Published October 23, 2020 | Version v1
Dataset Open

Dataset of clinical biomarkers for prediction of the arboviral infection severity using SIMON analysis

Authors/Creators

  • 1. University of Oxford

Contributors

Data collector:

Project leader:

  • 1. SUNY Upstate Medical University

Description

The SISA dataset was used for the identification of clinical biomarkers that can predict the severity of the arboviral infection severity using SIMON analysis, as described (https://doi.org/10.1101/2020.08.16.252767). The dataset contains clinical data from 543 individuals hospitalized due to the arboviral infection with Dengue, chikungunya, or Zika viruses from a surveillance study in Ecuador collected from 2013 to 2017 (Suppy et al., 2020, doi: 10.1371/journal.pntd.0007969). In the SISA dataset, we have excluded columns with high level of missing values (pregnancy, WomPreg and complete blood count test which was not performed for all donors and includes columns: PLT_count, Lymphocytes, CBC_N%, WBC_calc and CBC_HCT).  Additionally, 9 donors with missing values were removed and SIMON analysis was performed with this final dataset.

Notes

The clinical study and the initial data is described in the original publication: Sippy, R., Farrell, D.F., Lichtenstein, D.A., Nightingale, R., Harris, M.A., Toth, J., Hantztidiamantis, P., Usher, N., Cueva Aponte, C., Barzallo Aguilar, J., et al. (2020). Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection. PLoS neglected tropical diseases 14, e0007969.

Files

SISA.csv

Files (39.2 kB)

Name Size Download all
md5:e22b9af0ad9debdd1ccc86db09ab4bde
39.2 kB Preview Download

Additional details

Funding

European Commission
FluPRINT - Tracing the inFLUenza vaccine imPRINT on immune system to identify cellular signature of protection 796636