Faculty Bibliography

BACKGROUND AND PURPOSE/UNASSIGNED:Sleep disturbance after hemorrhagic stroke (intracerebral or subarachnoid hemorrhage) can impact rehabilitation, recovery, and quality of life. We sought to explore preclinical and clinical factors associated with sleep disturbance after hemorrhagic stroke assessed via the Quality of Life in Neurological Disorders (Neuro-QoL) short form sleep disturbance inventory. METHODS/UNASSIGNED:We telephonically completed the Neuro-QoL short form sleep disturbance inventory 3-months and 12-months after hemorrhagic stroke for patients >18-years-old hospitalized between January 2015 and February 2021. We examined the relationship between sleep disturbance (T-score >50) and social and neuropsychiatric history, systemic and neurological illness severity, medical complications, and temporality. RESULTS/UNASSIGNED:= .046). CONCLUSION/UNASSIGNED:This exploratory analysis did not demonstrate a sustained relationship between any preclinical or clinical factors and sleep disturbance after hemorrhagic stroke. Larger studies that include comparison to patients with ischemic stroke and healthy individuals and utilize additional techniques to evaluate sleep disturbance are needed.

INTRODUCTION/BACKGROUND:Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. METHODS:Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. RESULTS:[5.03-18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631-0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641-0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. CONCLUSION/CONCLUSIONS:We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

Introduction: Hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH) is a key predictor of poor prognosis and potentially amenable to treatment. This study aimed to build a classification model to predict HE in patients with ICH using deep learning algorithms without using advanced radiological features. Methods: Data from the ATACH-2 trial (Antihypertensive Treatment of Acute Cerebral Hemorrhage) was utilized. Variables included in the models were chosen as per literature consensus on salient variables associated with HE. HE was defined as increase in either >33% or 6 mL in hematoma volume in the first 24 h. Multiple machine learning algorithms were employed using iterative feature selection and outcome balancing methods. 70% of patients were used for training and 30% for internal validation. We compared the ML models to a logistic regression model and calculated AUC, accuracy, sensitivity and specificity for the internal validation models respective models. Results: Among 1000 patients included in the ATACH-2 trial, 924 had the complete parameters which were included in the analytical cohort. The median [interquartile range (IQR)] initial hematoma volume was 9.93.mm³ [5.03"“18.17] and 25.2% had HE. The best performing model across all feature selection groups and sampling cohorts was using an artificial neural network (ANN) for HE in the testing cohort with AUC 0.702 [95% CI, 0.631"“0.774] with 8 hidden layer nodes The traditional logistic regression yielded AUC 0.658 [95% CI, 0.641"“0.675]. All other models performed with less accuracy and lower AUC. Initial hematoma volume, time to initial CT head, and initial SBP emerged as most relevant variables across all best performing models. Conclusion: We developed multiple ML algorithms to predict HE with the ANN classifying the best without advanced radiographic features, although the AUC was only modestly better than other models. A larger, more heterogenous dataset is needed to further build and better generalize the models.

