Faculty Bibliography

Space occupying cerebral edema is a feared complication after large ischemic stroke, occurring in up to 30% of patients with middle cerebral artery (MCA) occlusion and peaking 2-4 days after injury. Little is known about the factors and outcomes associated with peak edema timing, especially after 96 h. We aimed to characterize differences and compare discharge status between patients who experienced maximum midline shift (MLS) or decompressive hemicraniectomy (DHC) in the acute (< 48 h), average (48-96 h), and subacute (> 96 h) groups. We performed a two-center, retrospective study of patients with ≥ 1/2 MCA territory infarct and MLS. We constructed a multivariable model to test the association of subacute peak edema and favorable discharge status, adjusting for various confounders. Of 321 eligible patients, 32%, 36%, and 32% experienced acute, average, and subacute peak edema. Subacute peak edema was significantly associated with higher odds of favorable discharge than acute peak edema (aOR, 2.05; 95% CI, 1.03-4.11). Subacute peak edema after large MCA stroke is associated with better discharge status compared to acute peak edema courses. Understanding how the timing of cerebral edema affects risk of unfavorable discharge has important implications for treatment decisions and prognostication.

BACKGROUND:Early prediction of hematoma expansion (HE) following nontraumatic intracerebral hemorrhage (ICH) may inform preemptive therapeutic interventions. We sought to identify how accurately machine learning (ML) radiomics models predict HE compared with expert clinicians using head computed tomography (HCT). METHODS:We used data from 900 study participants with ICH enrolled in the Antihypertensive Treatment of Acute Cerebral Hemorrhage 2 Study. ML models were developed using baseline HCT images, as well as admission clinical data in a training cohort (n = 621), and their performance was evaluated in an independent test cohort (n = 279) to predict HE (defined as HE by 33% or > 6 mL at 24 h). We simultaneously surveyed expert clinicians and asked them to predict HE using the same initial HCT images and clinical data. Area under the receiver operating characteristic curve (AUC) were compared between clinician predictions, ML models using radiomic data only (a random forest classifier and a deep learning imaging model) and ML models using both radiomic and clinical data (three random forest classifier models using different feature combinations). Kappa values comparing interrater reliability among expert clinicians were calculated. The best performing model was compared with clinical predication. RESULTS:The AUC for expert clinician prediction of HE was 0.591, with a kappa of 0.156 for interrater variability, compared with ML models using radiomic data only (a deep learning model using image input, AUC 0.680) and using both radiomic and clinical data (a random forest model, AUC 0.677). The intraclass correlation coefficient for clinical judgment and the best performing ML model was 0.47 (95% confidence interval 0.23-0.75). CONCLUSIONS:We introduced supervised ML algorithms demonstrating that HE prediction may outperform practicing clinicians. Despite overall moderate AUCs, our results set a new relative benchmark for performance in these tasks that even expert clinicians find challenging. These results emphasize the need for continued improvements and further enhanced clinical decision support to optimally manage patients with ICH.

BACKGROUND:Hemorrhagic stroke survivors may have cognitive impairment. We sought to identify preadmission and admission factors associated with cognitive impairment after hemorrhagic stroke. DESIGN/METHODS:Patients with nontraumatic intracerebral or subarachnoid hemorrhage (ICH or SAH) were assessed 3-months post-bleed using the Quality of Life in Neurological Disorders (Neuro-QoL) Cognitive Function short form. Univariate and multivariate analysis were used to evaluate the relationship between poor cognition (Neuro-QoL t-score ≤50) and preadmission and admission factors. RESULTS:Of 101 patients (62 ICH and 39 SAH), 51 (50 %) had poor cognition 3-months post-bleed. On univariate analysis, poor cognition was associated with (p < 0.05): age [66.0 years (52.0-77.0) vs. 54.5 years (40.8-66.3)]; private insurance (37.3 % vs. 74.0 %); BMI > 30 (13.7 % vs. 34.0 %); and admission mRS score > 0 (41.2 % vs. 14.0 %), NIHSS score [8.0 (2.0-17.0) vs. 0.5 (0.0-4.0)], and APACHE II score [16.0 (11.0-19.0) vs. 9.0 (6.0-14.3)]. On multivariate analysis, poor cognition was associated with mRS score > 0 [OR 4.97 (1.30-19.0), p = 0.019], NIHSS score [OR 1.14 (1.02-1.28), p = 0.026], private insurance [OR 0.21 (0.06-0.76), p = 0.017] and BMI > 30 [OR 0.13 (0.03-0.56), p = 0.006]. CONCLUSIONS:Cognitive impairment after hemorrhagic stroke is less common in patients with BMI > 30 and private insurance. Heightened surveillance for non-obese patients without private insurance is suggested. Additional investigation into the relationship between cognition and both BMI and insurance type is needed.

