Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates.

Autor: Koskinen M; Faculty of Medicine, University of Helsinki, Helsinki, Finland. miika.koskinen@hus.fi.; Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland. miika.koskinen@hus.fi.; Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland. miika.koskinen@hus.fi., Salmi JK; Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland., Loukola A; Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland., Mäkelä MJ; Division of Allergology, Skin and Allergy Hospital, Helsinki University Hospital and Helsinki University, Helsinki, Finland., Sinisalo J; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Heart and Lung Center, Helsinki University Hospital, and Helsinki University, Helsinki, Finland., Carpén O; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland.; HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland., Renkonen R; Faculty of Medicine, University of Helsinki, Helsinki, Finland.; HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Nov 02; Vol. 12 (1), pp. 18492. Date of Electronic Publication: 2022 Nov 02.
DOI: 10.1038/s41598-022-23090-3
Abstrakt: The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.
(© 2022. The Author(s).)
Databáze: MEDLINE
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