ArticleScientific reports2022
Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Pathways of Patients With Neurological Emergencies at Pediatric Emergency Department: A Process-Based Data Mining Study.European journal of neurology · 2026Article
- Population-based incidence and demographic patterns of traumatic joint dislocations in children and adolescents.Acta orthopaedica · 2025Article
- Survival and data-driven phenotypes in head and neck cancer.Scientific reports · 2025Article
- Comorbidities confound metabolomics studies of human disease.Scientific reports · 2024Article
- A comparative study of clinical trial and real-world data in patients with diabetic kidney disease.Scientific reports · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
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.
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