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Using a data-driven approach to define post-COVID conditions in US electronic health record data
Kathleen M Andersen1, Farid L Khan2, Peter W Park2
1Vaccines Real World Evidence, Pfizer Inc, New York, New York, United States of America.
Plos One
|April 5, 2024
Summary
A new data-driven definition identified post-COVID conditions (PCC) in 20% of individuals after COVID-19 infection. This definition, based on symptom changes, captured more cases than the U09.9 code, especially in older adults.
Area of Science:
- Medical Informatics
- Public Health
- Epidemiology
Background:
- Post-COVID conditions (PCC) present a significant public health challenge.
- Existing diagnostic criteria for PCC lack standardization and may not fully capture the patient experience.
- A data-driven approach is needed to accurately define and identify PCC.
Purpose of the Study:
- To develop a data-driven definition of post-COVID conditions (PCC) using electronic health record (EHR) data.
- To quantify the incidence of PCC based on symptom changes following a COVID-19 infection.
- To compare the performance of the data-driven definition against the ICD-10-CM code U09.9 for identifying PCC.
Main Methods:
- Retrospective cohort study utilizing a de-identified EHR dataset from April 2020 to September 2021.
- Developed a "COVID symptom score" based on new diagnoses weighted by incidence ratios compared to a control group.
- Compared the data-driven PCC definition with the U09.9 code for cases diagnosed in September 2021.
Main Results:
- The cohort included 588,611 individuals with COVID-19; 20% developed PCC.
- PCC incidence increased with age, affecting 7.8% (0-17), 17.3% (18-64), and 33.3% (65+).
- The data-driven definition identified 19.0% of September 2021 cases with PCC, compared to only 2.9% identified by the U09.9 code.
Conclusions:
- Symptom-based and U09.9 code-based definitions identify distinct patient populations.
- A combined approach may be necessary for maximal capture of PCC cases.
- The findings highlight the limitations of current coding systems and the need for robust, data-driven definitions, especially prior to widespread code utilization and consensus.
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