BACKGROUND:Life-threatening, space-occupying mass effect due to cerebral edema and/or hemorrhagic transformation is an early complication of patients with middle cerebral artery stroke. Little is known about longitudinal trajectories of laboratory and vital signs leading up to radiographic and clinical deterioration related to this mass effect. METHODS:We curated a retrospective data set of 635 patients with large middle cerebral artery stroke totaling 95,463 data points for 10 longitudinal covariates and 40 time-independent covariates. We assessed trajectories of the 10 longitudinal variables during the 72 h preceding three outcomes representative of life-threatening mass effect: midline shift ≥ 5 mm, pineal gland shift (PGS) > 4 mm, and decompressive hemicraniectomy (DHC). We used a "backward-looking" trajectory approach. Patients were aligned based on outcome occurrence time and the trajectory of each variable was assessed before that outcome by accounting for cases and noncases, adjusting for confounders. We evaluated longitudinal trajectories with Cox proportional time-dependent regression. RESULTS:Of 635 patients, 49.0% were female, and the mean age was 69 years. Thirty five percent of patients had midline shift ≥ 5 mm, 24.3% of patients had PGS > 4 mm, and 10.7% of patients underwent DHC. Backward-looking trajectories showed mild increases in white blood cell count (10-11 K/UL within 72 h), temperature (up to half a degree within 24 h), and sodium levels (1-3 mEq/L within 24 h) before the three outcomes of interest. We also observed a decrease in heart rate (75-65 beats per minute) 24 h before DHC. We found a significant association between increased white blood cell count with PGS > 4 mm (hazard ratio 1.05, p value 0.007). CONCLUSIONS:Longitudinal profiling adjusted for confounders demonstrated that white blood cell count, temperature, and sodium levels appear to increase before radiographic and clinical indicators of space-occupying mass effect. These findings will inform the development of multivariable dynamic risk models to aid prediction of life-threatening, space-occupying mass effect.

OBJECTIVE:This study assesses whether longitudinal quantitative pupillometry predicts neurological deterioration after large middle cerebral artery (MCA) stroke and determines how early changes are detectable. METHODS:This prospective, single-center observational cohort study included patients with large MCA stroke admitted to Boston Medical Center's intensive care unit (2019-2024). Associations between time-to-neurologic deterioration and quantitative pupillometry, including Neurological Pupil Index (NPi), were assessed using Cox proportional hazards models with time-dependent covariates adjusted for age, sex, and Alberta Stroke Program Early CT Score. Models using dilation velocity were compared with partial likelihood ratio tests. Pupillometric changes over 2-h intervals in the 12 h preceding deterioration were analyzed with linear mixed-effects modeling and Tukey's test. Matched referents (age, sex, stroke side, follow-up duration) were used for comparison. Optimal thresholds were identified using the Youden Index. RESULTS:Among 71 patients (mean age 66.5 years; 59.2% women), 32 (45.1%) experienced deterioration. A 1-unit decrease in NPi was associated with a higher hazard of deterioration (hazard ratio 2.46; 95% confidence interval 1.68-3.61). Dilation velocity improved model performance compared to NPi alone. NPi was significantly lower at 0-2 h (3.81 vs. 4.38, p = 0.001) and 2-4 h (3.71 vs. 4.38, p < 0.001) before deterioration compared to 10-12 h prior. Optimal thresholds were 4.01 for NPi, 0.49 mm/s for dilation velocity, and -0.15 change in NPi over 12 h. INTERPRETATION/CONCLUSIONS:Quantitative pupillometry predicts neurological deterioration in MCA stroke, with declines detectable up to 12 h prior. Dilation velocity shows promise as a novel biomarker. ANN NEUROL 2025.

