The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is ...essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care.
This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia.
Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years range, 21-88 years; 64% 36/55 male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years range, 27-88 years; 55% 22/40 male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease.
We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Purpose
To validate the T1‐ and T2‐weighted (T1w/T2w) MRI ratio technique in evaluating myelin in the neonatal brain.
Materials and Methods
T1w and T2w MR images of 10 term neonates with ...normal‐appearing brain parenchyma were obtained from a single 1.5 Tesla MRI and retrospectively analyzed. T1w/T2w ratio images were created with a postprocessing pipeline and qualitatively compared with standard clinical sequences (T1w, T2w, and apparent diffusion coefficient ADC). Quantitative assessment was also performed to assess the ratio technique in detecting areas of known myelination (e.g., posterior limb of the internal capsule) and very low myelination (e.g., optic radiations) using linear regression analysis and the Michelson Contrast equation, a measure of luminance contrast intensity.
Results
The ratio image provided qualitative improvements in the ability to visualize regional variation in myelin content of neonates. Linear regression analysis demonstrated a significant inverse relationship between the ratio intensity values and ADC values in the posterior limb of the internal capsule and the optic radiations (R2 = 0.96 and P < 0.001). The Michelson Contrast equation showed that contrast differences between these two regions for the ratio images were 1.6 times higher than T1w, 2.6 times higher than T2w, and 1.8 times higher than ADC (all P < 0.001). Finally, the ratio improved visualization of the corticospinal tract, one of the earliest myelinated pathways.
Conclusion
The T1w/T2w ratio accentuates contrast between myelinated and less myelinated structures and may enhance our diagnostic ability to detect myelination patterns in the neonatal brain.
Level of Evidence: 2
Technical Efficacy: Stage2
J. MAGN. RESON. IMAGING 2017;46:690–696
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Methamphetamine use causes spikes in blood pressure. Chronic hypertension is a major risk factor for cerebral small vessel disease (cSVD). The aim of this study is to investigate whether ...methamphetamine use increases the risk of cSVD. Consecutive patients with acute ischemic stroke at our medical center were screened for methamphetamine use and evidence of cSVD on MRI of the brain. Methamphetamine use was identified by self-reported history and/or positive urine drug screen. Propensity score matching was used to select non-methamphetamine controls. Sensitivity analysis was performed to assess the effect of methamphetamine use on cSVD. Among 1369 eligible patients, 61 (4.5%) were identified to have a history of methamphetamine use and/or positive urine drug screen. Compared with the non-methamphetamine group (n = 1306), the patients with methamphetamine abuse were significantly younger (54.5 ± 9.7 vs. 70.5 ± 12.4, p < 0.001), male (78.7% vs. 54.0%, p < 0.001) and White (78.7% vs. 50.4%, p < 0.001). Sensitivity analysis showed that methamphetamine use was associated with increased white matter hyperintensities, lacunes, and total burden of cSVD. The association was independent of age, sex, concomitant cocaine use, hyperlipidemia, acute hypertension, and stroke severity. Our findings suggest that methamphetamine use increases the risk of cSVD in young patients with acute ischemic stroke.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Autoimmune encephalitis is a category of autoantibody‐mediated neurological disorders that often presents a diagnostic challenge due to its variable clinical and imaging findings. The purpose of this ...image‐based review is to provide an overview of the major subtypes of autoimmune encephalitis and their associated autoantibodies, discuss their characteristic clinical and imaging features, and highlight several disease processes that may mimic imaging findings of autoimmune encephalitis. A literature search on autoimmune encephalitis was performed and publications from neuroradiology, neurology, and nuclear medicine literature were included. Cases from our institutional database that best exemplify major imaging features were presented.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Recently developed machine-learning algorithms have demonstrated strong performance in the detection of intracranial hemorrhage (ICH) and large vessel occlusion (LVO). However, their generalizability ...is often limited by geographic bias of studies. The aim of this study was to validate a commercially available deep learning-based tool in the detection of both ICH and LVO across multiple hospital sites and vendors throughout the U.S.
