The effects of prehospital TXA on mortality and neurologic outcomes in patients with traumatic intracranial hemorrhage: a subgroup analysis from the prehospital TXA for TBI trial.

BACKGROUND: In the Prehospital Tranexamic Acid (TXA) for TBI Trial, TXA administered within two hours of injury in the out-of-hospital setting did not reduce mortality in all patients with moderate/severe traumatic brain injury (TBI). We examined the association between TXA dosing arms, neurologic outcome, and mortality in patients with intracranial hemorrhage (ICH) on computed tomography (CT). METHODS: This was a secondary analysis of the Prehospital Tranexamic Acid for TBI Trial (ClinicalTrials.gov [NCT01990768]) that randomized adults with moderate/severe TBI (Glasgow Coma Scale<13) and systolic blood pressure > =90 mmHg within two hours of injury to a 2-gram out-of-hospital TXA bolus followed by an in-hospital saline infusion, a 1-gram out-of-hospital TXA bolus/1-gram in-hospital TXA infusion, or an out-of-hospital saline bolus/in-hospital saline infusion (placebo). This analysis included the subgroup with ICH on initial CT. Primary outcomes included 28-day mortality, 6-month Glasgow Outcome Scale-Extended (GOSE) < = 4, and 6-month Disability Rating Scale (DRS). Outcomes were modeled using linear regression with robust standard errors. RESULTS: The primary trial included 966 patients. Among 541 participants with ICH, 28-day mortality was lower in the 2-gram TXA bolus group (17%) compared to the other two groups (1-gram bolus/1-gram infusion 26%, placebo 27%). The estimated adjusted difference between the 2-gram bolus and placebo groups was -8·5 percentage points (95% CI, -15.9 to -1.0) and between the 2-gram bolus and 1-gram bolus/1-gram infusion groups was -10.2 percentage points (95% CI, -17.6 to -2.9). DRS at 6 months was lower in the 2-gram TXA bolus group than the 1-gram bolus/1-gram infusion (estimated difference -2.1 [95% CI, -4.2 to -0.02]) and placebo groups (-2.2 [95% CI, -4.3, -0.2]). Six-month GOSE did not differ among groups. CONCLUSIONS: A 2-gram out-of-hospital TXA bolus in patients with moderate/severe TBI and ICH resulted in lower 28-day mortality and lower 6-month DRS than placebo and standard TXA dosing. LEVEL OF EVIDENCE: Therapeutic/Care Management, Level II.

AI-generated CT body composition biomarkers associated with increased mortality risk in socioeconomically disadvantaged individuals.

PURPOSE: To evaluate the relationship between socioeconomic disadvantage using national area deprivation index (ADI) and CT-based body composition measures derived from fully automated artificial intelligence (AI) tools to identify body composition measures associated with increased risk for all-cause mortality and adverse cardiovascular events. METHODS: Fully automated AI body composition tools quantifying abdominal aortic calcium, abdominal fat (visceral [VAT], visceral-to-subcutaneous ratio [VSR]), and muscle attenuation (muscle HU) were applied to non-contrast CT examinations in adults undergoing screening CT colonography (CTC). Patients were partitioned into 5 socioeconomic groups based on the national ADI rank at the census block group level. Pearson correlation analysis was performed to determine the association between national ADI and body composition measures. One-way analysis of variance was used to compare means across groups. Odds ratios (ORs) were generated using high-risk, high specificity (90% specificity) body composition thresholds with the most disadvantaged groups being compared to the least disadvantaged group (ADI < 20). RESULTS: 7785 asymptomatic adults (mean age, 57 years; 4361:3424 F:M) underwent screening CTC from April 2004-December 2016. ADI rank data were available in 7644 patients. Median ADI was 31 (IQR 22-43). Aortic calcium, VAT, and VSR had positive correlation with ADI and muscle attenuation had a negative correlation with ADI (all p < .001). Compared with the least disadvantaged group, mean differences for the most disadvantaged group (ADI > 80) were: Aortic calcium (Agatston) = 567, VAT = 27 cm2, VSR = 0.1, and muscle HU = -6 HU (all p < .05). Compared with the least disadvantaged group, the most disadvantaged group had significantly higher odds of having high-risk body composition measures: Aortic calcium OR = 3.8, VAT OR = 2.5, VSR OR = 2.0, and muscle HU OR = 3.1(all p < .001). CONCLUSION: Fully automated CT body composition tools show that socioeconomic disadvantage is associated with high-risk body composition measures and can be used to identify individuals at increased risk for all-cause mortality and adverse cardiovascular events.

