PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.

Autor:	Zhang S; Department of Biostatistics, Vanderbilt University, Nashville, TN, USA., Strayer N; Posit PBC, Boston, MA, USA., Vessels T; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Choi K; Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA.; Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA., Wang GW; Department of Statistics, North Carolina State University., Li Y; Department of Biostatistics, Vanderbilt University, Nashville, TN, USA., Bejan CA; Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA., Hsi RS; Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA., Bick AG; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Velez Edwards DR; Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA., Savona MR; Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Philips EJ; Center for Drug Safety and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia., Pulley J; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA., Self WH; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA., Hopkins WC; Vanderbilt Institute for Clinical and Translational Science, Vanderbilt University Medical Center, Nashville, TN, USA., Roden DM; Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA., Smoller JW; Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA.; Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA.; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA., Ruderfer DM; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA.; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Xu Y; Department of Biostatistics, Vanderbilt University, Nashville, TN, USA.; Department of Biomedical informatics, Vanderbilt University, Nashville, TN, USA.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2023 Jul 30. Date of Electronic Publication: 2023 Jul 30.
DOI:	10.1101/2023.07.23.23293047
Abstrakt:	Motivation: Multimorbidity, characterized by the simultaneous occurrence of multiple diseases in an individual, is an increasing global health concern, posing substantial challenges to healthcare systems. Comprehensive understanding of disease-disease interactions and intrinsic mechanisms behind multimorbidity can offer opportunities for innovative prevention strategies, targeted interventions, and personalized treatments. Yet, there exist limited tools and datasets that characterize multimorbidity patterns across different populations. To bridge this gap, we used large-scale electronic health record (EHR) systems to develop the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME), which facilitates research in exploring and comparing multimorbidity patterns among multiple institutions, potentially leading to the discovery of novel and robust disease associations and patterns that are interoperable across different systems and organizations. Results: PheMIME integrates summary statistics from phenome-wide analyses of disease multimorbidities. These are currently derived from three major institutions: Vanderbilt University Medical Center, Mass General Brigham, and the UK Biobank. PheMIME offers interactive exploration of multimorbidity through multi-faceted visualization. Incorporating an enhanced version of associationSubgraphs, PheMIME enables dynamic analysis and inference of disease clusters, promoting the discovery of multimorbidity patterns. Once a disease of interest is selected, the tool generates interactive visualizations and tables that users can delve into multimorbidities or multimorbidity networks within a single system or compare across multiple systems. The utility of PheMIME is demonstrated through a case study on schizophrenia. Availability and Implementation: The PheMIME knowledge base and web application are accessible at https://prod.tbilab.org/PheMIME/. A comprehensive tutorial, including a use-case example, is available at https://prod.tbilab.org/PheMIME_supplementary_materials/. Furthermore, the source code for PheMIME can be freely downloaded from https://github.com/tbilab/PheMIME. Data Availability Statement: The data underlying this article are available in the article and in its online web application or supplementary material. Competing Interests: Competing Interest Statement The authors have declared no competing interest.
Databáze:	MEDLINE
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