ArticleBMC medical informatics and decision making2024
Identifying subgroups in heart failure patients with multimorbidity by clustering and network analysis.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- Care demand networks in maternity care - an innovative approach exploring the complexity of care demands with routine data: Retrospective observational study.International journal of nursing studies advances · 2026Article
- A foundational model encodes deep phenotyping data and enables diverse downstream applications.NPJ digital medicine · 2026Article
- Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Evaluating the kidney disease progression using a comprehensive patient profiling algorithm: A hybrid clustering approach.PloS one · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Misdiagnosis of Chronic Heart Failure in Patients with Type 2 Diabetes Mellitus in Primary Care: A Report of Two Cases and Literature Review.Vascular health and risk management · 2024Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
This study presents a workflow for identifying and characterizing patients with Heart Failure (HF) and multimorbidity utilizing data from Electronic Health Records. Multimorbidity, the co-occurrence of two or more chronic conditions, poses a significant challenge on healthcare systems. Nonetheless, understanding of patients with multimorbidity, including the most common disease interactions, risk factors, and treatment responses, remains limited, particularly for complex and heterogeneous conditions like HF. We conducted a clustering analysis of 3745 HF patients using demographics, comorbidities, laboratory values, and drug prescriptions. Our analysis revealed four distinct clusters with significant differences in multimorbidity profiles showing differential prognostic implications regarding unplanned hospital admissions. These findings underscore the considerable disease heterogeneity within HF patients and emphasize the potential for improved characterization of patient subgroups for clinical risk stratification through the use of EHR data.
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Registered trials
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.