CT-like images based on T1-weighted gradient echo MRI sequences for the assessment of fractures of the hand and wrist compared to CT

Objective To evaluate the performance of a 3D T1-weighted gradient-echo (3D T1GRE) computed tomography (CT)-like magnetic resonance imaging (MRI) sequence for detecting and assessing wrist and hand fractures compared to conventional CT. Methods Subjects with acute wrist or hand fracture in CT underwent additional 3 T MRI including a CT-like 3D T1GRE sequence and were compared to patients without fractures. Two radiologists assessed fracture morphology on both modalities according to the Arbeitsgemeinschaft Osteosynthese (AO) and graded image quality and diagnostic confidence on a 5-point Likert scale. Besides diagnostic test evaluation, differences in image quality and diagnostic confidence between CT-like MRI and CT were calculated using the Wilcoxon test. Agreement of AO classification between modalities and readers was assessed using Cohen’s Kappa. Results Twenty-eight patients with 43 fractures and 43 controls were included. Image quality (3D T1GRE 1.19 ± 0.37 vs. CT 1.22 ± 0.42; p = 0.65) and diagnostic confidence (3D T1GRE 1.28 ± 0.53 vs. CT 1.28 ± 0.55; p = 1.00) were rated excellent for both modalities. Regarding the AO classification, intra- (rater 1 and rater 2, κ = 0.89; 95% CI 0.80–0.97) and interrater agreement were excellent (3D T1GRE, κ = 0.82; 95% CI, 0.70–0.93; CT, κ = 0.85; 95% CI, 0.75–0.94). CT-like MRI showed excellent sensitivity, specificity and accuracy for fracture detection (reader 1: 1.00, 0.92, 0.96; reader 2: 0.98, 0.94, 0.96). Conclusion CT-like MRI is a comparable alternative to CT for assessing hand and wrist fractures, offering the advantage of avoiding radiation exposure

Development and benchmarking of a Deep Learning-based MRI-guided gross tumor segmentation algorithm for Radiomics analyses in extremity soft tissue sarcomas

Background: Volume of interest (VOI) segmentation is a crucial step for Radiomics analyses and radiotherapy (RT) treatment planning. Because it can be time-consuming and subject to inter-observer variability, we developed and tested a Deep Learning-based automatic segmentation (DLBAS) algorithm to reproducibly predict the primary gross tumor as VOI for Radiomics analyses in extremity soft tissue sarcomas (STS). Methods: A DLBAS algorithm was trained on a cohort of 157 patients and externally tested on an independent cohort of 87 patients using contrast-enhanced MRI. Manual tumor delineations by a radiation oncologist served as ground truths (GTs). A benchmark study with 20 cases from the test cohort compared the DLBAS predictions against manual VOI segmentations of two residents (ERs) and clinical delineations of two radiation oncologists (ROs). The ROs rated DLBAS predictions regarding their direct applicability. Results: The DLBAS achieved a median dice similarity coefficient (DSC) of 0.88 against the GTs in the entire test cohort (interquartile range (IQR): 0.11) and a median DSC of 0.89 (IQR 0.07) and 0.82 (IQR 0.10) in comparison to ERs and ROs, respectively. Radiomics feature stability was high with a median intraclass correlation coefficient of 0.97, 0.95 and 0.94 for GTs, ERs, and ROs, respectively. DLBAS predictions were deemed clinically suitable by the two ROs in 35% and 20% of cases, respectively. Conclusion: The results demonstrate that the DLBAS algorithm provides reproducible VOI predictions for radiomics feature extraction. Variability remains regarding direct clinical applicability of predictions for RT treatment planning

14-fold increased prevalence of rare glucokinase gene variant carriers in unselected Danish patients with newly diagnosed type 2 diabetes

AIMS: Rare variants in the glucokinase gene (GCK) cause Maturity-Onset Diabetes of the Young (MODY2/GCK-MODY). We investigated the prevalence of GCK variants, phenotypic characteristics, micro- and macrovascular disease at baseline and follow-up, and treatment among individuals with and without pathogenic GCK variants.METHODS: This is a cross-sectional study in a population-based cohort of 5,433 individuals without diabetes (Inter99 cohort) and in 2,855 patients with a new clinical diagnosis of type 2 diabetes (DD2 cohort) with sequencing of GCK. Phenotypic characteristics, presence of micro- and macrovascular disease and treatment information were available for patients in the DD2 cohort at baseline and after an average follow-up of 7.4 years.RESULTS: Twenty-two carriers of potentially deleterious GCK variants were found among patients with type 2 diabetes compared to three among 5,433 nondiabetic individuals [OR = 14.1 (95 % CI 4.2; 47.0), p = 8.9*10-6]. Patients with type 2 diabetes carrying GCK variants had significantly lower waist circumference, hip circumference and BMI, compared to non-carriers. Three GCK variant carriers with diabetes had microvascular complications during follow-up.CONCLUSIONS: Approximately 0.8% of Danish patients with newly diagnosed type 2 diabetes carry non-synonymous variants in GCK and resemble patients with GCK-MODY. Glucose-lowering treatment cessation should be considered in this subset of diabetes patients.</p

