Differences in the stride time and lower limb joint angles and their variability during distance running between treadmill and over-ground: a crossover study.

BACKGROUND: Treadmills have been used in laboratories to assess various measures related to walking and running. However, there has been some skepticism regarding their reliability as a representation of outdoor running. While marathon running has gained popularity as a form of physical activity, there have been few studies examining stride-to-stride variability after distance running, especially in relation to the duration and surface of running. This study compared stride time and lower limb joint angles during distance treadmill running and running over-ground. The hypothesis was that stride-to-stride variability would be influenced by running duration and surface, with greater variability observed during outdoor running. METHODS: Eleven runners participated in the study, running on a treadmill and over-ground for 31 minutes at their preferred speed. Inertial measurement units were used to measure stride time, total range of motion, and joint angles of the hip, knee, and ankle in different phases of the gait cycle in the sagittal plane movements. Mean and coefficient of variation of each parameter were compared between the initial and final 5 minutes of running on the treadmill and over-ground. RESULTS: There were no significant differences in stride time or its variability based on running duration or surface. However, mean and variability of certain lower limb joint angles were higher during outdoor running, supporting the hypothesis. Variability was higher in the initial duration of running as compared to final phase of running. CONCLUSIONS: These findings suggest that treadmill may not fully reflect the dynamics of running over-ground. It is important to consider variability in gait analysis and research, as well as the potential impact on training and clinical practice.

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well documented selection criteria for acute ischemic stroke treatment, however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with significant interobserver variabilities. We conducted a multi-reader, multi-case study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-based algorithm in order to analyze the impact of the software on clinicians' performance and interpretation time. MATERIALS AND METHODS: A total of 200 NCCT scans from 5 clinical sites (27 scanner models, 4 different vendors) were retrospectively collected. Reference standard was established through the consensus of three expert neuroradiologists who had access to baseline CTA and CTP data. Subsequently, eight additional clinicians (four typical ASPECTS reader and four senior neuroradiologists) analyzed the NCCT scans without and with the assistance of CINA-ASPECTS (Avicenna.AI, La Ciotat, France), a DLbased FDA-cleared and CE-marked algorithm designed to automatically compute ASPECTS. Differences were evaluated in both performance and interpretation time between the assisted and unassisted assessments. RESULTS: With software aid, readers demonstrated increased region-based accuracy from 72.4% to 76.5% (p<0.05), and increased ROC AUC from 0.749 to 0.788 (p<0.05). Notably, all readers exhibited an improved ROC AUC when utilizing the software. Moreover, use of the algorithm improved the score-based inter-observer reliability and correlation coefficient of ASPECTS evaluation by 0.222 and 0.087 (p<0.0001), respectively. Additionally, the readers' mean time spent analyzing a case was significantly reduced by 6% (p<0.05) when aided by the algorithm. CONCLUSIONS: With the assistance of the algorithm, readers' analyses were not only more accurate but also faster. Additionally, the overall ASPECTS evaluation exhibited greater consistency, less variabilities and higher precision compared to the reference standard. This novel tool has the potential to enhance patient selection for appropriate treatment by enabling physicians to deliver accurate and timely diagnosis of acute ischemic stroke. ABBREVIATIONS: ASPECTS = Alberta Stroke Program Early Computed Tomography Score; DL = Deep Learning; EIC = Early Ischemic Changes; ICC = Intraclass Correlation Coefficient; IS = Ischemic Stroke; ROC AUC = Receiver Operating Characteristics Area Under the Curve.

Diagnostic Performance of a Deep Learning-Powered Application for Aortic Dissection Triage Prioritization and Classification.