BACKGROUND:) are associated with important clinical outcomes during hospitalization and improve the performance of clinical prediction models of potentially lethal malignant edema (PLME). METHODS:using area under the curve. RESULTS:was significantly associated with higher odds of PLME when adjusting for confounders (OR 1.69, 95% CI 1.49 to 2.0), and improved model discrimination compared with existing models and models using baseline ASPECTS. Deep territory involvement (OR 2.46, 95% CI 1.53 to 4.01) and anterior territory involvement (OR 3.23, 95% CI 1.88 to 5.71) were significantly associated with PLME. CONCLUSIONS:information improved discrimination of established edema prediction models and could be used immediately to help facilitate clinical management decisions and prognostication.

BACKGROUND:Although it is well-known that intracerebral hemorrhage (ICH) is associated with physical and psychological morbidity, there is scant data on factors influencing social engagement after ICH. Understanding the relationship between functionality, psychological outcome and social engagement post-bleed may facilitate identification of patients at high risk for social isolation after ICH. METHODS:Patients ≥18-years-old with non-traumatic ICH from January 2015-March 2023 were identified from the Neurological Emergencies Outcomes at NYU (NEON) registry. Data on discharge functionality were collected from the medical record. 3-months post-bleed, patients/their legally-authorized representatives (LARs) were contacted to complete Neuro-QoL social engagement, anxiety, depression, and sleep inventories. Patients were stratified by ability to participate in social roles and activities (good=T-score>50, poor=T-score≤50) and satisfaction with social roles and activities (high=T-score>50 and low=T-score≤50). Univariate comparisons were performed to evaluate the relationship between post-bleed social engagement and both functionality and psychological outcome using Pearson's chi-square, Fisher's Exact test, and Mann-Whitney U tests. Multivariate logistic regression was subsequently performed using variables that were significant on univariate analysis (p<0.05). RESULTS:The social engagement inventories were completed for 55 patients with ICH; 29 (53 %) by the patient alone, 14 (25 %) by a LAR alone, and 12 (22 %) by both patient and LAR. 15 patients (27 %) had good ability to participate in social roles and activities and 10 patients (18 %) had high satisfaction with social roles and activities. Social engagement was associated with both functionality and psychological outcome on univariate analysis, but on multivariate analysis, it was only related to functionality; post-bleed ability to participate in social roles and activities was associated with discharge home, discharge GCS score, discharge mRS score, and discharge NIHSS score (p<0.05) and post-bleed satisfaction with social roles and activities was related to discharge mRS score and discharge NIHSS score (p<0.05). CONCLUSION/CONCLUSIONS:In patients with nontraumatic ICH, social engagement post-bleed was related to discharge functionality, even when controlling for depression, anxiety, and sleep disturbance.

