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Lara MK, Brabec JL, Hernan AE, Scott RC, Tyler AL, Mahoney JM. Network-based analysis predicts interacting genetic modifiers from a meta-mapping study of spike-wave discharge in mice. Genes Brain Behav 2024; 23:e12879. [PMID: 38444174 PMCID: PMC10915378 DOI: 10.1111/gbb.12879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 11/29/2023] [Accepted: 12/19/2023] [Indexed: 03/07/2024]
Abstract
Absence seizures are characterized by brief lapses in awareness accompanied by a hallmark spike-and-wave discharge (SWD) electroencephalographic pattern and are common to genetic generalized epilepsies (GGEs). While numerous genes have been associated with increased risk, including some Mendelian forms with a single causal allele, most cases of GGE are idiopathic and there are many unknown genetic modifiers of GGE influencing risk and severity. In a previous meta-mapping study, crosses between transgenic C57BL/6 and C3HeB/FeJ strains, each carrying one of three SWD-causing mutations (Gabrg2tm1Spet(R43Q) , Scn8a8j or Gria4spkw1 ), demonstrated an antagonistic epistatic interaction between loci on mouse chromosomes 2 and 7 influencing SWD. These results implicate universal modifiers in the B6 background that mitigate SWD severity through a common pathway, independent of the causal mutation. In this study, we prioritized candidate modifiers in these interacting loci. Our approach integrated human genome-wide association results with gene interaction networks and mouse brain gene expression to prioritize candidate genes and pathways driving variation in SWD outcomes. We considered candidate genes that are functionally associated with human GGE risk genes and genes with evidence for coding or non-coding allele effects between the B6 and C3H backgrounds. Our analyses output a summary ranking of gene pairs, one gene from each locus, as candidates for explaining the epistatic interaction. Our top-ranking gene pairs implicate microtubule function, cytoskeletal stability and cell cycle regulation as novel hypotheses about the source of SWD variation across strain backgrounds, which could clarify underlying mechanisms driving differences in GGE severity in humans.
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Affiliation(s)
- Montana Kay Lara
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
| | - Jeffrey L. Brabec
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
| | - Amanda E. Hernan
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
- Division of NeuroscienceNemours Children's HealthWilmingtonDelawareUSA
- Department of Psychological and Brain SciencesUniversity of DelawareNewarkDelawareUSA
| | - Rod C. Scott
- Division of NeuroscienceNemours Children's HealthWilmingtonDelawareUSA
- Department of Psychological and Brain SciencesUniversity of DelawareNewarkDelawareUSA
| | | | - J. Matthew Mahoney
- Department of Neurological SciencesUniversity of VermontBurlingtonVermontUSA
- The Jackson LaboratoryBar HarborMaineUSA
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LePrevost CE, Cofie LE, Nieuwsma J, Harwell EL, Rivera ND, Acevedo PA, Lee JGL. Community health worker outreach to farmworkers in rural North Carolina: Learning from adaptations to the SARS-CoV-2 pandemic. Health Expect 2024; 27:e14047. [PMID: 38613767 PMCID: PMC11015864 DOI: 10.1111/hex.14047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/18/2024] [Accepted: 04/03/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Community health workers represent a critical part of the health outreach and services for migrant and seasonal farmworkers ('farmworkers') in rural areas of the United States. PURPOSE We sought to identify adaptations to farmworker patient engagement and health outreach made by community health workers during the first 18 months of the COVID-19 pandemic. METHODS In this qualitative study, we used semi-structured interviews with community health workers from August 2020 to February 2022 (n = 21). Two coders used thematic analysis to identify three themes related to the experiences of community health workers in conducting health education and outreach to farmworkers prior to and following the onset of the pandemic. FINDINGS We found themes related to pre-pandemic outreach efforts to provide health education resource sharing with farmworkers and pandemic-related outreach efforts that included adoption of porch drops and distanced delivery of health education, adaptation of modes of health education and communication through technology and the internet, and taking on new roles related to COVID-19. Finally, we identified changes that reverted after the pandemic or will continue as adaptations. CONCLUSIONS Community health workers created practice-based innovations in outreach in response to the COVID-19 pandemic. These innovations included new COVID-19 related roles and new modes of health education and outreach, including the use of digital resources. The changes developed for emergency use in COVID-19, particularly related to internet and technology, have likely altered how community health workers conduct outreach in North Carolina going forward. Funders, community health worker training programs, and researchers should take note of these innovations. PATIENT OR PUBLIC CONTRIBUTION Community health workers who typically come from patient populations and provide critical navigation and connection with the health care system advised on the design and creation of this research project, including serving on an advisory board. Two authors have experience working as community health workers.
