Predictive value of lymphocyte-to-monocyte ratio in critically Ill patients with atrial fibrillation: A propensity score matching analysis

The role of systemic inflammatory indices in predicting atrial fibrillation and its complications: a narrative review

Atrial fibrillation (AF) is associated with increased morbidity and mortality. Inflammation and oxidative stress play critical roles in AF occurrence and its complications. Therefore, evaluating the circulating levels of inflammatory and oxidative stress biomarkers and their possible applications in AF diagnosis and management have been the focus of many efforts. The monocyte-to-high-density lipoprotein cholesterol ratio (MHR) and neutrophil-to-lymphocyte ratio (NLR) are two non-invasive, available, and established markers that serve as indicators of inflammation and oxidative stress. This review summarizes the current literature regarding alterations in the NLR, MHR, and other composite markers of systemic inflammation in AF patients. Moreover, this review discusses the clinical performance of these markers in predicting AF occurrence, recurrence, and disease outcomes. The PubMed, Scopus, and ScienceDirect online databases were searched for relevant studies using appropriate keywords, including "atrial fibrillation", "monocyte to high-density lipoprotein cholesterol ratio", and "neutrophil to lymphocyte ratio". The results of this review revealed the association of elevated levels of systemic inflammatory markers, specifically the NLR and MHR with AF and its complications. This finding indicates the potential role of subclinical inflammation in the development of AF, emphasizing its consideration in both the prevention and treatment of AF and associated complications. Despite these promising findings, the utilization of these markers in routine clinical settings faces challenges, including low specificity and sensitivity and varying cut-off values across different studies.

How Immunonutritional Markers Are Associated with Age, Sex, Body Mass Index and the Most Common Chronic Diseases in the Hospitalized Geriatric Population-A Cross Sectional Study

The aim of this study was to assess the relationship of different chronic diseases with immunonutritional markers in the senior population.

METHODS

this study included 1190 hospitalized geriatric patients. The criteria to participate were ability to communicate, given consent and C-reactive protein (CRP) lower than 6 mg/dL.

RESULTS

the mean age of the study population was 81.7 ± 7.6 years. NLR (neutrophil-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), MWR (monocyte-to-white blood cell ratio), SII (systemic immune-inflammation index), PNI (prognostic nutritional index) and CAR (C-reactive protein-to-albumin ratio) were related to age. NLR and MWR were higher, while LMR, PLR (platelet-to-lymphocyte ratio and SII were lower in men. All markers were related to BMI. NLR, LMR, LCR (lymphocyte-to-CRP ratio), MWR, PNI and CAR were related to several concomitant chronic diseases. In multivariate analyses, age and BMI were selected as independent predictors of all studied immunonutritional markers. Atrial fibrillation, diabetes mellitus and dementia appear most often in the models. PNI presented the most consistent statistical association with age, BMI and concomitant chronic diseases.

CONCLUSIONS

this study reveals the pivotal role of aging and BMI in inflammatory marker levels and the association of immunonutritional markers with different chronic diseases. Atrial fibrillation seems to have the most dominant connection to the immunonutritional markers.

Monocyte-to-lymphocyte ratio is a prognostic predictor for patients with non-small cell lung cancer treated with stereotactic body radiation therapy

Background

The monocyte-to-lymphocyte ratio (MLR), a systemic inflammation biomarker, has been shown to predict patient outcomes in several types of cancer. This study aimed to determine the association between MLR and local control (LC) and cause-specific survival (CSS) rates in patients with non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT).

Materials and methods

The median age of the 194 included participants (144 men, 50 women) was 80 (range, 50-96) years. The median follow-up period was 19 (range, 1-108) months. The LC and CSS rates were calculated using the Kaplan-Meier method. Univariate and multivariate Cox proportional hazard regression models were used to estimate the LC and CSS rates.

Results

Local recurrence was observed in 25 patients during the follow-up. Univariate Cox proportional hazards regression analysis revealed that MLR, performance status, and tumor diameter were significant factors for LC. Multivariate analysis showed MLR and tumor diameter as significant factors (p = 0.041 and 0.031, respectively). The 1- and 2-year LC rates for the lower and higher MLR groups were 97.5% and 97.5%, and 89.7% and 81.2%, respectively. During the follow-up period, 14 patients died due to NSCLC. Although MLR tended to predict CSS in univariate analysis (p = 0.086), none of the parameters was significant in predicting CSS. However, MLR as a continuous variable was a significant factor for CSS in the univariate analysis (p = 0.004).

Conclusions

Our data suggest that MLR is correlated with LC and CSS rates in NSCLC patients treated with SBRT.