This was a retrospective and multicenter study using anonymized data from two institutions. Eight hundred fourteen non-contrast CT cases and 378 CT angiography cases were analyzed to evaluate ICH and LVO, respectively. The tool's ability to detect and quantify ICH, LVO, and their various subtypes was assessed among multiple CT vendors and hospitals across the United States. Ground truth was based off imaging interpretations from two board-certified neuroradiologists.
There were 255 positive and 559 negative ICH cases. Accuracy was 95.6%, sensitivity was 91.4%, and specificity was 97.5% for the ICH tool. ICH was further stratified into the following subtypes: intraparenchymal, intraventricular, epidural/subdural, and subarachnoid with true positive rates of 92.9, 100, 94.3, and 89.9%, respectively. ICH true positive rates by volume small (<5 mL), medium (5-25 mL), and large (>25 mL) were 71.8, 100, and 100%, respectively. There were 156 positive and 222 negative LVO cases. The LVO tool demonstrated an accuracy of 98.1%, sensitivity of 98.1%, and specificity of 98.2%. A subset of 55 randomly selected cases were also assessed for LVO detection at various sites, including the distal internal carotid artery, middle cerebral artery M1 segment, proximal middle cerebral artery M2 segment, and distal middle cerebral artery M2 segment with an accuracy of 97.0%, sensitivity of 94.3%, and specificity of 97.4%.
Deep learning tools can be effective in the detection of both ICH and LVO across a wide variety of hospital systems. While some limitations were identified, specifically in the detection of small ICH and distal M2 occlusion, this study highlights a deep learning tool that can assist radiologists in the detection of emergent findings in a variety of practice settings.
Background:
COVID-19 has impacted healthcare in many ways, including presentation of acute stroke. Since time-sensitive thrombolysis is essential for reducing morbidity and mortality in acute stroke, ...any delays due to the pandemic can have serious consequences.
Methods:
We retrospectively reviewed the electronic medical records for patients presenting with acute ischemic stroke at a comprehensive stroke center in March–April 2020 (the early months of COVID-19) and compared to the same time period in 2019. Stroke metrics such as incidence, time to arrival, and immediate outcomes were assessed.
Results:
There were 48 acute ischemic strokes (of which 7 were transfers) in March–April 2020 compared to 64 (of which 12 were transfers) in 2019. The average last known well to arrival time (±SD) for stroke codes was 1,041 (±1682.1) min in 2020 and 554 (±604.9) min in 2019. Of the patients presenting directly to the ED with a known last known well time, 27.8% (10/36) presented in the first 4.5 h in 2020, in contrast to 40.5% (15/37) in 2019. Patients who died comprised 10.4% of the stroke cohort in 2020 (5/48) compared to 6.3% in 2019 (4/64).
Conclusions:
During the first 2 months of COVID-19, there were fewer overall stroke cases who presented to our hospital, and of these cases, there was delayed presentation in comparison to the same time period in 2019. Recognizing how stroke presentation may be affected by COVID-19 would allow for optimization of established stroke triage algorithms in order to ensure safe and timely delivery of stroke care during a pandemic.
Purpose
Despite the availability of commercial artificial intelligence (AI) tools for large vessel occlusion (LVO) detection, there is paucity of data comparing traditional machine learning and deep ...learning solutions in a real-world setting. The purpose of this study is to compare and validate the performance of two AI-based tools (RAPID LVO and CINA LVO) for LVO detection.
Materials and methods
This was a retrospective, single center study performed at a comprehensive stroke center from December 2020 to June 2021. CT angiography (
n
= 263) for suspected stroke were evaluated for LVO. RAPID LVO is a traditional machine learning model which primarily relies on vessel density threshold assessment, while CINA LVO is an end-to-end deep learning tool implemented with multiple neural networks for detection and localization tasks. Reasons for errors were also recorded.
Results
There were 29 positive and 224 negative LVO cases by ground truth assessment. RAPID LVO demonstrated an accuracy of 0.86, sensitivity of 0.90, specificity of 0.86, positive predictive value of 0.45, and negative predictive value of 0.98, while CINA demonstrated an accuracy of 0.96, sensitivity of 0.76, specificity of 0.98, positive predictive value of 0.85, and negative predictive value of 0.97.
Conclusion
Both tools successfully detected most anterior circulation occlusions. RAPID LVO had higher sensitivity while CINA LVO had higher accuracy and specificity. Interestingly, both tools were able to detect some, but not all M2 MCA occlusions. This is the first study to compare traditional and deep learning LVO tools in the clinical setting.
