BMC infectious diseases · 2026
Multimorbidity patterns and phenotype transitions in patients with clinician-coded long COVID: a multicenter US electronic health record cohort study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Wang Xiaofeng F, Huang Shuaiqi, Xu Yaomin, Zou Yan, Zhang Peng, Xie Yang, Tsuang Wayne M
Аннотация
BACKGROUND: Long 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.
METHODS: We 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.
RESULTS: Two 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.
CONCLUSIONS: Among 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.
METHODS: We 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.
RESULTS: Two 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.
CONCLUSIONS: Among 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.