Association of the Neutrophil-to-Lymphocyte Ratio with 90-Day Functional Outcomes in Patients with Acute Ischemic Stroke

Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999-2018)

Background

Our study aims to utilize unsupervised machine learning methods to perform inflammation clustering on stroke patients via novel CBC-derived inflammatory indicators (NLR, PLR, NPAR, SII, SIRI, and AISI), evaluate the mortality risk among these different clusters and construct prognostic models to provide reference for clinical management.

Methods

A cross-sectional analysis was conducted using data from stroke participants in the U.S. NHANES 1999-2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves.

Results

A total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. Weighted multivariate logistic regression analysis revealed that high SIRI and NPAR levels were significantly positively correlated with increased all-cause mortality risk in stroke patients (p < 0.001), independent of potential confounders; Consensus clustering divided patients into two inflammatory subgroups via SIRI and NPAR, with subgroup 2 having significantly higher markers and mortality risks than subgroup 1 (p < 0.001); LASSO regression analysis showed subgroup 2 had higher risk scores and shorter overall survival than subgroup 1 [HR, 1.99 (1.61-2.45), p < 0.001]; WQS regression, Cox regression, and machine learning methods identified NPAR and SIRI as hub prognostic inflammatory markers; The nomogram prognostic model with NPAR and SIRI demonstrated the best net benefit for predicting 1, 3, 5 and 10-year overall survival in stroke patients.

Conclusion

This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (p < 0.001) for stroke patients. Patients would been divided into 2 inflammatory subtypes via them, with subtype 2 having higher values and mortality risks (p < 0.001). It suggests that enhanced monitoring and management for patients with high SIRI and NPAR levels to improve survival outcomes.

Analysis of Prognostic Risk Factors in Patients with Complete Revascularization After Thrombectomy for Acute Anterior Circulation Large Vessel Occlusion

OBJECTIVE

Prognostic risk factors were analyzed for patients with acute anterior circulation large vessel occlusion stroke who achieved modified Treatment in Cerebral Ischemia (mTICI) 3 grade by endovascular treatment.

METHODS

Patients with Acute ischemic stroke with mTICI=3 grade after endovascular treatment from June 2019 to September 2024, at the Eighth Clinical College of Guangzhou University of Traditional Chinese Medicine, were retrospectively analyzed. Data related to patients' baseline data, risk factors, test data, and surgical data were collected, and the primary endpoint was 90-day poor functional outcome, defined as patients' modified Rankin score >2 at 90 days after surgery. The predictive effect was evaluated using receiver operating characteristic curve analysis, and multivariate logistic regression analysis was used to explore the independent correlation between inflammatory markers and prognosis.

RESULTS

A total of 103 eligible patients were included. Multivariate logistic regression analysis showed that the neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR] = 1.10, 95% confidence interval [CI]: 1.01∼1.20, P = 0.021) at 24 hours after surgery, 7-day National Institute of Health stroke scale (NIHSS) score: (OR = 1.15, 95% CI: 1.07∼1.23, P < 0.001), diabetes history: (OR = 9.60, 95% CI: 2.41∼38.26, P = 0.001), was independently associated with poor prognosis in patients with mTICI = grade 3 after endovascular therapy. Receiver operating characteristic curve analysis: NLR (area under the curve [AUC] = 0.687, 95% CI: 0.584∼0.790) at 24 hours after surgery, NIHSS (AUC = 0.826, 95% CI: 0.746∼0.906), and diabetes history (AUC = 0.667, 95% CI: 0.563∼0.771). Three-marker combined predictive indicators (AUC = 0.889, 95%CI: 0.829∼0.949) CONCLUSIONS: In patients with acute anterior large vascular occlusive stroke with mTICI = grade 3 after endovascular therapy, NIHSS score at 7 days after surgery, NLR within 24 hours after surgery, and history of diabetes were independent influencing factors for poor prognosis. Patients with a history of diabetes who had a NLR ≥6.7 within 24 hours and a NIHSS score ≥4.5 at 7 days after surgery were more likely to have a poor prognosis.

Long Non-Coding H19 in Lymphocytes: Prognostic Value in Acute Ischemic Stroke Patients

Acute ischemic stroke (AIS) is a cerebrovascular disease that seriously affects the physical and mental health and quality of life of patients. However, there is a lack of reliable prognostic prediction methods. The main objective of this study was to investigate the prognostic value of long non-coding RNA (lncRNA) H19 in lymphocytes of patients with AIS, and to construct a prognostic prediction model for AIS including lncRNA H19 in lymphocytes, which would provide new ideas for the prognostic evaluation of AIS. Poor prognosis was defined when the patient's modified Rankin scale (mRS) score at 3 months after AIS onset was greater than 2. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the level of lncRNA H19 in lymphocytes. Spearman correlation analysis revealed a positive correlation between lncRNA H19 and mRS score at 3 months after AIS onset (r = 0.1977, p = 0.0032), while lncRNA H19 was negatively correlated with white blood cells counts, lymphocytes counts, and neutrophils counts. Logistic regression analysis identified lncRNA H19 as an independent predictor of poor prognosis (OR = 3.062 [1.69-5.548], p < 0.001). Moreover, a nomogram prediction model incorporating lncRNA H19 in lymphocytes demonstrated effective discrimination, calibration, and clinical applicability in predicting AIS outcomes. The findings suggest that lncRNA H19 in lymphocytes could be a valuable prognostic indicator and a potential pharmacological target for AIS patients, and might be a novel pathway for enhanced prognostic evaluation and targeted therapeutic strategies.