International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium

Abstract

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Keywords: Databases; Outcomes research; Viral infection.

Fig. 1

Percentages of patients along with 95% confidence intervals in each a country and b sex age groups.

Fig. 2

Adjusted 7-day average new case rate per 100K by country based on 4CE contributors along with 95% confidence intervals compared to 7-day average new case rate collected by Johns Hopkins Center for Systems Science and Engineering (JHU CSSE).

Fig. 3. Laboratory tests representative of renal function (creatinine), systemic inflammation (C-reactive protein), coagulopathy (D-dimer), liver function (total bilirubin), and immune response (white blood cell count) visualized relative to date of diagnosis of COVID-19.

The top row shows weighted means and 95% confidence intervals across all patients. The second row shows unweighted country- (thick lines) and site-level (thin lines) means. The third and fourth rows show the number of patients and sites, respectively, contributing laboratory tests of each type on a given day.

Fig. 4

Drop-off in laboratory tests reported for creatinine relative to the day with the largest number of laboratory tests reported.

Fig. 5. Laboratory variation across countries and within sites for creatinine test performed within a day of diagnosis.

Values for mean value and standard deviation (SD) in creatinine level are shown. Large variations exist within sites and are often larger than the between country variation. The overall SD across countries was 1.47 while the SD within sites was 1.39. Standard deviation for countries was 1.64, 1.31, 1.13, and 1.62 within France, Germany, Italy, and the US, respectively.

Fig. 6

Overview of data collection and analysis.