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Kerexeta-Sarriegi J, García-Navarro T, Rollan-Martinez-Herrera M, Larburu N, Espejo-Mambié MD, Beristain Iraola A, Graña M. Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach. Int J Med Inform 2024; 188:105466. [PMID: 38761458 DOI: 10.1016/j.ijmedinf.2024.105466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/08/2024] [Accepted: 04/22/2024] [Indexed: 05/20/2024]
Abstract
BACKGROUND Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management. OBJECTIVE The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients. METHODS This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation. RESULTS The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field. CONCLUSION The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.
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Affiliation(s)
- Jon Kerexeta-Sarriegi
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain; Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain.
| | - Teresa García-Navarro
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain
| | - María Rollan-Martinez-Herrera
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Servicio de Pediatría, Hospital Universitario Marqués de Valdecilla, 39011 Santander, Cantabria, Spain; Instituto de Investigación Sanitaria IDIVAL. Grupo de Epidemiología y Salud Pública, 39008 Santander, Cantabria, Spain
| | - Nekane Larburu
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain
| | - Moisés D Espejo-Mambié
- Universidad de Alcalá, Facultad de Medicina y Ciencias de la Salud, Departamento de Biología de Sistemas, Alcalá de Henares, Spain; Asunción Klinika, 20400 Tolosa, Gipuzkoa, Spain
| | - Andoni Beristain Iraola
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain; Biogipuzkoa Health Research Institute, Bioengineering Area, Group of E-Health, 20014 San Sebastián, Spain; Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain
| | - Manuel Graña
- Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 San Sebastián, Spain
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Herzeel C, D’Hondt E, Vandeweerd V, Botermans W, Akand M, Van der Aa F, Wuyts R, Verachtert W. A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development. PLOS DIGITAL HEALTH 2023; 2:e0000384. [PMID: 37992021 PMCID: PMC10664923 DOI: 10.1371/journal.pdig.0000384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 09/22/2023] [Indexed: 11/24/2023]
Abstract
We present the Patient Trajectory Analysis Library (PTRA), a software package for explorative analysis of patient development. PTRA provides the tools for extracting statistically relevant trajectories from the medical event histories of a patient population. These trajectories can additionally be clustered for visual inspection and identifying key events in patient progression. The algorithms of PTRA are based on a statistical method developed previously by Jensen et al, but we contribute several modifications and extensions to enable the implementation of a practical tool. This includes a new clustering strategy, filter mechanisms for controlling analysis to specific cohorts and for controlling trajectory output, a parallel implementation that executes on a single server rather than a high-performance computing (HPC) cluster, etc. PTRA is furthermore open source and the code is organized as a framework so researchers can reuse it to analyze new data sets. We illustrate our tool by discussing trajectories extracted from the TriNetX Dataworks database for analyzing bladder cancer development. We show this experiment uncovers medically sound trajectories for bladder cancer.
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Affiliation(s)
| | | | - Valerie Vandeweerd
- Janssen Research & Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Wouter Botermans
- Janssen Research & Development, a division of Janssen Pharmaceutica NV, Beerse, Belgium
| | - Murat Akand
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
| | - Frank Van der Aa
- Department of Urology, University Hospitals Leuven, Leuven, Belgium
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Paik H, Lee J, Jeong CS, Park JS, Lee JH, Rappoport N, Kim Y, Sohn HY, Jo C, Kim J, Cho SB. Identification of a pleiotropic effect of ADIPOQ on cardiac dysfunction and Alzheimer's disease based on genetic evidence and health care records. Transl Psychiatry 2022; 12:389. [PMID: 36114174 PMCID: PMC9481623 DOI: 10.1038/s41398-022-02144-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/21/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
Observations of comorbidity in heart diseases, including cardiac dysfunction (CD) are increasing, including and cognitive impairment, such as Alzheimer's disease and dementia (AD/D). This comorbidity might be due to a pleiotropic effect of genetic variants shared between CD and AD/D. Here, we validated comorbidity of CD and AD/D based on diagnostic records from millions of patients in Korea and the University of California, San Francisco Medical Center (odds ratio 11.5 [8.5-15.5, 95% Confidence Interval (CI)]). By integrating a comprehensive human disease-SNP association database (VARIMED, VARiants Informing MEDicine) and whole-exome sequencing of 50 brains from individuals with and without Alzheimer's disease (AD), we identified missense variants in coding regions including APOB, a known risk factor for CD and AD/D, which potentially have a pleiotropic role in both diseases. Of the identified variants, site-directed mutation of ADIPOQ (268 G > A; Gly90Ser) in neurons produced abnormal aggregation of tau proteins (p = 0.02), suggesting a functional impact for AD/D. The association of CD and ADIPOQ variants was confirmed based on domain deletion in cardiac cells. Using the UK Biobank including data from over 500000 individuals, we examined a pleiotropic effect of the ADIPOQ variant by comparing CD- and AD/D-associated phenotypic evidence, including cardiac hypertrophy and cognitive degeneration. These results indicate that convergence of health care records and genetic evidences may help to dissect the molecular underpinnings of heart disease and associated cognitive impairment, and could potentially serve a prognostic function. Validation of disease-disease associations through health care records and genomic evidence can determine whether health conditions share risk factors based on pleiotropy.