BACKGROUND AND PURPOSE/OBJECTIVE:Non-O blood types are known to be associated with thromboembolic complications (TECs) in population-based studies. TECs are known drivers of morbidity and mortality in intracerebral hemorrhage (ICH) patients, yet the relationships of blood type on TECs in this patient population are unknown. We sought to explore the relationships between ABO blood type and TECs in ICH patients. METHODS:Consecutive adult ICH patients enrolled into a prospective observational cohort study with available ABO blood type data were analyzed. Patients with cancer history, prior thromboembolism, and baseline laboratory evidence of coagulopathy were excluded. The primary exposure variable was blood type (non-O versus O). The primary outcome was composite TEC, defined as pulmonary embolism, deep venous thrombosis, ischemic stroke or myocardial infarction, during the hospital stay. Relationships between blood type, TECs and clinical outcomes were separately assessed using logistic regression models after adjusting for sex, ethnicity and ICH score. RESULTS:Of 301 ICH patients included for analysis, 44% were non-O blood type. Non-O blood type was associated with higher admission GCS and lower ICH score on baseline comparisons. We identified TECs in 11.6% of our overall patient cohort. . Although TECs were identified in 9.9% of non-O blood type patients compared to 13.0% in O blood type patients, we did not identify a significant relationship of non-O blood type with TECs (adjusted OR=0.776, 95%CI: 0.348-1.733, p=0.537). The prevalence of specific TECs were also comparable in unadjusted and adjusted analyses between the two cohorts. In additional analyses, we identified that TECs were associated with poor 90-day mRS (adjusted OR=3.452, 95% CI: 1.001-11.903, p=0.050). We did not identify relationships between ABO blood type and poor 90-day mRS (adjusted OR=0.994, 95% CI:0.465-2.128, p=0.988). CONCLUSIONS:We identified that TECs were associated with worse ICH outcomes. However, we did not identify relationships in ABO blood type and TECs. Further work is required to assess best diagnostic and prophylactic and treatment strategies for TECs to improve ICH outcomes.

BACKGROUND AND OBJECTIVES/OBJECTIVE:Mortality after intracerebral hemorrhage (ICH) is common. Neighborhood socioeconomic status (nSES) is an important social determinant of health (SDoH) that can affect clinical outcome. We hypothesize that SDoH, including nSES, contribute to differences in withdrawal of life-sustaining therapies (WLSTs) and mortality in patients with ICH. METHODS:tests. We performed multivariable analysis using backward stepwise logistic regression. RESULTS:≤ 0.01 for both). In multivariable analysis adjusting for age and clinical severity scores, patients who lived in zip codes with high-income levels were more likely to have WLST (adjusted odds ratio [aOR] 1.88; 95% CI 1.29-2.74) and mortality before discharge (aOR 1.5; 95% CI 1.06-2.13). DISCUSSION/CONCLUSIONS:SDoH, including nSES, are associated with WLST after ICH. This has important implications for the care and management of patients with ICH.

OBJECTIVE/UNASSIGNED:A significant number of patients develop anxiety after stroke. The objective of this study was to identify risk factors for anxiety after hemorrhagic stroke that may facilitate diagnosis and treatment. METHODS/UNASSIGNED:Patients admitted between January 2015 and February 2021 with nontraumatic hemorrhagic stroke (intracerebral [ICH] or subarachnoid [SAH] hemorrhage) were assessed telephonically 3 and 12 months after stroke with the Quality of Life in Neurological Disorders Anxiety Short Form to evaluate the relationships between poststroke anxiety (T score >50) and preclinical social and neuropsychiatric history, systemic and neurological illness severity, and in-hospital complications. RESULTS/UNASSIGNED:Of 71 patients who completed the 3-month assessment, 28 (39%) had anxiety. There was a difference in Glasgow Coma Scale (GCS) scores on admission between patients with anxiety (median=14, interquartile range [IQR]=12-15) and those without anxiety (median=15, IQR=14-15) (p=0.034), and the incidence of anxiety was higher among patients with ICH (50%) than among those with SAH (20%) (p=0.021). Among patients with ICH, anxiety was associated with larger median ICH volume (25 cc [IQR=8-46] versus 8 cc [IQR=3-13], p=0.021) and higher median ICH score (2 [IQR=1-3] versus 1 [IQR=0-1], p=0.037). On multivariable analysis with GCS score, hemorrhage type, and neuropsychiatric history, only hemorrhage type remained significant (odds ratio=3.77, 95% CI=1.19-12.05, p=0.024). Of the 39 patients who completed the 12-month assessment, 12 (31%) had anxiety, and there was a difference in mean National Institutes of Health Stroke Scale scores between patients with (5 [IQR=3-12]) and without (2 [IQR=0-4]) anxiety (p=0.045). There was fair agreement (κ=0.38) between the presence of anxiety at 3 and 12 months. CONCLUSIONS/UNASSIGNED:Hemorrhage characteristics and factors assessed with neurological examination on admission are associated with the development of poststroke anxiety.

