Striving for Socioeconomic Equity in Ischemic Stroke Care: Imaging and Acute Treatment Utilization From a Comprehensive Stroke Center

Association between Heat Vulnerability Index and Stroke Severity

BACKGROUND

Socioeconomically disadvantaged neighborhoods are particularly vulnerable to heat-related illnesses. We aim to investigate the association between the heat vulnerability index (HVI), an established neighborhood-level metric of heat-related mortality risk, and acute ischemic stroke (AIS) severity.

METHODS

We conducted a retrospective analysis of consecutive AIS admissions to a comprehensive stroke center between 2012 and 2021. Stroke severity was defined upon admission based on the National Institutes of Health Stroke Scale (NIHSS). Demographic, socioeconomic, and clinical characteristics were extracted from electronic health records. HVI status was assigned using residential ZIP codes. Multivariable logistic regression analyses were performed.

RESULTS

Of 3429 AIS admissions, 1123 (32.8%) were from high-HVI (scores 4-5) neighborhoods and 868 (25.3%) had severe stroke (NIHSS score ≥ 10). In the multivariable regression model with stepwise selection, a high HVI was independently associated with severe stroke (adjusted odds ratio: 1.40 [95% confidence interval 1.16-1.69]).

CONCLUSIONS

The association between a high HVI and severe stroke underscores the importance of targeting policy interventions to mitigate heat-related illness in socioeconomically disadvantaged neighborhoods.

Social Determinants of Health Framework to Identify and Reduce Barriers to Imaging in Marginalized Communities

Social determinants of health (SDOH) are conditions influencing individuals' health based on their environment of birth, living, working, and aging. Addressing SDOH is crucial for promoting health equity and reducing health outcome disparities. For conditions such as stroke and cancer screening where imaging is central to diagnosis and management, access to high-quality medical imaging is necessary. This article applies a previously described structural framework characterizing the impact of SDOH on patients who require imaging for their clinical indications. SDOH factors can be broadly categorized into five sectors: economic stability, education access and quality, neighborhood and built environment, social and community context, and health care access and quality. As patients navigate the health care system, they experience barriers at each step, which are significantly influenced by SDOH factors. Marginalized communities are prone to disparities due to the inability to complete the required diagnostic or screening imaging work-up. This article highlights SDOH that disproportionately affect marginalized communities, using stroke and cancer as examples of disease processes where imaging is needed for care. Potential strategies to mitigate these disparities include dedicating resources for clinical care coordinators, transportation, language assistance, and financial hardship subsidies. Last, various national and international health initiatives are tackling SDOH and fostering health equity.

Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT

BACKGROUND

Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations.

METHODS

The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. The median of volume, overlap, and distance segmentation metrics were determined for agreement in lesion segmentations between (1) three experts, (2) the majority model and each expert, and (3) the random model and each expert. The two sided Wilcoxon signed rank test was used to compare performances (1) to 2) and (1) to (3). We further compared volumes with the 24 hour follow-up diffusion weighted imaging (DWI, final infarct core) and correlations with clinical outcome (modified Rankin Scale (mRS) at 90 days) with the Spearman method.

RESULTS

The random model outperformed the inter-expert agreement ((1) to (2)) and the majority model ((1) to (3)) (dice 0.51±0.04 vs 0.36±0.05 (P<0.0001) vs 0.45±0.05 (P<0.0001)). The random model predicted volume correlated with clinical outcome (0.19, P<0.05), whereas the median expert volume and majority model volume did not. There was no significant difference when comparing the volume correlations between random model, median expert volume, and majority model to 24 hour follow-up DWI volume (P>0.05, n=51).

CONCLUSION

The random model for ischemic injury delineation on non-contrast CT surpassed the inter-expert agreement ((1) to (2)) and the performance of the majority model ((1) to (3)). We showed that the random model volumetric measures of the model were consistent with 24 hour follow-up DWI.

