DOI: 10.1161/str.55.suppl_1.tp234 ISSN: 0039-2499

Abstract TP234: A Predictive Model for Early Neurologic Deterioration in Small Subcortical Infarcts

Eric D Goldstein, Liqi Shu, Edgar R Lopez-Navarro, Dixon Yang, Jose Gutierrez, Shadi Yaghi
  • Advanced and Specialized Nursing
  • Cardiology and Cardiovascular Medicine
  • Neurology (clinical)

Introduction: Roughly a quarter of patients with an acute small subcortical infarct may develop early neurologic deterioration (END) hallmarked by progressive or stuttering neurologic deficits. Importantly, END results in worse short- and long-term functional outcomes. However, despite its importance, there is an inability to identify those at risk for END, thereby creating a barrier to designing future treatment and preventative trials. We aimed to generate a simple predictive scoring model for END in patients with an acute SSI.

Methods: We performed a retrospective chart review of all adult patients admitted to Rhode Island Hospital with an acute SSI between 2015 and 2022. Clinical and demographic data were extracted. Radiographic variables included: 1)infarct signal intensity ratio ≤ 1.15 (analogous to the MR-WITNESS trial), 2) infarct morphology, and 3) infarct size. The primary outcome was END, defined as a ≥ 2-point increase in NIHSS or progressive disabling stroke-related deficits while hospitalized attributed to the index acute SSI. Standard descriptive statistical techniques were used. We then employed a backward stepwise logistic regression with a 0.05 threshold to discern factors associated with END, with each factor contributing one point to the model. An external validation cohort was ascertained from Columbia University. The area under the curve (AUC) was used to assess the utility of the predictive model.

Results: A total of 252 patients met inclusion criteria. Demographic and clinical histories did not differ amongst the primary or validation cohorts. A signal intensity ratio ≤ 1.15, “finger-like” SSI morphology, and the presence of SSI on > 3 axial diffusion weighted imaging slices were predictive of END. Allocating one point for each domain (minimum 0, maximum 3) yielded an AUC of 0.88. A score of 2 points captured 91.6% of all END cases. Applying this model to the external validation cohort (n=43) yielded an AUC of 0.68.

Conclusion: In sum, we found that a radiographic ordinal model including signal intensity ratio ≤ 1.15, “finger-like” SSI morphology, and infarct presence on > 3 axial diffusion weight imaging slices was helpful in predicting END. Future studies are needed to expand the external cohort size to test this model’s validity.

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