Correlation Analysis of Neutrophil/Albumin Ratio and Leukocyte Count/Albumin Ratio with Ischemic Stroke Severity

Association between lower neutrophil-to-albumin ratio and early neurological improvement in patients with acute cerebral infarction after intravenous thrombolysis

Elevated neutrophil counts and decreased albumin levels have been linked to an unfavorable prognosis in acute cerebral infarction (ACI). The objective of this study is to explore the correlation between the neutrophil-to-albumin ratio (NAR) and the early neurological improvement (ENI) of ACI patients following intravenous thrombolysis (IVT). ACI patients who underwent IVT between June 2019 and June 2023 were enrolled. The severity of ACI was assessed using the National Institutes of Health Stroke Scale (NIHSS). ENI was defined as a reduction in NIHSS score of ≥ 4 or complete resolution of neurological deficit within 24 h after IVT. Propensity score match (PSM) and logistic regression analysis were used to explore the correlation between these variables and the early neurological outcomes of patients. A total of 545 ACI patients were included, with 253 (46.4 %) experiencing ENI. Among the 193 pairs of patients after PSM, there was a significant association between NAR and ENI (OR, 0.89; 95 % CI, 0.85-0.94; p < 0.001). The restricted cubic splines analysis revealed a significant nonlinear correlation between NAR and ENI (p for nonlinear = 0.0004; p for overall = 0.0002). The optimal cutoff for predicting ENI was determined as a NAR level of 10.20, with sensitivity and specificity values of 73.6 % and 60.9 %. NAR levels are associated with ENI in ACI patients after IVT. The decreased levels of NAR indicate an increased likelihood of post-thrombolysis ENI in ACI patients.

Prediction of Osteoporotic Hip Fracture Outcome: Comparative Accuracy of 27 Immune-Inflammatory-Metabolic Markers and Related Conceptual Issues

Objectives: This study, based on the concept of immuno-inflammatory-metabolic (IIM) dysregulation, investigated and compared the prognostic impact of 27 indices at admission for prediction of postoperative myocardial injury (PMI) and/or hospital death in hip fracture (HF) patients. Methods: In consecutive HF patient (n = 1273, mean age 82.9 ± 8.7 years, 73.5% females) demographics, medical history, laboratory parameters, and outcomes were recorded prospectively. Multiple logistic regression and receiver-operating characteristic analyses (the area under the curve, AUC) were used to establish the predictive role for each biomarker. Results: Among 27 IIM biomarkers, 10 indices were significantly associated with development of PMI and 16 were indicative of a fatal outcome; in the subset of patients aged >80 years with ischaemic heart disease (IHD, the highest risk group: 90.2% of all deaths), the corresponding figures were 26 and 20. In the latter group, the five strongest preoperative predictors for PMI were anaemia (AUC 0.7879), monocyte/eosinophil ratio > 13.0 (AUC 0.7814), neutrophil/lymphocyte ratio > 7.5 (AUC 0.7784), eosinophil count < 1.1 × 109/L (AUC 0.7780), and neutrophil/albumin × 10 > 2.4 (AUC 0.7732); additionally, sensitivity was 83.1-75.4% and specificity was 82.1-75.0%. The highest predictors of in-hospital death were platelet/lymphocyte ratio > 280.0 (AUC 0.8390), lymphocyte/monocyte ratio < 1.1 (AUC 0.8375), albumin < 33 g/L (AUC 0.7889), red cell distribution width > 14.5% (AUC 0.7739), and anaemia (AUC 0.7604), sensitivity 88.2% and above, and specificity 85.1-79.3%. Internal validation confirmed the predictive value of the models. Conclusions: Comparison of 27 IIM indices in HF patients identified several simple, widely available, and inexpensive parameters highly predictive for PMI and/or in-hospital death. The applicability of IIM biomarkers to diagnose and predict risks for chronic diseases, including OP/OF, in the preclinical stages is discussed.

Beyond diagnosis: Leveraging routine blood and urine biomarkers to predict severity and functional outcome in acute ischemic stroke

Background

The initial severity of acute ischemic stroke (AIS) is a crucial predictor of the disease outcome. In this study, blood and urine biomarkers from patients with AIS were measured to estimate stroke severity and predict long-term stroke outcomes.

