About

ISARIC 4C UK national COVID data analysis platform

The ISARIC Coronavirus Clinical Characterisation Consortium (4C) is the largest observational study of hospitalised patients with COVID-19 anyhwere in the world. Data are held in an integrated, accessible analysis platform from across multiple UK studies:

  • prospective clinical data from 78603 cases
  • biological measurements from research samples of blood, respiratory secretions, urine, and stool from 2459 cases
  • the GenOMICC study: whole genome sequence data from 3442 cases
  • follow-up clinical and biological data generated by the Post-Hospitalisation for COVID-19 follow-up study (PHOSP-COVID)
  • deep immunological phenotyping data from across the UK Coronavirus Immunology Consortium (UK-CIC)
  • linked patient records within the NHS Data Safe Haven
Figure 1: ISARIC 4C study and data analysis platform

Back-end infrastructure

ISARIC 4C has developed a clinical and research data integration platform to facilitate integrative analyses of multi-omic, serial disease profiling, stratified by clinical phenotype and outcome. This is hosted on nationally-leading, exabyte-scale computational infrastructure including state-of-the-art security systems for protection of identifiable data, and high-performance CPU/GPU computing (the Edinburgh Parallel Compute Centre, EPCC, and ARCHER/ARCHER2).

This platform now serves as a hub for a coordinated UK national research response to COVID-19.

Earning trust from data contributors

The default position is that data are contributed under embargo, prohibiting publication or general release until authorised by the data contributor. All contributors will agree to abide by this rule in good faith. Embargoed data will be available to other contributors during the embargo period, and will be released into the open analysis platform at or before the time of the first pre-print report.

A critical determinant of success is building sufficient trust among contributors to ensure that data are contributed in an accessible format as early as possible. Data sharing within the ISARIC 4C consortium continues to have the support and goodwill of contributors, because: - there is a palpable urgency created by the COVID-19 crisis; - the platform has earned the trust of contributors and will maintain it by enforcing embargo rules; - there is a clear expectation from patients, the public, funders and government; - there is primary benefit to data contributors to gain access to other unpublished data and analysis platforms.

Principles

ISARIC 4C is built on top of existing pandemic preparedness infrastructure, designed, established, maintained and tested during the interpandemic period (fig. 1),1 and harmonised across the world.2 It is an open-access national resource: we have already shared data on 78603 participants and 4273 samples with 26 external groups.

The success of ISARIC 4C is largely due to the following foundational principles:

  • no group, funder, collaborator or other party will have exclusive access to data or samples
  • consortium resources (samples, data and funds) will be prioritised according to likelihood of rapid impact on the COVID-19 pandemic
  • all data generated using ISARIC 4C resources is shared in a machine-readable format within the Integrated Analysis Platform

Open analysis platform for deidentified data

The analysis platform is being used to provide itegrated analyses of genetic associations with multiple phenotypes,3 functional genomics,4 and multi-omics critical illness trajectories,5 within the largest clinical study of COVID-19 anywhere in the world.6

Figure 2: Overlapping data for surviving members of the ISARIC 4C cohort.

The platform hosts overlapping datasets from across the UK. Individual patient consent enables sharing of linked whole-genome sequence data, whole-blood transcriptomics, proteomics, cytokine measurements, viral load and sequence, and clinical data. This will enable a range of discovery science with direct therapeutic applications, including subphenotype classification and extended causal inference using Mendelian randomisation and related approaches.

Providing clean, linked, deidentified data in a format that is easily accessible to researchers from a range of backgrounds requires staff with a high level of skill in clinical epidemiology, data science, and software engineering. Data will be systematically cleansed and linked, missing data completed in an iterative process interacting with analysis teams, and presented in curated flat files and through an integrated relational database. This will be presented to users through four interfaces:

  1. a user-friendly browsable interface enabling data selection and subgrouping through dropdown menus to subset patient populations by clinical and biological data and run de novo GWAS analyses using a GPU platform (GOLEM, Tenesa group), multivariable regression, propensity-matching, unsupervised clustering and other analyses.

  2. flexible analysis through bespoke, secure virtual machines operated through a command line interface providing access to R, Python, and other software as required by the user.

  3. a well-documented application programming interface (API) enabling external computational queries. This allows all data in the ISARIC 4C platform to contribute to federated data analysis frameworks at national and international level. Collaborating groups such as OPENSafely and Genomics England will be able to run queries seamlessly from external platforms.

  4. a limited, anonymised, downloadable dataset comprising key variables from all participants.

