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Privacy-Protecting, Reliable Response Data Discovery Using COVID-19 Patient Observations
Jihoon Kim1, Larissa Neumann1,2,3, Paulina Paul1
1UC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA.
Medrxiv : the Preprint Server for Health Sciences
|September 30, 2020
Summary
COVID-19 research needs multi-center data. A privacy-preserving web portal analyzed data from 202 hospitals, finding no significant mortality differences by gender or ethnicity after age adjustment.
Area of Science:
- Clinical Medicine
- Public Health
- Health Informatics
Background:
- Urgent need for COVID-19 clinical course insights.
- Multi-center data crucial but centralization challenging.
- Privacy-preserving methods enable distributed research.
Approach:
- Launched a public Questions & Answers web portal (https://covid19questions.org).
- Analyzed comorbidities, medications, and lab tests.
- Utilized data from 202 hospitals (59,074 COVID-19 patients) in USA and Germany.
Key Points:
- 8.6% of non-ICU hospitalizations led to readmission within seven days.
- Hospitalized COVID-19 patient mortality showed no significant difference by gender or ethnicity, adjusted for age.
- Comorbidities, medications, and lab tests were analyzed.
Conclusions:
- Distributed data analysis is feasible for COVID-19 research.
- Web portals can facilitate multi-center data insights.
- Key clinical outcomes like readmission rates and mortality factors were identified.
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