Methods

The medical records of patients with AIS between October 2016 and May 2020 were retrospectively analyzed. The relationships of blood and urine biomarkers with stroke severity at admission were evaluated in patients with AIS. Predictive models for initial stroke severity and long-term prognosis were then developed using a panel of identified biomarkers.

Results

A total of 2229 patients were enrolled. Univariate analysis revealed 12 biomarkers associated with the National Institutes of Health Stroke Scale scores at admission. The area under the curve values for predicting initial stroke severity and long-term prognosis on the basis of these biomarkers were 0.7465, 0.7470, and 0.8061, respectively. Among multiple tested machine-learning, eXtreme gradient boosting exhibited the highest effectiveness in predicting 90-day modified Rankin Scale scores. SHapley Additive exPlanations revealed fasting glucose, albumin, hemoglobin, prothrombin time, and urine-specific gravity to be the top five most crucial biomarkers.

Conclusion

These findings demonstrate that clinically available blood and urine biomarkers can effectively estimate initial stroke severity and predict long-term prognosis in patients with AIS. Our results provide a scientific basis for developing tailored clinical treatment and management strategies for AIS, through incorporating liquid biomarkers into stroke risk assessment and patient care protocols for patients with AIS.

Assessing the predictive value of neutrophil percentage to albumin ratio for ICU admission in ischemic stroke patients

Background

Acute ischemic stroke (AIS) remains a substantial global health challenge, contributing to increased morbidity, disability, and mortality. This study aimed at investigating the predictive value of the neutrophil percentage to albumin ratio (NPAR) in determining intensive care unit (ICU) admission among AIS patients.

Methods

A retrospective observational study was conducted, involving AIS cases admitted to a tertiary hospital in Jordan between 2015 and 2020. Lab data were collected upon admission, and the primary outcome was ICU admission during hospitalization. Descriptive and inferential analyses were performed using SPSS version 29.

Results

In this study involving 364 AIS patients, a subset of 77 (21.2%) required admission to the ICU during their hospital stay, most frequently within the first week of admission. Univariable analysis revealed significantly higher NPAR levels in ICU-admitted ischemic stroke patients compared to those who were not admitted (23.3 vs. 15.7, p < 0.001), and multivariable regression models confirmed that higher NPAR (≥19.107) independently predicted ICU admission in ischemic stroke patients (adjusted odds ratio [aOR] = 4.85, 95% CI: 1.83-12.83). Additionally, lower GCS scores and higher neutrophil-to-lymphocyte ratio (NLR) were also associated with increased likelihood of ICU admission. In terms of predictive performance, NPAR showed the highest accuracy with an AUC of 0.885, sensitivity of 0.805, and specificity of 0.854, using a cutoff value of 19.107. NPAR exhibits an AUC of 0.058, significantly outperforming NLR (Z = 2.782, p = 0.005).

Conclusion

NPAR emerged as a robust independent predictor of ICU admission in ischemic stroke patients, surpassing the predictive performance of the NLR.

Prediction of early neurological deterioration in acute ischemic stroke patients treated with intravenous thrombolysis

A proportion of acute ischemic stroke (AIS) patients suffer from early neurological deterioration (END) within 24 hours following intravenous thrombolysis (IVT), which greatly increases the risk of poor prognosis of these patients. Therefore, we aimed to explore the predictors of early neurological deterioration of ischemic origin (ENDi) in AIS patients after IVT and develop a nomogram prediction model. This study collected 244 AIS patients with post-thrombolysis ENDi as the derivation cohort and 155 patients as the validation cohort. To establish a nomogram prediction model, risk factors were identified by multivariate logistic regression analysis. The results showed that neutrophil to lymphocyte ratio (NLR) (OR 2.616, 95% CI 1.640-4.175, P < 0.001), mean platelet volume (MPV) (OR 3.334, 95% CI 1.351-8.299, P = 0.009), body mass index (BMI) (OR 1.979, 95% CI 1.285-3.048, P = 0.002) and atrial fibrillation (AF) (OR 8.012, 95% CI 1.341-47.873, P = 0.023) were significantly associated with ENDi. The area under the curve of the prediction model constructed from the above four factors was 0.981 (95% CI 0.961-1.000) and the calibration curve was close to the ideal diagonal line. Therefore, this nomogram prediction model exhibited good discrimination and calibration power and might be a reliable and easy-to-use tool to predict post-thrombolysis ENDi in AIS patients.