AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults.

OBJECTIVES: To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population. METHODS: Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile. RESULTS: A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT. CONCLUSION: Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures. CLINICAL RELEVANCE STATEMENT: Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients. KEY POINTS: Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.

Post-contrast CT liver attenuation alone is superior to the liver-spleen difference for identifying moderate hepatic steatosis.

OBJECTIVE: To assess the diagnostic performance of post-contrast CT for predicting moderate hepatic steatosis in an older adult cohort undergoing a uniform CT protocol, utilizing hepatic and splenic attenuation values. MATERIALS AND METHODS: A total of 1676 adults (mean age, 68.4 ± 10.2 years; 1045M/631F) underwent a CT urothelial protocol that included unenhanced, portal venous, and 10-min delayed phases through the liver and spleen. Automated hepatosplenic segmentation for attenuation values (in HU) was performed using a validated deep-learning tool. Unenhanced liver attenuation < 40.0 HU, corresponding to > 15% MRI-based proton density fat, served as the reference standard for moderate steatosis. RESULTS: The prevalence of moderate or severe steatosis was 12.9% (216/1676). The diagnostic performance of portal venous liver HU in predicting moderate hepatic steatosis (AUROC = 0.943) was significantly better than the liver-spleen HU difference (AUROC = 0.814) (p < 0.001). Portal venous phase liver thresholds of 80 and 90 HU had a sensitivity/specificity for moderate steatosis of 85.6%/89.6%, and 94.9%/74.7%, respectively, whereas a liver-spleen difference of -40 HU and -10 HU had a sensitivity/specificity of 43.5%/90.0% and 92.1%/52.5%, respectively. Furthermore, livers with moderate-severe steatosis demonstrated significantly less post-contrast enhancement (mean, 35.7 HU vs 47.3 HU; p < 0.001). CONCLUSION: Moderate steatosis can be reliably diagnosed on standard portal venous phase CT using liver attenuation values alone. Consideration of splenic attenuation appears to add little value. Moderate steatosis not only has intrinsically lower pre-contrast liver attenuation values (< 40 HU), but also enhances less, typically resulting in post-contrast liver attenuation values of 80 HU or less. CLINICAL RELEVANCE STATEMENT: Moderate steatosis can be reliably diagnosed on post-contrast CT using liver attenuation values alone. Livers with at least moderate steatosis enhance less than those with mild or no steatosis, which combines with the lower intrinsic attenuation to improve detection. KEY POINTS: The liver-spleen attenuation difference is frequently utilized in routine practice but appears to have performance limitations. The liver-spleen attenuation difference is less effective than liver attenuation for moderate steatosis. Moderate and severe steatosis can be identified on standard portal venous phase CT using liver attenuation alone.

Abdominal CT Body Composition Thresholds Using Automated AI Tools for Predicting 10-year Adverse Outcomes.

Background CT-based body composition measures derived from fully automated artificial intelligence tools are promising for opportunistic screening. However, body composition thresholds associated with adverse clinical outcomes are lacking. Purpose To determine population and sex-specific thresholds for muscle, abdominal fat, and abdominal aortic calcium measures at abdominal CT for predicting risk of death, adverse cardiovascular events, and fragility fractures. Materials and Methods In this retrospective single-center study, fully automated algorithms for quantifying skeletal muscle (L3 level), abdominal fat (L3 level), and abdominal aortic calcium were applied to noncontrast abdominal CT scans from asymptomatic adults screened from 2004 to 2016. Longitudinal follow-up documented subsequent death, adverse cardiovascular events (myocardial infarction, cerebrovascular event, and heart failure), and fragility fractures. Receiver operating characteristic (ROC) curve analysis was performed to derive thresholds for body composition measures to achieve optimal ROC curve performance and high specificity (90%) for 10-year risks. Results A total of 9223 asymptomatic adults (mean age, 57 years ± 7 [SD]; 5152 women and 4071 men) were evaluated (median follow-up, 9 years). Muscle attenuation and aortic calcium had the highest diagnostic performance for predicting death, with areas under the ROC curve of 0.76 for men (95% CI: 0.72, 0.79) and 0.72 for women (95% CI: 0.69, 0.76) for muscle attenuation. Sex-specific thresholds were higher in men than women (P < .001 for muscle attenuation for all outcomes). The highest-performing markers for risk of death were muscle attenuation in men (31 HU; 71% sensitivity [164 of 232 patients]; 72% specificity [1114 of 1543 patients]) and aortic calcium in women (Agatston score, 167; 70% sensitivity [152 of 218 patients]; 70% specificity [1427 of 2034 patients]). Ninety-percent specificity thresholds for muscle attenuation for both risk of death and fragility fractures were 23 HU (men) and 13 HU (women). For aortic calcium and risk of death and adverse cardiovascular events, 90% specificity Agatston score thresholds were 1475 (men) and 735 (women). Conclusion Sex-specific thresholds for automated abdominal CT-based body composition measures can be used to predict risk of death, adverse cardiovascular events, and fragility fractures. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Ohliger in this issue.

