Optimising care for acute ischaemic stroke: Early detection, treatment and outcomes

Abstract

Abstract Background and Aim: Stroke management in pre-hospital and the early acute phase is crucial for timely and effective treatment to optimise care and outcomes. This thesis investigates the efficacy of machine learning (ML) models and traditional stroke scales for early detection of large vessel occlusion (LVO), evaluates novel visual impairment screening tools in emergency departments, and analyses outcomes of endovascular thrombectomy (EVT) in stroke patients with atrial fibrillation (AF). Methods: A systematic review and meta-analysis compared pre-hospital stroke scales and ML models for detecting LVO, where the ML models were based on clinical data (e.g., neurological examination findings and demographic characteristics). The effectiveness of Vision-Face-Arm-Speech-Time (V-FAST) checklist in detecting visual impairments was assessed against National Institutes of Health Stroke Scale (NIHSS) and orthoptist assessments in a hyperacute emergency setting. Outcomes of EVT in patients with AF were analysed, focusing on the impact of bridging thrombolysis (BT) and comparing sex and age differences in functional recovery and reperfusion success in a federated network and a nationwide cohort. Results: ML models showed higher discriminative performance than traditional stroke scales but faced challenges in real-world application due to variability and potential biases. The V-FAST checklist improved detection of several key visual impairments (visual field deficits, eye movement abnormalities, reading difficulties and visual extinction) in hyperacute settings. Its purpose is to serve as a screening tool to identify patients requiring comprehensive orthoptic assessment, although it was less effective in identifying complex eye movements disorders. AF status did not significantly impact haemorrhagic complications or mortality following EVT, and bridging thrombolysis (IV thrombolysis prior to EVT) offered a survival benefit in anticoagulated patients with AF. Females with AF had higher odds of good functional outcomes at 90 days compared to those without, and males with AF had higher successful reperfusion rates, especially in older groups. Conclusions: ML models can enhance early detection capabilities for LVO in pre-hospital settings, although their real-world application is limited by methodological and sample heterogeneity. The V-FAST checklist, evaluated within emergency department settings, shows improved detection of visual impairments in acute stroke care. AF status does not significantly impact EVT outcomes, supporting its safe use. Furthermore, the observed sex and age differences in EVT outcomes call for personalised stroke management approaches. Together, these findings present a cohesive research focus on improving acute stroke care across the entire patient journey, from early recognition and pre-hospital triage (via ML and stroke scales) to targeted symptom screening in emergency settings (V-FAST for visual deficits) and evaluation of outcomes in high-risk populations (AF patients undergoing EVT, including those on oral anticoagulants, and across demographic groups). This comprehensive approach supports the refinement of diagnostic and therapeutic protocols to enhance stroke care across diverse clinical environments and patient demographics.