Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems
Strayer, N.; Vessels, T. J.; Choi, K. W.; Zhang, S.; Li, Y.; Sharber, B.; Hsi, R. S.; Bejan, C. A.; Bick, A. G.; Balko, J. M.; Johnson, D. B.; Wheless, L. E.; Wells, Q. S.; Shah, R. V.; Phillips, E. J.; Self, W. H.; Pulley, J. M.; Wilkins, C. H.; Chen, Q.; Hartert, T.; Savona, M. R.; Shyr, Y.; Roden, D. M.; Smoller, J. W.; Ruderfer, D. M.; Xu, Y.
Show abstract
BackgroundElectronic 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. MethodsUtilizing 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. FindingsAnalyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendalls{tau} = 0.643) and comorbidity strengths (Pearson{rho} = 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 networks ability to uncover clinically relevant disease relationships and provide novel insights. InterpretationOur 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. FundingVUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data 95%
- Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs 94%
- Novel clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health 93%
Similar papers in this journal
Similar papers in this journal
- Pretrained Patient Trajectories for Adverse Drug Event Prediction Using Common Data Model-based Electronic Health Records 93%
- Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID 92%
- The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy 91%
Similar papers in this journal
- Construction and optimization of multi-platform precision pathways for precision medicine 93%
- Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study 92%
- Synteny: a high throughput web tool to streamline causal gene prioritisation and provide insight into protein function 91%
Similar papers in this journal
- Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data 92%
- Multi-ancestry omic Mendelian randomization revealing putative drug targets of COVID-19 severity 91%
- A systematic analysis of the contribution of genetics to multimorbidity and comparisons with primary care data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.