Faculty Bibliography

OBJECTIVE/UNASSIGNED:Emotional and behavioral dyscontrol (EBD), a neuropsychiatric complication of stroke, leads to patient and caregiver distress and challenges to rehabilitation. Studies of neuropsychiatric sequelae in stroke are heavily weighted toward ischemic stroke. This study was designed to compare risk of EBD following intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) and to identify risk factors for EBD following hemorrhagic stroke. METHODS/UNASSIGNED:The authors conducted a prospective cohort study of patients hospitalized for nontraumatic hemorrhagic stroke between 2015 and 2021. Patients or legally authorized representatives completed the Quality of Life in Neurological Disorders (Neuro-QOL) EBD short-form inventory 3 months after hospitalization. Univariable and multivariable analyses identified risk factors for EBD after hemorrhagic stroke. RESULTS/UNASSIGNED:The incidence of EBD was 21% (N=15 of 72 patients) at 3 months after hemorrhagic stroke. Patients with ICH were more likely to develop EBD; 93% of patients with EBD (N=14 of 15) had ICH compared with 56% of patients without EBD (N=32 of 57). The median Glasgow Coma Scale (GCS) score at hospital admission was lower among patients who developed EBD (13 vs. 15 among those without EBD). Similarly, admission scores on the National Institutes of Health Stroke Scale (NIHSS) and the Acute Physiology and Chronic Health Evaluation II (APACHE II) were higher among patients with EBD (median NIHSS score: 7 vs. 2; median APACHE II score: 17 vs. 11). Multivariable analyses identified hemorrhage type (ICH) and poor admission GCS score as predictors of EBD 3 months after hemorrhagic stroke. CONCLUSIONS/UNASSIGNED:Patients with ICH and a low GCS score at admission are at increased risk of developing EBD 3 months after hemorrhagic stroke and may benefit from early intervention.

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.

OBJECTIVES/OBJECTIVE:Hospital readmissions are associated with poor health outcomes including illness severity and medical complications. The objective of this study was to identify characteristics associated with 30-day post-stroke readmission in an academic urban hospital network. MATERIALS AND METHODS/METHODS:We collected data on patients admitted with stroke from 2017 through 2022 who were readmitted within 30 days of discharge and compared them to a subset of non-readmitted stroke patients. Chart review was used to collect demographics, characteristics of the stroke, co-morbid conditions, in-hospital complications, and post-discharge care. Univariate analyses followed by regression analysis were used to assess characteristics associated with post-stroke readmission. RESULTS:We identified 4743 patients with stroke (18 % hemorrhagic, mean age 70.1 (standard deviation (SD) 17.2), 47.3 % female) discharged from the stroke services, of whom 282 (5.9 %) patients were readmitted within 30 days of index hospitalization. Univariate analyses identified 18 significantly different features between admitted and readmitted patients. Regression analysis revealed characteristics associated with readmission included private insurance (odds ratio (OR) 0.4, confidence interval (CI) 0.3-0.6, p < 0.001), comorbid peripheral vascular disease (PVD) (OR 2.7, CI 1.3-5.5, p = 0.009), malignancy (OR 1.6, CI 1.0-2.6, p = 0.04), seizure (OR 3.4, CI 1.4-8.2, p = 0.007), thrombolytic administration (OR 0.4, CI 0.2-0.7, p = 0.003), undergoing thrombectomy (OR 5.4, CI 2.9-10.1, p < 0.001), and higher discharge modified Rankin Scale score (OR 1.2, CI 1.0-1.3, p = 0.047). CONCLUSIONS:Our data demonstrate that thrombectomy, high discharge Rankin score, comorbid malignancy, seizure or PVD, and lack of thrombolytic administration or private insurance predict readmission.

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.

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.

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 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.

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: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.