Automated large vessel occlusion (LVO) tools allow for prompt identification of positive LVO cases, but little is known about their role in acute stroke triage when implemented in a real-world ...setting. The purpose of this study was to evaluate the automated LVO detection tool's impact on acute stroke workflow and clinical outcomes.
Consecutive patients with a computed tomography angiography (CTA) presenting with suspected acute ischemic stroke were compared before and after the implementation of an AI tool, RAPID LVO (RAPID 4.9, iSchemaView, Menlo Park, CA). Radiology CTA report turnaround times (TAT), door-to-treatment times, and the NIH stroke scale (NIHSS) after treatment were evaluated.
A total of 439 cases in the pre-AI group and 321 cases in the post-AI group were included, with 62 (14.12%) and 43 (13.40%) cases, respectively, receiving acute therapies. The AI tool demonstrated a sensitivity of 0.96, a specificity of 0.85, a negative predictive value of 0.99, and a positive predictive value of 0.53. Radiology CTA report TAT significantly improved post-AI (mean 30.58 min for pre-AI vs. 22 min for post-AI,
< 0.0005), notably at the resident level (
< 0.0003) but not at higher levels of expertise. There were no differences in door-to-treatment times, but the NIHSS at discharge was improved for the pre-AI group adjusted for confounders (parameter estimate = 3.97,
< 0.01).
Implementation of an automated LVO detection tool improved radiology TAT but did not translate to improved stroke metrics and outcomes in a real-world setting.
Background: Specialized hypothalamic systems that increase food intake might also increase ethanol intake. To test this possibility, morphine and receptor‐specific opioid agonists were microinjected ...in the paraventricular nucleus (PVN) of rats that had learned to drink ethanol. To cross‐validate the results, naloxone methiodide (m‐naloxone), an opioid antagonist, was microinjected with the expectation that it would have the opposite effect of morphine and the specific opioid agonists.
Methods: Sprague–Dawley rats were trained, without sugar, to drink 4 or 7% ethanol and were then implanted with chronic brain cannulas aimed at the PVN. After recovery, those drinking 7% ethanol, with food and water available, were injected with 2 doses each of morphine or m‐naloxone. To test for receptor specificity, 2 doses each of the μ‐receptor agonist d‐Ala2,N‐Me‐Phe4,Gly5‐ol‐Enkephalin (DAMGO), δ‐receptor agonist d‐Ala‐Gly‐Phe‐Met‐NH2 (DALA), or κ‐receptor agonist U‐50,488H were injected. DAMGO was also tested in rats drinking 4% ethanol without food or water available. As an anatomical control for drug reflux, injections were made 2 mm dorsal to the PVN.
Results: A main result was a significant increase in ethanol intake induced by PVN injection of morphine. The opposite effect was produced by m‐naloxone. The effects of morphine and m‐naloxone were exclusively on intake of ethanol, even though food and water were freely available. In the analysis with specific receptor agonists, PVN injection of the δ‐agonist DALA significantly increased 7% ethanol intake without affecting food or water intake. This is in contrast to the κ‐agonist U‐50,488H, which decreased ethanol intake, and the μ‐agonist DAMGO, which had no effect on ethanol intake in the presence or absence of food and water. In the anatomical control location 2 mm dorsal to the PVN, no drug caused any significant changes in ethanol, food, or water intake, providing evidence that the active site was close to the cannula tip.
Conclusions: The δ‐opioid receptor agonist in the PVN increased ethanol intake in strong preference over food and water, while the κ‐opioid agonist suppressed ethanol intake. Prior studies show that learning to drink ethanol stimulates PVN expression and production of the peptides enkephalin and dynorphin, which are endogenous agonists for the δ‐ and κ‐receptors, respectively. These results suggest that enkephalin via the δ‐opioid system can function locally within a positive feedback circuit to cause ethanol intake to escalate and ultimately contribute to the abuse of ethanol. This is in contrast to dynorphin via the κ‐opioid system, which may act to counter this escalation. Naltrexone therapy for alcoholism may act, in part, by blocking the enkephalin‐triggered positive feedback cycle.
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