Assessment of Vessel Density on Non-Contrast Computed Tomography to Detect Basilar Artery Occlusion

Introduction: Basilar artery occlusion (BAO) may be clinically occult due to variable and non-specific symptomatology. We evaluated the qualitative and quantitative determination of a hyperdense basilar artery (HDBA) on non-contrast computed tomography (NCCT) brain for the diagnosis of BAO.Methods: We conducted a case control study of patients with confirmed acute BAO vs a control group of suspected acute stroke patients without BAO. Two EM attending physicians, one third-year EM resident, and one medical student performed qualitative and quantitative assessments for the presence of a HDBA on axial NCCT images. Our primary outcome measures were sensitivity and specificity for BAO. Our secondary outcomes were inter-rater and intra-rater reliability of the qualitative and quantitative assessments.Results: We included 60 BAO and 65 control patients in our analysis. Qualitative assessment of the hyperdense basilar artery sign was poorly sensitive (54%–72%) and specific (55%–89%). Quantitative measurement improved the specificity of hyperdense basilar artery assessment for diagnosing BAO, with a threshold of 61.0–63.8 Hounsfield units demonstrating relatively high specificity of 85%–94%. There was moderate inter-rater agreement for the qualitative assessment of HDBA (Fleiss’ kappa statistic 0.508, 95% confidence interval: 0.435–0.581). Agreement improved for quantitative assessments, but still fell in the moderate range (Shrout-Fleiss intraclass correlation coefficient: 0.635). Intra-rater reliability for the quantitative assessments of the two attending physician reviewers demonstrated substantial consistency.Conclusion: Our results highlight the importance of carefully examining basilar artery density when interpreting the NCCT of patients with altered consciousness or other signs and symptoms concerning for an acute basilar artery occlusion. If the Hounsfield unit density of the basilar artery exceeds 61 Hounsfield units, BAO should be highly suspected

Designing out Medical Error: An Interdisciplinary Approach to the Design of Healthcare Equipment

Medical error is an internationally recognised problem, with major financial and human costs (Gray; 2003, de Vries; et.al. 2008). The design of hospital equipment, devices and environments can contribute to the problem. Clinical staff often have to cope with confusing interfaces and equipment, making their tasks difficult and potentially dangerous. There are calls to rethink the approach to design in healthcare. Design should acknowledge the real world issues users face in the hospital environment. A collaborative approach is required to understand these issues, (Karsh & Scanlon, 2007). This paper outlines the methodologies used in two interdisciplinary case study projects, revealing the importance of a clear set of working methods and detailing the approach taken at each point. The resulting designs aim to better support healthcare processes, reducing the instance of medical error and ultimately saving lives

Assessment of Vessel Density on Non-Contrast Computed Tomography to Detect Basilar Artery Occlusion

Introduction: Basilar artery occlusion (BAO) may be clinically occult due to variable and non-specific symptomatology. We evaluated the qualitative and quantitative determination of a hyperdense basilar artery (HDBA) on non-contrast computed tomography (NCCT) brain for the diagnosis of BAO.Methods: We conducted a case control study of patients with confirmed acute BAO vs a control group of suspected acute stroke patients without BAO. Two EM attending physicians, one third-year EM resident, and one medical student performed qualitative and quantitative assessments for the presence of a HDBA on axial NCCT images. Our primary outcome measures were sensitivity and specificity for BAO. Our secondary outcomes were inter-rater and intra-rater reliability of the qualitative and quantitative assessments.Results: We included 60 BAO and 65 control patients in our analysis. Qualitative assessment of the hyperdense basilar artery sign was poorly sensitive (54%–72%) and specific (55%–89%). Quantitative measurement improved the specificity of hyperdense basilar artery assessment for diagnosing BAO, with a threshold of 61.0–63.8 Hounsfield units demonstrating relatively high specificity of 85%–94%. There was moderate inter-rater agreement for the qualitative assessment of HDBA (Fleiss’ kappa statistic 0.508, 95% confidence interval: 0.435–0.581). Agreement improved for quantitative assessments, but still fell in the moderate range (Shrout-Fleiss intraclass correlation coefficient: 0.635). Intra-rater reliability for the quantitative assessments of the two attending physician reviewers demonstrated substantial consistency.Conclusion: Our results highlight the importance of carefully examining basilar artery density when interpreting the NCCT of patients with altered consciousness or other signs and symptoms concerning for an acute basilar artery occlusion. If the Hounsfield unit density of the basilar artery exceeds 61 Hounsfield units, BAO should be highly suspected

Automated detection of intracranial large vessel occlusions using Viz.ai software: Experience in a large, integrated stroke network

Abstract Background and purpose Endovascular thrombectomy is an evidence‐based treatment for large vessel occlusion (LVO) stroke. Commercially available artificial intelligence has been designed to detect the presence of an LVO on computed tomography angiogram (CTA). We compared Viz.ai‐LVO (San Francisco, CA, USA) to CTA interpretation by board‐certified neuroradiologists (NRs) in a large, integrated stroke network. Methods From January 2021 to December 2021, we compared Viz.ai detection of an internal carotid artery (ICA) or middle cerebral artery first segment (MCA‐M1) occlusion to the gold standard of CTA interpretation by board‐certified NRs for all code stroke CTAs. On a monthly basis, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Trend analyses were conducted to evaluate for any improvement of LVO detection by the software over time. Results 3851 patients met study inclusion criteria, of whom 220 (5.7%) had an ICA or MCA‐M1 occlusion per NR. Sensitivity and specificity were 78.2% (95% CI 72%–83%) and 97% (95% CI 96%–98%), respectively. PPV was 61% (95% CI 55%–67%), NPV 99% (95% CI 98%–99%), and accuracy was 95.9% (95% CI 95.3%–96.5%). Neither specificity or sensitivity improved over time in the trend analysis. Conclusions Viz.ai‐LVO has high specificity and moderately high sensitivity to detect an ICA or proximal MCA occlusion. The software has the potential to streamline code stroke workflows and may be particularly impactful when emergency access to NRs or vascular neurologists is limited