Opportunistic Screening: Radiology Scientific Expert Panel.

Radiologic tests often contain rich imaging data not relevant to the clinical indication. Opportunistic screening refers to the practice of systematically leveraging these incidental imaging findings. Although opportunistic screening can apply to imaging modalities such as conventional radiography, US, and MRI, most attention to date has focused on body CT by using artificial intelligence (AI)-assisted methods. Body CT represents an ideal high-volume modality whereby a quantitative assessment of tissue composition (eg, bone, muscle, fat, and vascular calcium) can provide valuable risk stratification and help detect unsuspected presymptomatic disease. The emergence of "explainable" AI algorithms that fully automate these measurements could eventually lead to their routine clinical use. Potential barriers to widespread implementation of opportunistic CT screening include the need for buy-in from radiologists, referring providers, and patients. Standardization of acquiring and reporting measures is needed, in addition to expanded normative data according to age, sex, and race and ethnicity. Regulatory and reimbursement hurdles are not insurmountable but pose substantial challenges to commercialization and clinical use. Through demonstration of improved population health outcomes and cost-effectiveness, these opportunistic CT-based measures should be attractive to both payers and health care systems as value-based reimbursement models mature. If highly successful, opportunistic screening could eventually justify a practice of standalone "intended" CT screening.

Technical Adequacy of Fully Automated Artificial Intelligence Body Composition Tools: Assessment in a Heterogeneous Sample of External CT Examinations.

BACKGROUND. Clinically usable artificial intelligence (AI) tools analyzing imaging studies should be robust to expected variations in study parameters. OBJECTIVE. The purposes of this study were to assess the technical adequacy of a set of automated AI abdominal CT body composition tools in a heterogeneous sample of external CT examinations performed outside of the authors' hospital system and to explore possible causes of tool failure. METHODS. This retrospective study included 8949 patients (4256 men, 4693 women; mean age, 55.5 ± 15.9 years) who underwent 11,699 abdominal CT examinations performed at 777 unique external institutions with 83 unique scanner models from six manufacturers with images subsequently transferred to the local PACS for clinical purposes. Three independent automated AI tools were deployed to assess body composition (bone attenuation, amount and attenuation of muscle, amount of visceral and sub-cutaneous fat). One axial series per examination was evaluated. Technical adequacy was defined as tool output values within empirically derived reference ranges. Failures (i.e., tool output outside of reference range) were reviewed to identify possible causes. RESULTS. All three tools were technically adequate in 11,431 of 11,699 (97.7%) examinations. At least one tool failed in 268 (2.3%) of the examinations. Individual adequacy rates were 97.8% for the bone tool, 99.1% for the muscle tool, and 98.9% for the fat tool. A single type of image processing error (anisometry error, due to incorrect DICOM header voxel dimension information) accounted for 81 of 92 (88.0%) examinations in which all three tools failed, and all three tools failed whenever this error occurred. Anisometry error was the most common specific cause of failure of all tools (bone, 31.6%; muscle, 81.0%; fat, 62.8%). A total of 79 of 81 (97.5%) anisometry errors occurred on scanners from a single manufacturer; 80 of 81 (98.8%) occurred on the same scanner model. No cause of failure was identified for 59.4% of failures of the bone tool, 16.0% of failures of the muscle tool, or 34.9% of failures of the fat tool. CONCLUSION. The automated AI body composition tools had high technical adequacy rates in a heterogeneous sample of external CT examinations, supporting the generalizability of the tools and their potential for broad use. CLINICAL IMPACT. Certain causes of AI tool failure related to technical factors may be largely preventable through use of proper acquisition and reconstruction protocols.

Automated Deep Learning Artificial Intelligence Tool for Spleen Segmentation on CT: Defining Volume-Based Thresholds for Splenomegaly.

