, 70 ± 11 years) were studied (median follow-up 54 ± 25 months, 24 deaths). (3) Results: The main determinants of SIRI and SII were creatinine and brain natriuretic peptide (BNP) (multivariate regression). Patients with higher SIRI (>75th percentile, 4.9) and SII (>75th percentile, 1257.5) had lower survival rates than those in the low SIRI/SII group (Kaplan-Meier analysis). Univariate Cox regression revealed that high SIRI and SII were associated with mortality (HR: 2.6, 95% CI: 1.1-5.8, p < 0.05; 2.2, 1-4.9, p ≤ 0.05, respectively); however, these associations lost their significance after multivariate adjustment. (4) Conclusions: SIRI and SII association with mortality was significantly affected by confounding factors in our population, especially creatinine and BNP, which are associated with both the inflammatory indices and the outcome.

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.