Delays in the diagnosis of ischaemic stroke presenting with persistent reduced level of consciousness: A systematic review

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography

BACKGROUND

Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO.

METHODS

We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature.

RESULTS

Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity.

CONCLUSIONS

Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Bilateral Stroke: A Case Report

We present the case of a 74-year-old woman, functionally independent in her daily activities, with a medical history notable for hypertension and dyslipidemia. She presented to the emergency room with an altered level of consciousness, opening her eyes only to pain, no verbal response, and flexion withdrawal from pain coupled with a Glasgow Coma Scale of 7. The intensive care unit was promptly summoned, and the patient was intubated and admitted to intensive care. Comprehensive laboratory assessments revealed no abnormalities and an initial cerebral CT scan showed no acute changes. A subsequent CT scan performed 24 hours post-event disclosed bilateral ischemia affecting the territories of the anterior and middle cerebral arteries. Regrettably, this catastrophic event precluded any potential for recovery. Consequently, the decision was made not to pursue further investigations to determine the underlying cause. The medical team opted for supportive treatment and comfort measures. Tragically, the patient died on the 37th day of hospital admission.