Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio are Important Indicators for Predicting in-Hospital Death in Elderly AMI Patients

Dynamic change of neutrophil-to-lymphocyte ratio and its predictive value of prognosis in acute ischemic stroke

OBJECTIVE

The present research aimed to explore the dynamic change of the neutrophil-to-lymphocyte ratio (NLR) and its relationship with functional outcome following an acute ischemic stroke (AIS), whether receiving intravenous thrombolysis (IVT) or not.

METHODS

We retrospectively analyzed data that were prospectively acquired from patients with AIS treated with IVT or not. For patients receiving IVT, the NLR was based on a blood test performed prior to IVT (d0) and at different time points after disease onset (d1, d3, d7). In addition, in the non-IVT group, the NLR was obtained at different time points after disease onset (d1, d3, d7). Follow-ups were performed 3 months after onset via telephone. In addition, a good outcome was defined as a modified Rankin scale (mRS) ≤1; a poor outcome means 2 ≤ mRS ≤ 6.

RESULTS

A total of 204 AIS patients were included in this study. The NLR presented a dynamic change as it increased to its peak at day 1 and gradually declined to its baseline at day 7, no matter whether patients were receiving IVT or not. Patients with poor outcomes have a higher NLR at various time points. The results of multivariate logistic regression analysis demonstrated that the National Institutes of Health Stroke Scale (NIHSS), NLR d1, NLR d3, and NLR d7 were independently associated with functional outcomes. The area under the receiver operating characteristic curve of NLR in predicting outcomes was as follows: NLR d3 demonstrated robust predictive power within the IVT therapy cohort, whereas NLR d7 was predictive in the non-IVT cohort. However, the most potent predictor emerged as the combination of NIHSS and NLR.

CONCLUSION

NLR has the potential to predicate diagnosis for AIS, especially when combined with the NIHSS score.

Effect of Elevated Neutrophil-to-Lymphocyte Ratio on Adverse Outcomes in Patients With Myocardial Infarction: A Systematic Review and Meta-Analysis

Myocardial infarction (MI), a leading cause of morbidity and mortality globally, is characterized by an underlying inflammatory process driven by atherosclerosis. The neutrophil-to-lymphocyte ratio (NLR), a readily available and cost-effective marker of systemic inflammation, has emerged as a potential predictor of adverse outcomes in patients with MI. This meta-analysis aimed to evaluate the association between elevated NLR and the risk of major adverse cardiovascular events (MACE) and all-cause mortality in patients with MI. A comprehensive literature search was conducted across multiple databases, including Embase, Web of Science, PubMed, and OVID Medicine, to identify relevant studies published from January 1, 2011, onward. Studies reporting the effect of NLR values on MACE and mortality in adult patients with MI, including both ST-elevation (STEMI) and non-ST-elevation (NSTEMI) subtypes, were included. Data extraction and quality assessment were performed independently by multiple authors. The meta-analysis included 37 studies, comprising a total of 18 studies evaluating the risk of MACE and 30 studies assessing all-cause mortality. The pooled analysis revealed a significantly increased risk of MACE (odds ratio [OR] 1.86, 95% confidence interval [CI] 1.53-2.28, P < 0.01) and all-cause mortality (OR 2.29, 95% CI 1.94-2.70, P < 0.01) in patients with elevated NLR compared to those without elevated NLR. Subgroup analyses stratified by follow-up duration and study design further supported the consistent association between elevated NLR and adverse outcomes. In conclusion, this meta-analysis demonstrates a significant association between elevated NLR and an increased risk of MACE and all-cause mortality in patients with MI. These findings highlight the potential clinical utility of NLR as a prognostic marker and underscore the importance of further research to validate its predictive value and establish optimal cutoff values for risk stratification in this patient population.

Investigating the interplay of smoking, cardiovascular risk factors, and overall cardiovascular disease risk: NHANES analysis 2011-2018

BACKGROUND

This study explores the intricate relationship between smoking, cardiovascular disease (CVD) risk factors and their combined impact on overall CVD risk, utilizing data from NHANES 2011-2018.

