Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions.

OBJECTIVE: We aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions. MATERIALS AND METHODS: We preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution. RESULTS: The smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline. DISCUSSION: The low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows. CONCLUSION: Blockchain technology can be used to support federated data analysis among multiple institutions.

Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence.

Accurate, highly specific immunoassays for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are needed to evaluate seroprevalence. This study investigated the concordance of results across four immunoassays targeting different antigens for sera collected at the beginning of the SARS-CoV-2 pandemic in the United States. Specimens from All of Us participants contributed between January and March 2020 were tested using the Abbott Architect SARS-CoV-2 IgG (immunoglobulin G) assay (Abbott) and the EuroImmun SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (EI). Participants with discordant results, participants with concordant positive results, and a subset of concordant negative results by Abbott and EI were also tested using the Roche Elecsys anti-SARS-CoV-2 (IgG) test (Roche) and the Ortho-Clinical Diagnostics Vitros anti-SARS-CoV-2 IgG test (Ortho). The agreement and 95% confidence intervals were estimated for paired assay combinations. SARS-CoV-2 antibody concentrations were quantified for specimens with at least two positive results across four immunoassays. Among the 24,079 participants, the percent agreement for the Abbott and EI assays was 98.8% (95% confidence interval, 98.7%, 99%). Of the 490 participants who were also tested by Ortho and Roche, the probability-weighted percentage of agreement (95% confidence interval) between Ortho and Roche was 98.4% (97.9%, 98.9%), that between EI and Ortho was 98.5% (92.9%, 99.9%), that between Abbott and Roche was 98.9% (90.3%, 100.0%), that between EI and Roche was 98.9% (98.6%, 100.0%), and that between Abbott and Ortho was 98.4% (91.2%, 100.0%). Among the 32 participants who were positive by at least 2 immunoassays, 21 had quantifiable anti-SARS-CoV-2 antibody concentrations by research assays. The results across immunoassays revealed concordance during a period of low prevalence. However, the frequency of false positivity during a period of low prevalence supports the use of two sequentially performed tests for unvaccinated individuals who are seropositive by the first test. IMPORTANCE What is the agreement of commercial SARS-CoV-2 immunoglobulin G (IgG) assays during a time of low coronavirus disease 2019 (COVID-19) prevalence and no vaccine availability? Serological tests produced concordant results in a time of low SARS-CoV-2 prevalence and no vaccine availability, driven largely by the proportion of samples that were negative by two immunoassays. The CDC recommends two sequential tests for positivity for future pandemic preparedness. In a subset analysis, quantified antinucleocapsid and antispike SARS-CoV-2 IgG antibodies do not suggest the need to specify the antigen targets of the sequential assays in the CDC's recommendation because false positivity varied as much between assays targeting the same antigen as it did between assays targeting different antigens.

Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020.

BACKGROUND: With limited severe acute respiratory syndrome coronavirus (SARS-CoV-2) testing capacity in the United States at the start of the epidemic (January-March 2020), testing was focused on symptomatic patients with a travel history throughout February, obscuring the picture of SARS-CoV-2 seeding and community transmission. We sought to identify individuals with SARS-CoV-2 antibodies in the early weeks of the US epidemic. METHODS: All of Us study participants in all 50 US states provided blood specimens during study visits from 2 January to 18 March 2020. Participants were considered seropositive if they tested positive for SARS-CoV-2 immunoglobulin G (IgG) antibodies with the Abbott Architect SARS-CoV-2 IgG enzyme-linked immunosorbent assay (ELISA) and the EUROIMMUN SARS-CoV-2 ELISA in a sequential testing algorithm. The sensitivity and specificity of these ELISAs and the net sensitivity and specificity of the sequential testing algorithm were estimated, along with 95% confidence intervals (CIs). RESULTS: The estimated sensitivities of the Abbott and EUROIMMUN assays were 100% (107 of 107 [95% CI: 96.6%-100%]) and 90.7% (97 of 107 [83.5%-95.4%]), respectively, and the estimated specificities were 99.5% (995 of 1000 [98.8%-99.8%]) and 99.7% (997 of 1000 [99.1%-99.9%]), respectively. The net sensitivity and specificity of our sequential testing algorithm were 90.7% (97 of 107 [95% CI: 83.5%-95.4%]) and 100.0% (1000 of 1000 [99.6%-100%]), respectively. Of the 24 079 study participants with blood specimens from 2 January to 18 March 2020, 9 were seropositive, 7 before the first confirmed case in the states of Illinois, Massachusetts, Wisconsin, Pennsylvania, and Mississippi. CONCLUSIONS: Our findings identified SARS-CoV-2 infections weeks before the first recognized cases in 5 US states.

How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles.

OBJECTIVE: This study aims at reviewing novel coronavirus disease (COVID-19) datasets extracted from PubMed Central articles, thus providing quantitative analysis to answer questions related to dataset contents, accessibility and citations. METHODS: We downloaded COVID-19-related full-text articles published until 31 May 2020 from PubMed Central. Dataset URL links mentioned in full-text articles were extracted, and each dataset was manually reviewed to provide information on 10 variables: (1) type of the dataset, (2) geographic region where the data were collected, (3) whether the dataset was immediately downloadable, (4) format of the dataset files, (5) where the dataset was hosted, (6) whether the dataset was updated regularly, (7) the type of license used, (8) whether the metadata were explicitly provided, (9) whether there was a PubMed Central paper describing the dataset and (10) the number of times the dataset was cited by PubMed Central articles. Descriptive statistics about these seven variables were reported for all extracted datasets. RESULTS: We found that 28.5% of 12 324 COVID-19 full-text articles in PubMed Central provided at least one dataset link. In total, 128 unique dataset links were mentioned in 12 324 COVID-19 full text articles in PubMed Central. Further analysis showed that epidemiological datasets accounted for the largest portion (53.9%) in the dataset collection, and most datasets (84.4%) were available for immediate download. GitHub was the most popular repository for hosting COVID-19 datasets. CSV, XLSX and JSON were the most popular data formats. Additionally, citation patterns of COVID-19 datasets varied depending on specific datasets. CONCLUSION: PubMed Central articles are an important source of COVID-19 datasets, but there is significant heterogeneity in the way these datasets are mentioned, shared, updated and cited.

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations.

There is an urgent need to answer questions related to COVID-19's clinical course and associations with underlying conditions and health outcomes. Multi-center data are necessary to generate reliable answers, but centralizing data in a single repository is not always possible. Using a privacy-protecting strategy, we launched a public Questions & Answers web portal (https://covid19questions.org) with analyses of comorbidities, medications and laboratory tests using data from 202 hospitals (59,074 COVID-19 patients) in the USA and Germany. We find, for example, that 8.6% of hospitalizations in which the patient was not admitted to the ICU resulted in the patient returning to the hospital within seven days from discharge and that, when adjusted for age, mortality for hospitalized patients was not significantly different by gender or ethnicity.

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

COVID-19: indexed data speed up solutions

Letter to the Editor