The Interplay Between Human Leukocyte Antigen Antibody Profile and COVID-19 Vaccination in Waitlisted Renal Transplant Patients.

CONTEXT.­: Mass COVID-19 vaccination is mandated in vulnerable populations in our renal transplant waitlist cohort. However, the anti-human leukocyte antigen (anti-HLA) profile after COVID-19 vaccination is controversial, and the side effects are yet to be discerned. OBJECTIVE.­: To evaluate the status of HLA antibodies in waitlist renal transplant patients before and 3 weeks after each vaccination and if comorbidities are associated with the HLA antibody profile. DESIGN.­: A total of 59 waitlisted kidney transplant patients were included in this study. The anti-HLA antibodies were analyzed before and 6 months after their last COVID-19 vaccination. The mean fluorescence intensity change in the anti-HLA antibody levels was used to classify patients into 3 groups: high inducers, low inducers, and noninducers. RESULTS.­: There were significant HLA antibody profile changes after COVID-19 vaccination, showing 21 antibodies generated against HLA class I antigens and 7 against HLA class II antigens to their baseline. Compared with the noninducers, the high and low inducers showed a higher prevalence of COVID-19 infection, COVID-19 vaccine type, and background hypertension history. CONCLUSIONS.­: Our data suggest that COVID-19 vaccination propagates anti-HLA class I and II antibodies for waitlisted renal transplant patients. The clinical significance of these antibodies needs further study. Furthermore, comorbidities, such as history of COVID-19 infection and hypertension, supplemented this effect. Anti-HLA antibody monitoring may be warranted in vaccinated, waitlisted renal transplant patients with COVID-19 vaccinations, and a history of COVID-19 infection or hypertension.

Evaluating the efficacy of combination and single-agent immunotherapies in real-world patterns of disease progression and survival of metastatic melanoma patients.

The objective of this study is to describe survival outcomes in patients with metastatic melanoma in a real-world setting receiving combination and single-agent immunotherapy outside the clinical trial context. We conducted a retrospective single-institution study of patients with metastatic melanoma in a real-world setting. Survival was calculated using log-rank test. Contingency tables were analyzed using Fisher's Exact test. CD8 + T-cell densities were measured using quantitative immunofluorescence and analyzed using Mann-Whitney U test. The median overall survival (OS) for 132 patients was 45.3 months. Brain metastasis did not confer a higher risk of death relative to liver and/or bone disease (39.53 versus 30.00 months, respectively; P  = 0.687). Anti-PD-1 monotherapy was the most common first-line treatment, received by 49.2% of patients. There was no significant difference in OS between patients receiving single-agent anti-PD-1 and combination anti-PD-1 plus CTLA-4 (39.4 months versus undefined; P  = 0.643). Patients treated with combination therapy were more likely to be alive without progression at the last follow-up than those who received monotherapy (70.4% versus 49.2%; P  = 0.0408). Median OS was 21.8 months after initiation of second-line therapy after anti-PD-1 monotherapy. CD8+ T-cell densities were higher in patients who achieved disease control on first-line immunotherapy ( P  = 0.013). In a real-world setting, patients with metastatic melanoma have excellent survival rates, and treatment benefit can be achieved even after progression on first-line therapy. Combination immunotherapy may produce more favorable long-term outcomes in a real-world setting. High pretreatment CD8+ T-cell infiltration correlates with immunotherapy efficacy.

The predictive and prognostic value of peripheral blood antigen-specific memory B cells in phospholipase A2 receptor-associated membranous nephropathy.

Background: Phospholipase A2 receptor-associated membranous nephropathy (PLA2R-MN) is an anti-PLA2R antibody (PLA2R-Ab) mediated autoimmune kidney disease. Although antibody titer correlates closely with disease activity, whether it can provide longer-term predictions on disease course and progression is unclear. Rituximab, a B-cell depletion therapy, has become the first-line treatment option for PLA2R-MN; however, the response to Rituximab varies among patients. Methods: We developed a flow cytometry-based test that detects and quantifies PLA2R antigen-specific memory B cells (PLA2R-MBCs) in peripheral blood, the primary source for PLA2R-Ab production upon disease relapse. We applied the test to 159 blood samples collected from 28 patients with PLA2R-MN (at diagnosis, during and after immunosuppressive treatment, immunological remission, and relapse) to evaluate the relationship between circulating PLA2R-MBC levels and disease activity. Results: The level of PLA2R-MBCs in healthy controls (n=56) is less than or equal to 1.5% of the total MBC compartment. High circulating PLA2R-MBC levels were detected in two patients post-Rituximab despite achieving immunologic and proteinuric remission, as well as in two patients with negative serum autoantibody but increasing proteinuria. Elimination of these cells with Rituximab improved clinical outcomes. Moreover, five patients exhibited elevated PLA2R-MBC levels before disease relapse, followed by a rapid decline to baseline when relapse became clinically evident. COVID-19 vaccination or SARS-CoV-2 infection significantly affected the dynamics of circulating PLA2R-MBCs. Conclusions: This study suggests that monitoring PLA2R-MBC levels in patients with PLA2R-MN may help refine and individualize immunosuppressive therapy and predict disease course and progression. The technology and findings may also have broader applications in the clinical management of other autoimmune diseases.

