Evidence map›Paper›PMID 38700929›Full record

ArticleOnline journal of public health informatics2024

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

Monika Maripuri, Andrew Dey, Jacqueline Honerlaw, Chuan Hong, Yuk-Lam Ho, Vidisha Tanukonda, Alicia W Chen, Vidul Ayakulangara Panickan, Xuan Wang, Harrison G Zhang and 13 more

Abstract read
In one paragraph

Article in Online journal of public health informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. A national atlas to improve the study of Canadians living with long COVID (post-COVID-19 condition).Canadian journal of public health = Revue canadienne de sante publique · 2026
    Article
  6. Article
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Monika MaripuriVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0009-0003-8848-6171
Andrew DeyVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0001-8890-8387
Jacqueline HonerlawVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5872-359X
Chuan HongDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0001-7056-9559
Yuk-Lam HoVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3305-3830
Vidisha TanukondaVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0009-0001-8103-3474
Alicia W ChenVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0009-0002-4417-6932
Vidul Ayakulangara PanickanDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0003-0616-0403
Xuan WangDepartment of Population Health Sciences, University of Utah, Salt Lake City, UT, United States.ORCID https://orcid.org/0000-0002-0011-5663
Harrison G ZhangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0001-6679-1464
Doris YangDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5188-2571
Malarkodi Jebathilagam SamayamuthuDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0003-0061-8196
Michele MorrisDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-3255-5727
Shyam VisweswaranDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0002-2079-8684
Brendin Beaulieu-JonesDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-1208-8025
Rachel RamoniOffice of Research and Development, US Department of Veterans Affairs, Washington, DC, United States.ORCID https://orcid.org/0000-0002-1459-9638
Sumitra MuralidharOffice of Research and Development, US Department of Veterans Affairs, Washington, DC, United States.ORCID https://orcid.org/0000-0001-8417-9068
J Michael GazianoVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5384-9767
Katherine LiaoDivision of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, United States.ORCID https://orcid.org/0000-0002-4797-3200
Zongqi XiaDepartment of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.ORCID https://orcid.org/0000-0003-1500-2589
Gabriel A BratDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3928-5931
Tianxi CaiDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.ORCID https://orcid.org/0000-0002-5379-2502
Kelly ChoVeterans Affairs Boston Healthcare System, Boston, MA, United States.ORCID https://orcid.org/0000-0003-1727-7076

Funding

Leveraging electronic health records to optimize treatment selection and response in multiple sclerosisR01NS098023 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI Zongqi Xia · 2016 to 2026
$4.6M
Real-world impact of the COVID-19 pandemic in people with multiple sclerosisR01NS124882 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI XIA, ZONGQI · 2022 to 2024
$1.2M
NINDS NIH HHS R01 NS098023NINDS NIH HHS R01 NS124882
6 · The paper itself

Abstract

backgroundPost-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear.

objectiveThis study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions.

methodsPatients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic.

resultsOverall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems.

conclusionsThis is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Indexed as

algorithm validationchart reviewCOVID-19electronic health recordsInternational Classification of Diseaseslong COVID-19PASCpostacute sequelae of SARS-CoV-2U09.9 ICD-10 codeveterans

Identifiers

PMID38700929
PMCPMC11073632

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.