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Affiliation(s)
- Catherine E. LePrevost
- Department of Applied Ecology, College of Agriculture and Life SciencesNC State UniversityRaleighNorth CarolinaUSA
| | - Leslie E. Cofie
- Department of Health Education and Promotion, College of Health and Human PerformanceEast Carolina UniversityGreenvilleNorth CarolinaUSA
| | - Julianna Nieuwsma
- Department of Applied Ecology, College of Agriculture and Life SciencesNC State UniversityRaleighNorth CarolinaUSA
| | - Emery L. Harwell
- Department of Applied Ecology, College of Agriculture and Life SciencesNC State UniversityRaleighNorth CarolinaUSA
| | - Natalie D. Rivera
- NC Farmworker Health Program, Office of Rural HealthNC Department of Health and Human ServicesRaleighNorth CarolinaUSA
| | - Paula A. Acevedo
- Department of Health Education and Promotion, College of Health and Human PerformanceEast Carolina UniversityGreenvilleNorth CarolinaUSA
| | - Joseph G. L. Lee
- Department of Health Education and Promotion, College of Health and Human PerformanceEast Carolina UniversityGreenvilleNorth CarolinaUSA
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3
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Tran SD, Lin J, Galvez C, Rasmussen LV, Pacheco J, Perottino GM, Rahbari KJ, Miller CD, John JD, Theros J, Vogel K, Dinh PV, Malik S, Ramzan U, Tegtmeyer K, Mohindra N, Johnson JL, Luo Y, Kho A, Sosman J, Walunas TL. Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach. Front Immunol 2024; 15:1331959. [PMID: 38558818 PMCID: PMC10978703 DOI: 10.3389/fimmu.2024.1331959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Immune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors. Methods We conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs. Results Logistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43). Discussion Our machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.
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Affiliation(s)
- Steven D. Tran
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jean Lin
- Department of Medicine, Division of Rheumatology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Carlos Galvez
- Hematology and Oncology, University of Illinois Health, Chicago, IL, United States
| | - Luke V. Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Jennifer Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | | | - Kian J. Rahbari
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Charles D. Miller
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jordan D. John
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jonathan Theros
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kelly Vogel
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Patrick V. Dinh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Sara Malik
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Umar Ramzan
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Kyle Tegtmeyer
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Nisha Mohindra
- Department of Medicine, Division of Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, United States
| | - Jodi L. Johnson
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, United States
- Departments of Pathology and Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Abel Kho
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Medicine, Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Jeffrey Sosman
- Department of Medicine, Division of Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL, United States
| | - Theresa L. Walunas
- Center for Health Information Partnerships, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Medicine, Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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Liu JTC, Chow SSL, Colling R, Downes MR, Farré X, Humphrey P, Janowczyk A, Mirtti T, Verrill C, Zlobec I, True LD. Engineering the future of 3D pathology. J Pathol Clin Res 2024; 10:e347. [PMID: 37919231 PMCID: PMC10807588 DOI: 10.1002/cjp2.347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/04/2023]
Abstract
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.
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Affiliation(s)
- Jonathan TC Liu
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
- Department of BioengineeringUniversity of WashingtonSeattleUSA
| | - Sarah SL Chow
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
| | | | | | | | - Peter Humphrey
- Department of UrologyYale School of MedicineNew HavenCTUSA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGAUSA
- Geneva University HospitalsGenevaSwitzerland
| | - Tuomas Mirtti
- Helsinki University Hospital and University of HelsinkiHelsinkiFinland
- Emory University School of MedicineAtlantaGAUSA
| | - Clare Verrill
- John Radcliffe HospitalUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Inti Zlobec
- Institute for Tissue Medicine and PathologyUniversity of BernBernSwitzerland
| | - Lawrence D True
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
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Jeong E, Malin B, Nelson SD, Su Y, Li L, Chen Y. Revealing the dynamic landscape of drug-drug interactions through network analysis. Front Pharmacol 2023; 14:1211491. [PMID: 37860114 PMCID: PMC10583566 DOI: 10.3389/fphar.2023.1211491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
Introduction: The landscape of drug-drug interactions (DDIs) has evolved significantly over the past 60 years, necessitating a retrospective analysis to identify research trends and under-explored areas. While methodologies like bibliometric analysis provide valuable quantitative perspectives on DDI research, they have not successfully delineated the complex interrelations between drugs. Understanding these intricate relationships is essential for deciphering the evolving architecture and progressive transformation of DDI research structures over time. We utilize network analysis to unearth the multifaceted relationships between drugs, offering a richer, more nuanced comprehension of shifts in research focus within the DDI landscape. Methods: This groundbreaking investigation employs natural language processing, techniques, specifically Named Entity Recognition (NER) via ScispaCy, and the information extraction model, SciFive, to extract pharmacokinetic (PK) and pharmacodynamic (PD) DDI evidence from PubMed articles spanning January 1962 to July 2023. It reveals key trends and patterns through an innovative network analysis approach. Static network analysis is deployed to discern structural patterns in DDI research, while evolving network analysis is employed to monitor changes in the DDI research trend structures over time. Results: Our compelling results shed light on the scale-free characteristics of pharmacokinetic, pharmacodynamic, and their combined networks, exhibiting power law exponent values of 2.5, 2.82, and 2.46, respectively. In these networks, a select few drugs serve as central hubs, engaging in extensive interactions with a multitude of other drugs. Interestingly, the networks conform to a densification power law, illustrating that the number of DDIs grows exponentially as new drugs are added to the DDI network. Notably, we discovered that drugs connected in PK and PD networks predominantly belong to the same categories defined by the Anatomical Therapeutic Chemical (ATC) classification system, with fewer interactions observed between drugs from different categories. Discussion: The finding suggests that PK and PD DDIs between drugs from different ATC categories have not been studied as extensively as those between drugs within the same categories. By unearthing these hidden patterns, our study paves the way for a deeper understanding of the DDI landscape, providing valuable information for future DDI research, clinical practice, and drug development focus areas.
