Predicting prognosis in patients with stroke treated with intravenous alteplase through blood pressure changes: A machine learning-based approach

Enhancing ischemic stroke management: leveraging machine learning models for predicting patient recovery after Alteplase treatment

AIM

Ischemic stroke remains a leading global cause of morbidity and mortality, emphasizing the need for timely treatment strategies. This study aimed to develop a machine learning model to predict clinical outcomes in ischemic stroke patients undergoing Alteplase therapy.

METHODS

Data from 457 ischemic stroke patients were analyzed, including 50 demographic, clinical, laboratory, and imaging variables. Five machine learning algorithms - k-nearest neighbors (KNN), support vector machines (SVM), Naïve Bayes (NB), decision trees (DT), and random forest (RF) - were applied for constructing models. Additional feature importance analysis were p to identify high-impact predictors.

RESULTS

The Random Forest model showed the highest predictive reliability, outperforming other algorithms in sensitivity (0.97 ± 0.02) and F-measure (0.96 ± 0.02). feature importance analysis identified NIH1C (LOC commands (eye and hand movements)), NIH1B (LOC questions (birthday and age recall)), and NIH_noValue (the absence of any stroke characteristics) as the most influential predictors. Using only the top-ranked features identified from the feature importance analysis, the model maintained comparable performance, suggesting a streamlined yet effective predictive approach.

CONCLUSION

Our findings highlight the potential of machine learning in optimizing ischemic stroke treatment outcomes. Random Forest, in particular, proved effective as a decision-support tool, offering clinicians valuable insights for more tailored treatment approaches.

Extracellular cold-inducible RNA-binding protein in CNS injury: molecular insights and therapeutic approaches

Central nervous system (CNS) injuries, such as ischemic stroke (IS), intracerebral hemorrhage (ICH) and traumatic brain injury (TBI), are a significant global burden. The complex pathophysiology of CNS injury is comprised of primary and secondary injury. Inflammatory secondary injury is incited by damage-associated molecular patterns (DAMPs) which signal a variety of resident CNS cells and infiltrating immune cells. Extracellular cold-inducible RNA-binding protein (eCIRP) is a DAMP which acts through multiple immune and non-immune cells to promote inflammation. Despite the well-established role of eCIRP in systemic and sterile inflammation, its role in CNS injury is less elucidated. Recent literature suggests that eCIRP is a pleiotropic inflammatory mediator in CNS injury. eCIRP is also being evaluated as a clinical biomarker to indicate prognosis in CNS injuries. This review provides a broad overview of CNS injury, with a focus on immune-mediated secondary injury and neuroinflammation. We then review what is known about eCIRP in CNS injury, and its known mechanisms in both CNS and non-CNS cells, identifying opportunities for further study. We also explore eCIRP's potential as a prognostic marker of CNS injury severity and outcome. Next, we provide an overview of eCIRP-targeting therapeutics and suggest strategies to develop these agents to ameliorate CNS injury. Finally, we emphasize exploring novel molecular mechanisms, aside from neuroinflammation, by which eCIRP acts as a critical mediator with significant potential as a therapeutic target and prognostic biomarker in CNS injury.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach

Background

Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis.

Objective

This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach.

Methods

In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model.

Results

Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set.

Conclusion

The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.