This multicenter retrospective study evaluated the diagnostic performance of a deep learning (DL)-based application for detecting, classifying, and highlighting suspected aortic dissections (ADs) on chest and thoraco-abdominal CT angiography (CTA) scans. CTA scans from over 200 U.S. and European cities acquired on 52 scanner models from six manufacturers were retrospectively collected and processed by CINA-CHEST (AD) (Avicenna.AI, La Ciotat, France) device. The diagnostic performance of the device was compared with the ground truth established by the majority agreement of three U.S. board-certified radiologists. Furthermore, the DL algorithm's time to notification was evaluated to demonstrate clinical effectiveness. The study included 1303 CTAs (mean age 58.8 ± 16.4 years old, 46.7% male, 10.5% positive). The device demonstrated a sensitivity of 94.2% [95% CI: 88.8-97.5%] and a specificity of 97.3% [95% CI: 96.2-98.1%]. The application classified positive cases by the AD type with an accuracy of 99.5% [95% CI: 98.9-99.8%] for type A and 97.5 [95% CI: 96.4-98.3%] for type B. The application did not miss any type A cases. The device flagged 32 cases incorrectly, primarily due to acquisition artefacts and aortic pathologies mimicking AD. The mean time to process and notify of potential AD cases was 27.9 ± 8.7 s. This deep learning-based application demonstrated a strong performance in detecting and classifying aortic dissection cases, potentially enabling faster triage of these urgent cases in clinical settings.

Performance and clinical utility of an artificial intelligence-enabled tool for pulmonary embolism detection.

PURPOSE: Diagnosing pulmonary embolism (PE) is still challenging due to other conditions that can mimic its appearance, leading to incomplete or delayed management and several inter-observer variabilities. This study evaluated the performance and clinical utility of an artificial intelligence (AI)-based application designed to assist clinicians in the detection of PE on CT pulmonary angiography (CTPA). PATIENTS AND METHODS: CTPAs from 230 US cities acquired on 57 scanner models from 6 different vendors were retrospectively collected. Three US board certified expert radiologists defined the ground truth by majority agreement. The same cases were analyzed by CINA-PE, an AI-driven algorithm capable of detecting and highlighting suspected PE locations. The algorithm's performance at a per-case and per-finding level was evaluated. Furthermore, cases with PE not mentioned in the clinical report but correctly detected by the algorithm were analyzed. RESULTS: A total of 1204 CTPAs (mean age 62.1 years ± 16.6[SD], 44.4 % female, 14.9 % positive) were included in the study. Per-case sensitivity and specificity were 93.9 % (95%CI: 89.3 %-96.9 %) and 94.8 % (95%CI: 93.3 %-96.1 %), respectively. Per-finding positive predictive value was 89.5 % (95%CI: 86.7 %-91.9 %). Among the 196 positive cases, 29 (15.6 %) were not mentioned in the clinical report. The algorithm detected 22/29 (76 %) of these cases, leading to a reduction in the miss rate from 15.6 % to 3.8 % (7/186). CONCLUSIONS: The AI-based application may improve diagnostic accuracy in detecting PE and enhance patient outcomes through timely intervention. Integrating AI tools in clinical workflows can reduce missed or delayed diagnoses, and positively impact healthcare delivery and patient care.

Generating colloquial radiology reports with large language models.

OBJECTIVES: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM). MATERIALS AND METHODS: Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability. RESULTS: Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices. CONCLUSION: We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.

Artificial Intelligence Efficacy as a Function of Trainee Interpreter Proficiency: Lessons from a Randomized Controlled Trial.