Geographic and Temporal Trends in COVID-Associated Acute Kidney Injury in the National COVID Cohort Collaborative.

BACKGROUND: AKI is associated with mortality in patients hospitalized with coronavirus disease 2019 (COVID-19); however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. METHODS: Electronic health record data were obtained from 53 health systems in the United States in the National COVID Cohort Collaborative. We selected hospitalized adults diagnosed with COVID-19 between March 6, 2020, and January 6, 2022. AKI was determined with serum creatinine and diagnosis codes. Time was divided into 16-week periods (P1-6) and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. RESULTS: Of a total cohort of 336,473, 129,176 (38%) patients had AKI. Fifty-six thousand three hundred and twenty-two (17%) lacked a diagnosis code but had AKI based on the change in serum creatinine. Similar to patients coded for AKI, these patients had higher mortality compared with those without AKI. The incidence of AKI was highest in P1 (47%; 23,097/48,947), lower in P2 (37%; 12,102/32,513), and relatively stable thereafter. Compared with the Midwest, the Northeast, South, and West had higher adjusted odds of AKI in P1. Subsequently, the South and West regions continued to have the highest relative AKI odds. In multivariable models, AKI defined by either serum creatinine or diagnostic code and the severity of AKI was associated with mortality. CONCLUSIONS: The incidence and distribution of COVID-19-associated AKI changed since the first wave of the pandemic in the United States. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_08_08_CJN0000000000000192.mp3.

hACE2-Induced Allosteric Activation in SARS-CoV versus SARS-CoV-2 Spike Assemblies Revealed by Structural Dynamics.

SARS-CoV and SARS-CoV-2 cell entry begins when spike glycoprotein (S) docks with the human ACE2 (hACE2) receptor. While the two coronaviruses share a common receptor and architecture of S, they exhibit differences in interactions with hACE2 as well as differences in proteolytic processing of S that trigger the fusion machine. Understanding how those differences impact S activation is key to understand its function and viral pathogenesis. Here, we investigate hACE2-induced activation in SARS-CoV and SARS-CoV-2 S using hydrogen/deuterium-exchange mass spectrometry (HDX-MS). HDX-MS revealed differences in dynamics in unbound S, including open/closed conformational switching and D614G-induced S stability. Upon hACE2 binding, notable differences in transduction of allosteric changes were observed extending from the receptor binding domain to regions proximal to proteolytic cleavage sites and the fusion peptide. Furthermore, we report that dimeric hACE2, the native oligomeric form of the receptor, does not lead to any more pronounced structural effect in S compared to saturated monomeric hACE2 binding. These experiments provide mechanistic insights into receptor-induced activation of Sarbecovirus spike proteins.

Colonic Perforation Secondary to COVID-19 Induced Hemorrhagic Colitis in a Patient Heterozygous for Factor V Leiden.

Up to 17.6% of COVID-19 positive patients present with gastrointestinal symptoms and bowel wall abnormalities have been described in up to 31% of COVID-19 positive patients. Here, we present a case of a 40-year-old male diagnosed with COVID-19 complicated by hemorrhagic colitis leading to colonic perforation. CT scan of abdomen and pelvis demonstrated markedly distended descending and sigmoid colon with poorly defined wall, pneumatosis, and pneumoperitoneum. The patient was taken for emergent exploratory laparotomy for extended left hemicolectomy, partial omentectomy, transverse colostomy creation, abdominal washout, repair of small bowel, and appendectomy. The patient was brought back for repeat exploratory laparotomy with ICG perfusion assessment. Patient was found to be heterozygous for factor V Leiden mutation and was never vaccinated for COVID-19. Our case demonstrates a novel use for indocyanine green (ICG) to assess perfusion and underscores the importance of completing a thorough hypercoagulable evaluation following COVID-19 induced thrombotic event.

