ALB-dNLR Score Predicts Mortality in Coronary Artery Disease Patients After Percutaneous Coronary Intervention

Development and validation of a nomogram for predicting mortality in patients with acute severe traumatic brain injury: A retrospective analysis

BACKGROUND

Recent evidence links the prognosis of traumatic brain injury (TBI) to various factors, including baseline clinical characteristics, TBI specifics, and neuroimaging outcomes. This study focuses on identifying risk factors for short-term survival in severe traumatic brain injury (sTBI) cases and developing a prognostic model.

METHODS

Analyzing 430 acute sTBI patients from January 2018 to December 2023 at the 904th Hospital's Neurosurgery Department, this retrospective case-control study separated patients into survival outcomes: 288 deceased and 142 survivors. It evaluated baseline, clinical, hematological, and radiological data to identify risk and protective factors through univariate and Lasso regression. A multivariate model was then formulated to pinpoint independent prognostic factors, assessing their relationships via Spearman's correlation. The model's accuracy was gauged using the Receiver Operating Characteristic (ROC) curve, with additional statistical analyses for quantitative factors and model effectiveness. Internal validation employed ROC, calibration curves, Decision Curve Analysis (DCA), and Clinical Impact Curves (CIC) to assess model discrimination, utility, and accuracy. The International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) and Corticosteroid Randomization After Significant Head injury (CRASH) models were also compared through multivariate regression.

RESULTS

Factors like unilateral and bilateral pupillary non-reactivity at admission, the derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), D-dimer to fibrinogen ratio (DFR), infratentorial hematoma, and Helsinki CT score were identified as independent risk factors (OR > 1), whereas serum albumin emerged as a protective factor (OR < 1). The model showed superior predictive performance with an AUC of 0.955 and surpassed both IMPACT and CRASH models in predictive accuracy. Internal validation confirmed the model's high discriminative capability, clinical relevance, and effectiveness.

CONCLUSIONS

Short-term survival in sTBI is significantly influenced by factors such as pupillary response, dNLR, PLR, DFR, serum albumin levels, infratentorial hematoma occurrence, and Helsinki CT scores at admission. The developed nomogram accurately predicts sTBI outcomes, offering significant clinical utility.

Novel predictive factor for erectile dysfunction: systemic immune inflammation index

Systemic immune inflammation index (SII) is a global parameter that comprehensively reflects body inflammation, this study aims to assess the correlation between this index and erectile dysfunction (ED). This cross-sectional study incorporated 164 ED patients and 95 healthy adult males. The collection of general demographic information and pertinent hematological data from the participants enabled the computation of corresponding SII values. Statistical analysis, encompassing descriptive statistics as well as normality and logistic regression analyses, was carried out employing SPSS version 26. The findings of the univariate analysis revealed a noteworthy distinction in triglyceride levels (TG) (P = 0.017) and SII (P < 0.001) between ED patients and the healthy population. Subsequent multivariate logistic regression analysis unveiled a significant association between SII (odd ratio (OR):1.012, 95% confidence interval (CI):1.008-1.015; P < 0.001) and the occurrence of ED. Since the impact value is not clearly visible, SII/100 is utilized to magnify the effect value one hundredfold. The regression analysis results indicate that the OR value of SII/100 is 3.171, and the 95% CI is 2.339-4.298 (P < 0.001). The Receiver Operating Characteristic (ROC) curve analysis ascertained an AUC of 0.863 (P < 0.001) for SII, with a determined cut-off value of 391.53(109/L), exhibiting a sensitivity of 81.7% and specificity of 83.2%. Moreover, when comparing patients with varying degrees of ED severity, both univariate (P < 0.001) and subsequent multivariate logistic regression analyses (OR: 1.007, 95% CI: 1.004-1.010; P < 0.001) underscored the significance of the SII value. At this point, SII/100 OR: 1.971, 95% CI: 1.508-2.576 (P < 0.001). The ROC curve analysis in this context demonstrated an AUC of 0.799 (P < 0.001), with a determined cut-off value of 746.63(109/L), featuring a sensitivity of 60.6% and specificity of 91.6%. These discerned outcomes affirm a correlation between SII and ED, establishing its potential not only in predicting the onset of ED but also in differentiating among various levels of ED severity.

Association of inflammatory markers based on routine blood with prognosis in patients underwent percutaneous coronary intervention

Inflammation contributes to the pathophysiological processes of coronary artery disease. We evaluated the association between inflammatory biomarkers, neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), systemic inflammatory index, platelet-lymphocyte ratio, and 1-year all-cause mortality in patients underwent percutaneous coronary intervention (PCI). In this retrospective cohort, we consecutively enrolled 4651 patients who underwent PCI. Baseline demographic details, clinical data, and laboratory parameters on admission were analyzed. The primary outcome was 1-year all-cause mortality after PCI. We performed Cox regression and restricted cubic spline analysis to assessed the association between the inflammatory biomarkers and the clinical outcome. The area under the curve from receiver operating characteristic analysis was determined for the ability to classify mortality outcomes. A total of 4651 patients were included. Of these, 198 (4.26%) died on follow-up. Univariate Cox regression showed that NLR (heart rate [HR]: 1.070, 95% confidence interval [CI]: 1.060-1.082, P < .001), RDW (HR: 1.441, 95% CI 1.368-1.518, P < .001), systemic inflammatory index (HR: 1.000, 95% CI 1.000-3.180, P < .001), platelet-lymphocyte ratio (HR: 3.812, 95% CI 1.901-3.364, P < .001) were significant predictors of 1-year all-cause mortality. After adjusting for other confounders in multivariate analysis, NLR (HR: 01.038, 95% CI 1.022-1.054, P < .001) and RDW (HR: 1.437, 95% CI 1.346-1.535, P < .001) remained significant predictors. Restricted cubic spline analysis showed the relationship between RDW, NLR, and 1-year all-cause mortality was linear after adjusting for the covariables (P for non-linearity < 0.001). The multivariable adjusted model led to improvement in the area under the curve to 0.83 (P < .05). Nomogram was created to predict the probability of 1 year mortality. Among the laboratory indices, RDW and NLR showed the best performance for mortality risk prediction. Multivariate predictive models significantly improved risk stratification.