Deidentified data will be available openly to bona fide researchers for unrestricted analyses

Data safe haven

A linked, secure NHS data safe haven will provide access to identifiable data, and data collected without individual patient consent, for qualified, approved researchers performing research to improve patient care. This incorporates full ISARIC COVID case report forms for 46,000 patients, together with health records linkage (CAG section 251 and PBPP approvals in place).

This will enable detailed, rich clinical analyses with corrections for confounding and bias caused by social factors, comorbid illness and medications, and opens a range of detailed information to characterise acute disease using clinical measurements acquired from electronic health records.

References

1.Dunning, J.W., Merson, L., Rohde, G.G.U., Gao, Z., Semple, M.G., Tran, D., Gordon, A., Olliaro, P.L., Khoo, S.H., Bruzzone, R., Horby, P., Cobb, J.P., Longuere, K.-S., Kellam, P., Nichol, A., Brett, S., Everett, D., Walsh, T.S., Hien, T.-T., Yu, H., Zambon, M., Ruiz-Palacios, G., Lang, T., Akhvlediani, T., ISARIC Working Group 3, ISARIC Council, Hayden, F.G., Marshall, J., Webb, S., Angus, D.C., Shindo, N., van der Werf, S., Openshaw, P.J.M., Farrar, J., Carson, G. & Baillie, J.K. Open source clinical science for emerging infections. The Lancet Infectious Diseases 14, 8–9(2014).

2.Akhvlediani, T., Ali, S.M., Angus, D.C., Arabi, Y.M., Ashraf, S., Baillie, J.K., Bakamutumaho, B., Beane, A., Bozza, F., Brett, S.J., Bruzzone, R., Carson, G., Castle, L., Christian, M., Cobb, J.P., Cummings, M.J., D’Ortenzio, E., Jong, M.D. de, Denis, E., Derde, L.P.G., Dobell, E., Dondorp, A.M., Dunning, J.W., Everett, D., Farrar, J., Fowler, R., Gamage, D., Gao, Z., Gomersall, C.D., Gordon, A.C., Haniffa, R., Hardwick, H., Hashmi, M., Hayat, M., Hayden, F.G., Ho, A., Horby, P., Horby, P.W., Jamieson, N., Jawad, I., John, M., Kennon, K., Khaskheli, S., Khoo, S.H., Lang, T., Lee, J., Ling, L., Marshall, J.C., Memon, M.I., Mentré, F., Merson, L., Moore, S., Murthy, S., Nichol, A., O’Donnell, M.R., Olliaro, P.L., Olliaro, P., Openshaw, P.J., Parke, R., Pereira, R., Plotkin, D., Pritchard, M., Rabindrarajan, E., Ramakrishnan, N., Richards, T., Ruiz-Palacios, G.M., Russell, C.D., Scott, J.T., Semple, M.G., Shindo, N., Sigfrid, L., Somers, E.C., Taqi, A., Turtle, L., Thevarajan, I., Vijayaraghavan, B.K.T., Udayanga, I., Werf, S. van der, Vatrinet, R., Vecham, P.K. & Webb, S. Global outbreak research: Harmony not hegemony. The Lancet Infectious Diseases 0, (2020).

3.Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nature Genetics 50, 1593–1599(2018).

4.Forrest, A. R. R., Kawaji, H., Rehli, M., Baillie, J.K., et al A promoter-level mammalian expression atlas. Nature 507, 462–470(2014).

5.Neyton, L., Zheng, X., Skouras, C., Wilson, A.B., Gutmann, M.U., Yau, C., Uings, I.J., Rao, F.V., Nicolas, A., Marshall, C., Wilson, L.-M., Baillie, J.K. & Mole, D.J. Multiomic definition of generalizable endotypes in human acute pancreatitis. bioRxiv 539569(2019).doi:10/gf2zwn

6.Docherty, A.B., Harrison, E.M., Green, C.A., Hardwick, H.E., Pius, R., Norman, L., Holden, K.A., Read, J.M., Dondelinger, F., Carson, G., Merson, L., Lee, J., Plotkin, D., Sigfrid, L., Halpin, S., Jackson, C., Gamble, C., Horby, P.W., Nguyen-Van-Tam, J.S., Ho, A., Russell, C.D., Dunning, J., Openshaw, P.J., Baillie, J.K. & Semple, M.G. Features of 200.167em133 uk patients in hospital with covid-19 using the isaric who clinical characterisation protocol: Prospective observational cohort study. BMJ (Clinical research ed.) 369, m1985(2020).