BACKGROUND. Splenomegaly historically has been assessed on imaging by use of potentially inaccurate linear measurements. Prior work tested a deep learning artificial intelligence (AI) tool that automatically segments the spleen to determine splenic volume. OBJECTIVE. The purpose of this study is to apply the deep learning AI tool in a large screening population to establish volume-based splenomegaly thresholds. METHODS. This retrospective study included a primary (screening) sample of 8901 patients (4235 men, 4666 women; mean age, 56 ± 10 [SD] years) who underwent CT colonoscopy (n = 7736) or renal donor CT (n = 1165) from April 2004 to January 2017 and a secondary sample of 104 patients (62 men, 42 women; mean age, 56 ± 8 years) with end-stage liver disease who underwent contrast-enhanced CT performed as part of evaluation for potential liver transplant from January 2011 to May 2013. The automated deep learning AI tool was used for spleen segmentation, to determine splenic volumes. Two radiologists independently reviewed a subset of segmentations. Weight-based volume thresholds for splenomegaly were derived using regression analysis. Performance of linear measurements was assessed. Frequency of splenomegaly in the secondary sample was determined using weight-based volumetric thresholds. RESULTS. In the primary sample, both observers confirmed splenectomy in 20 patients with an automated splenic volume of 0 mL; confirmed incomplete splenic coverage in 28 patients with a tool output error; and confirmed adequate segmentation in 21 patients with low volume (< 50 mL), 49 patients with high volume (> 600 mL), and 200 additional randomly selected patients. In 8853 patients included in analysis of splenic volumes (i.e., excluding a value of 0 mL or error values), the mean automated splenic volume was 216 ± 100 [SD] mL. The weight-based volumetric threshold (expressed in milliliters) for splenomegaly was calculated as (3.01 × weight [expressed as kilograms]) + 127; for weight greater than 125 kg, the splenomegaly threshold was constant (503 mL). Sensitivity and specificity for volume-defined splenomegaly were 13% and 100%, respectively, at a true craniocaudal length of 13 cm, and 78% and 88% for a maximum 3D length of 13 cm. In the secondary sample, both observers identified segmentation failure in one patient. The mean automated splenic volume in the 103 remaining patients was 796 ± 457 mL; 84% (87/103) of patients met the weight-based volume-defined splenomegaly threshold. CONCLUSION. We derived a weight-based volumetric threshold for splenomegaly using an automated AI-based tool. CLINICAL IMPACT. The AI tool could facilitate large-scale opportunistic screening for splenomegaly.

Utility of Fully Automated Body Composition Measures on Pretreatment Abdominal CT for Predicting Survival in Patients With Colorectal Cancer.

BACKGROUND. CT examinations contain opportunistic body composition data with potential prognostic utility. Previous studies have primarily used manual or semiautomated tools to evaluate body composition in patients with colorectal cancer (CRC). OBJECTIVE. The purpose of this article is to assess the utility of fully automated body composition measures derived from pretreatment CT examinations in predicting survival in patients with CRC. METHODS. This retrospective study included 1766 patients (mean age, 63.7 ± 14.4 [SD] years; 862 men, 904 women) diagnosed with CRC between January 2001 and September 2020 who underwent pretreatment abdominal CT. A panel of fully automated artificial intelligence-based algorithms was applied to portal venous phase images to quantify skeletal muscle attenuation at the L3 lumbar level, visceral adipose tissue (VAT) area and subcutaneous adipose tissue (SAT) area at L3, and abdominal aorta Agatston score (aortic calcium). The electronic health record was reviewed to identify patients who died of any cause (n = 848). ROC analyses and logistic regression analyses were used to identify predictors of survival, with attention to highest- and lowest-risk quartiles. RESULTS. Patients who died, compared with patients who survived, had lower median muscle attenuation (19.2 vs 26.2 HU, p < .001), SAT area (168.4 cm2 vs 197.6 cm2, p < .001), and aortic calcium (620 vs 182, p < .001). Measures with highest 5-year AUCs for predicting survival in patients without (n = 1303) and with (n = 463) metastatic disease were muscle attenuation (0.666 and 0.701, respectively) and aortic calcium (0.677 and 0.689, respectively). A combination of muscle attenuation, SAT area, and aortic calcium yielded 5-year AUCs of 0.758 and 0.732 in patients without and with metastases, respectively. Risk of death was increased (p < .05) in patients in the lowest quartile for muscle attenuation (hazard ratio [HR] = 1.55) and SAT area (HR = 1.81) and in the highest quartile for aortic calcium (HR = 1.37) and decreased (p < .05) in patients in the highest quartile for VAT area (HR = 0.79) and SAT area (HR = 0.76). In 423 patients with available BMI, BMI did not significantly predict death (p = .75). CONCLUSION. Fully automated CT-based body composition measures including muscle attenuation, SAT area, and aortic calcium predict survival in patients with CRC. CLINICAL IMPACT. Routine pretreatment body composition evaluation could improve initial risk stratification of patients with CRC.

MRI features predict tumor grade in isocitrate dehydrogenase (IDH)-mutant astrocytoma and oligodendroglioma.

