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OBJECTIVE: Delayed cerebral ischemia (DCI) is a leading complication of aneurysmal subarachnoid hemorrhage (SAH) and electroencephalography (EEG) is increasingly used to evaluate DCI risk. Our goal is to develop an automated DCI prediction algorithm integrating multiple EEG features over time. METHODS: We assess 113 moderate to severe grade SAH patients to develop a machine learning model that predicts DCI risk using multiple EEG features. RESULTS: Multiple EEG features discriminate between DCI and non-DCI patients when aligned either to SAH time or to DCI onset. DCI and non-DCI patients have significant differences in alpha-delta ratio (0.08 vs 0.05, p < 0.05) and percent alpha variability (0.06 vs 0.04, p < 0.05), Shannon entropy (p < 0.05) and epileptiform discharge burden (205 vs 91 discharges per hour, p < 0.05) based on whole brain and vascular territory averaging. Our model improves predictions by emphasizing the most informative features at a given time with an area under the receiver-operator curve of 0.73, by day 5 after SAH and good calibration between 48–72 hours (calibration error 0.13). CONCLUSIONS: Our proposed model obtains good performance in DCI prediction. SIGNIFICANCE: We leverage machine learning to enable rapid, automated, multi-featured EEG assessment and has the potential to increase the utility of EEG for DCI prediction.

Objective To investigate whether epileptiform discharge burden can identify those at risk for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH). Methods Retrospective analysis of 113 moderate to severe grade SAH patients who had continuous EEG (cEEG) recordings during their hospitalization. We calculated the burden of epileptiform discharges (ED), measured as number of ED per hour. Results We find that many SAH patients have an increase in ED burden during the first 3–10 days following rupture, the major risk period for DCI. However, those who develop DCI have a significantly higher hourly burden from days 3.5–6 after SAH vs. those who do not. ED burden is higher in DCI patients when assessed in relation to the onset of DCI (area under the receiver operator curve 0.72). Finally, specific trends of ED burden over time, assessed by group-based trajectory analysis, also help stratify DCI risk. Conclusions These results suggest that ED burden is a useful parameter for identifying those at higher risk of developing DCI after SAH. The higher burden rate associated with DCI supports the theory of metabolic supply-demand mismatch which contributes to this complication. Significance ED burden is a novel biomarker for predicting those at high risk of DCI.

This retrospective case-controlled study was performed to evaluate whether Epileptiform Activity, suspected clinical seizures, and/or 2HELPS2B/S after nontraumatic Intraparenchymal Hemorrhage or Subarachnoid Hemorrhage can predict Epilepsy. Hundred and thirty-two patients were included-29 (Epilepsy), 103 (Control Group). After matching, the average effect for all three risk factors was significant as follows: (1) Epileptiform Activity (p = 0.012, odds ratio 3.14), (2) suspected seizures (p = 0.021, odds ratio 3.78), and (3) 2HELPS2B/S score (p 0.001, odds ratio 4.94). This study shows that Epileptiform Activity, suspected seizures, and particularly, the 2HELPS2B/S score in the acute phase are risk factors for the development of epilepsy after nontraumatic intraparenchymal and subarachnoid hemorrhage.

Background and Objectives Effective deprescribing requires shared decision making between a patient and their clinician, and should be used when implementing evidence-based deprescribing conversations. As part of the Shed-MEDS clinical trial, this study assessed barriers and enablers that influence patient decision making in deprescribing to inform future implementation efforts and adaptations. Research Design and Methods Shed-MEDS, a randomized controlled deprescribing trial, included hospitalized older adults discharging to post-acute care facilities. A trained clinician reviewed each participant’s medical history and medication list to identify medications with potential for deprescribing. The study clinician then conducted a semistructured patient-centered deprescribing interview to determine patient (or surrogate) concerns about medications and willingness to deprescribe. Reeve et al.’s (2013) framework was used to categorize barriers and enablers to deprescribing from the patient’s perspective, including “appropriateness of cessation,” “fear,” “dislike of a medication,” “influences,” and “process of cessation.” Results Overall, participants/surrogates (N = 177) agreed with 63% (883 total medications) of the study clinician’s deprescribing recommendations. Thematic analysis revealed that “appropriateness” of a medication was the most common barrier (88.2%) and enabler (67.3%) to deprescribing. Other deprescribing enablers were in the following domains: “influences” (22.7%), “process” (22.5%), “pragmatic” (19.4%), and “dislike” (5.3%). Discussion and Implications Use of a semistructured deprescribing interview conversation tool allowed study clinicians to elicit individual barriers and enablers to deprescribing from the patient’s perspective. Participants in this study expressed more agreement than disagreement with study clinicians’ deprescribing recommendations. These results should inform future implementation efforts that incorporate a patient-centered framework during deprescribing conversations. Clinical Trials Registration Number NCT02979353

