Posterior circulation ischaemic stroke diagnosis and management

The enhanced reliability of higher national institute of health stroke scale thresholds over the conventional 6-point scale

INTRODUCTION

It is still uncertain if higher thresholds on National Institute of Health Stroke Scale (NIHSS) are better predictors of large infarctions than the conventional 6-point cutoff.

METHODS

We used 6-point and higher NIHSS thresholds including 8, 9, and 10-point to predict relative infarct areas, expressed as percentage of the affected hemisphere on axial brain computed tomography images, beginning at 5% with 5% increments each time until reaching the 40% cutoff for large infarctions, or achieving 100% sensitivity. Results were compared using area under the receiver operating characteristic curves (AUROC).

RESULTS

We enrolled 151 patients of acute ischemic stroke (Mean age: 62.88 years ± 12.71; Female: 48.34%). 77 patients (50.99%) exhibited left hemisphere strokes, while 74 (49%) had right hemisphere involvement. Sensitivity values of the 6-point for infarcts measuring 5%, 10%, 20%, 30%, and 40% were 62%, 64%, 77%, 82%, and 100%, respectively. At 40% infarct-size, 8-point achieved comparable results (52%, 55%, 69%, 76%, 100%), closely aligning with the 9-point (50%, 53%, 69%, 76%, 100%). The10-point was slightly trailing behind in sensitivity at 40% infarct-core (96%). Moreover, higher thresholds exhibited improved false-positive rates (FPR). At 40% infarct size, the FPRs of 6, 8, 9, and 10 points were 39%, 27%, 27%, and 21% respectively. Higher thresholds had augmented AUROC values (0.86, 0.86, 0.89) as compared to the 6-point (0.80). Logistic regression identified 14-point as definitive cutoff for large infarctions.

CONCLUSION

Higher thresholds can better differentiate small and medium infarcts as true-negatives and substantially reduce false-positive referrals for mechanical thrombectomy.

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

BACKGROUND

Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management.

METHODS

We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew's correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance.

RESULTS

The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value.

CONCLUSION

This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient's clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.

Treatment of Acute Ischemic Stroke: The Last 30 Years of Trials and Tribulations

The landscape of acute ischemic stroke management has undergone a substantial transformation over the past 3 decades, mirroring our enhanced comprehension of the pathology and progress in diagnostic techniques, therapeutic interventions, and preventive measures. The 1990s marked a pivotal moment in stroke care with the integration of intravenous thrombolytics. However, the most significant paradigm shift in recent years has undoubtedly been the advent of endovascular thrombectomy. This article endeavors to deliver an exhaustive analysis of this revolutionary progression.

Endovascular therapy of acute vertebrobasilar occlusions: influence of first-line strategy in the Endovascular Treatment in Ischemic Stroke (ETIS) Registry

BACKGROUND

The choice of the first-line technique in vertebrobasilar occlusions (VBOs) remains challenging. We aimed to report outcomes in a large cohort of patients and to compare the efficacy and safety of contact aspiration (CA) and combined technique (CoT) as a first-line endovascular technique in patients with acute VBOs.

METHODS

We retrospectively analyzed clinical and neuroradiological data of patients with VBOs from the prospective, multicenter, observational Endovascular Treatment in Ischemic Stroke (ETIS) Registry in France between January 2015 and August 2023. The primary outcome was the first pass effect (FPE) rate, whereas modified Thrombolysis In Cerebral Infarction (mTICI) 2b-3 and 2c-3, number of passes, need for rescue strategy, modified Rankin Scale (mRS) 0-2, mortality, and symptomatic intracranial hemorrhage (sICH) were secondary outcomes. We performed univariate and multivariate analyses to investigate differences between the two groups.

RESULTS

Among the 583 included patients (mean age 66.2 years, median National Institutes of Health Stroke Scale (NIHSS) 13, median posterior circulation Alberta Stroke Program Early CT Score (pc-ASPECTS) 8), 393 were treated with CA alone and 190 with CoT. Procedures performed with CA were shorter compared with CoT (28 vs 47 min, P<0.0001); however, no differences were observed in terms of FPE (CA 43.3% vs CoT 38.4%, P=0.99), and successful final recanalization (mTICI 2b-3, CA 92.4% vs CoT 91.8%, P=0.74) did not differ between the two groups. Functional independence and sICH rates were also similar, whereas mortality was significantly lower in the CA group (34.5% vs 42.9%; OR 1.79, 95% CI 1.03 to 3.11).

CONCLUSIONS

We observed no differences in FPE, mTICI 2b-3, sICH, and functional independence between the two study groups. First-line CA was associated with shorter procedures and lower mortality rates than CoT.

A smartphone pupillometry tool for detection of acute large vessel occlusion

OBJECTIVES

Pupillary light reflex (PLR) parameters can be used as quantitative biomarkers of neurological function. Since digital infrared pupillometry is expensive, we sought to examine alterations in PLR parameters using a smartphone quantitative pupillometry platform in subjects with acute ischemic stroke (AIS).

MATERIALS AND METHODS

Patients were enrolled if they presented to the emergency department as a stroke code activation and had evidence of a large vessel occlusion (LVO) on computed tomography angiography. Controls were enrolled from hospital staff. A smartphone pupillometer was used in AIS patients with LVO pre-mechanical thrombectomy, immediately post-thrombectomy, and at 24 h post-thrombectomy. Clinical and demographic data were collected, along with the proprietary Neurological Pupil index (NPi) score from the NPi-200 digital infrared pupillometer. PLR parameters were compared using mean differences. The absolute and non-absolute inter-eye difference in each parameter for each subject were also analyzed by measuring 1 - (R:L) to determine alteration in the equilibrium between subject pupils. The NPi was analyzed for mean differences between cohorts.

RESULTS

Healthy controls (n = 132) and AIS patients (n = 31) were enrolled. Significant differences were observed in PLR parameters for healthy subjects when compared to pre-thrombectomy subjects in both mean and absolute inter-eye differences after post hoc Bonferroni correction. The proprietary NPi score was not significantly different for all groups and comparisons.

CONCLUSIONS

Significant alterations in the PLR were observed in AIS patients with LVO before thrombectomy, indicating the potential use of smartphone pupillometry for detection of LVO.