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Roni RG, Tsipi H, Ofir BA, et al. Disease Evolution and Risk-Based Disease Trajectories in Congestive Heart Failure Patients. J Biomed Inform. 2021;103949doi: 10.1016/j.jbi.2021.103949.
Roni, R. G., Tsipi, H., Ofir, B. A., Nir, S., & Robert, K. (2021). Disease Evolution and Risk-Based Disease Trajectories in Congestive Heart Failure Patients. Journal of biomedical informatics, 103949. https://doi.org/10.1016/j.jbi.2021.103949
Roni, Ramon-Gonen, et al. "Disease Evolution and Risk-Based Disease Trajectories in Congestive Heart Failure Patients." Journal of biomedical informatics vol. (2021): 103949. doi: https://doi.org/10.1016/j.jbi.2021.103949
Roni RG, Tsipi H, Ofir BA, Nir S, Robert K. Disease Evolution and Risk-Based Disease Trajectories in Congestive Heart Failure Patients. J Biomed Inform. 2021 Dec 04;103949. doi: 10.1016/j.jbi.2021.103949. Epub 2021 Dec 04. PMID: 34875386.
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J Biomed Inform. 2021 Dec 04;103949. doi: 10.1016/j.jbi.2021.103949. Epub 2021 Dec 04.

Disease Evolution and Risk-Based Disease Trajectories in Congestive Heart Failure Patients.

Journal of biomedical informatics

Ramon-Gonen Roni, Heart Tsipi, Ben-Assuli Ofir, Shlomo Nir, Klempfner Robert

Affiliations

  1. Bar-Ilan university, Israel. Electronic address: [email protected].
  2. Ono Academic College, Israel. Electronic address: [email protected].
  3. Ono Academic College, Israel. Electronic address: [email protected].
  4. The Leviev Heart Center, Sheba Medical Center, Israel. Electronic address: [email protected].
  5. The Leviev Heart Center, Sheba Medical Center, Israel. Electronic address: [email protected].

PMID: 34875386 DOI: 10.1016/j.jbi.2021.103949

Abstract

Congestive Heart Failure (CHF) is among the most prevalent chronic diseases worldwide, and is commonly associated with comorbidities and complex health conditions. Consequently, CHF patients are typically hospitalized frequently, and are at a high risk of premature death. Early detection of an envisaged patient disease trajectory is crucial for precision medicine. However, despite the abundance of patient-level data, cardiologists currently struggle to identify disease trajectories and track the evolution patterns of the disease over time, especially in small groups of patients with specific disease subtypes. The present study proposed a five-step method that allows clustering CHF patients, detecting cluster similarity, and identifying disease trajectories, and promises to overcome the existing difficulties. This work is based on a rich dataset of patients' records spanning ten years of hospital visits. The dataset contains all the health information documented in the hospital during each visit, including diagnoses, lab results, clinical data, and demographics. It utilizes an innovative Cluster Evolution Analysis (CEA) method to analyze the complex CHF population where each subject is potentially associated with numerous variables. We have defined sub-groups for mortality risk levels, which we used to characterize patients' disease evolution by refined data clustering in three points in time over ten years, and generating patients' migration patterns across periods. The results elicited 18, 23, and 25 clusters respective to the first, second, and third visits, uncovering clinically interesting small sub-groups of patients. In the following post-processing stage, we identified meaningful patterns. The analysis yielded fine-grained patient clusters divided into several finite risk levels, including several small-sized groups of high-risk patients. Significantly, the analysis also yielded longitudinal patterns where patients' risk levels changed over time. Four types of disease trajectories were identified: decline, preserved state, improvement, and mixed-progress. This stage is a unique contribution of the work. The resulting fine partitioning and longitudinal insights promise to significantly assist cardiologists in tailoring personalized interventions to improve care quality. Cardiologists could utilize these results to glean previously undetected relationships between symptoms and disease evolution that would allow a more informed clinical decision-making and effective interventions.

Copyright © 2021. Published by Elsevier Inc.

Keywords: Cluster Evolution Analysis; Congestive Heart Failure; Disease Trajectories; K-Means; Machine Learning; Sequential Pattern Mining

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this pa

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