International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium
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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.
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SciScore for 10.1101/2020.04.13.20059691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Data Availability: Data files for daily counts, demographics, diagnosis, and labs datasets are available at https://covidclinical.net. Informed Consent/IRB Statement: Each institution reported obtaining proper institutional review board approval for data sharing. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Visualizations were implemented using Python and Altair (http://altair-viz.github.io/) in Jupyter Notebooks (https://jupyter.org), all of which are freely available on the website. Pythonsuggested: (IPython, RRID:SCR_001658)Resu…
SciScore for 10.1101/2020.04.13.20059691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Data Availability: Data files for daily counts, demographics, diagnosis, and labs datasets are available at https://covidclinical.net. Informed Consent/IRB Statement: Each institution reported obtaining proper institutional review board approval for data sharing. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Visualizations were implemented using Python and Altair (http://altair-viz.github.io/) in Jupyter Notebooks (https://jupyter.org), all of which are freely available on the website. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There are a multitude of limitations to this study, not least of which is that it is observational and subject to a variety of biases. Perhaps the most severe is that study data is limited to those patients who were seen at or admitted to hospitals, due to severity of illness or other possibly biasing characteristics. Limitations also include heavy right censoring where patient absence can be due to death or discharge, variations in ICD annotations for conditions existing prior to the COVID-19-related admission, delays in updating billing codes or in uploading EHR data to the local analytic data repository. Furthermore, potentially confounding interactions between comorbidities, chronic diseases and their treatments and lifestyle or exposures were not taken into consideration. Again, because of these limitations we were careful to avoid making more than basic and descriptive conclusions. Over the coming weeks, we will work to quantify these biases and adjust for them, if we can. This will include adding data types as well as disaggregating the data to the patient level if and when permitted by IRBs. For the present, with the current limited knowledge of the clinical course of patients suffering from COVID-19, these results add to this small knowledge base. Our paper strikingly shows the power of harmonized data extraction from EHRs to rapidly study pandemics like COVID-19. We invite others to join the 4CE consortium by sending a note to 4CE@i2b2foundation.org.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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