METHODS

Participants were categorized based on the presence of CVD, and we compared their demographic, social, and clinical characteristics. We utilized logistic regression models, receiver operating characteristics (ROC) analysis, and the chi-squared test to examine the associations between variables and CVD risk.

RESULTS

Significant differences in characteristics were observed between those with and without CVD. Serum cotinine levels exhibited a dose-dependent association with CVD risk. The highest quartile of cotinine levels corresponded to a 2.33-fold increase in risk. Smoking, especially in conjunction with lower HDL-c, significantly increases CVD risk. Combinations of smoking with hypertension, central obesity, diabetes, and elevated triglycerides also contributed to increased CVD risk. Waist-to-Height Ratio, Visceral Adiposity Index, A Body Shape Index, Conicity Index, Triglyceride-Glucose Index, Neutrophil, Mean platelet volume and Neutrophil to Lymphocyte ratio demonstrated significant associations with CVD risk, with varying levels of significance post-adjustment. When assessing the combined effect of smoking with multiple risk factors, a combination of smoking, central obesity, higher triglycerides, lower HDL-c, and hypertension presented the highest CVD risk, with an adjusted odds ratio of 14.18.

CONCLUSION

Smoking, when combined with central obesity, higher triglycerides, lower HDL-c, and hypertension, presented the highest CVD risk, with an adjusted odds ratio of 14.18.

The correlation between albumin-corrected anion gap and prognosis in patients with acute myocardial infarction

AIMS

Acute myocardial infarction (AMI) is a cardiovascular disease with high morbidity and mortality. We collected patients with AMI from the Medical Information Mart for Intensive Care IV (v2.0) database and explored the association between serum albumin-corrected anion gap (ACAG) level and mortality in patients with AMI.

METHODS AND RESULTS

Data of adult patients with AMI were collected. According to the 360 day prognosis, patients were divided into survival and non-survival groups. Based on the ACAG level, patients were then divided into normal and high ACAG groups. Cox hazard proportional models and restricted cubic splines (RCSs) were used to investigate the correlation between ACAG and mortality. Kaplan-Meier curves were created to compare the cumulative survival rates between the high and normal ACAG groups. The receiver operating characteristic (ROC) curve was used to analyse the predictive value of ACAG for the prognosis of patients with AMI. Sensitivity and subgroup analyses were conducted to revalidate the results. Finally, 1783 patients were included. Elevated ACAG (>20 mmol/L) was significantly associated with 30 and 360 day mortality (P < 0.001). Adjusted for multiple confounding factors, the Cox proportional hazard analysis showed that elevated ACAG (>20 mmol/L) was an independent risk factor of increased all-cause mortality in patients with AMI (hazard ratio 1.423, 95% confidence interval 1.206-1.678, P < 0.001). RCS analysis further showed that there was a non-linear trend relationship between ACAG and the risk of all-cause mortality at 30 and 360 days (χ2 = 10.750, P = 0.013; χ2 = 13.960, P = 0.003). Kaplan-Meier survival curves showed that the 30 and 360 day cumulative survival rates of patients with AMI were significantly lower (log-rank test, χ2 = 98.880, P < 0.001; χ2 = 105.440, P < 0.001) in the high ACAG group. ROC curve analysis showed that the area under the curve (AUC) of ACAG was 0.651, while the AUC of anion gap (AG) was 0.609, indicating that ACAG had a higher predictive value for 360 day mortality than AG. When combined with Sequential Organ Failure Assessment score, the predictive performance of ACAG for 360 day mortality was better, with an AUC of 0.699. Sensitivity and subgroup analyses were conducted suggesting the stability of our results.

CONCLUSIONS

Elevated serum ACAG (≥20 mmol/L) is an independent risk factor for short-term and long-term mortality in critically ill patients with AMI, and it may assist clinicians and nurses identifying high-risk patients.

A Prediction Model Based on Systemic Immune-Inflammatory Index Combined with Other Predictors for Major Adverse Cardiovascular Events in Acute Myocardial Infarction Patients

Objective

To evaluate the prognostic value of the systemic immune-inflammatory index (SII) for predicting in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction (AMI) and establish a relevant nomogram.

