Jennifer A. Frontera

Background and Purpose: Prognostication after acute ischemic stroke often relies on limited variables and simple risk scores, despite richer information being available at admission. We developed a multimodal AI model using admission data to predict modified Rankin Scale (mRS) outcomes and compared it to established tools. Methods: In a retrospective study of ischemic stroke/TIA patients, we trained three modality-specific models on admission non-contrast head CT, history and physical notes, and structured clinical variables, and combined them in a weighted-average ensemble. We predicted binary (mRS 0-2 versus 3-6) and ordinal mRS (0-6) outcomes at discharge and 90 days. Performance on an external test cohort was compared with THRIVE and SPAN-100 scores using AUROC, AUPRC, Brier score, mean absolute error (MAE), and quadratic weighted kappa (QWK). Results: A total of 6,915 patients were split into training, validation and testing cohorts in a 3:1:1 ratio. For discharge binary mRS (n=1596), the multimodal ensemble achieved significantly better discrimination (AUROC 0.859, AUPRC 0.858) with 25-61% lower Brier scores than THRIVE or SPAN-100 (all p<0.001). For 90-day binary mRS (n=207), the model also outperformed both THRIVE and SPAN-100 (AUROC 0.838, AUPRC 0.805, with 3-38% lower Brier scores). Ordinal mRS prediction showed similarly strong performance with significantly better QWK at discharge and numerically lower MAE. The multimodal ensemble model reassigned about one-third of patients to different risk categories versus THRIVE and was closer to the true discharge outcome in ∼74% of discordant cases. Conclusions: We developed a well-calibrated multimodal AI model for prediction of discharge and 90-day post-stroke functional outcomes using only data present at the time of admission. This model outperforms existing prognostic tools and can support early clinical decision-making.

INTRODUCTION: Though brain fog is common in Long‐coronavirus disease 2019 (Long‐COVID), the incidence of mild cognitive impairment (MCI) is unknown. METHODS: In an observational cohort study, recovered COVID‐positive, Long‐COVID, and COVID‐negative subjects underwent blinded evaluation using National Alzheimer's Coordinating Center (NACC) and National Institute on Aging (NIA) ‐Alzheimer's Association diagnostic criteria for dementia and MCI. The cumulative incidence of MCI was calculated for each group, and the hazard of MCI was compared between groups. RESULTS: Among 260 subjects, the cumulative incidence of MCI over 4.4 years was higher with Long‐COVID (27%) versus recovered‐COVID (5%) or COVID‐negative status (1%). There was a higher hazard of MCI for patients with Long‐COVID compared to those without (hazard ratio [HR] 3.93, 95% confidence interval [CI] 1.86–8.31, p < 0.001), and specifically for the Alzheimer's disease (AD) ‐related MCI subtype (HR 3.20, 95% confidence interval [CI] 1.14–9.00, p = 0.027). DISCUSSION: The cumulative incidence and adjusted hazard of MCI (and specifically AD‐related MCI) at 4.4 years was significantly higher among Long‐COVID patients compared to recovered‐COVID and COVID‐negative controls.

Objective: Cervical artery dissection (CeAD) may be limited to the extracranial extradural space or extend to the intradural space. Intradural extension can potentially increase the risk of stroke and subarachnoid hemorrhage. However, the factors associated with intradural extension and its impact on clinical outcome remain unclear. Methods: This was a secondary analysis of the STOP‐CAD observational, multi‐center study. Patients with CeAD and intradural extension (CeADid) were compared with those with pure CeAD extradural dissections (CeADed) using multiple regression analyses. Results: Of 4,023 patients with CeAD, 534 (13.3%) had CeADid. In comparison to patients with CeADed, those with CeADid more often had clinical overt stroke or transient ischemic attack (TIA) at presentation, acute infarcts on imaging, a vertebral artery affected, and severe stenosis of the involved vessel ( p < 0.001 for all). In contrast, carotid involvement and complete occlusions were more frequent in patients with CeADed ( p < 0.001 for both). CeADid was associated with a shift in the distribution of scores on the modified Rankin Scale (mRS) toward worse functional outcome (odds ratio [OR] = 0.76, 95% confidence interval [CI] = 0.62–0.92) but the odds for favorable outcomes (mRS = 0–2) did not differ between the groups after appropriate adjustments on multivariate analysis. CeADid was independently associated with higher mortality at 180 days on multivariate analysis (adjusted OR = 2.84, 95% CI = 1.50–5.38). Interpretation: CeADid is associated with more severe clinical presentation, a shift toward less favorable outcomes, and higher mortality rates. These findings suggest that CeADid may represent a high‐risk type of CeAD. ANN NEUROL 2026;99:1189–1197