Rapid Approximate Subset-Based Spectra Prediction for Electron Ionization-Mass Spectrometry.

Mass spectrometry is a vital tool in the analytical chemist's toolkit, commonly used to identify the presence of known compounds and elucidate unknown chemical structures. All of these applications rely on having previously measured spectra for known substances. Computational methods for predicting mass spectra from chemical structures can be used to augment existing spectral databases with predicted spectra from previously unmeasured molecules. In this paper, we present a method for prediction of electron ionization-mass spectra (EI-MS) of small molecules that combines physically plausible substructure enumeration and deep learning, which we term rapid approximate subset-based spectra prediction (RASSP). The first of our two models, FormulaNet, produces a probability distribution over chemical subformulae to achieve a state-of-the-art forward prediction accuracy of 92.9% weighted (Stein) dot product and database lookup recall (within top 10 ranked spectra) of 98.0% when evaluated against the NIST 2017 Mass Spectral Library. The second model, SubsetNet, produces a probability distribution over vertex subsets of the original molecule graph to achieve similar forward prediction accuracy and superior generalization in the high-resolution, low-data regime. Spectra predicted by our best model improve upon the previous state-of-the-art spectral database lookup error rate by a factor of 2.9×, reducing the lookup error (top 10) from 5.7 to 2.0%. Both models can train on and predict spectral data at arbitrary resolution. Source code and predicted EI-MS spectra for 73.2M small molecules from PubChem will be made freely accessible online.

Prophages Present in Acinetobacter pittii Influence Bacterial Virulence, Antibiotic Resistance, and Genomic Rearrangements.

Introduction: Antibiotic resistance and virulence are common among bacterial populations, posing a global clinical challenge. The bacterial species Acinetobacter pittii, an infectious agent in clinical environments, has shown increasing rates of antibiotic resistance. Viruses that integrate as prophages into A. pittii could be a potential cause of this pathogenicity, as they often contain antibiotic resistance or virulence factor gene sequences. Methods: In this study, we analyzed 25 A. pittii strains for potential prophages. Using virulence factor databases, we identified many common and virulent prophages in A. pittii. Results: The analysis also included a specific catalogue of the virulence factors and antibiotic resistance genes contributed by A. pittii prophages. Finally, our results illustrate multiple similarities between A. pittii and its bacterial relatives with regard to prophage integration sites and prevalence. Discussion: These findings provide a broader insight into prophage behavior that can be applied to future studies on similar species in the Acinetobacter calcoaceticus-baumannii complex.

COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality.

Background: Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. Methods: Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. Results: Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. Conclusions: Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.

Structural dynamics reveal isolate-specific differences at neutralization epitopes on HIV Env.

The envelope glycoprotein (Env) is the sole target for neutralizing antibodies against HIV and the most rapidly evolving, variable part of the virus. High-resolution structures of Env trimers captured in the pre-fusion, closed conformation have revealed a high degree of structural similarity across diverse isolates. Biophysical data, however, indicate that Env is highly dynamic, and the level of dynamics and conformational sampling is believed to vary dramatically between HIV isolates. Dynamic differences likely influence neutralization sensitivity, receptor activation, and overall trimer stability. Here, using hydrogen/deuterium-exchange mass spectrometry (HDX-MS), we have mapped local dynamics across native-like Env SOSIP trimers from diverse isolates. We show that significant differences in epitope order are observed across most sites targeted by broadly neutralizing antibodies. We also observe isolate-dependent conformational switching that occurs over a broad range of timescales. Lastly, we report that hyper-stabilizing mutations that dampen dynamics in some isolates have little effect on others.

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset.

OBJECTIVE: The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. MATERIALS AND METHODS: The National COVID Cohort Collaborative (N3C) table of laboratory measurement data-over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. RESULTS: Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors' records lacked units). DISCUSSION: The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. CONCLUSION: The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.

Structural determinants of the dominant conformational epitopes of phospholipase A2 receptor in primary membranous nephropathy.