Automated detection of incidental abdominal aortic aneurysms on computed tomography.

PURPOSE: To detect and assess abdominal aortic aneurysms (AAAs) on CT in a large asymptomatic adult patient population using fully-automated deep learning software. MATERIALS AND METHODS: The abdominal aorta was segmented using a fully-automated deep learning model trained on 66 manually-segmented abdominal CT scans from two datasets. The axial diameters of the segmented aorta were extracted to detect the presence of AAAs-maximum axial aortic diameter greater than 3 cm were labeled as AAA positive. The trained system was then externally-validated on CT colonography scans of 9172 asymptomatic outpatients (mean age, 57 years) referred for colorectal cancer screening. Using a previously-validated automated calcified atherosclerotic plaque detector, we correlated abdominal aortic Agatston and volume scores with the presence of AAA. RESULTS: The deep learning software detected AAA on the external validation dataset with a sensitivity, specificity, and AUC of 96%, (95% CI 89%, 100%), 96% (96%, 97%), and 99% (98%, 99%) respectively. The Agatston and volume scores of reported AAA-positive cases were statistically significantly greater than those of reported AAA-negative cases (p < 0.0001). Using plaque alone as a AAA detector, at a threshold Agatston score of 2871, the sensitivity and specificity were 84% (73%, 94%) and 87% (86%, 87%), respectively. CONCLUSION: Fully-automated detection and assessment of AAA on CT is feasible and accurate. There was a strong statistical association between the presence of AAA and the quantity of abdominal aortic calcified atherosclerotic plaque.

Comparing fully automated AI body composition measures derived from thin and thick slice CT image data.

PURPOSE: To compare fully automated artificial intelligence body composition measures derived from thin (1.25 mm) and thick (5 mm) slice abdominal CT data. METHODS: In this retrospective study, fully automated CT-based body composition algorithms for quantifying bone attenuation, muscle attenuation, muscle area, liver attenuation, liver volume, spleen volume, visceral-to-subcutaneous fat ratio (VSR) and aortic calcium were applied to both thin (1.25 × 0.625 mm) and thick (5 × 3 mm) abdominal CT series from two patient cohorts: unenhanced scans in asymptomatic adults undergoing colorectal cancer screening, and post-contrast scans in patients with colorectal cancer. Body composition measures derived from thin and thick slice data were compared, including correlation coefficients and Bland-Altman analysis. RESULTS: A total of 9882 CT scans (mean age, 57.0 years; 4527 women, 5355 men) were evaluated, including 8947 non-contrast and 935 contrast-enhanced CT exams. Very strong positive correlation was observed for all soft tissue measures: muscle attenuation (r2 = 0.97), muscle area (r2 = 0.98), liver attenuation (r2 = 0.99), liver volume (r2 = 0.98) and spleen volume (r2 = 0.99), VSR (r2 = 0.98), and aortic calcium (r2 = 0.92); (p < 0.001 for all). Moderate positive correlation was observed for bone attenuation (r2 = 0.35). Bland-Altman analysis showed strong agreement for muscle attenuation, muscle area, liver attenuation, liver volume and spleen volume. Mean percentage differences amongst body composition measures were less than 5% for VSR (4.6%), muscle area (- 0.5%), liver attenuation (0.4%) and liver volume (2.7%) and less than 10% for muscle attenuation (- 5.5%) and spleen volume (5.1%). For aortic calcium, thick slice overestimated for Agatston scores between 0 and 100 and > 400 burden in 3.1% and 0.3% relative to thin slice, respectively, but underestimated scores between 100 and 400. CONCLUSION: Automated body composition measures derived from thin and thick abdominal CT data are strongly correlated and show agreement, particularly for soft tissue applications, making it feasible to use either series for these CT-based body composition algorithms.