Methods

This study included 954 AMI patients. We examined three inflammatory factors (SII, platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR)) to see which one predicts in-hospital MACEs better. The predictors were subsequently screened using bidirectional stepwise regression method, and a MACE nomogram was constructed via logistic regression analysis. The predictive value of the model was evaluated using the area under the curve (AUC), sensitivity and specificity. In addition, the clinical utility of the nomogram was evaluated using decision curve analysis. We also compared the nomogram with the Global Registry of Acute Coronary Events (GRACE) scoring system.

Results

334 (35.0%) patients had MACEs. The SII (AUC =0.684) had a greater predictive value for in-hospital MACEs in AMI patients than the PLR (AUC =0.597, P<0.001) or NLR (AUC=0.654, P=0.01). The area under the curve (AUC) of the SII-based multivariable model for predicting MACEs, which was based on the SII, Killip classification, left ventricular ejection fraction, age, urea nitrogen (BUN) concentration and electrocardiogram-based diagnosis, was 0.862 (95% CI: 0.833-0.891). Decision curve and calibration curve analysis revealed that SII-based multivariable model demonstrated a good fit and calibration and provided positive net benefits than the model without SII. The predictive value of the SII-based multivariable model was greater than that of the GRACE scoring system (P<0.001).

Conclusion

SII is a promising, reliable biomarker for identifying AMI patients at high risk of in-hospital MACEs, and SII-based multivariable model may serve as a quick and easy tool to identify these patients.

Elevated ApoB/apoA-1 is Associated with in-Hospital Mortality in Elderly Patients with Acute Myocardial Infarction

Background

Apolipoprotein B/apolipoprotein A-1 (apoB/apoA-1) has been shown to be strongly associated with the risk of future cardiovascular disease, but the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with acute myocardial infarction(AMI) is inconclusive.

Aim

To investigate the association between apoB/apoA-1 and the risk of in-hospital death in elderly patients with AMI.

Methods

From December 2015 to December 2021, a total of 1495 elderly AMI patients (aged ≥ 60 years) with complete clinical history data were enrolled in the Second Hospital of Dalian Medical University. Outcome was defined as all-cause mortality during hospitalization. Multivariate logistic regression and restricted spline cubic (RCS) models were used to evaluate the association between apoB/apoA-1 and in-hospital mortality risk, respectively. Receiver operating characteristic(ROC) curves were used to evaluate the predictive value of apoB/apoA-1 for in-hospital mortality events. Discordance analysis was performed when apoB/apoA-1 and LDL-C/HDL-C were not in concordance.

Results

(1) A total of 128 patients (8.6%) died during hospitalization. Patients in the death group had higher apoB/apoA-1 than those in the non-death group, but lower apoA-1 levels than those in the non-death group, and the difference was statistically significant (P < 0.05); (2) Multivariate logistic regression analysis showed that apoB/apoA-1 was associated with the risk of in-hospital death in elderly AMI patients [Model 3 OR = 3.524 (1.622-7.659), P = 0.001]; (3) ROC curve analysis showed that apoB/apoA-1 (AUC = 0.572, P = 0.011) had some predictive value for the risk of in-hospital death in elderly AMI patients; (4) RCS models showed a linear dose-response relationship between apoB/apoA-1 and in-hospital death after adjusting for confounders (P for non-linearity = 0.762).

Conclusion

ApoB/apoA-1 is associated with the risk of in-hospital death in elderly patients with AMI, and is superior to other blood lipid parameters and blood lipid ratio.

The Systemic Immune Inflammatory Response Index Can Predict the Clinical Prognosis of Patients with Initially Diagnosed Coronary Artery Disease

Background

Recently, the systemic immune inflammatory response index (SIIRI), a novel and expanded inflammatory response marker, has been an independent predictor of lesion severity in patients with acute coronary syndrome (ACS). However, its predictive role in patients with initially diagnosed coronary artery disease (CAD) remains to be explored.

Patients and Methods

We evaluated 959 patients with CAD undergoing an initial coronary intervention. Each patient had laboratory measurements, including blood cell counts, taken after admission and before interventional treatment. The primary endpoint was major cardiovascular events (MACEs), defined as cardiovascular death, nonfatal myocardial infarction(MI), and nonfatal stroke. The secondary endpoints included MACEs and readmission for congestive heart failure(HF).