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Affiliation(s)
- Hyojung Paik
- Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
- Bakar Computational Health Sciences Institute, University of California San Francisco, 550 16th Street, San Francisco, CA, 94143, USA
- Department of Pediatrics, School of Medicine, University of California San Francisco, 550 16th Street, San Francisco, CA, 94143, USA
- Department of Data and HPC Science, University of Science and Technology, Daejeon, 34113, Republic of Korea
| | - Junehawk Lee
- Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Chan-Seok Jeong
- Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Jun Sung Park
- Biomedical Science and Engineering Interdisciplinary Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Jeong Ho Lee
- Biomedical Science and Engineering Interdisciplinary Program, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Nadav Rappoport
- Bakar Computational Health Sciences Institute, University of California San Francisco, 550 16th Street, San Francisco, CA, 94143, USA
- Departement of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beersheba, 8410501, Israel
| | - Younghoon Kim
- Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Hee-Young Sohn
- Division of Brain Disease Research, Department for Chronic Disease Convergence Research, Korea National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Chulman Jo
- Division of Brain Disease Research, Department for Chronic Disease Convergence Research, Korea National Institute of Health, Chungcheongbuk-do, 28159, Republic of Korea
| | - Jimin Kim
- Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, 34141, Republic of Korea
| | - Seong Beom Cho
- Department of Bio-Medical Informatics, Gachon University, College of Medicine, Incheon, 21565, Republic of Korea.
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Künnapuu K, Ioannou S, Ligi K, Kolde R, Laur S, Vilo J, Rijnbeek PR, Reisberg S. Trajectories: a framework for detecting temporal clinical event sequences from health data standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. JAMIA Open 2022; 5:ooac021. [PMID: 35571357 PMCID: PMC9097714 DOI: 10.1093/jamiaopen/ooac021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 03/05/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Objective
To develop a framework for identifying temporal clinical event trajectories from Observational Medical Outcomes Partnership-formatted observational healthcare data.
Materials and Methods
A 4-step framework based on significant temporal event pair detection is described and implemented as an open-source R package. It is used on a population-based Estonian dataset to first replicate a large Danish population-based study and second, to conduct a disease trajectory detection study for type 2 diabetes patients in the Estonian and Dutch databases as an example.
Results
As a proof of concept, we apply the methods in the Estonian database and provide a detailed breakdown of our findings. All Estonian population-based event pairs are shown. We compare the event pairs identified from Estonia to Danish and Dutch data and discuss the causes of the differences. The overlap in the results was only 2.4%, which highlights the need for running similar studies in different populations.
Conclusions
For the first time, there is a complete software package for detecting disease trajectories in health data.
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Affiliation(s)
| | - Solomon Ioannou
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Kadri Ligi
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Sven Laur
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jaak Vilo
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec, Tartu, Estonia
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sulev Reisberg
- STACC, Tartu, Estonia
- Institute of Computer Science, University of Tartu, Tartu, Estonia
- Quretec, Tartu, Estonia
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