BACKGROUND AND PURPOSE: Recently, artificial intelligence tools have been deployed with increasing speed in educational and clinical settings. However, the use of artificial intelligence by trainees across different levels of experience has not been well-studied. This study investigates the impact of artificial intelligence assistance on the diagnostic accuracy for intracranial hemorrhage and large-vessel occlusion by medical students and resident trainees. MATERIALS AND METHODS: This prospective study was conducted between March 2023 and October 2023. Medical students and resident trainees were asked to identify intracranial hemorrhage and large-vessel occlusion in 100 noncontrast head CTs and 100 head CTAs, respectively. One group received diagnostic aid simulating artificial intelligence for intracranial hemorrhage only (n = 26); the other, for large-vessel occlusion only (n = 28). Primary outcomes included accuracy, sensitivity, and specificity for intracranial hemorrhage/large-vessel occlusion detection without and with aid. Study interpretation time was a secondary outcome. Individual responses were pooled and analyzed with the t test; differences in continuous variables were assessed with ANOVA. RESULTS: Forty-eight participants completed the study, generating 10,779 intracranial hemorrhage or large-vessel occlusion interpretations. With diagnostic aid, medical student accuracy improved 11.0 points (P < .001) and resident trainee accuracy showed no significant change. Intracranial hemorrhage interpretation time increased with diagnostic aid for both groups (P < .001), while large-vessel occlusion interpretation time decreased for medical students (P < .001). Despite worse performance in the detection of the smallest-versus-largest hemorrhages at baseline, medical students were not more likely to accept a true-positive artificial intelligence result for these more difficult tasks. Both groups were considerably less accurate when disagreeing with the artificial intelligence or when supplied with an incorrect artificial intelligence result. CONCLUSIONS: This study demonstrated greater improvement in diagnostic accuracy with artificial intelligence for medical students compared with resident trainees. However, medical students were less likely than resident trainees to overrule incorrect artificial intelligence interpretations and were less accurate, even with diagnostic aid, than the artificial intelligence was by itself.

Comparison of muscle activity in school students while carrying backpacks and trolley bags.

BACKGROUND: Trolley bags have gained popularity among students, but there is limited research comparing them to backpack-style school bags. OBJECTIVE: This study aimed to compare how carrying a backpack versus a trolley bag affects the activity of trunk and lower limb muscles in secondary school students. METHODS: Electromyographic activity was measured in 25 students (13.4±1.1 years) as they walked on level ground and up/down stairs while carrying both types of bags. The activity of the gastrocnemius, tibialis anterior, semitendinosus, rectus femoris, lumbar erector spinae, and rectus abdominis muscles was assessed on both the dominant and non-dominant sides. RESULTS: The study found significantly reduced muscle activation in most of the targeted muscles when walking on level ground with the trolley bag and when going up/down stairs with the backpack. CONCLUSIONS: Lifting a trolley bag depends on the slope of the walking surface and is more efficient on level ground, while carrying a backpack is more efficient when going up and down stairs. Since it is not practical to switch bags when encountering stairs in schools, a bag with a mixed model design incorporating features of both trolley and backpack may be more beneficial and practical for students to use. Students, parents, and teachers should be aware of the injury risks associated with carrying different types of bags.

Effectiveness of Robotic Intervention on Improving Social Development and Participation of Children with Autism Spectrum Disorder - A Randomised Controlled Trial.

Evidence-based robotic intervention programmes for children with autism spectrum disorder (ASD) have been limited. As yet, there is insufficient evidence to inform therapists, teachers, and service providers on effectiveness of robotic intervention to enhance social development and participation of children with ASD in a real context. This study used a randomised controlled trial to test the efficacy of robotic intervention programmes in enhancing the social development and participation of children with ASD. 60 children with ASD were included. The participants were randomly assigned to the following groups: (1) robotic intervention programme (n = 20), (2) human-instructed programme (n = 20), and (3) control group (n = 20). Both the performance-based behavioural change in social communication and parent-reported change in social responsiveness were evaluated. The participants in the robotic intervention group demonstrated statistically significant changes in both the performance-based assessment and parent-reported change in social participation. Significant differences were found in the communication and reciprocal social interactions scores between the experimental group and the control and comparison groups in the performance-based assessment (p < 0.01). The effectiveness of robotic intervention programme to enhance the social communication and participation was confirmed. Future studies may also consider adding a maintenance phase to document how the effects of the intervention carry over to the participants over a longer period. (Clinical trial number: NCT04879303; Date of registration: 10 May 2021).

Tracking Bilateral Lower Limb Kinematics of Distance Runners on Treadmill Using a Single Inertial Measurement Unit.