BACKGROUND:The utility of head computed tomography (CT) in predicting elevated intracranial pressure (ICP) is known to be limited in traumatic brain injury; however, few data exist in patients with spontaneous intracranial hemorrhage. METHODS:We conducted a retrospective review of prospectively collected data in patients with nontraumatic intracranial hemorrhage (subarachnoid hemorrhage [SAH] or intraparenchymal hemorrhage [IPH]) who underwent external ventricular drain (EVD) placement. Head CT scans performed immediately prior to EVD placement were quantitatively reviewed for features suggestive of elevated ICP, including temporal horn diameter, bicaudate index, basal cistern effacement, midline shift, and global cerebral edema. The modified Fisher score (mFS), intraventricular hemorrhage score, and IPH volume were also measured, as applicable. We calculated the accuracy, positive predictive value (PPV), and negative predictive value (NPV) of these radiographic features for the coprimary outcomes of elevated ICP (> 20 mm Hg) at the time of EVD placement and at any time during the hospital stay. Multivariable backward stepwise logistic regression analysis was performed to identify significant radiographic factors associated with elevated ICP. RESULTS:Of 608 patients with intracranial hemorrhages enrolled during the study time frame, 243 (40%) received an EVD and 165 (n = 107 SAH, n = 58 IPH) had a preplacement head CT scan available for rating. Elevated opening pressure and elevated ICP during hospitalization were recorded in 48 of 152 (29%) and 103 of 165 (62%), respectively. The presence of ≥ 1 radiographic feature had only 32% accuracy for identifying elevated opening pressure (PPV 30%, NPV 58%, area under the curve [AUC] 0.537, 95% asymptotic confidence interval [CI] 0.436-0.637, P = 0.466) and 59% accuracy for predicting elevated ICP during hospitalization (PPV 63%, NPV 40%, AUC 0.514, 95% asymptotic CI 0.391-0.638, P = 0.820). There was no significant association between the number of radiographic features and ICP elevation. Head CT scans without any features suggestive of elevated ICP occurred in 25 of 165 (15%) patients. However, 10 of 25 (40%) of these patients had elevated opening pressure, and 15 of 25 (60%) had elevated ICP during their hospital stay. In multivariable models, mFS (adjusted odds ratio [aOR] 1.36, 95% CI 1.10-1.68) and global cerebral edema (aOR 2.93, 95% CI 1.27-6.75) were significantly associated with elevated ICP; however, their accuracies were only 69% and 60%, respectively. All other individual radiographic features had accuracies between 38 and 58% for identifying intracranial hypertension. CONCLUSIONS:More than 50% of patients with spontaneous intracranial hemorrhage without radiographic features suggestive of elevated ICP actually had ICP > 20 mm Hg during EVD placement or their hospital stay. Morphological head CT findings were only 32% and 59% accurate in identifying elevated opening pressure and ICP elevation during hospitalization, respectively.

Use of thoracoabdominal normothermic regional perfusion (TA-NRP) during donation after circulatory death (DCD) is an important advance in organ donation. Prior to establishing TA-NRP, the brachiocephalic, left carotid, and left subclavian arteries are ligated, thereby eliminating anterograde brain blood flow via the carotid and vertebral arteries. While theoretical concerns have been voiced that TA-NRP after DCD may restore brain blood flow via collaterals, there have been no studies to confirm or refute this possibility. We evaluated brain blood flow using intraoperative transcranial Doppler (TCD) in two DCD TA-NRP cases. Pre-extubation, anterior and posterior circulation brain blood flow waveforms were present in both cases, similar to the waveforms detected in a control patient on mechanical circulatory support undergoing cardiothoracic surgery. Following declaration of death and initiation of TA-NRP, no brain blood flow was detected in either case. Additionally, there was absence of brainstem reflexes, no response to noxious stimuli and no respiratory effort. These TCD results demonstrate that DCD with TA-NRP did not restore brain blood flow.