PURPOSE: Nearly all literature for predicting tumor grade in astrocytoma and oligodendroglioma pre-dates the molecular classification system. We investigated the association between contrast enhancement, ADC, and rCBV with tumor grade separately for IDH-mutant astrocytomas and molecularly-defined oligodendrogliomas. METHODS: For this retrospective study, 44 patients with IDH-mutant astrocytomas (WHO grades II, III, or IV) and 39 patients with oligodendrogliomas (IDH-mutant and 1p/19q codeleted) (WHO grade II or III) were enrolled. Two readers independently assessed preoperative MRI for contrast enhancement, ADC, and rCBV. Inter-reader agreement was calculated, and statistical associations between MRI metrics and WHO grade were determined per reader. RESULTS: For IDH-mutant astrocytomas, both readers found a stepwise positive association between contrast enhancement and WHO grade (Reader A: OR 7.79 [1.97, 30.80], p = 0.003; Reader B: OR 6.62 [1.70, 25.82], p = 0.006); both readers found that ADC was negatively associated with WHO grade (Reader A: OR 0.74 [0.61, 0.90], p = 0.002); Reader B: OR 0.80 [0.66, 0.96], p = 0.017), and both readers found that rCBV was positively associated with WHO grade (Reader A: OR 2.33 [1.35, 4.00], p = 0.002; Reader B: OR 2.13 [1.30, 3.57], p = 0.003). For oligodendrogliomas, both readers found a positive association between contrast enhancement and WHO grade (Reader A: OR 15.33 [2.56, 91.95], p = 0.003; Reader B: OR 20.00 [2.19, 182.45], p = 0.008), but neither reader found an association between ADC or rCBV and WHO grade. CONCLUSIONS: Contrast enhancement predicts WHO grade for IDH-mutant astrocytomas and oligodendrogliomas. ADC and rCBV predict WHO grade for IDH-mutant astrocytomas, but not for oligodendrogliomas.

Factors Contributing to Computed Tomography Trauma Scan Times at a Tertiary Center: Improving Emergency Department Trauma Imaging Workflow Through Targeted Interventions.

PURPOSES: The aims of the study are to identify factors contributing to computed tomography (CT) trauma scan turnaround time variation and to evaluate the effects of an automated intervention on time metrics. METHODS: Throughput metrics were captured via picture archiving and communication system from January 1, 2018, to December 16, 2019, and included 17,709 CT trauma scans from our institution. Initial data showed that imaging technologist variation played a significant role in trauma imaging turnaround time. In December 2019, we implemented a 2-pronged intervention: (1) educational intervention to techs and (2) modified trauma CT abdomen/pelvis to autogenerate and autosend reformats to picture archiving and communication system. A total of 13,169 trauma CT scans were evaluated from the postintervention period taking place from January 2020 to March 2021. Throughput metrics such as last image to first report interval and emergency department length of stay were captured and compared with performing technologist, time of day, and weekday versus weekend scans. RESULTS: Substantial variability among trauma CT scans was observed. For CT trauma abdomen/pelvis, the interval from last image to initial report decreased from 26.4 to 24.0 minutes ( P = 0.001) while the interval between first and last image time decreased from 11.4 to 4.2 minutes ( P < 0.001). Emergency department length of stay also decreased from 3.9 to 3.7 hours ( P < 0.0001) in the postintervention period. Variation among imaging technologist was statistically significant and became less significant after intervention ( P = 0.09, P = 0.54). CONCLUSIONS: Factors such as imaging technologist variability, time of day, and day of the week of trauma scans played a significant role in CT trauma turnaround time variability. Automation interventions can help with efficiency in image turnaround time.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver. Materials and Methods In this retrospective study, liver volumes were successfully derived with use of a deep learning tool for asymptomatic outpatient adults who underwent multidetector CT for colorectal cancer screening (unenhanced) or renal donor evaluation (contrast-enhanced) at a single medical center between April 2004 and December 2016. The performance of the craniocaudal and maximal three-dimensional (3D) linear measures was assessed. The manual liver volume results were compared with the automated results in a subset of renal donors in which the entire liver was included at both precontrast and postcontrast CT. Unenhanced liver volumes were standardized to a postcontrast equivalent, reflecting a correction of 3.6%. Linear regression analysis was performed to assess the major patient-specific determinant or determinants of liver volume among age, sex, height, weight, and body surface area. Results A total of 3065 patients (mean age ± standard deviation, 54 years ± 12; 1639 women) underwent multidetector CT for colorectal screening (n = 1960) or renal donor evaluation (n = 1105). The mean standardized automated liver volume ± standard deviation was 1533 mL ± 375 and demonstrated a normal distribution. Patient weight was the major determinant of liver volume and demonstrated a linear relationship. From this result, a linear weight-based upper limit of normal hepatomegaly threshold volume was derived: hepatomegaly (mL) = 14.0 × (weight [kg]) + 979. A craniocaudal threshold of 19 cm was 71% sensitive (49 of 69 patients) and 86% specific (887 of 1030 patients) for hepatomegaly, and a maximal 3D linear threshold of 24 cm was 78% sensitive (54 of 69) and 66% specific (678 of 1030). In the subset of 189 patients, the median difference in hepatic volume between the deep learning tool and the semiautomated or manual method was 2.3% (38 mL). Conclusion A simple weight-based threshold for hepatomegaly derived by using a fully automated CT-based liver volume segmentation based on deep learning provided an objective and more accurate assessment of liver size than linear measures. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Sosna in this issue.