Anti-phospholipase A2 receptor autoantibody (PLA2R-Ab) plays a critical role in the pathogenesis of primary membranous nephropathy (PMN), an autoimmune kidney disease characterized by immune deposits in the glomerular subepithelial spaces and proteinuria. However, the mechanism of how PLA2R-Abs interact with the conformational epitope(s) of PLA2R has remained elusive. PLA2R is a single transmembrane helix receptor containing ten extracellular domains that begin with a CysR domain followed by a FnII and eight CTLD domains. Here, we examined the interactions of PLA2R-Ab with the full PLA2R protein, N-terminal domain truncations, and C-terminal domain deletions under either denaturing or physiological conditions. Our data demonstrate that the PLA2R-Abs against the dominant epitope (the N-terminal CysR-CTLD1 triple domain) possess weak cross-reactivities to the C-terminal domains beyond CTLD1. Moreover, both the CysR and CTLD1 domains are required to form a conformational epitope for PLA2R-Ab interaction, with FnII serving as a linker domain. Upon close examination, we also observed that patients with newly diagnosed PMN carry two populations of PLA2R-Abs in sera that react to the denatured CysR-CTLD3 (the PLA2R-Ab1) and denatured CysR-CTLD1 (the PLA2R-Ab2) domain complexes on Western blots, respectively. Furthermore, the PLA2R-Ab1 appeared at an earlier time point than PLA2R-Ab2 in patients, whereas the increased levels of PLA2R-Ab2 coincided with the worsening of proteinuria. In summary, our data support that an integrated folding of the three PLA2R N-terminal domains, CysR, FnII, and CTLD1, is a prerequisite to forming the PLA2R conformational epitope and that the dominant epitope-reactive PLA2R-Ab2 plays a critical role in PMN clinical progression.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

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 >99% or antigen <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.

The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction.

Background: The majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy. Methods and Findings: In a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients. Conclusions: This is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

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.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery.

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.

The Interrelations Between Different Causes of Dizziness: A Conceptual Framework for Understanding Vestibular Disorders.

BACKGROUND: According to population-based studies that estimate disease prevalence, the majority of patients evaluated at dizziness clinics receive a single vestibular diagnosis. However, accumulating literature supports the notion that different vestibular disorders are interrelated and often underdiagnosed. OBJECTIVE: Given the complexity and richness of these interrelations, we propose that a more inclusive conceptual framework to vestibular diagnostics that explicitly acknowledges this web of association will better inform vestibular differential diagnosis. METHODS: A narrative review was performed using PubMed database. Articles were included if they defined a cohort of patients, who were given specific vestibular diagnosis. The interrelations among vestibular disorders were analyzed and placed within a conceptual framework. RESULTS: The frequency of patients currently receiving multiple vestibular diagnoses in dizziness clinic is approximately 3.7% (1263/33 968 patients). The most common vestibular diagnoses encountered in the dizziness clinic include benign paroxysmal positional vertigo (BPPV), vestibular migraine, vestibular neuritis, and Ménière's disease. CONCLUSIONS: A review of the literature demonstrates an intricate web of interconnections among different vestibular disorders such as BPPV, vestibular migraine, Ménière's disease, vestibular neuritis, bilateral vestibulopathy, superior canal dehiscence syndrome, persistent postural perceptual dizziness, anxiety, head trauma, and aging, among others.

CT angiography depicted lumbar spinal artery anatomic course: defining a safer zone for lumbar sympathetic and splanchnic blocks.

INTRODUCTION: Determining safer techniques for lumbar injections is an important goal in pain medicine. This study aims to characterize the location of the T10-L5 spinal arteries using CT angiogram scans to define a safer approach for sympathetic and splanchnic blocks that minimizes intra-arterial injection. METHODS: CT angiograms of 68 patients were included this study. The path of the spinal arteries from the aorta origin along the vertebral body to the neural foramina was traced on axial CT images. The sagittal plane of the vertebral body was divided into nine quadrants to map the path of a spinal artery at a vertebral level. At a given vertebral level and laterality, the presence of an artery as well as the quadrants the artery traveled in along its path were recorded. RESULTS: At the anterior vertebral body, >90% of the spinal arteries were observed either at or below the pedicle level. At the middle portion of the vertebral body from T11 to L3, >80% of the spinal arteries were found at the pedicle level. For the posterior portion of the vertebral bodies at L4 and L5, the spinal arteries terminated almost universally below the pedicle level. For other levels at the posterior vertebral bodies, the spinal arteries were equivocally located at or below the pedicle level. CONCLUSION: Using routine anatomic landmarks visible on CT imaging, we classified the anatomic course of low thoracic and lumbar spinal arteries originating from the aorta into the neural foraminal space. A safe recommendation to avoid intra-arterial injection for a splanchnic or lumbar sympathetic is to start above the pedicle and add a slight caudal angulation to the needle trajectory to avoid disc injury at the anterolateral vertebral body.