BACKGROUND:Limited data exists evaluating predictors of long-term outcomes after hospitalization for COVID-19. METHODS:We conducted a prospective, longitudinal cohort study of patients hospitalized for COVID-19. The following outcomes were collected at 6 and 12-months post-diagnosis: disability using the modified Rankin Scale (mRS), activities of daily living assessed with the Barthel Index, cognition assessed with the telephone Montreal Cognitive Assessment (t-MoCA), Neuro-QoL batteries for anxiety, depression, fatigue and sleep, and post-acute symptoms of COVID-19. Predictors of these outcomes, including demographics, pre-COVID-19 comorbidities, index COVID-19 hospitalization metrics, and life stressors, were evaluated using multivariable logistic regression. RESULTS:Of 790 COVID-19 patients who survived hospitalization, 451(57%) completed 6-month (N = 383) and/or 12-month (N = 242) follow-up, and 77/451 (17%) died between discharge and 12-month follow-up. Significant life stressors were reported in 121/239 (51%) at 12-months. In multivariable analyses, life stressors including financial insecurity, food insecurity, death of a close contact and new disability were the strongest independent predictors of worse mRS, Barthel Index, depression, fatigue, and sleep scores, and prolonged symptoms, with adjusted odds ratios ranging from 2.5 to 20.8. Other predictors of poor outcome included older age (associated with worse mRS, Barthel, t-MoCA, depression scores), baseline disability (associated with worse mRS, fatigue, Barthel scores), female sex (associated with worse Barthel, anxiety scores) and index COVID-19 severity (associated with worse Barthel index, prolonged symptoms). CONCLUSIONS:Life stressors contribute substantially to worse functional, cognitive and neuropsychiatric outcomes 12-months after COVID-19 hospitalization. Other predictors of poor outcome include older age, female sex, baseline disability and severity of index COVID-19.

OBJECTIVE:We explored the relationship between markers of infection and inflammation and mortality in patients with acute ischemic stroke who underwent thrombectomy. METHODS:We performed retrospective chart review of stroke patients who underwent thrombectomy at two tertiary academic centers between December 2018 and November 2020. Associations between discharge mortality, WBC count, neutrophil percentage, fever, culture data, and antibiotic treatment were analyzed using the Wilcoxon rank sum test, Student's t-test, and Fisher's exact test. Independent predictors of mortality were identified with multivariable analysis. Analyses were repeated excluding COVID-positive patients. RESULTS:Of 248 patients who underwent thrombectomy, 41 (17 %) died prior to discharge. Mortality was associated with admission WBC count (11 [8-14] vs. 9 [7-12], p = 0.0093), admission neutrophil percentage (78 % ± 11 vs. 71 % ± 14, p = 0.0003), peak WBC count (17 [13-22] vs. 12 [9-15], p < 0.0001), fever (71 % vs. 27 %, p < 0.0001), positive culture (44 % vs. 15 %, p < 0.0001), and days treated with antibiotics (3 [1-7] vs. 1 [0-4], p < 0.0001). After controlling for age, admission NIHSS and post-thrombectomy ASPECTS score, mortality was associated with admission WBC count (OR 13, CI 1.32-142, p = 0.027), neutrophil percentage (OR 1.03, CI 1.0-1.07, p = 0.045), peak WBC count (OR 301, CI 24-5008, p < 0.0001), fever (OR 24.2, CI 1.77-332, p < 0.0001), and positive cultures (OR 4.24, CI 1.87-9.62, p = 0.0006). After excluding COVID-positive patients (n = 14), peak WBC count, fever and positive culture remained independent predictors of mortality. CONCLUSION/CONCLUSIONS:Markers of infection and inflammation are associated with discharge mortality after thrombectomy. Further study is warranted to investigate the causal relationship of these markers with clinical outcome.