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.

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.

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction.

PURPOSE: The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT. METHODS: This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.8 [SD] years; 4020 men, 5046 women) undergoing unenhanced low-dose abdominal CT for colorectal cancer screening. A previously validated artificial intelligence (AI) tool was used to assess cross-sectional visceral and subcutaneous adipose tissue areas (SAT and VAT), as well as their ratio (VSR) at the L1 and L3 levels. Post-CT survival prediction was compared using area under the ROC curve (ROC AUC) and hazard ratios (HRs). RESULTS: Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.2-11.6 years), during which 5.9% died (532/9066). No significant difference (p > 0.05) for mortality was observed between L1 and L3 VAT and SAT at 10-year ROC AUC. However, L3 measures were significantly better for VSR at 10-year AUC (p < 0.001). HRs comparing worst-to-best quartiles for mortality at L1 vs. L3 were 2.12 (95% CI, 1.65-2.72) and 2.22 (1.74-2.83) for VAT; 1.20 (0.95-1.52) and 1.16 (0.92-1.46) for SAT; and 2.26 (1.7-2.93) and 3.05 (2.32-4.01) for VSR. In women, the corresponding HRs for VSR were 2.58 (1.80-3.69) (L1) and 4.49 (2.98-6.78) (L3). CONCLUSION: Automated CT-based measures of visceral fat (VAT and VSR) at L1 are predictive of survival, although overall measures of adiposity at L1 level are somewhat inferior to the standard L3-level measures. Utilizing predictive L1-level fat measures could expand opportunistic screening to chest CT imaging.

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.

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.

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

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.

Predicting opioid-induced oversedation in hospitalised patients: a multicentre observational study.

OBJECTIVES: Opioid-induced respiratory depression (OIRD) and oversedation are rare but potentially devastating adverse events in hospitalised patients. We investigated which features predict an individual patient's risk of OIRD or oversedation; and developed a risk stratification tool that can be used to aid point-of-care clinical decision-making. DESIGN: Retrospective observational study. SETTING: Twelve acute care hospitals in a large not-for-profit integrated delivery system. PARTICIPANTS: All inpatients ≥18 years admitted between 1 July 2016 and 30 June 2018 who received an opioid during their stay (163 190 unique hospitalisations). MAIN OUTCOME MEASURES: The primary outcome was occurrence of sedation or respiratory depression severe enough that emergent reversal with naloxone was required, as determined from medical record review; if naloxone reversal was unsuccessful or if there was no evidence of hypoxic encephalopathy or death due to oversedation, it was not considered an oversedation event. RESULTS: Age, sex, body mass index, chronic obstructive pulmonary disease, concurrent sedating medication, renal insufficiency, liver insufficiency, opioid naïvety, sleep apnoea and surgery were significantly associated with risk of oversedation. The strongest predictor was concurrent administration of another sedating medication (adjusted HR, 95% CI=3.88, 2.48 to 6.06); the most common such medications were benzodiazepines (29%), antidepressants (22%) and gamma-aminobutyric acid analogue (14.7%). The c-statistic for the final model was 0.755. The 24-point Oversedation Risk Criteria (ORC) score developed from the model stratifies patients as high (>20%, ≥21 points), moderate (11%-20%, 10-20 points) and low risk (≤10%, <10 points). CONCLUSIONS: The ORC risk score identifies patients at high risk for OIRD or oversedation from routinely collected data, enabling targeted monitoring for early detection and intervention. It can also be applied to preventive strategies-for example, clinical decision support offered when concurrent prescriptions for opioids and other sedating medications are entered that shows how the chosen combination impacts the patient's risk.

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.

Neurologic Symptoms Caused by Mature Cystic Teratoma of the Thymus.

This case report describes an unusual presentation of a rare mature cystic teratoma of the thymus. It was indistinguishable from other anterior mediastinal masses without surgical resection and histologic diagnosis. Malignant thymic masses and mediastinal masses that cause compression of the heart and surrounding vessels have been reported to cause paresthesia. However, this case documents a mediastinal teratoma, specifically a benign thymic teratoma, that presented with symptoms of sensory dysfunction among other neurologic deficits. Complete surgical resection of the teratoma was performed without complications, and all symptoms resolved.