Naples Prognostic Score and Prediction of Left Ventricular Ejection Fraction in STEMI Patients

The Naples score is a new prognostic score developed according to inflammatory and nutritional status and frequently evaluated in cancer patients. The present study aimed to evaluate using the Naples prognostic score (NPS) to predict the development of decreased left ventricular ejection fraction (LVEF) after acute ST-segment elevation myocardial infarction (STEMI). The study has a multicenter and retrospective design and included 2280 patients with STEMI who underwent primary percutaneous coronary intervention (pPCI) between 2017 and 2022. All participants were divided into 2 groups according to their NPS. The relationship between these 2 groups and LVEF was evaluated. The low-Naples risk group (Group-1) included 799 patients, and the high-Naples risk group (Group-2) had 1481 patients. Hospital mortality, shock, and no-reflow rates were found to be higher in Group 2 compared with Group 1 (P < .001, P = .032, P = .004). The NPS was significantly inversely associated with discharge LVEF (B coefficient: -1.51, 95% CI-2.26; -.76, P = .001). NPS, a simple and easily calculated risk score, may help identify high-risk STEMI patients. To the best of our knowledge, the present study is the first to demonstrate the relationship between low LVEF and NPS in patients with STEMI.

Development and Validation of a Predictive Model Based on LASSO Regression: Predicting the Risk of Early Recurrence of Atrial Fibrillation after Radiofrequency Catheter Ablation

BACKGROUND

Although recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF.

PURPOSE

To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA.

METHODS

This study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model's discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model.

RESULTS

In a 3-month follow-up of AF patients (n = 136) who underwent RFCA, there were 47 recurrences of and 89 non-recurrences of AF after RFCA. P, PLR, RDW, LDL, and CRI-II were associated with early recurrence of AF after RFCA in patients with AF (p < 0.05). We developed a predictive model using LASSO regression, incorporating four robust factors (PLR, RDW, LDL, CRI-II). The AUC of this prediction model was 0.7248 (95% CI 0.6342-0.8155), and the AUC of the internal validation using the bootstrap method was 0.8403 (95% CI 0.7684-0.9122). The model demonstrated a strong predictive capability, along with favorable calibration and clinical applicability. The Hosmer-Lemeshow test indicated that there was good consistency between the predicted and observed values. Additionally, DCA highlighted the model's advantages in terms of its clinical application.

CONCLUSIONS

We have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations.

Association between the systemic immune-inflammation index and outcomes among atrial fibrillation patients with diabetes undergoing radiofrequency catheter ablation

PURPOSE

To investigate the relationship between the incidence of atrial fibrillation (AF) recurrence and the levels of the systemic immune-inflammatory index (SII, platelet × neutrophil/lymphocyte ratio) in patients with AF and diabetes mellitus (DM) undergoing after radiofrequency catheter ablation (RFCA).

PATIENTS AND METHODS

Preoperative SII levels were determined in AF patients with DM undergoing RFCA. Restricted cubic splines were used to determine the correlation between SII and the risk of AF recurrence. Multivariate-adjusted logistic regression models were constructed to determine the relationship between SII levels and AF recurrence. The predictive value of the clinical model and combined with the SII index was estimated by the area under the receiver-operating characteristic curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

A total of 204 patients with AF and DM who underwent RFCA in our hospital were included. Seventy-seven patients had AF recurred during a mean follow-up of 20 months. Restricted cubic spline analysis showed that when SII ≥ 444.77 × 109 /L, there was a positive correlation with the incidence of AF recurrence. In addition, adding the SII to the predictive model for AF recurrence after RFCA in patients with DM and AF could contribute to an increase in C-statistics (0.798 vs. 0.749, p = .034). After SII was incorporated into the clinical model, the comprehensive discrimination and net reclassification tended to improve (IDI and NRI > 0, p < .05).

CONCLUSION

SII was independently and positively associated with recurrence after the first catheter ablation in patients with DM and AF.

Development and Validation of a Risk Nomogram Model for Predicting Recurrence in Patients with Atrial Fibrillation After Radiofrequency Catheter Ablation

Purpose

This study aimed to develop and validate a risk nomogram model for predicting the risk of atrial fibrillation recurrence after radiofrequency catheter ablation.

Patients and Methods

A retrospective observational study was conducted using data from 485 patients with atrial fibrillation who underwent the first radiofrequency ablation in our hospital from January 2018 to June 2021. All patients were randomized into training cohort (70%; n=340) and validation cohort (30%; n=145). Univariate and multivariate logistic regression analyses were used to identify independent risk factors. The predictive nomogram model was established by using R software. The nomogram was developed and evaluated based on differentiation, calibration, and clinical efficacy by concordance statistic (C-statistic), calibration plots, and decision curve analysis (DCA), respectively.

Results

The nomogram was established by four variables including left atrial diameter (OR 1.057, 95% CI 1.010–1.107, P=0.018), left ventricular ejection fraction (OR 0.943, 95% CI 0.905–0.982, P=0.005), type of atrial fibrillation (OR 2.164, 95% CI: 1.262–3.714), and systemic inflammation score (OR 1.905, 95% CI 1.408–2.577). The C-statistic of the nomogram was 0.741 (95% CI: 0.689–0.794) in the training cohort and 0.750 (95% CI: 0.670–0.831) in the validation cohort. The calibration plots showed good agreement between the predictions and observations in the training and validation cohorts. Decision curve analysis and clinical impact curves indicated the clinical utility of the predictive nomogram.

Conclusion

The nomogram model has good discrimination and accuracy, which can screen high-risk groups intuitively and individually, and has a certain predictive value for atrial fibrillation recurrence in patients after radiofrequency ablation.