Albumin combined with neutrophil-to-lymphocyte ratio score and outcomes in patients with acute coronary syndrome treated with percutaneous coronary intervention

BACKGROUND

Evidence about the association between albumin combined with neutrophil-to-lymphocyte ratio score (ANS) and survival outcomes in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) is rare. This study aimed to evaluate the prognostic value of ANS in patients with ACS undergoing PCI by propensity score matching (PSM) analysis.

PATIENTS AND METHODS

Patients with ACS undergoing PCI were consecutively enrolled in this prospective cohort study from January 2016 to December 2018. The albumin and neutrophil-to-lymphocyte ratio cutoff values for predicting major adverse cardiovascular events (MACEs) were calculated using receiver operating characteristic curves. Survival analysis was performed using Kaplan-Meier estimates, the Cox proportional hazard regression models and PSM. The study endpoint was the occurrence of a MACE, which included all-cause mortality and rehospitalization for severe heart failure during follow-up.

RESULTS

Overall, 1549 patients with adequate specimens were identified and assigned into different groups for comparison. Before and after PSM, the Kaplan-Meier curves showed that a higher ANS value was associated with a higher risk of MACEs (all P  < 0.001). The multivariate Cox proportional hazard regression model showed that the ANS (per 1 score increase) [hazard ratio (HR), 2.016; 95% confidence interval (CI), 1.329-3.057; P  = 0.001 vs. HR, 2.166; 95% CI, 1.344-3.492; P  = 0.002] was an independent predictor for MACEs.

CONCLUSION

This study tentatively confirms that ANS may be a valuable clinical indicator to identify high-risk ACS patients after PCI. More high-quality prospective studies are needed in the future.

Serum a-1 antitrypsin as a novel biomarker in chronic heart failure

AIMS

Chronic heart failure (CHF) remains a major health issue worldwide. In the present study, we aimed to identify novel circulating biomarkers for CHF using serum proteomics technology and to validate the biomarker in three independent cohorts.

METHODS AND RESULTS

The isobaric tags for relative and absolute quantitation technology was utilized to identify the potential biomarkers of CHF. The validation was conducted in three independent cohort. Cohort A included 223 patients with ischaemic heart disease (IHD) and 321 patients with ischaemic heart failure (IHF) from the CORFCHD-PCI study. Cohort B recruited 817 patients with IHD and 1139 patients with IHF from the PRACTICE study. Cohort C enrolled 559 non-ischaemic heart disease patients with CHF (n = 316) or without CHF (n = 243). We found the expression of a-1 antitrypsin (AAT) was elevated significantly in patients with CHF compared with that in the patients with stable IHD using statistical and bioinformatics analyses. In a validation study, there was a significant difference between patients with stable IHD and patients with IHF in AAT concentration either in cohort A (1.35 ± 0.40 vs. 1.64 ± 0.56, P < 0.001) or in cohort B (1.37 ± 0.42 vs. 1.70 ± 0.48, P < 0.001). The area under the receiver operating characteristic curve was 0.70 [95% confidence interval (CI): 0.66 to 0.74, P < 0.001] in cohort A and 0.74 (95% CI: 0.72 to 0.76, P < 0.001) in cohort B. Furthermore, AAT was negative correlated with left ventricular ejection fraction (r = -0.261, P < 0.001). After adjusting for confounders using a multivariate logistic regression analysis, AAT remained an independent association with CHF in both cohort A (OR = 3.14, 95% CI: 1.667 to 5.90, P < 0.001) and cohort B (OR = 4.10, 95% CI: 2.97 to 5.65, P < 0.001). This association was also validated in cohort C (OR = 1.86, 95% CI: 1.02 to 3.38, P = 0.043).

CONCLUSIONS

The present study suggests that serum AAT is a reliable biomarker for CHF in a Chinese population.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Nomograms Based on the Albumin/Neutrophil-to-Lymphocyte Ratio Score for Predicting Coronary Artery Disease or Subclinical Coronary Artery Disease

Purpose

To develop and validate two nomograms incorporating the albumin/neutrophil-to-lymphocyte ratio score (ANS) for predicting the risk of coronary artery disease (CAD) or subclinical CAD.

Patients and Methods

Four hundred fifty patients with suspected CAD who underwent coronary computed tomographic angiography were consecutively enrolled between September 2015 and June 2017. Nomograms were established based on independent predictors of CAD or subclinical CAD.

Results

In total, 437 patients with suspected CAD who underwent coronary computed tomographic angiography were included. Male sex, age ≥65 years, smoking, hypertension, diabetes, dyslipidemia, ischemic stroke, and ANS were independent predictors of CAD and subclinical CAD. The areas under the curve of each nomogram were 0.799 (95% CI: 0.752-0.846) and 0.809 (95% CI: 0.762-0.856), respectively. The calibration curve and decision curve analysis showed good performance for the diagnostic nomograms. The prediction of CAD or subclinical CAD by the ANS was not modified by the independent predictors (all, p for interaction >0.05).

Conclusion

Our ANS-based nomograms can provide accurate and individualized risk predictions for patients with suspected CAD or subclinical CAD.