Screening Digital Breast Tomosynthesis: Radiation Dose Among Patients With Breast Implants.

OBJECTIVE: To compare the mean glandular dose (MGD), cancer detection rate (CDR), and recall rate (RR) among screening examinations of patients with breast implants utilizing various digital breast tomosynthesis (DBT)-based imaging protocols. METHODS: This IRB-approved retrospective study included 1998 women with breast implants who presented for screening mammography between December 10, 2013, and May 29, 2020. Images were obtained using various protocol combinations of DBT and 2D digital mammography. Data collected included MGD, implant type and position, breast density, BI-RADS final assessment category, CDR, and RR. Statistical analysis utilized type II analysis of variance and the chi-square test. RESULTS: The highest MGD was observed in the DBT only protocol, while the 2D only protocol had the lowest (10.29 mGy vs 5.88 mGy, respectively). Statistically significant difference in MGD was observed across protocols (P < 0.0001). The highest per-view MGD was among DBT full-field (FF) views in both craniocaudal and mediolateral oblique projections (P < 0.0001). No significant difference was observed in RR among protocols (P = 0.17). The combined 2D (FF only) + DBT implant-displaced (ID) views protocol detected the highest number of cancers (CDR, 7.2 per 1000), but this was not significantly different across protocols (P = 0.48). CONCLUSION: The combination of 2D FF views and DBT ID views should be considered for women with breast implants in a DBT-based screening practice when aiming to minimize radiation exposure without compromising the sensitivity of cancer detection. Avoidance of DBT FF in this patient population is recommended to minimize radiation dose.

Tranexamic acid administration in the field does not affect admission thromboelastography after traumatic brain injury.

BACKGROUND: No Food and Drug Administration-approved medication improves outcomes following traumatic brain injury (TBI). A forthcoming clinical trial that evaluated the effects of two prehospital tranexamic acid (TXA) dosing strategies compared with placebo demonstrated no differences in thromboelastography (TEG) values. We proposed to explore the impact of TXA on markers of coagulation and fibrinolysis in patients with moderate to severe TBI. METHODS: Data were extracted from a placebo-controlled clinical trial in which patients 15 years or older with TBI (Glasgow Coma Scale, 3-12) and systolic blood pressure of ≥90 mm Hg were randomized prehospital to receive placebo bolus/placebo infusion (placebo), 1 g of TXA bolus/1 g of TXA infusion (bolus maintenance), or 2 g of TXA bolus/placebo infusion (bolus only). Thromboelastography was performed, and coagulation measures including prothrombin time, activated partial thromboplastin time, international ratio, fibrinogen, D-dimer, plasmin-antiplasmin (PAP), thrombin antithrombin, tissue plasminogen activator, and plasminogen activator inhibitor 1 were quantified at admission and 6 hours later. RESULTS: Of 966 patients receiving study drug, 700 had laboratory tests drawn at admission and 6 hours later. There were no statistically significant differences in TEG values, including LY30, between groups (p > 0.05). No differences between prothrombin time, activated partial thromboplastin time, international ratio, fibrinogen, thrombin antithrombin, tissue plasminogen activator, and plasminogen activator inhibitor 1 were demonstrated across treatment groups. Concentrations of D-dimer in TXA treatment groups were less than placebo at 6 hours (p < 0.001). Concentrations of PAP in TXA treatment groups were less than placebo on admission (p < 0.001) and 6 hours (p = 0.02). No differences in D-dimer and PAP were observed between bolus maintenance and bolus only. CONCLUSION: While D-dimer and PAP levels reflect a lower degree of fibrinolysis following prehospital administration of TXA when compared with placebo in a large prehospital trial of patients with TBI, TEG obtained on admission and 6 hours later did not demonstrate any differences in fibrinolysis between the two TXA dosing regimens and placebo. LEVEL OF EVIDENCE: Diagnostic test, level III.

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.