Distance running related injuries are common, and many ailments have been associated with faulty posture. Conventional measurement of running kinematics requires sophisticated motion capture system in laboratory. In this study, we developed a wearable solution to accurately predict lower limb running kinematics using a single inertial measurement unit placed on the left lower leg. The running data collected from participants was used to train a model using long short-term memory (LSTM) neural networks with an inter-subject approach that predicted lower limb kinematics with an average accuracy of 80.2%, 85.8%, and 69.4% for sagittal hip, knee and ankle joint angles respectively for the ipsilateral limb. A comparable accuracy range was observed for the contralateral limb. The average RMSE (root mean squared error) of sagittal hip, knee and ankle were 8.76°, 13.13°, and 9.67° respectively for the ipsilateral limb. Analysis of contralateral limb kinematics was performed. The model established in this study can be used as a monitoring device to track essential running kinematics in natural running environments. Besides, the wearable solution can be an integral part of a real-time gait retraining biofeedback system for injury prevention and rehabilitation.

Posttreatment Surveillance Imaging After Radiation for Head and Neck Cancer.

Importance: The role of surveillance imaging after treatment for head and neck cancer is controversial and evidence to support decision-making is limited. Objective: To determine the use of surveillance imaging in asymptomatic patients with head and neck cancer in remission after completion of chemoradiation. Design, Setting, and Participants: This was a retrospective, comparative effectiveness research review of adult patients who had achieved a complete metabolic response to initial treatment for head and neck cancer as defined by having an unequivocally negative positron emission tomography (PET) scan using the PET response criteria in solid tumors (PERCIST) scale within the first 6 months of completing therapy. The medical records of 501 consecutive patients who completed definitive radiation therapy (with or without chemotherapy) for newly diagnosed squamous cell carcinoma of the head and neck between January 2014 and June 2022 were reviewed. Exposure: Surveillance imaging was defined as the acquisition of a PET with computed tomography (CT), magnetic resonance imaging (MRI), or CT of the head and neck region in the absence of any clinically suspicious symptoms and/or examination findings. For remaining patients, subsequent surveillance after the achievement of a complete metabolic response to initial therapy was performed on an observational basis in the setting of routine follow-up using history-taking and physical examination, including endoscopy. This expectant approach led to imaging only in the presence of clinically suspicious symptoms and/or physical examination findings. Main Outcome and Measures: Local-regional control, overall survival, and progression-free survival based on assignment to either the surveillance imaging or expectant management cohort. Results: This study included 340 patients (mean [SD] age, 59 [10] years; 201 males [59%]; 88 Latino patients [26%]; 145 White patients [43%]) who achieved a complete metabolic response during this period. There was no difference in 3-year local-regional control, overall survival, progression-free survival, or freedom from distant metastasis between patients treated with surveillance imaging vs those treated expectantly. Conclusions and Relevance: In this comparative effectiveness research, imaging-based surveillance failed to improve outcomes compared with expectant management for patients who were seemingly in remission after completion of primary radiation therapy for head and neck cancer.

Current role of portable MRI in diagnosis of acute neurological conditions.

Neuroimaging is an inevitable component of the assessment of neurological emergencies. Magnetic resonance imaging (MRI) is the preferred imaging modality for detecting neurological pathologies and provides higher sensitivity than other modalities. However, difficulties such as intra-hospital transport, long exam times, and availability in strict access-controlled suites limit its utility in emergency departments and intensive care units (ICUs). The evolution of novel imaging technologies over the past decades has led to the development of portable MRI (pMRI) machines that can be deployed at point-of-care. This article reviews pMRI technologies and their clinical implications in acute neurological conditions. Benefits of pMRI include timely and accurate detection of major acute neurological pathologies such as stroke and intracranial hemorrhage. Additionally, pMRI can be potentially used to monitor the progression of neurological complications by facilitating serial measurements at the bedside.

Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID-19 pandemic.

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID-19 pandemic. Toward reliable decision-making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual-level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration-death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS-CoV-2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood-based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per-patient ICU stay estimates using anonymized patient-level UCI hospital data.

