Evidence map›Paper›PMID 42380873›Full record

ArticleBMC infectious diseases2026

Multimorbidity patterns and phenotype transitions in patients with clinician-coded long COVID: a multicenter US electronic health record cohort study.

Xiaofeng F Wang, Shuaiqi Huang, Yaomin Xu, Yan Zou, Peng Zhang, Yang Xie, Wayne M Tsuang

Abstract readMulticenter Study
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xiaofeng F Wang *Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH, USA. wangx6@ccf.org.
Shuaiqi Huang *Department of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH, USA.
Yaomin XuDepartment of Health Data Science and Biostatistics, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Yan ZouDepartment of Quantitative Health Sciences, Cleveland Clinic, 9500 Euclid Ave/JJN3, Cleveland, OH, USA.
Peng ZhangDepartment of Pulmonary and Critical Care Medicine, Cleveland Clinic, Cleveland, OH, USA.
Yang XieDepartment of Health Data Science and Biostatistics, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Wayne M TsuangCenter for Environment, Place, and Health Research, Cleveland Clinic, Cleveland, OH, USA.

Funding

Stochastic Deep Learning for Electronic Health Records: Localizing Learning with Massive and Fragmented DataR01GM152717 · NIGMS · PURDUE UNIVERSITY · PI LIANG, FAMING, WANG, XIAOFENG · 2023 to 2025
$600k
NIGMS NIH HHS 1R01GM152717-01NIGMS NIH HHS R01 GM152717
6 · The paper itself

Abstract

backgroundLong COVID is clinically heterogeneous, but longitudinal changes in documented chronic disease burden and transitions in multimorbidity phenotypes after infection are not well characterized in routine care.

methodsWe considered 425,614 patients with clinician-coded long COVID (ICD-10-CM U09.9) and a definable COVID-19 index date between October 1, 2021 and September 16, 2024 using deidentified electronic health record data from Epic Cosmos, a multicenter US network. Pre-index and post-index windows were defined as days - 365 to - 1 and days 91 to 455 relative to infection, respectively; follow-up was available through December 15, 2025. Chronic condition groups derived from ICD-10-CM codes were compared across windows using adjusted generalized estimating equation models. K-modes clustering was used to identify multimorbidity phenotypes, and multinomial regression was used to estimate adjusted transition probabilities.

resultsTwo pre-index phenotypes were identified: low burden (87.9%) and multimorbid (12.1%). Four post-index phenotypes emerged: low burden (63.3%), multimorbid/systemic (21.0%), respiratory-dominant (4.3%), and high-utilization/low-coded multimorbidity (11.3%). Higher baseline multimorbidity was associated with greater probability of transition to the multimorbid/systemic phenotype, whereas respiratory-dominant and high-utilization phenotypes arose from both baseline groups. Increases were concentrated in neurologic/autonomic, respiratory, hypercoagulable, endocrine/metabolic, sleep-related, and symptom-based domains. The high-utilization/low-coded phenotype was younger, predominantly female, and had greater emergency department and outpatient use. Fewer changes reached statistical significance in children than in adults.

conclusionsAmong patients with clinician-coded long COVID, chronic disease burden increased after infection and diversified into interpretable post-index phenotypes with distinct utilization profiles, supporting phenotype-informed follow-up and health system planning.

Indexed as

COVID-19MultimorbidityAdolescentAdultAgedChronic DiseaseCohort StudiesElectronic Health RecordsFemaleHumansMaleMiddle AgedPhenotypePost-Acute COVID-19 SyndromeSARS-CoV-2United StatesElectronic health recordsHealth care utilizationLong COVIDMultimorbidityPhenotypes

Identifiers

PMID42380873
PMCPMC13591880

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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.