Abstract TP160: The Non-stenosing Carotid Artery Plaque In Embolic Stroke Of Undetermined Source: A Retrospective Study

Introduction: Atherosclerotic cervical ICA disease is one of the major causes of ischemic stroke. The risk of stroke from mild to moderate stenoses (i.e., <50% stenosis) might be underestimated. Further investigation is mandated to describe the association between high-risk plaque features and ESUS.

Methods: This was a retrospective observational study. Using the stroke registry of our hospital's system between June 20th 2016 and June 20th 2021, we reviewed data for patients diagnosed with ESUS according to previously published definition criteria. Using CTA, we analyzed laterality of high-risk plaque features in relation to the stroke side, and then we identified the incidence of recurrent stroke events.

Results: Out of 1779 patients with cryptogenic ischemic stroke, only 152 met the inclusion criteria for ESUS. We Compared high-risk plaque features ipsilateral to stroke side as to contralaterally. There were significantly more ulcerations defined as >1 mm depression (19.08% vs 5.26%, p<.0001), plaque thickness >3 mm (19.08% vs 7.24%, p=0.001), and plaque length >1cm (13.16% vs 5.92%, p=0.0218). Also, there was a significant difference in stenosis of ipsilateral to stroke when compared contralaterally, especially for stenoses of 10-30% and 31-49% (17.76% vs 10.53% and 5.26% vs 2.63%, respectively. p=0.0327). There was also a significant difference in plaque component; both components (soft and calcified) and only soft plaque (42.76% vs 23.68% and 17.76% vs 9.21%, respectively. p<.0001) were more prevalent ipsilaterally. In total, 17 patients were found to have a recurrent stroke event, 8 patients had an ipsilateral stroke to the index event, 7 had a bilateral and 2 had a contralateral event.

Conclusion: ESUS is more commonly found ipsilateral to high-risk plaque features. The small number of our sample is definitely a limitation. Further large and multicenter studies aiming to form precise prediction models and scoring systems are needed to help guide treatment.

Abstract P405: A Time-Series Forecast Model to Assess Vital Sign Waveform Variability Prior to Vasospasm

Introduction: Symptomatic vasospasm (SV) is a complication of aneurysmal subarachnoid hemorrhage (aSAH) and can lead to cerebral infarction. Changes in vital trends, such as heart rate (HR) and mean arterial blood pressure (MAP), have been associated with SV in aSAH. Real-time assessment of instantaneous vital sign waveform data could improve detection of vital sign variability associated with vasospasm. However, no model using instantaneous waveform data exists to predict SV. We hypothesize that autoregressive integrated moving average (ARIMA) analysis, a time-series forecast model, is a useful approach to assess the variability of vital sign waveforms associated with SV.

Methods: In this small case-control study, vital signs of patients admitted to the neuroICU with aSAH were obtained using a software-based analytics platform, Sickbay. HR and MAP from 15 aSAH patients were continuously obtained from ECG and arterial line waveforms. Ten patients developed neurologic deficits attributed to angiographically-confirmed SV (Det). Five controls (Con) without SV were matched based on age. 3 Det and 3 Con were randomly selected for further analysis. For Det, waveforms were analyzed at 5-second intervals for 48 hours prior to clinical deterioration. For Con, waveforms were analyzed at a random 48-hour interval.

Results: Visually, MAP and not HR was more variable in Det than in Con patients (Figure). The ARIMA model plotted the forecasted-fit for each delta-variable waveform. The MAP confidence interval margins were significantly larger for Det patients compared to the Con patient. This trend was consistent across all other patients.

Conclusion: ARIMA is a useful tool to assess HR and MAP waveform variations prior to SV in aSAH. Larger studies are required to solidify this concept and further explore the combination of data analytics platform and ARIMA to predict neurological deterioration in SV.