Fast acquisition with seamless stage translation (FASST) for a trimodal x-ray breast imaging system.

PURPOSE: A major technical obstacle to bringing x-ray multicontrast (i.e., attenuation, phase, and dark-field) imaging methodology to clinical use is the prolonged data acquisition time caused by the phase stepping procedure. The purpose of this work was to introduce a fast acquisition with seamless stage translation (FASST) technique to a prototype multicontrast breast imaging system for reduced image acquisition time that is clinically acceptable. METHODS: The prototype system was constructed based on a Hologic full-field digital mammography + digital breast tomosynthesis combination system. During each FASST acquisition process, a motorized stage holding a diffraction grating travels continuously with a constant velocity, and a train of 15 short x-ray pulses (35 ms each) was delivered by using the Zero-Degree Tomo mode of the Hologic system. Standard phase retrieval was applied to the 15 subimages without spatial interpolation to avoid spatial resolution loss. The method was evaluated using a physical phantom, a bovine udder specimen, and a freshly resected mastectomy specimen. The FASST technique was experimentally compared with single-shot acquisition methods and the standard phase stepping method. RESULTS: The image acquisition time of the proposed method is 3.7 s. In comparison, conventional phase stepping took 105 s using the same prototype imaging system. The mean glandular dose of both methods was matched at 1.3 mGy. No artifacts or spatial resolution loss was observed in images produced by FASST. In contrast, the single-shot methods led to spatial resolution loss and residual moiré artifacts. CONCLUSIONS: The FASST technique reduces the data acquisition time of the prototype multicontrast x-ray breast imaging system to 3.7 s, such that it is comparable to a clinical digital breast tomosynthesis exam.

Optimal configuration for stability and magnetic resonance imaging quality in temporary external fixation of tibial plateau fractures.

INTRODUCTION: Temporary external fixation has been widely utilized in the stabilization of plateau fractures while waiting for an optimization of the soft tissue conditions before subsequent permanent internal fixation. Simultaneously, MRI is beneficial in the assessment of concomitant damage to ligaments and menisci so that these injuries could be promptly identified, and surgical planning executed at the time of definitive fixation of the bony injury. Increasing numbers of side-bars and pins have been previously suggested to increase frame rigidity, but at the same time, several studies have indicated the presence of MRI artifacts which may obscure key anatomical structures, even when MRI-compatible fixation devices are used. This study aims to identify, among six potential configurations, the construct that maximizes stability while most minimizing the number of MRI artifacts generated among different configurations commonly used. HYPOTHESIS: There is one or more configurations among the others that maximize stability while preserving a clinically acceptable level of MRI quality. MATERIAL AND METHODS: Six constructs were recreated on cadaveric specimens and identified by the disposition of the bars: H, Anterior, Flash, Hashtag, Rhomboid, and Diamond. Stage one evaluated the amount of artifact produced during MRI on instrumented cadaveric legs, as well as the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) at five specific regions of interest. Stage two assessed the amount of compressional and torsional stiffness of the configurations on bone surrogate models. RESULTS: Image artifacts were not detected within the knee joint for all considered constructs. In terms of SNR The H, Anterior, Hashtag, and Diamond configurations were not significantly different from their control (p>0.366) while the others were significantly different (p<0.03). The values of CNR found for the H and Hashtag configurations were not significantly different from their controls (p>0.07) while the remaining configurations were significantly different (p<0.03). In compression, the H and Diamond configurations had similar stiffness (p=0.468) of 35.78N/mm and 31.44N/mm, respectively, and were stiffer than the other configurations. In torsion, the constructs have shown different stiffness (p<0.001) with a minimum value of 0.66 Nm/deg for the Rhomboid configuration, which was significantly less stiff than the Anterior configuration (1.20 Nm/deg [p<0.001]). There was no difference between the Diamond and H configurations (p=0.177) or between them and the Hashtag configuration (p=0.215). DISCUSSION: An external fixator construct directly bridging the femur and tibia without interconnections is the most stable and produces MRI scans without image artifacts that would interfere with diagnostic quality. LEVEL OF EVIDENCE: V, basic science study, diagnostic imaging and mechanical testing.