Association of neutrophil to lymphocyte ratio and D-dimer with functional outcome in patients with cerebral venous sinus thrombosis.

BACKGROUND: Investigations on the risk factors for the prognosis of cerebral venous sinus thrombosis (CVST) are limited. This study aimed to explore whether specific inflammatory factors and coagulation indictors are associated with functional outcome in patients treated for CVST. METHODS: This retrospective study included 137 patients admitted to our hospital between January 2010 and October 2021. The functional outcome was assessed with the modified Rankin Scale (mRS) score at discharge. Patients were divided into two groups, 102 patients with favorable outcomes (mRS 0-1) and 35 patients with poor outcomes (mRS 2-6). The clinical indexes were compared between two groups. Multivariable logistic regression was performed to identify the independent influencing factors for poor outcomes of CVST patients. The prognostic indicators were analyzed using the receiver operating characteristic (ROC) curve. RESULTS: Compared with the favorable outcome group, the incidence of impaired consciousness and brain lesion, the levels of D-dimer, RDW, neutrophil count, neutrophil to lymphocyte ratio (NLR) and red blood cell distribution width to platelet ratio (%) on admission were significantly higher in the poor outcome group, while the level of lymphocyte count was significantly lower. After multivariable logistic regression analysis, baseline D-dimer level (odds ratio (OR), 1.180; 95% confidence interval (CI), 1.019-1.366, P = 0.027) and NLR (OR, 1.903; 95%CI, 1.232-2.938, P = 0.004) were significantly associated with unfavorable outcome at discharge. The ROC curve analysis showed that the areas under the curve of D-dimer, NLR and their combined detection for predicting worse outcome were 0.719, 0.707 and 0.786, respectively. CONCLUSIONS: Elevated D-dimer level and NLR on admission were associated with an increased risk of poor functional outcome in patients with CVST.

Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.

Background and purpose: Corpus callosum (CC) infarction is an extremely rare subtype of cerebral ischemic stroke, however, the symptoms of cognitive impairment often fail to attract early attention of patients, which seriously affects the long-term prognosis, such as high mortality, personality changes, mood disorders, psychotic reactions, financial burden and so on. This study seeks to develop and validate models for early predicting the risk of subjective cognitive decline (SCD) after CC infarction by machine learning (ML) algorithms. Methods: This is a prospective study that enrolled 213 (only 3.7%) CC infarction patients from a nine-year cohort comprising 8,555 patients with acute ischemic stroke. Telephone follow-up surveys were carried out for the patients with definite diagnosis of CC infarction one-year after disease onset, and SCD was identified by Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. Based on the significant features selected by the least absolute shrinkage and selection operator (LASSO), seven ML models including Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naïve Bayes (GNB), Complement Naïve Bayes (CNB), and Support vector machine (SVM) were established and their predictive performances were compared by different metrics. Importantly, the SHapley Additive exPlanations (SHAP) was also utilized to examine internal behavior of the highest-performance ML classifier. Results: The Logistic Regression (LR)-model performed better than other six ML-models in SCD predictability after the CC infarction, with the area under the receiver characteristic operator curve (AUC) of 77.1% in the validation set. Using LASSO and SHAP analysis, we found that infarction subregions of CC infarction, female, 3-month modified Rankin Scale (mRS) score, age, homocysteine, location of angiostenosis, neutrophil to lymphocyte ratio, pure CC infarction, and number of angiostenosis were the top-nine significant predictors in the order of importance for the output of LR-model. Meanwhile, we identified that infarction subregion of CC, female, 3-month mRS score and pure CC infarction were the factors which independently associated with the cognitive outcome. Conclusion: Our study firstly demonstrated that the LR-model with 9 common variables has the best-performance to predict the risk of post-stroke SCD due to CC infarcton. Particularly, the combination of LR-model and SHAP-explainer could aid in achieving personalized risk prediction and be served as a decision-making tool for early intervention since its poor long-term outcome.