BACKGROUND: Intracranial atherosclerosis (ICAS) is associated with an increased risk of early recurrent ischemic stroke. We evaluated whether biomarkers of impaired distal perfusion—specifically, anterior circulation borderzone infarct, and hypoperfusion mismatch volume—were associated with recurrent ischemic stroke within 90 days. METHODS: The BIORISK ICAS (Biomarkers and Recurrence Risk in Symptomatic Intracranial Atherosclerosis) is a multicenter retrospective international study (35 sites) that included hospitalized patients with symptomatic ICAS (50%–99% luminal stenosis of the intracranial vertebral, basilar, distal internal carotid, or proximal middle cerebral artery) from January 2019 to June 2024. The primary outcome was recurrent ischemic stroke in the territory of the symptomatic artery within 90 days. In the primary analysis, the exposure was an acute anterior circulation borderzone infarct. In secondary analysis of the subset with presentation within 72 hours of last known normal and perfusion imaging completed, the exposure of interest was prespecified as hypoperfusion mismatch volume at T _max (time to maximum) threshold of 6 seconds, dichotomized at the Youden index. We performed multivariable Cox regression to test associations between exposure variables and the outcome, adjusting for clinically relevant variables and those associated with the outcome ( P <0.1). RESULTS: Of 2050 patients with symptomatic ICAS, 1737 (84.7%) presented within 72 hours of symptom onset, among whom 509 (29.3%) underwent perfusion imaging. The primary analysis included 1891 patients; 174 (9.2%) patients had recurrent ischemic stroke in the symptomatic arterial territory. In adjusted Cox regression models, there was an association between anterior circulation borderzone infarct and recurrent ischemic stroke at 90 days (adjusted hazard ratio, 1.40 [95% CI, 1.02–1.93]). In the perfusion imaging analysis, hypoperfusion mismatch of ≥10 mL was associated with recurrent ischemic stroke (adjusted hazard ratio, 1.83 [95% CI, 1.03–3.28]). CONCLUSIONS: Biomarkers of impaired distal perfusion, anterior circulation borderzone infarct, and hypoperfusion mismatch were associated with increased risk of recurrent ischemic stroke. These findings support the use of perfusion imaging in ICAS as well as future trials investigating early reperfusion in high-risk patients with ICAS.

The Stroke Treatment Academic Industry Roundtable convened a workshop regarding artificial intelligence (AI) and innovative clinical trial designs during the Stroke Treatment Academic Industry Roundtable XIII meeting on March 28, 2025. This forum brought together stroke physicians and researchers, and industry representatives to discuss the current use and future opportunities for AI and novel trial designs in acute stroke trials. AI already plays a substantial role in the treatment of acute stroke with regards to imaging but is poised to have a much larger impact in clinical care and research trials over the coming years. The quality and understanding of the data are used to train the AI, the human element needed to ensure training is successful, and the clinician and trialist at the bedside, the humans “in the loop,” will be necessary to maximize AI’s effectiveness in clinical practice and trials. Platform trials address multiple scientific questions in an area of medicine simultaneously within the same trial structure by sharing controls across multiple interventions. While platform trials increase efficiency and potentially decrease the time needed to answer important clinical scientific questions, they also can introduce complexity to standard workflows. Future acute ischemic stroke clinical trials should incorporate elements of pragmatic and patient-centered trial design when possible. Pragmatic trials aim to assess the effectiveness of treatments when they are implemented into routine clinical care rather than under idealized conditions. AI models and platform, pragmatic, and patient-centered trial designs are new tools to answer important clinical questions, but understanding how they work, their best uses, and their limitations is critical for accelerating successful new treatments for stroke.