Fully Automated Deep Learning Tool for Sarcopenia Assessment on CT: L1 Versus L3 Vertebral Level Muscle Measurements for Opportunistic Prediction of Adverse Clinical Outcomes.

BACKGROUND. Sarcopenia is associated with adverse clinical outcomes. CT-based skeletal muscle measurements for sarcopenia assessment are most commonly performed at the L3 vertebral level. OBJECTIVE. The purpose of this article is to compare the utility of fully automated deep learning CT-based muscle quantitation at the L1 versus L3 level for predicting future hip fractures and death. METHODS. This retrospective study included 9223 asymptomatic adults (mean age, 57 ± 8 [SD] years; 4071 men, 5152 women) who underwent unenhanced low-dose abdominal CT. A previously validated fully automated deep learning tool was used to assess muscle for myosteatosis (by mean attenuation) and myopenia (by cross-sectional area) at the L1 and L3 levels. Performance for predicting hip fractures and death was compared between L1 and L3 measures. Performance for predicting hip fractures and death was also evaluated using the established clinical risk scores from the fracture risk assessment tool (FRAX) and Framingham risk score (FRS), respectively. RESULTS. Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.1-11.6 years), yielding hip fractures and death in 219 (2.4%) and 549 (6.0%) patients, respectively. L1-level and L3-level muscle attenuation measurements were not different in 2-, 5-, or 10-year AUC for hip fracture (p = .18-.98) or death (p = .19-.95). For hip fracture, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRAX score were 0.717, 0.709, and 0.708, respectively. For death, 5-year AUCs for L1-level muscle attenuation, L3-level muscle attenuation, and FRS were 0.737, 0.721, and 0.688, respectively. Lowest quartile hazard ratios (HRs) for hip fracture were 2.20 (L1 attenuation), 2.45 (L3 attenuation), and 2.53 (FRAX score), and for death were 3.25 (L1 attenuation), 3.58 (L3 attenuation), and 2.82 (FRS). CT-based muscle cross-sectional area measurements at L1 and L3 were less predictive for hip fracture and death (5-year AUC ≤ 0.571; HR ≤ 1.56). CONCLUSION. Automated CT-based measurements of muscle attenuation for myosteatosis at the L1 level compare favorably with previously established L3-level measurements and clinical risk scores for predicting hip fracture and death. Assessment for myopenia was less predictive of outcomes at both levels. CLINICAL IMPACT. Alternative use of the L1 rather than L3 level for CT-based muscle measurements allows sarcopenia assessment using both chest and abdominal CT scans, greatly increasing the potential yield of opportunistic CT screening.

Validation of a simplified comorbidity evaluation predicting clinical outcomes among patients with coronavirus disease 2019 - A multicenter retrospective observation study.

OBJECTIVES: We compared and validated the performance accuracy of simplified comorbidity evaluation compared to the Charlson Comorbidity Index (CCI) predicting COVID-19 severity. In addition, we also determined whether risk prediction of COVID-19 severity changed during different COVID-19 pandemic outbreaks. METHODS: We enrolled all patients whose SARS-CoV-2 PCR tests were performed at six different hospital Emergency Departments in 2020. Patients were divided into three groups based on the various COVID-19 outbreaks in the US (first wave: March-May 2020, second wave: June-September 2020, and third wave: October-December 2020). A simplified comorbidity evaluation was used as an independent risk factor to predict clinical outcomes using multivariate logistic regressions. RESULTS: A total of 22,248 patients were included, for which 7023 (32%) patients tested COVID-19 positive. Higher percentages of COVID-19 patients with more than three chronic conditions had worse clinical outcomes (i.e., hospital and intensive care unit admissions, receiving invasive mechanical ventilations, and in-hospital mortality) during all three COVID-19 outbreak waves. CONCLUSIONS: This simplified comorbidity evaluation was validated to be associated with COVID clinical outcomes. Such evaluation did not perform worse when compared with CCI to predict in-hospital mortality.

Vertebral Artery Tortuosity and Morphometric Characteristics of Patients with Recurrent Cervical Artery Dissection.