Background: Propofol is a key component for the management of sedation and shivering during targeted temperature management (TTM) following cardiac arrest. The cardiac depressant effects of propofol have not been described during TTM and may be especially relevant given the stress to the myocardium following cardiac arrest. The purpose of this study is to describe hemodynamic changes associated with propofol administration during TTM. Methods: This single center, retrospective cohort study evaluated adult patients who received a propofol infusion for at least 30 minutes during TTM. The primary outcome was the change in cardiovascular Sequential Organ Failure Assessment (cvSOFA) score 30 minutes after propofol initiation. Secondary outcomes included change in systolic blood pressure (SBP), mean arterial pressure (MAP), heart rate (HR), and vasopressor requirements (VR) expressed as norepinephrine equivalents at 30, 60, 120, 180, and 240 minutes after propofol initiation. A multivariate regression was performed to assess the influence of propofol and body temperature on MAP, while controlling for vasopressor dose and cardiac arrest hospital prognosis (CAHP) score. Results: The cohort included 40 patients with a median CAHP score of 197. The goal temperature of 33°C was achieved for all patients. The median cvSOFA score was 1 at baseline and 0.5 at 30 minutes, with a non-significant change after propofol initiation ( P = .96). SBP and MAP reductions were the greatest at 60 minutes (17 and 8 mmHg; P

BACKGROUND AND OBJECTIVES: Seizures (SZs) and other SZ-like patterns of brain activity can harm the brain and contribute to in-hospital death, particularly when prolonged. However, experts qualified to interpret EEG data are scarce. Prior attempts to automate this task have been limited by small or inadequately labeled samples and have not convincingly demonstrated generalizable expert-level performance. There exists a critical unmet need for an automated method to classify SZs and other SZ-like events with expert-level reliability. This study was conducted to develop and validate a computer algorithm that matches the reliability and accuracy of experts in identifying SZs and SZ-like events, known as “ictal-interictal-injury continuum” (IIIC) patterns on EEG, including SZs, lateralized and generalized periodic discharges (LPD, GPD), and lateralized and generalized rhythmic delta activity (LRDA, GRDA), and in differentiating these patterns from non-IIIC patterns. METHODS: We used 6,095 scalp EEGs from 2,711 patients with and without IIIC events to train a deep neural network, SPaRCNet, to perform IIIC event classification. Independent training and test data sets were generated from 50,697 EEG segments, independently annotated by 20 fellowship-trained neurophysiologists. We assessed whether SPaRCNet performs at or above the sensitivity, specificity, precision, and calibration of fellowship-trained neurophysiologists for identifying IIIC events. Statistical performance was assessed by the calibration index and by the percentage of experts whose operating points were below the model's receiver operating characteristic curves (ROCs) and precision recall curves (PRCs) for the 6 pattern classes. RESULTS: SPaRCNet matches or exceeds most experts in classifying IIIC events based on both calibration and discrimination metrics. For SZ, LPD, GPD, LRDA, GRDA, and “other” classes, SPaRCNet exceeds the following percentages of 20 experts—ROC: 45%, 20%, 50%, 75%, 55%, and 40%; PRC: 50%, 35%, 50%, 90%, 70%, and 45%; and calibration: 95%, 100%, 95%, 100%, 100%, and 80%, respectively. DISCUSSION: SPaRCNet is the first algorithm to match expert performance in detecting SZs and other SZ-like events in a representative sample of EEGs. With further development, SPaRCNet may thus be a valuable tool for an expedited review of EEGs. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that among patients with epilepsy or critical illness undergoing EEG monitoring, SPaRCNet can differentiate (IIIC) patterns from non-IIIC events and expert neurophysiologists.

Cells were first gated on size and singularity by forward scatter and side scatter. Nonviable cells were excluded by live/dead gating. CD3+ live cells were gated on CD4 and CD8. CD3+FoxP3+CD4+ cells were designated as Tregs. TIM-3 vs. PD-1 expression was assessed for each lymphocyte population.

Cells were first gated on size and singularity by forward scatter and side scatter. Nonviable cells were excluded by live/dead gating. Live cells were gated on CD11c and CD11b, then on CD45 and F4/80 to identify macrophage and dendritic cell phenotypes. Finally myeloid cells were gated for expression of TIM-3. Final populations were (A) F4/80+CD45hiCD11c-CD11bhi (B) F4/80+CD45dimCD11bhi (C) F4/80+CD45hiCD11c+CD11b+ and (D) F4/80-CD45dimCD11c+CD11b-.