Results

During a mean follow-up period of 33.3±9.9 months, 229 (23.9%) MACEs were recorded. ROC curve analysis displayed that the best cut-off value of SIIRI for predicting MACEs was 247.17*1018/L2. Kaplan-Meier survival curve analysis showed that the survival rate of the low SIIRI group was higher than that of the high SIIRI group (P<0.001). Compared with the low SIIRI group, the high SIIRI group had a significantly higher risk of MACEs (187 cases (39.53%) vs.42 patients (8.64%), P<0.001). Univariate and multivariate Cox regression analyses displayed that high SIIRI levels were independently associated with the occurrence of MACEs in patients with initially diagnosed CAD undergoing percutaneous coronary intervention (PCI) (adjusted hazard ratio [HR]: 3.808, 95% confidence interval [CI%]: 2.643-5.486, P<0.001). Adding SIIRI to conventional risk factor models improved the predictive value of MACEs.

Conclusion

Elevated SIIRI is associated with adverse cardiovascular prognosis in patients with initially diagnosed CAD. SIIRI can be a simple and practical index to identify high-risk patients with CAD after PCI.

Assessment of Inflammatory Hematological Ratios (NLR, PLR, MLR, LMR and Monocyte/HDL-Cholesterol Ratio) in Acute Myocardial Infarction and Particularities in Young Patients

Cardiovascular disease, particularly coronary artery disease (CAD), remains a predominant cause of mortality globally. Factors such as atherosclerosis and inflammation play significant roles in the pathogenesis of CAD. The nexus between inflammation and CAD is underscored by the role of immune cells, such as neutrophils, lymphocytes, monocytes, and macrophages. These cells orchestrate the inflammatory process, a core component in the initiation and progression of atherosclerosis. The activation of these pathways and the subsequent lipid, fibrous element, and calcification accumulation can result in vessel narrowing. Hematological parameters derived from routine blood tests offer insights into the underlying inflammatory state. Recent studies have highlighted the potential of inflammatory hematological ratios, such as the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, monocyte/lymphocyte ratio and lymphocyte/monocyte ratio. These parameters are not only accessible and cost-effective but also mirror the degree of systemic inflammation. Several studies have indicated a correlation between these markers and the severity, prognosis, and presence of CAD. Despite the burgeoning interest in the relationship between inflammatory markers and CAD, there remains a paucity of data exploring these parameters in young patients with acute myocardial infarction. Such data could offer valuable insights into the unique pathophysiology of early-onset CAD and improve risk assessment and predictive strategies.

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

Aim

Inflammatory response is closely associated with poor prognosis in elderly patients with acute myocardial infarction (AMI). The aim of this study was to develop an easy-to-use predictive model based on medical history data at admission, systemic immune inflammatory index (SII), and systemic inflammatory response index (SIRI) to predict the risk of in-hospital mortality in elderly patients with AMI.

Methods

We enrolled 1550 elderly AMI patients (aged ≥60 years) with complete medical history data and randomized them 5:5 to the training and validation cohorts. Univariate and multivariate logistic regression analyses were used to screen risk factors associated with outcome events (in-hospital death) and to establish a nomogram. The discrimination, calibration, and clinical application value of nomogram were evaluated based on receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.

Results

The results of multivariate logistic regression showed that age, body mass index (BMI), previous stroke, diabetes, SII, and SIRI were associated with in-hospital death, and these indicators will be included in the final prediction model, which can be obtained by asking the patient's medical history and blood routine examination in the early stage of admission and can improve the utilization rate of the prediction model. The areas under the ROC curve for the training and validation cohorts nomogram were 0.824 (95% CI 0.796 to 0.851) and 0.809 (95% CI 0.780 to 0.836), respectively. Calibration curves and DCA showed that nomogram could better predict the risk of in-hospital mortality in elderly patients with AMI.

Conclusion

The nomogram constructed by combining SII, SIRI, and partial medical history data (age, BMI, previous stroke, and diabetes) at admission has a good predictive effect on the risk of in-hospital death in elderly patients with AMI.