Simple approach to quantify hypoxic-ischemic brain injury severity from computed tomography imaging files after cardiac arrest.

BACKGROUND: Grey-white ratio (GWR) can estimate severity of cytotoxic cerebral edema secondary to hypoxic-ischemic brain injury after cardiac arrest and predict progression to death by neurologic criteria (DNC). Current approaches to calculating GWR are not standardized and have variable interrater reliability. We tested if measures of variance of brain density on early computed tomographic (CT) imaging after cardiac arrest could predict DNC. METHODS: We performed a retrospective cohort study, identifying post-arrest patients treated between 2011 and 2020 at our single center. We extracted demographic data from our registry and Digital Imaging and Communication in Medicine (DICOM) files for each patient's first brain CT. We analyzed slices 15-20 of each DICOM, corresponding to the level of the basal ganglia while accommodating differences in patient anatomy. We extracted pixel arrays and converted the radiodensities to Hounsfield units (HU). To focus on brain tissue densities, we excluded HU > 60 and < 10. We calculated the variance of each patient's HU distribution and the difference between the means of a two-group Gaussian finite mixture model. We compared these novel metrics to existing measures of cerebral edema, then randomly divided our data into 80% training and 20% test sets and used logistic regression to predict DNC. RESULTS: Of 1,133 included subjects, 457 (40%) were female, mean (standard deviation) age was 58 (16) years, and 115 (10%) progressed to DNC. CTs were obtained a median [interquartile range] of 4.2 [2.8-5.7] hours post-arrest. Our novel measures correlated weakly with GWR. HU variance, but not difference between mixture model means, differed significantly between subjects with and without sulcal or cistern effacement. GWR outperformed our novel measures in predicting progression to DNC with an area under the receiver operating characteristic curve (AUC) of 0.82, compared to HU variance (AUC = 0.73) and the difference between mixture model means (AUC = 0.56). CONCLUSION: There are differences in the distribution of HU on post-arrest CT in patients with qualitative measures of cerebral edema. Current methods to quantify cerebral edema outperform simple measures of attenuation variance on early brain CT. Further analyses could investigate if these measures of variance, or other distributional characteristics of brain density, have improved predictive performance on brain CTs obtained later in the clinical course or derived from discrete regions of anatomical interest.

Diagnostic yield of computed tomography after non-traumatic out-of-hospital cardiac arrest.

AIM: Determine the frequency with which computed tomography (CT) after out-of-hospital cardiac arrest (OHCA) identifies clinically important findings. METHODS: We included non-traumatic OHCA patients treated at a single center from February 2019 to February 2021. Clinical practice was to obtain CT head in comatose patients. Additionally, CT of the cervical spine, chest, abdomen, and pelvis were obtained if clinically indicated. We identified CT imaging obtained within 24 hours of emergency department (ED) arrival and summarized radiology findings. We used descriptive statistics to summarize population characteristics and imaging results, report their frequencies and, post hoc, compared time from ED arrival to catheterization between patients who did and did not undergo CT. RESULTS: We included 597 subjects, of which 491 (82.2%) had a CT obtained. Time to CT was 4.1 hours [2.8-5.7]. Most (n = 480, 80.4%) underwent CT head, of which 36 (7.5%) had intracranial hemorrhage and 161 (33.5%) had cerebral edema. Fewer subjects (230, 38.5%) underwent a cervical spine CT, and 4 (1.7%) had acute vertebral fractures. Most subjects (410, 68.7%) underwent a chest CT, and abdomen and pelvis CT (363, 60.8%). Chest CT abnormalities included rib or sternal fractures (227, 55.4%), pneumothorax (27, 6.6%), aspiration or pneumonia (309, 75.4%), mediastinal hematoma (18, 4.4%) and pulmonary embolism (6, 3.7%). Significant abdomen and pelvis findings were bowel ischemia (24, 6.6%) and solid organ laceration (7, 1.9%). Most subjects that had CT imaging deferred were awake and had shorter time to catheterization. CONCLUSIONS: CT identifies clinically important pathology after OHCA.

Evaluating novel methods of outcome assessment following cardiac arrest.

INTRODUCTION: We compared novel methods of long-term follow-up after resuscitation from cardiac arrest to a query of the National Death Index (NDI). We hypothesized use of the electronic health record (EHR), and internet-based sources would have high sensitivity for identifying decedents identified by the NDI. METHODS: We performed a retrospective study including patients treated after cardiac arrest at a single academic center from 2010 to 2018. We evaluated two novel methods to ascertain long-term survival and modified Rankin Scale (mRS): 1) a structured chart review of our health system's EHR; and 2) an internet-based search of: a) local newspapers, b) Ancestry.com, c) Facebook, d) Twitter, e) Instagram, and f) Google. If a patient was not reported deceased by any source, we considered them to be alive. We compared results of these novel methods to the NDI to calculate sensitivity. We queried the NDI for 200 in-hospital decedents to evaluate sensitivity against a true criterion standard. RESULTS: We included 1,097 patients, 897 (82%) alive at discharge and 200 known decedents (18%). NDI identified 197/200 (99%) of known decedents. The EHR and local newspapers had highest sensitivity compared to the NDI (87% and 86% sensitivity, respectively). Online sources identified 10 likely decedents not identified by the NDI. Functional status estimated from EHR, and internet sources at follow up agreed in 38% of alive patients. CONCLUSIONS: Novel methods of outcome assessment are an alternative to NDI for determining patients' vital status. These methods are less reliable for estimating functional status.

Author Correction: Early Pastoral Economies and Herding Transitions in Eastern Eurasia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Early Pastoral Economies and Herding Transitions in Eastern Eurasia.

While classic models for the emergence of pastoral groups in Inner Asia describe mounted, horse-borne herders sweeping across the Eurasian Steppes during the Early or Middle Bronze Age (ca. 3000-1500 BCE), the actual economic basis of many early pastoral societies in the region is poorly characterized. In this paper, we use collagen mass fingerprinting and ancient DNA analysis of some of the first stratified and directly dated archaeofaunal assemblages from Mongolia's early pastoral cultures to undertake species identifications of this rare and highly fragmented material. Our results provide evidence for livestock-based, herding subsistence in Mongolia during the late 3rd and early 2nd millennia BCE. We observe no evidence for dietary exploitation of horses prior to the late Bronze Age, ca. 1200 BCE - at which point horses come to dominate ritual assemblages, play a key role in pastoral diets, and greatly influence pastoral mobility. In combination with the broader archaeofaunal record of Inner Asia, our analysis supports models for widespread changes in herding ecology linked to the innovation of horseback riding in Central Asia in the final 2nd millennium BCE. Such a framework can explain key broad-scale patterns in the movement of people, ideas, and material culture in Eurasian prehistory.