BACKGROUND: Cervical Artery Dissection is an important cause of stroke in the young. Data on incidence and associations of recurrence in patients with cervical artery dissection are lacking. Increased Vertebral Artery Tortuosity Index has been reported in patients with cervical artery dissection and associated with earlier age of arterial dissection in patients with connective tissue disease. OBJECTIVE: To test the hypothesis that increased vertebral artery tortuosity is associated with recurrent cervical artery dissection. METHODS: We reviewed data from a single-center registry of cervical artery dissection patients enrolled between 2011-2021. CT angiography was reviewed for neck length, vertebral artery dominance, and vertebral artery tortuosity index. Incidence rate of recurrent dissection was calculated using Poisson regression. Differences between groups were analyzed using the Kruskal-Wallis rank sum test and Fisher's exact test. RESULTS: The cohort included 155 patients: women (56%), mean (SD) age 42 (±10) years, and 116 single and 39 multiple artery dissections. Eleven (7.1%) had a recurrence with an incidence rate (95% CI) of 1.91 (1.06, 3.44) per 100 person-years. Vertebral artery tortuosity did not differ significantly between single and recurrent groups (median (IQR) 46.81 (40.85, 53.91) vs 44.97 (40.68, 50.62) p = 0.388). Morphometric characteristics of height, neck length, and BMI were not associated with recurrence. There was no difference in vertebral artery tortuosity by dissection location (carotid vs vertebral). CONCLUSION: In this single center cohort of patients with cervical artery dissection, there was no difference in VTI between single and recurrent groups.

Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence.

Background Radiologists are proficient in differentiating between chest radiographs with and without symptoms of pneumonia but have found it more challenging to differentiate coronavirus disease 2019 (COVID-19) pneumonia from non-COVID-19 pneumonia on chest radiographs. Purpose To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of abnormalities at chest radiography. Materials and Methods In this retrospective study, a deep neural network, CV19-Net, was trained, validated, and tested on chest radiographs in patients with and without COVID-19 pneumonia. For the chest radiographs positive for COVID-19, patients with reverse transcription polymerase chain reaction results positive for severe acute respiratory syndrome coronavirus 2 with findings positive for pneumonia between February 1, 2020, and May 30, 2020, were included. For the non-COVID-19 chest radiographs, patients with pneumonia who underwent chest radiography between October 1, 2019, and December 31, 2019, were included. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to characterize diagnostic performance. To benchmark the performance of CV19-Net, a randomly sampled test data set composed of 500 chest radiographs in 500 patients was evaluated by the CV19-Net and three experienced thoracic radiologists. Results A total of 2060 patients (5806 chest radiographs; mean age, 62 years ± 16 [standard deviation]; 1059 men) with COVID-19 pneumonia and 3148 patients (5300 chest radiographs; mean age, 64 years ± 18; 1578 men) with non-COVID-19 pneumonia were included and split into training and validation and test data sets. For the test set, CV19-Net achieved an AUC of 0.92 (95% CI: 0.91, 0.93). This corresponded to a sensitivity of 88% (95% CI: 87, 89) and a specificity of 79% (95% CI: 77, 80) by using a high-sensitivity operating threshold, or a sensitivity of 78% (95% CI: 77, 79) and a specificity of 89% (95% CI: 88, 90) by using a high-specificity operating threshold. For the 500 sampled chest radiographs, CV19-Net achieved an AUC of 0.94 (95% CI: 0.93, 0.96) compared with an AUC of 0.85 (95% CI: 0.81, 0.88) achieved by radiologists. Conclusion CV19-Net was able to differentiate coronavirus disease 2019-related pneumonia from other types of pneumonia, with performance exceeding that of experienced thoracic radiologists. © RSNA, 2021 Online supplemental material is available for this article.

Impact of emergency department opioid use on future health resource utilization among patients with migraine.