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28–120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings

Is Weight Loss Associated with Less Progression of Changes in Knee Articular Cartilage among Obese and Overweight Patients as Assessed with MR Imaging over 48 Months? Data from the Osteoarthritis Initiative

Purpose To investigate the association of weight loss with progression of cartilage changes at magnetic resonance (MR) imaging over 48 months in overweight and obese participants compared with participants of stable weight. Materials and Methods The institutional review boards of the four participating centers approved this HIPAA-compliant study. Included were (a) 640 participants (mean age, 62.9 years ± 9.1 [standard deviation]; 398 women) who were overweight or obese (body mass index cutpoints of 25 and 30 kg/m2, respectively) from the Osteoarthritis Initiative, with risk factors for osteoarthritis or mild to moderate radiographic findings of osteoarthritis, categorized into groups with (a) weight loss of more than 10% (n = 82), (b) weight loss of 5%-10% (n = 238), or (c) stable weight (n = 320) over 48 months. Participants were frequency-matched for age, sex, baseline body mass index, and Kellgren-Lawrence score. Two radiologists assessed cartilage and meniscus defects on right knee 3-T MR images at baseline and 48 months by using the modified Whole-Organ Magnetic Resonance Imaging Score (WORMS). Progression of the subscores was compared between the weight loss groups by using multivariable logistic regression models. Results Over 48 months, adjusted mean increase of cartilage WORMS was significantly smaller in the 5%-10% weight loss group (1.6; 95% confidence interval [CI]: 1.3, 1.9; P = .002) and even smaller in the group with more than 10% weight loss (1.0; 95% CI: 0.6, 1.4; P = .001) when compared with the stable weight group (2.3; 95% CI: 2.0, 2.7). Moreover, percentage of weight change was significantly associated with increase in cartilage WORMS (β = 0.2; 95% CI: 0.02, 0.4; P = .007). Conclusion Participants who lost weight over 48 months showed significantly lower cartilage degeneration, as assessed with MR imaging; rates of progression were lower with greater weight loss. © RSNA, 2017

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives: To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants: In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction &gt;99% or antigen &lt;1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures: Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results: The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission

Demonstration of Interoperability Between MIDRC and N3C: A COVID-19 Severity Prediction Use Case

Interoperability between data sources, one of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management, can enable multi-modality research. The purpose of our study was to investigate the potential for interoperability between an imaging resource, the Medical Imaging and Data Resource Center (MIDRC), and a clinical record resource, the National COVID Cohort Collaborative (N3C). The use case was the prediction of COVID-19 severity, defined as evidence for invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice in the N3C clinical record. Patient-level matching between MIDRC and N3C was identified using Privacy Preserving Record Linking via an honest broker. We identified positive COVID-19 tests and chest radiograph procedures in N3C and used the interval between them to identify images with matching intervals in MIDRC. Of the 236 patients (306 unique images) meeting initial inclusion criteria in MIDRC, 117 patients (and 139 unique images) remained after date interval matching between repositories and exclusion of patients with multiple potential matches. The Charlson Comorbidity Index (CCI) and the minimum mean arterial pressure (MAP) on the day of the chest radiograph were used as clinical indicators. The AUC in the task of predicting severe COVID-19 was evaluated using the computer-extracted imaging index alone (MIDRC), clinical indicators alone (N3C), and both together. Our model combining imaging and clinical indicators (CCI over 2 and MAP below 70) to predict severe COVID had an AUC of 0.73 (95% CI 0.62–0.84), and the models including imaging or clinical indicators alone were 0.67 (95% CI 0.56–0.79) and 0.69 (95% CI 0.59–0.80), respectively. This study highlights the potential for cross-platform data sharing to facilitate future multi-modality research and broader collaborative studies

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND METHODS: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. RESULTS: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. CONCLUSIONS: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19

Increased Incidence of Vestibular Disorders in Patients With SARS-CoV-2

OBJECTIVE: Determine the incidence of vestibular disorders in patients with SARS-CoV-2 compared to the control population. STUDY DESIGN: Retrospective. SETTING: Clinical data in the National COVID Cohort Collaborative database (N3C). METHODS: Deidentified patient data from the National COVID Cohort Collaborative database (N3C) were queried based on variant peak prevalence (untyped, alpha, delta, omicron 21K, and omicron 23A) from covariants.org to retrospectively analyze the incidence of vestibular disorders in patients with SARS-CoV-2 compared to control population, consisting of patients without documented evidence of COVID infection during the same period. RESULTS: Patients testing positive for COVID-19 were significantly more likely to have a vestibular disorder compared to the control population. Compared to control patients, the odds ratio of vestibular disorders was significantly elevated in patients with untyped (odds ratio [OR], 2.39; confidence intervals [CI], 2.29-2.50; CONCLUSIONS: The incidence of vestibular disorders differed between COVID-19 variants and was significantly elevated in COVID-19-positive patients compared to the control population. These findings have implications for patient counseling and further research is needed to discern the long-term effects of these findings