Background: There is a paucity of data comparing neuropsychiatric testing in patients with post‐acute COVID‐19 symptoms (“long‐COVID”) to COVID‐19 patients without long‐term symptoms and to COVID‐19 negative controls. Method: We conducted a cross‐sectional study of patients with and without prior laboratory‐confirmed COVID‐19 (COV+ vs COV‐), who had no previous history of dementia/cognitive impairment. Long‐COVID was defined as subjective symptoms (WHO symptom questionnaire) lasting >1 month after index SARS‐CoV‐2 infection. All patients underwent UDS3 neuropsychiatric testing and received a formal cognitive diagnosis (with respective etiology following NACC criteria) by a consensus group of physicians blinded to their long‐COVID status as: normal, mild‐cognitive impairment (MCI), or dementia. Cognitive test scores, CDR, and physician diagnoses were compared between patients with and without long‐COVID using Fisher's exact and Mann‐Whitney U tests and multivariable logistic regression models were constructed to adjust for differences in age, sex, time from initial COVID diagnosis and number of COVID‐19 infections. Result: We enrolled 279 patients: N = 51 (18%) COV‐, N = 228 (82%) COV+, N = 122 (44%) with long‐COVID, median age 70 years (IQR 63‐76), and 62% female. Compared to COV‐ subjects and COV+ subjects without long‐COVID, participants with long‐COVID had significantly worse cognitive test results (≥1 cognitive battery abnormal in 64% with long‐COVID compared to 46% without, p = 0.003), as well as worse global CDR and CDR sum of boxes scores (26% CDR>0 among long‐COVID vs. 6% without long‐COVID, p p p = 0.038), and MCI related to psychiatric diagnoses (11% vs. 1%, p p = 0.025), and specifically MCI due to Alzheimer's (aOR 4.4, 95% CI 1.1‐18.3, p = 0.041), but not MCI due to psychiatric disease. Conclusion: Long‐COVID patients had significantly worse cognitive test scores and higher rates of MCI (Alzheimer's type) diagnoses compared to both COV‐ and COV+ subjects without long‐COVID symptoms.

Background: While mechanisms underlying post‐COVID‐19 cognitive impairment are unknown, increased blood‐brain barrier (BBB) permeability and immune alterations have been posited as possible contributing factors. Methods: We conducted a cross‐sectional study of patients with and without prior laboratory‐confirmed COVID‐19 (COV+ vs COV‐), with no previous history of dementia/cognitive impairment. Brain fog was defined as the subjective symptoms of memory loss, confusion, and/or difficulty concentrating lasting >1 month after index SARS‐CoV‐2 infection. Fasting plasma biomarkers of inflammation (cytokines), and BBB disruption (Simoa SP‐X), as well as phosphorylated tau (pTau) were measured using Simoa HD‐X technology. Patients underwent concurrent UDS3 neuropsychiatric testing and received a formal cognitive diagnosis by a consensus group of physicians following NACC criteria. Plasma biomarker levels were compared between patients with and without brain fog, and with and without a diagnosis of MCI using Mann‐Whitney U tests. Results: We enrolled 279 patients: N = 51 (18%) COV‐, N = 228 (82%) COV+, N = 96 (34%) with post‐COVID brain fog, and N = 35 (13%) COV+ patients with newly diagnosed MCI post‐COVID. Several plasma cytokine levels (TNF‐a, IL‐4, IL‐10, IL‐22) were significantly lower in those with brain fog compared to those without (all p ≤0.01), while markers of BBB permeability were higher (heparin‐binding epidermal growth factor 10.1 pg/mL in brain fog patients vs 7.7 pg/mL without, p p = 0.055; and placental growth factor 12.4 vs 11.4 pg/mL, p = 0.090). Among COV+ patients diagnosed with MCI, TNF‐a was significantly lower compared to cognitively normal subjects (1.3 vs. 1.5 pg/mL, p = 0.031), while pTau‐217 was significantly higher in those with MCI due to Alzheimer's (0.21 vs. 0.13 pg/mL, p = 0.009) compared to those without. Conclusions: Lower cytokine levels, increased markers of BBB disruption, and altered markers of tau pathology were observed in patients with brain fog and COV+ subjects with MCI, which may suggest immune exhaustion, increased BBB permeability, and disrupted tau processing as possible mechanisms of post‐COVID‐19 brain fog.