Purpose: Checkpoint molecules like programmed death-1 (PD-1) and T-cell immunoglobulin mucin-3 (TIM-3) are negative immune regulators that may be upregulated in the setting of glioblastoma multiforme. Combined PD-1 blockade and stereotactic radiosurgery (SRS) have been shown to improve antitumor immunity and produce long-term survivors in a murine glioma model. However, tumor-infiltrating lymphocytes (TIL) can express multiple checkpoints, and expression of ≥2 checkpoints corresponds to a more exhausted T-cell phenotype. We investigate TIM-3 expression in a glioma model and the antitumor efficacy of TIM-3 blockade alone and in combination with anti-PD-1 and SRS.Experimental Design: C57BL/6 mice were implanted with murine glioma cell line GL261-luc2 and randomized into 8 treatment arms: (i) control, (ii) SRS, (iii) anti-PD-1 antibody, (iv) anti-TIM-3 antibody, (v) anti-PD-1 + SRS, (vi) anti-TIM-3 + SRS, (vii) anti-PD-1 + anti-TIM-3, and (viii) anti-PD-1 + anti-TIM-3 + SRS. Survival and immune activation were assessed.Results: Dual therapy with anti-TIM-3 antibody + SRS or anti-TIM-3 + anti-PD-1 improved survival compared with anti-TIM-3 antibody alone. Triple therapy resulted in 100% overall survival (P < 0.05), a significant improvement compared with other arms. Long-term survivors demonstrated increased immune cell infiltration and activity and immune memory. Finally, positive staining for TIM-3 was detected in 7 of 8 human GBM samples.Conclusions: This is the first preclinical investigation on the effects of dual PD-1 and TIM-3 blockade with radiation. We also demonstrate the presence of TIM-3 in human glioblastoma multiforme and provide preclinical evidence for a novel treatment combination that can potentially result in long-term glioma survival and constitutes a novel immunotherapeutic strategy for the treatment of glioblastoma multiforme. Clin Cancer Res; 23(1); 124–36. ©2016 AACR.

Each mouse was individually imaged with cone beam computed tomography (CBCT) using the SARRP with a 65 kVp and 0.7 mA beam. Using the treatment planning system from the SARRP system (Muriplan) and the CBCT, the target was exactly placed 3mm below the skull's burr hole. The planning system calculated the x-ray beam's (220kVp and 13mA) time of exposure according to the prescribed dose and moved the motorized couch to its target location, after which a 3-mm beam centered on the burr hole and underlying tumor was used to administer a total of 10 Gy radiation per animal at a rate of 1.9 Gy/min (18). The isodose distribution is shown on the figure provided. The dose to adjacent organs or rest of the brain is insignificant as the beam's penumbra demonstrates complete drop off on the edge of the field (17, 50, 51)

Antibodies used for flow cytometry (clones and dilutions)

Gating strategy to assess for surface expression of TIM-3 on (A) CD4+ and (B) CD8+ T cells isolated from peripheral lymph nodes, lungs, livers, spleens, and brains.

Introduction: Positive lumpectomy margins are associated with an increased risk for ipsilateral tumor recurrence, and necessitate additional procedures to achieve acceptable margins. Re-excision rates are higher in patients with ductal carcinoma in situ (DCIS) versus those with invasive carcinoma. The purpose of this study was to investigate the clinicopathologic characteristics, surgical techniques, and other outcomes associated with re-excision over time following primary breast conserving surgery for DCIS. Methods: Our institutional Breast Cancer Database was queried for patients treated with lumpectomy as the primary procedure for pure DCIS from 2010-2021. Cases of microinvasive disease were excluded. The primary endpoint was the rate of re-excision following lumpectomy. Variables of interest included patient demographics and clinicopathologic characteristics. As a secondary analysis, adjusted odds ratios for the association of MarginProbe® with re-excisions were calculated using a multivariable logistic regression model controlling for age, tumor size, breast density and DCIS subtype. Individual comparisons of continuous variables were based on t-tests, group proportions compared with one-way ANOVA, and categorical variables compared using Chi Square tests. Results: Out of a total of 695 patients in our cohort, 221 (32%) had re-excisions. There was a statistically significant 2% decline per year in the number of re-excisions (p=0.005). Patients who underwent re-excision had larger size of DCIS (2.37 cm vs 1.37 cm, p Table 1.Clinicopathologic CharacteristicsVariableNo Re-excision (n=313)Re-Excision (n=100)P-ValueAge; mean (SD)61.48 (12.01)58.04 (13.39)0.016Age of Menopause; mean (SD)50.22 (5.18)48.13 (5.45)0.004Size of DCIS; mean (SD)1.37 (1.25)2.37 (2.00)0 and Citation Format: Joshua A Feinberg, Charles DiMaggio, Nakisa Pourkey, Jennifer Chun Kim, Jenny Goodgal, Amber Guth, Deborah Axelrod, Freya Schnabel. Meeting the challenge of successful one-stage lumpectomy for DCIS [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-18-11.

Considering the COVID-19 pandemic where concomitant occurrence of ARDS and severe acute brain injury (sABI) has increasingly coemerged, we synthesize existing data regarding the simultaneous management of both conditions. Our aim is to provide readers with fundamental principles and concepts for the management of sABI and ARDS, and highlight challenges and conflicts encountered while managing concurrent disease. Up to 40% of patients with sABI can develop ARDS. Although there are trials and guidelines to support the mainstays of treatment for ARDS and sABI independently, guidance on concomitant management is limited. Treatment strategies aimed at managing severe ARDS may at times conflict with the management of sABI. In this narrative review, we discuss the physiological basis and risks involved during simultaneous management of ARDS and sABI, summarize evidence for treatment decisions, and demonstrate these principles using hypothetical case scenarios. Use of invasive or noninvasive monitoring to assess brain and lung physiology may facilitate goal-directed treatment strategies with the potential to improve outcome. Understanding the pathophysiology and key treatment concepts for comanagement of these conditions is critical to optimizing care in this high-acuity patient population.