Gender-Based Disparity in Acute Stroke Imaging Utilization and the Impact on Treatment and Outcomes: 2012 to 2021

INTRODUCTION

Prior studies have revealed significant socio-economic disparities in neuro-imaging and treatment utilization for patients with acute ischemic stroke (AIS). In this study, we sought to evaluate whether a sex-based disparity exists in neuro-imaging and to determine its etiology and association with acute treatment and outcomes.

MATERIALS AND METHODS

This was a retrospective study of consecutive patients with AIS admitted to a comprehensive stroke center between 2012 and 2021. Patient demographic and clinical characteristics, neuro-imaging, acute treatment, and early clinical outcomes were extracted from the electronic medical records. Trend analysis, bivariate analysis of patient characteristics by sex, and multivariable logistic regression analyses were conducted.

RESULTS

Of the 7,540 AIS episodes registered from 2012 to 2021, 47.9% were female patients. After adjusting for demographic, clinical, and temporal factors, significantly higher utilization of CTA was found for male patients (odds ratio = 1.20 [95% confidence interval 1.07-1.34]), particularly from socio-economically advantaged groups, and in years 2015 and 2019, representing the years endovascular thrombectomy recommendations changed. Despite this, male patients had significantly lower intravenous thrombolysis utilization (odds ratio = 0.83 [95% confidence interval 0.71-0.96]) and similar endovascular thrombectomy rates as female patients. There were no significant sex differences in early clinical outcomes, and no relevant clinical or demographic factors explained the CT angiography utilization disparity.

CONCLUSION

Despite higher CT angiography utilization in socio-economically advantaged male patients with AIS, likely overutilization due to implicit biases following guideline updates, the rates of acute treatment, and early clinical outcomes were unaffected.

Lost to Follow-Up: A Nationwide Analysis of Patients With Transient Ischemic Attack Discharged From Emergency Departments With Incomplete Imaging

PURPOSE

Imaging guidelines for transient ischemic attack (TIA) recommend that patients undergo urgent brain and neurovascular imaging within 48 hours of symptom onset. Prior research suggests that most patients with TIA discharged from the emergency department (ED) do not complete recommended TIA imaging workup during their ED encounters. The purpose of this study was to determine the nationwide percentage of patients with TIA discharged from EDs with incomplete imaging workup who complete recommended imaging after discharge.

METHODS

Patients discharged from EDs with the diagnosis of TIA were identified from the Medicare 5% sample for 2017 and 2018 using International Classification of Diseases, tenth rev, Clinical Modification codes. Imaging performed was identified using Current Procedural Terminology codes. Incomplete imaging workup was defined as a TIA encounter without cross-sectional brain, brain-vascular, and neck-vascular imaging performed within the subsequent 30 days of the initial ED encounter. Patient- and hospital-level factors associated with incomplete TIA imaging were analyzed in a multivariable logistic regression.

RESULTS

In total, 6,346 consecutive TIA encounters were analyzed; 3,804 patients (59.9%) had complete TIA imaging workup during their ED encounters. Of the 2,542 patients discharged from EDs with incomplete imaging, 761 (29.9%) completed imaging during the subsequent 30 days after ED discharge. Among patients with TIA imaging workup completed after ED discharge, the median time to completion was 5 days. For patients discharged from EDs with incomplete imaging, the odds of incomplete TIA imaging at 30 days after discharge were highest for black (odds ratio, 1.84; 95% confidence interval, 1.27-2.66) and older (≥85 years of age; odds ratio, 2.41; 95% confidence interval, 1.78-3.26) patients. Reference values were age cohort 65 to 69 years; male gender; white race; no co-occurring diagnoses of hypertension, hyperlipidemia, or diabetes mellitus; household income > $63,029; hospital in the Northeast region; urban hospital location; hospital size > 400 beds; academically affiliated hospital; and facility with access to MRI.

CONCLUSIONS

Most patients discharged from EDs with incomplete TIA imaging workup do not complete recommended imaging within 30 days after discharge.