OBJECTIVES/OBJECTIVE:The objective of this study was to determine factors associated with negative disease-related stigma after hemorrhagic stroke. MATERIALS AND METHODS/METHODS:Patients with non-traumatic hemorrhage (ICH or SAH) admitted between January 2015 and February 2021 were assessed by telephone 3-months after discharge using the Quality of Life in Neurological Disorders (Neuro-QoL) Negative Disease-Related Stigma Short Form inventory. We evaluated the relationship between disease-related stigma (T-score >50) and pre-stroke demographics, admission data, and poor functional outcome (3-month mRS score 3-5 and Barthel Index <100). RESULTS:We included 89 patients (56 ICH and 33 SAH). The median age was 63 (IQR 50-69), 43 % were female, and 67 % graduated college. Admission median GCS score was 15 (IQR 13-15) and APACHE II score was 12 (IQR 9-17). 31 % had disease-related stigma. On univariate analysis, disease-related stigma was associated with female sex, non-completion of college, GCS score, APACHE II score, and 3-month mRS score (all p < 0.05). On multivariate analysis, disease-related stigma was associated with female sex (OR = 3.72, 95 % CI = 1.23-11.25, p = 0.02) and 3-month Barthel Index <100 (OR = 3.46, 95 % CI = 1.13-10.64, p = 0.03) on one model, and female sex (OR = 3.75, 95 % CI = 1.21-11.58, p = 0.02) and 3-month mRS score 3-5 (OR = 4.23, 95 % CI = 1.21-14.75, p = 0.02) on a second model. CONCLUSION/CONCLUSIONS:Functional outcome and female sex are associated with disease-related stigma 3-months after hemorrhagic stroke. Because stigma may negatively affect recovery, there is a need to understand the relationship between these factors to mitigate stroke-related stigma.

Historically, investigators have not differentiated between patients with and without hemorrhagic transformation (HT) in large core ischemic stroke at risk for life-threatening mass effect (LTME) from cerebral edema. Our objective was to determine whether LTME occurs faster in those with HT compared to those without. We conducted a two-center retrospective study of patients with ≥ 1/2 MCA territory infarct between 2006 and 2021. We tested the association of time-to-LTME and HT subtype (parenchymal, petechial) using Cox regression, controlling for age, mean arterial pressure, glucose, tissue plasminogen activator, mechanical thrombectomy, National Institute of Health Stroke Scale, antiplatelets, anticoagulation, temperature, and stroke side. Secondary and exploratory outcomes included mass effect-related death, all-cause death, disposition, and decompressive hemicraniectomy. Of 840 patients, 358 (42.6%) had no HT, 403 (48.0%) patients had petechial HT, and 79 (9.4%) patients had parenchymal HT. LTME occurred in 317 (37.7%) and 100 (11.9%) had mass effect-related deaths. Parenchymal (HR 8.24, 95% CI 5.46-12.42, p < 0.01) and petechial HT (HR 2.47, 95% CI 1.92-3.17, p < 0.01) were significantly associated with time-to-LTME and mass effect-related death. Understanding different risk factors and sequelae of mass effect with and without HT is critical for informed clinical decisions.

BACKGROUND/UNASSIGNED:) stroke. Little is known about longitudinal trajectories of laboratory and vital signs leading up to radiographic and clinical deterioration related to this mass effect. METHODS/UNASSIGNED:). We used a "backward looking" trajectory approach. Patients were aligned according to the time of outcome occurrence and the trajectory of each variable was assessed prior to that outcome by accounting for both cases and non-cases. RESULTS/UNASSIGNED:Of 635 patients, 49% were female, and mean age was 69 years. Thirty five percent of patients had MLS ≥ 5mm, 24.1% had PGS >4mm, and DHC occurred in 10.7%. For the three outcomes of interest, backward-looking trajectories showed mild increases in white blood cell count (10 up to 11 K/UL within 72 hours), temperature (up to half a degree within 24 hours), and sodium (1-3 mEq/L within 24 hours) leading up to outcomes. We also observed a decrease in heart rate (75 - 65 beats per minute) 24 hours prior to DHC. CONCLUSIONS/UNASSIGNED:Univariable longitudinal profiling showed that temperature, white blood cell count, and sodium increase prior to radiographic and clinical indicators of space-occupying mass effect. These findings will inform development of multivariable dynamic risk models to aid prediction of life-threatening space-occupying mass effect.