Influence of Lateral Sitting Wedges on the Rasterstereographically Measured Scoliosis Angle in Patients Aged 10-18 Years with Adolescent Idiopathic Scoliosis.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional axial deviation of the spine diagnosed in adolescence. Despite a long daily sitting duration, there are no studies on whether scoliosis can be positively influenced by sitting on a seat wedge. For the prospective study, 99 patients with AIS were measured with the DIERS formetric III 4D average, in a standing position, on a level seat and with three differently inclined seat wedges (3°, 6° and 9°). The rasterstereographic parameters 'scoliosis angle' and 'lateral deviation RMS' were analysed. The side (ipsilateral/contralateral) on which the optimal correcting wedge was located in relation to the lumbar/thoraco-lumbar convexity was investigated. It was found that the greatest possible correction of scoliosis occurred with a clustering in wedges with an elevation on the ipsilateral side of the convexity. This clustering was significantly different from a uniform distribution (p < 0.001; chi-square = 35.697 (scoliosis angle); chi-square = 54.727 (lateral deviation RMS)). It should be taken into account that the effect of lateral seat wedges differs for individual types of scoliosis and degrees of severity. The possibility of having a positive effect on scoliosis while sitting holds great potential, which is worth investigating in follow-up studies.

Exergaming and education: a relational model for games selection and evaluation.

Exergaming, or technology-driven physical exercise, has gained popularity in recent years. Its applications include physical education, health promotion, and rehabilitation. Although studies have obtained promising results regarding the positive effects of exergaming, the outcomes of exergaming for different populations remain undetermined. Inconsistencies in the literature on this topic have multiple potential explanations, including the content and demand of the exergames and the capability of the exergamer. A model with a sound theoretical framework is required to facilitate matching between games and gamers. This article proposes a relational model based on a matrix of Bloom's taxonomy of learning domains and the performance components of exergames. Appropriate matching of the physical demands of an exergame and the ability of the exergamer would enhance the effective usage of exergaming for individuals with various needs. This theory-based exergame model is developed to promote the general development, physical status, and psychosocial well-being of students, older adults, and individuals with rehabilitation needs. This model may provide a resource for future research on the application, effectiveness, and design of exergaming.

Impact of an automated large vessel occlusion detection tool on clinical workflow and patient outcomes.

Purpose: 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. Materials and methods: 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. Results: 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, p < 0.0005), notably at the resident level (p < 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, p < 0.01). Conclusion: 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.

Methamphetamine use increases the risk of cerebral small vessel disease in young patients with acute ischemic stroke.

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.

Neutralizing Monoclonal Antibody Use and COVID-19 Infection Outcomes.