OBJECTIVE: The purpose of this study was to evaluate the subsequent health resource utilization (HRU) between patients with migraine who received opioid medications at their emergency department (ED) visits ("opioid recipients") versus patients with migraine who did not receive opioid medications at their ED visits ("non-recipients"). BACKGROUND: Previous studies have found that opioid use is common among patients with migraine at emergency settings. Medication overuse, especially the use of opioids, is associated with migraine progression, which can ultimately lead to substantial health resource use and costs. There is limited evidence on opioid use specifically in emergency settings and its impact on future HRU among people with migraine. METHOD: This retrospective cohort study used electronic health record data from the Baylor Scott & White Health between December 2013 and April 2017. Adult patients who had at least 6 months of continuous enrollment before (baseline or pre-index) and after (follow-up) the first date they had an ED visit with a diagnosis of migraine (defined as index date) were enrolled in the study. Opioid use and HRU during follow-up period between opioid recipients and non-recipients were summarized and compared. RESULTS: A total of 788 patients met the eligibility criteria and were included in this study. During the 6-month follow-up period, compared to patients with migraine who were non-recipients at their index ED visits, opioid recipients had significantly more all-cause (3.6 [SD = 6.3] vs. 1.9 [SD = 4.8], p < 0.0001) and migraine-related (1.6 [SD = 4.2] vs. 0.6 [SD = 2.1], p < 0.0001) opioid prescriptions (RXs), and more all-cause (2.6 [SD = 4.3] vs. 1.6 [SD = 2.6], p = 0.002) and migraine-related (0.6 [SD = 1.4] vs. 0.3 [SD = 0.8], p = 0.001) ED visits. In addition, opioid recipients had higher risk of future migraine-related ED visits controlling for covariates (HR = 1.49, 95% CI = 1.09-2.03, p = 0.013). Factors that were significantly (p < 0.05) related to future migraine-related ED visits include previous opioid use (HR = 2.12, 95% CI = 1.24-3.65, p = 0.007), previous ED visits (HR = 2.38, 95% CI = 1.23-4.58, p = 0.010), hypertension (HR = 1.46, 95% CI = 1.07-2.00, p = 0.017), age between 45 and 64 years (HR = 0.68, 95% CI = 0.48-0.97, p = 0.033), female sex (HR = 1.82, 95% CI = 1.12-2.86, p = 0.015), and tobacco use disorder (HR = 1.45, 95% CI = 1.07-1.97, p = 0.017). Sub-analyses were restricted to the group of patients who were opioid naïve at baseline (n = 274, defined as having ≤1 opioid RXs during the 6-month pre-index period). Patients who were baseline opioid naïve but received opioids during their index ED visits were more likely to have future migraine-related ED visits compared to patients who were baseline opioid naïve and did not receive any opioids during their index ED visits, controlling for covariates (HR = 2.90, 95% CI = 1.54-5.46, p = 0.001). CONCLUSION: Opioid use among patients with migraine presenting to the ED is associated with increased future HRU, which highlights the need for optimizing migraine management in emergency settings.

Safety and image quality of MR-conditional external fixators for 1.5 Tesla extremity MR.

PURPOSE: To evaluate the safety and image quality of extremity MR examinations performed with two MR conditional external fixators located in the MR bore. MATERIALS AND METHODS: Single-center retrospective study of a prospectively maintained imaging dataset that evaluated MR examinations of extremities in patients managed with external fixations instrumentation and imaged on a single 1.5T MR scanner. The fixation device was one of two MR-conditional instrumentation systems: DuPuy Synthes (aluminum, stainless steel, carbonium and Kevlar) or Dolphix temporary fixation system (PEEK-CA30). Safety events were recorded by the performing MR radiologic technologist. A study musculoskeletal radiologist assessed all sequences to evaluate for image quality, signal- and contrast-to-noise ratios (SNR/CNR), and injury patterns/findings. RESULTS: In the 13 men and 9 women with a mean age of 42 years (range 18 to 72 years), most patients (19/22 patients; 86%) were involved with trauma resulting in extremity injury requiring external fixation. MR examinations included 19 knee, 2 ankle, and 1 elbow examinations. There were no adverse safety events, heating that caused patient discomfort, fixation dislodgement/perturbment, or early termination of MR examinations. All examinations were of diagnostic quality. Fat-suppressed proton density sequences had significantly higher SNR and CNR compared to STIR (p = 0.01 to 0.04). The lower SNR of STIR and increased quality of fat-suppressed proton density during the study period led to the STIR sequence being dropped in standard MR protocol. CONCLUSION: MR of the extremity using the two study MR conditional external fixators within the MR bore is safe and feasible.

Dinutuximab in adult-onset chemotherapy refractory high-risk neuroblastoma.

INTRODUCTION: Neuroblastoma is the most common extracranial solid tumor in pediatrics but is considerably uncommon in adults, with approximately 1 case per 10 million diagnosed per year and is associated with poor prognosis. There are no standard treatment protocols for adult-onset neuroblastomas and there are only a few published case reports on neuroblastoma in adults. CASE REPORT: We report our treatment experience in a 41-year-old female diagnosed with high-risk, poorly differentiated neuroblastoma. MANAGEMENT AND OUTCOME: Our patient received two cycles of dinutuximab adapted from the Children's Oncology Group ANBL1221 protocol. The patient experienced pain, neuropathy, pruritus, and infusion reactions which were managed with supportive care. Due to the lack of tumor regression, dinutuximab was omitted from future treatments. Currently, the patient is asymptomatic from her disease and remains off of all therapy and pain medication. DISCUSSION: While dinutuximab has produced promising outcomes in pediatric patients, it is not without potentially severe adverse effects. Serious reactions of capillary leak syndrome, infusion reactions, pain, and neuropathy have been reported. Clinicians must be cognizant of the treatment-related toxicities associated with dinutuximab therapy, ranging from pain, neuropathy, pruritus, and infusion reactions as explored in this patient case.

Effect of Out-of-Hospital Tranexamic Acid vs Placebo on 6-Month Functional Neurologic Outcomes in Patients With Moderate or Severe Traumatic Brain Injury.

