دورية أكاديمية
Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.
| العنوان: | Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems. |
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| المؤلفون: | Strayer N; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA., Vessels T; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.; Center for Digital Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; 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., Zhang S; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA., Li Y; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA., Han L; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.; Center for Digital Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Sharber B; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Hsi RS; Department of Urology, Vanderbilt University Medical Center, Nashville, TN, USA., Bejan CA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA., Bick AG; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Balko JM; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Johnson DB; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Wheless LE; Tennessee Valley Health System VA Hospital, Nashville, TN, USA.; Department of Dermatology, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Wells QS; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Philips EJ; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia., Pulley JM; Department of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA.; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA., Self WH; Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA., Chen Q; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA., Hartert T; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Wilkins CH; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA., Savona MR; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA., Shyr Y; Department of Biostatistics, 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; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.; Center for Digital Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA., Xu Y; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA. |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2024 May 27. Date of Electronic Publication: 2024 May 27. |
| نوع المنشور: | Journal Article; Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD. Competing Interests: JWS is a member of the Scientific Advisory Board of Sensorium Therapeutics (with equity) and has received grant support from Biogen, Inc. He is the principal investigator of a collaborative study of the genetics of depression and bipolar disorder sponsored by 23andMe, for which 23andMe provides analysis time as in-kind support but no payments. DMR has served on advisory boards for Illumina and Alkermes and has received research funds unrelated to this work from PTC Therapeutics. All other authors declare no competing interests. |
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| معلومات مُعتمدة: | UL1 TR000445 United States TR NCATS NIH HHS; S10 RR025141 United States RR NCRR NIH HHS; RC2 GM092618 United States GM NIGMS NIH HHS; P50 GM115305 United States GM NIGMS NIH HHS; U01 HG006378 United States HG NHGRI NIH HHS; U19 HL065962 United States HL NHLBI NIH HHS; R01 MH118233 United States MH NIMH NIH HHS; R01 HD074711 United States HD NICHD NIH HHS; R21 DK127075 United States DK NIDDK NIH HHS; UL1 RR024975 United States RR NCRR NIH HHS; R01 NS032830 United States NS NINDS NIH HHS; U01 HG004798 United States HG NHGRI NIH HHS; UL1 TR002243 United States TR NCATS NIH HHS |
| فهرسة مساهمة: | Keywords: cross-institutional interoperability; electronic health records (EHR); multimorbidity; network analysis; real-world data analysis; reproducibility |
| تواريخ الأحداث: | Date Created: 20240408 Latest Revision: 20240608 |
| رمز التحديث: | 20240608 |
| مُعرف محوري في PubMed: | PMC10996752 |
| DOI: | 10.1101/2024.03.28.24305045 |
| PMID: | 38585743 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2024.03.28.24305045 |
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