The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic

BackgroundThe COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty. ObjectivesCreate a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions. MethodsWe constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. Expected OutcomesO_LIAccelerate generation of new knowledge and increase scholarly productivity with particular local relevance C_LIO_LIImprove the institutional academic climate by: O_LIBroadening research scope C_LIO_LIExpanding research capability to more diverse group of stakeholders including clinical and research-based faculty and trainees C_LIO_LIEnhancing interdepartmental collaborations C_LI C_LI ConclusionsThe DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

Predicting patients with false negative SARS-CoV-2 testing at hospital admission: A retrospective multi-center study

ImportanceFalse negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied. ObjectiveTo characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR. DesignRetrospective cohort study. SettingFive hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. Participants: Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization. ExposureWe developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested. Main Outcomes and MeasuresThis model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive. ResultsWe included 31,459 hospitalized adult patients; 2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients. Conclusion and RelevanceWe show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.

A neutrophil activation signature predicts critical illness and mortality in COVID-19

Pathologic immune hyperactivation is emerging as a key feature of critical illness in COVID-19, but the mechanisms involved remain poorly understood. We carried out proteomic profiling of plasma from cross-sectional and longitudinal cohorts of hospitalized patients with COVID-19 and analyzed clinical data from our health system database of over 3,300 patients. Using a machine learning algorithm, we identified a prominent signature of neutrophil activation, including resistin, lipocalin-2, HGF, IL-8, and G-CSF, as the strongest predictors of critical illness. Neutrophil activation was present on the first day of hospitalization in patients who would only later require transfer to the intensive care unit, thus preceding the onset of critical illness and predicting increased mortality. In the health system database, early elevations in developing and mature neutrophil counts also predicted higher mortality rates. Altogether, we define an essential role for neutrophil activation in the pathogenesis of severe COVID-19 and identify molecular neutrophil markers that distinguish patients at risk of future clinical decompensation.

Biomechanical Analysis of a Pedicle Screw-Rod System with a Novel Cross-Link Configuration

STUDY DESIGN: The strength effects of a pedicle screw-rod system supplemented with a novel cross-link configuration were biomechanically evaluated in porcine spines. PURPOSE: To assess the biomechanical differences between a conventional cross-link pedicle screw-rod system versus a novel cross-link instrumentation, and to determine the effect of the cross-links. OVERVIEW OF LITERATURE: Transverse cross-link systems affect torsional rigidity, but are thought to have little impact on the sagittal motion of spinal constructs. We tested the strength effects in pullout and flexion-compression tests of novel cross-link pedicle screw constructs using porcine thoracic and lumbar vertebrae. METHODS: Five matched thoracic and lumbar vertebral segments from 15 porcine spines were instrumented with 5.0-mm pedicle screws, which were then connected with 6.0-mm rods after partial corpectomy in the middle vertebral body. The forces required for construct failure in pullout and flexion-compression tests were examined in a randomized manner for three different cross-link configurations: un-cross-link control, conventional cross-link, and cross-link passing through the base of the spinous process. Statistical comparisons of strength data were analyzed using Student's t-tests. RESULTS: The spinous process group required a significantly greater pullout force for construct failure than the control group (p=0.036). No difference was found between the control and cross-link groups, or the cross-link and spinous process groups in pullout testing. In flexion-compression testing, the spinous processes group required significantly greater forces for construct failure than the control and cross-link groups (p<0.001 and p=0.003, respectively). However, there was no difference between the control and cross-link groups. CONCLUSIONS: A novel cross-link configuration that features cross-link devices passing through the base of the spinous processes increased the mechanical resistance in pullout and flexion-compression testing compared to un-cross-link constructs. This configuration provided more resistance to middle-column damage under flexion-compression testing than conventional cross-link configuration.