Importance: Traumatic brain injury (TBI) is the leading cause of death and disability due to trauma. Early administration of tranexamic acid may benefit patients with TBI. Objective: To determine whether tranexamic acid treatment initiated in the out-of-hospital setting within 2 hours of injury improves neurologic outcome in patients with moderate or severe TBI. Design, Setting, and Participants: Multicenter, double-blinded, randomized clinical trial at 20 trauma centers and 39 emergency medical services agencies in the US and Canada from May 2015 to November 2017. Eligible participants (N = 1280) included out-of-hospital patients with TBI aged 15 years or older with Glasgow Coma Scale score of 12 or less and systolic blood pressure of 90 mm Hg or higher. Interventions: Three interventions were evaluated, with treatment initiated within 2 hours of TBI: out-of-hospital tranexamic acid (1 g) bolus and in-hospital tranexamic acid (1 g) 8-hour infusion (bolus maintenance group; n = 312), out-of-hospital tranexamic acid (2 g) bolus and in-hospital placebo 8-hour infusion (bolus only group; n = 345), and out-of-hospital placebo bolus and in-hospital placebo 8-hour infusion (placebo group; n = 309). Main Outcomes and Measures: The primary outcome was favorable neurologic function at 6 months (Glasgow Outcome Scale-Extended score >4 [moderate disability or good recovery]) in the combined tranexamic acid group vs the placebo group. Asymmetric significance thresholds were set at 0.1 for benefit and 0.025 for harm. There were 18 secondary end points, of which 5 are reported in this article: 28-day mortality, 6-month Disability Rating Scale score (range, 0 [no disability] to 30 [death]), progression of intracranial hemorrhage, incidence of seizures, and incidence of thromboembolic events. Results: Among 1063 participants, a study drug was not administered to 96 randomized participants and 1 participant was excluded, resulting in 966 participants in the analysis population (mean age, 42 years; 255 [74%] male participants; mean Glasgow Coma Scale score, 8). Of these participants, 819 (84.8%) were available for primary outcome analysis at 6-month follow-up. The primary outcome occurred in 65% of patients in the tranexamic acid groups vs 62% in the placebo group (difference, 3.5%; [90% 1-sided confidence limit for benefit, -0.9%]; P = .16; [97.5% 1-sided confidence limit for harm, 10.2%]; P = .84). There was no statistically significant difference in 28-day mortality between the tranexamic acid groups vs the placebo group (14% vs 17%; difference, -2.9% [95% CI, -7.9% to 2.1%]; P = .26), 6-month Disability Rating Scale score (6.8 vs 7.6; difference, -0.9 [95% CI, -2.5 to 0.7]; P = .29), or progression of intracranial hemorrhage (16% vs 20%; difference, -5.4% [95% CI, -12.8% to 2.1%]; P = .16). Conclusions and Relevance: Among patients with moderate to severe TBI, out-of-hospital tranexamic acid administration within 2 hours of injury compared with placebo did not significantly improve 6-month neurologic outcome as measured by the Glasgow Outcome Scale-Extended. Trial Registration: ClinicalTrials.gov Identifier: NCT01990768.

The effect of vertical split-flow patient management on emergency department throughput and efficiency.

BACKGROUND: To address emergency department overcrowding operational research seeks to identify efficient processes to optimize flow of patients through the emergency department. Vertical flow refers to the concept of utilizing and assigning patients virtual beds rather than to an actual physical space within the emergency department to care of low acuity patients. The aim of this study is to evaluate the impact of vertical flow upon emergency department efficiency and patient satisfaction. METHODS: Prospective pre/post-interventional cohort study of all intend-to-treat patients presenting to the emergency department during a two-year period before and after the implementation of a vertical flow model. RESULTS: In total 222,713 patient visits were included in the analysis with 107,217 patients presenting within the pre-intervention and 115,496 in the post-intervention groups. The results of the regression analysis demonstrate an improvement in throughput across the entire ED patient population, decreasing door to departure time by 17 min (95% CI 15-18) despite an increase in patient volume. No statistically significant difference in patient satisfaction scores were found between the pre- and post-intervention. CONCLUSIONS: Initiation of a vertical split flow model was associated with improved ED efficiency.

Implications of Clostridium septicum in Vascular Surgery: A Case Report and Outcomes Literature Review.

Clostridium septicum is a rare organism, accounting for approximately 1.3% of clostridial infections and is associated with synchronous colonic malignancy. This case report describes a patient successfully treated for a mycotic aortic aneurysm secondary to C. septicum bacteremia. Subsequent evaluation confirmed a colonic malignancy, prompting early intervention. A systematic literature review revealing a rate of gastrointestinal malignancy in this patient population is 71%, and hematologic malignancy is 3.9%, with the remaining cohort of patients having an unknown source. Infectious involvement of the aorta and associated vascular structures portends a poor prognosis with a 57% mortality rate. Our case and updated review demonstrates the implications of C. septicum vascular seeding and subsequent treatment outcomes.