Importance: Evidence on the effectiveness and safety of COVID-19 therapies across a diverse population with varied risk factors is needed to inform clinical practice. Objective: To assess the safety of neutralizing monoclonal antibodies (nMAbs) for the treatment of COVID-19 and their association with adverse outcomes. Design, Setting, and Participants: This retrospective cohort study included 167 183 patients from a consortium of 4 health care systems based in California, Minnesota, Texas, and Utah. The study included nonhospitalized patients 12 years and older with a positive COVID-19 laboratory test collected between November 9, 2020, and January 31, 2022, who met at least 1 emergency use authorization criterion for risk of a poor outcome. Exposure: Four nMAb products (bamlanivimab, bamlanivimab-etesevimab, casirivimab-imdevimab, and sotrovimab) administered in the outpatient setting. Main Outcomes and Measures: Clinical and SARS-CoV-2 genomic sequence data and propensity-adjusted marginal structural models were used to assess the association between treatment with nMAbs and 4 outcomes: all-cause emergency department (ED) visits, hospitalization, death, and a composite of hospitalization or death within 14 days and 30 days of the index date (defined as the date of the first positive COVID-19 test or the date of referral). Patient index dates were categorized into 4 variant epochs: pre-Delta (November 9, 2020, to June 30, 2021), Delta (July 1 to November 30, 2021), Delta and Omicron BA.1 (December 1 to 31, 2021), and Omicron BA.1 (January 1 to 31, 2022). Results: Among 167 183 patients, the mean (SD) age was 47.0 (18.5) years; 95 669 patients (57.2%) were female at birth, 139 379 (83.4%) were White, and 138 900 (83.1%) were non-Hispanic. A total of 25 241 patients received treatment with nMAbs. Treatment with nMAbs was associated with lower odds of ED visits within 14 days (odds ratio [OR], 0.76; 95% CI, 0.68-0.85), hospitalization within 14 days (OR, 0.52; 95% CI, 0.45-0.59), and death within 30 days (OR, 0.14; 95% CI, 0.10-0.20). The association between nMAbs and reduced risk of hospitalization was stronger in unvaccinated patients (14-day hospitalization: OR, 0.51; 95% CI, 0.44-0.59), and the associations with hospitalization and death were stronger in immunocompromised patients (hospitalization within 14 days: OR, 0.31 [95% CI, 0.24-0.41]; death within 30 days: OR, 0.13 [95% CI, 0.06-0.27]). The strength of associations of nMAbs increased incrementally among patients with a greater probability of poor outcomes; for example, the ORs for hospitalization within 14 days were 0.58 (95% CI, 0.48-0.72) among those in the third (moderate) risk stratum and 0.41 (95% CI, 0.32-0.53) among those in the fifth (highest) risk stratum. The association of nMAb treatment with reduced risk of hospitalizations within 14 days was strongest during the Delta variant epoch (OR, 0.37; 95% CI, 0.31-0.43) but not during the Omicron BA.1 epoch (OR, 1.29; 95% CI, 0.68-2.47). These findings were corroborated in the subset of patients with viral genomic data. Treatment with nMAbs was associated with a significant mortality benefit in all variant epochs (pre-Delta: OR, 0.16 [95% CI, 0.08-0.33]; Delta: OR, 0.14 [95% CI, 0.09-0.22]; Delta and Omicron BA.1: OR, 0.10 [95% CI, 0.03-0.35]; and Omicron BA.1: OR, 0.13 [95% CI, 0.02-0.93]). Potential adverse drug events were identified in 38 treated patients (0.2%). Conclusions and Relevance: In this study, nMAb treatment for COVID-19 was safe and associated with reductions in ED visits, hospitalization, and death, although it was not associated with reduced risk of hospitalization during the Omicron BA.1 epoch. These findings suggest that targeted risk stratification strategies may help optimize future nMAb treatment decisions.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.

PURPOSE: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. METHODS: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. RESULTS: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. CONCLUSIONS: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

Ordering Characteristics Predictive of Noncontrast CT Head Positivity in the Emergency Department.

RATIONALE AND OBJECTIVES: Recent decades have seen a steady increase in noncontrast head CT utilization in the emergency department with a concurrent rise in the practice of physician assistants (PAs) and nurse practitioners (NPs). The goal of this study was to identify ordering and patient characteristics predictive of positive noncontrast head CTs in the ED. We hypothesized NP/PAs would have lower positivity rates compared to physicians, suggestive of relative overutilization. MATERIALS AND METHODS: We retrospectively identified ED patients who underwent noncontrast head CTs at a single institution: a nonlevel 1 trauma center, during a 7-year period, recording examination positivity, ordering provider training/experience, and multiple additional ordering/patient attributes. Exam positivity was defined as any intracranial abnormality necessitating a change in acute management, such as acute hemorrhage, hydrocephalus, herniation, or worsening prior findings. RESULTS: 6624 patients met inclusion criteria. 4.6% (280/6107) of physician exams were positive while 3.7% (19/517) of NP/PA exams were positive; however, differences were not significant. Increasing provider experience was not associated with positivity. Attributes with increased positivity were patient age (p < 0.001), daytime exam (p < 0.05), and indications regarding malignancy (p < 0.001) or focal neurologic deficit (p = 0.001). Attributes with decreased positivity were indications of trauma (p < 0.001) or vertigo/dizziness (p < 0.05). CONCLUSION: We found no significant difference in rates of exam positivity between physicians and NP/PAs, even accounting for years of experience. This suggests increasing utilization of head CTs in the ED is not due to the increasing presence of NP/PAs, and may be reflective of general practice trends and clear diagnostic algorithms leading to head CT.