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Affiliation(s)
- Eugene Jeong
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bradley Malin
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - Scott D. Nelson
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yu Su
- Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States
| | - You Chen
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
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Abstract
T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.
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Affiliation(s)
- Hai Yang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
| | - Jason Cham
- Department of Medicine, Scripps Green Hospital, La Jolla, CA, United States
| | - Brian Patrick Neal
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Zenghua Fan
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Tao He
- Department of Mathematics, San Francisco State University, San Francisco, CA, United States
| | - Li Zhang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, United States
- Department of Medicine, University of California San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
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Hall-Swan S, Slone J, Rigo MM, Antunes DA, Lizée G, Kavraki LE. PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure. Front Immunol 2023; 14:1108303. [PMID: 37187737 PMCID: PMC10175663 DOI: 10.3389/fimmu.2023.1108303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
Introduction Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe. Methods Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs. Results and discussion We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.
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Affiliation(s)
- Sarah Hall-Swan
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Jared Slone
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Mauricio M. Rigo
- Department of Computer Science, Rice University, Houston, TX, United States
| | - Dinler A. Antunes
- Department of Biology and Biochemistry, University of Houston, Houston, TX, United States
| | - Gregory Lizée
- Department of Melanoma Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lydia E. Kavraki
- Department of Computer Science, Rice University, Houston, TX, United States
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Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Front Pharmacol 2023; 14:1113007. [PMID: 37180722 PMCID: PMC10169664 DOI: 10.3389/fphar.2023.1113007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.
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Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
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Douville NJ, Larach DB, Lewis A, Bastarache L, Pandit A, He J, Heung M, Mathis M, Wanderer JP, Kheterpal S, Surakka I, Kertai MD. Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury. Front Genet 2023; 14:1094908. [PMID: 37124606 PMCID: PMC10133500 DOI: 10.3389/fgene.2023.1094908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
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Affiliation(s)
- Nicholas J. Douville
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Daniel B. Larach
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anita Pandit
- Center for Statistical Genetics and Precision Health Initiative, University of Michigan, Ann Arbor, MI, United States
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Heung
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Michael Mathis
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
- Center for Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, MI, United States
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States
| | - Jonathan P. Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sachin Kheterpal
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, MI, United States
| | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Miklos D. Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States
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10
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Chakravarty D, Schafer JW, Porter LL. Distinguishing features of fold-switching proteins. Protein Sci 2023; 32:e4596. [PMID: 36782353 PMCID: PMC9951197 DOI: 10.1002/pro.4596] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/30/2023] [Accepted: 02/09/2023] [Indexed: 02/15/2023]
Abstract
Though many folded proteins assume one stable structure that performs one function, a small-but-increasing number remodel their secondary and tertiary structures and change their functions in response to cellular stimuli. These fold-switching proteins regulate biological processes and are associated with autoimmune dysfunction, severe acute respiratory syndrome coronavirus-2 infection, and more. Despite their biological importance, it is difficult to computationally predict fold switching. With the aim of advancing computational prediction and experimental characterization of fold switchers, this review discusses several features that distinguish fold-switching proteins from their single-fold and intrinsically disordered counterparts. First, the isolated structures of fold switchers are less stable and more heterogeneous than single folders but more stable and less heterogeneous than intrinsically disordered proteins (IDPs). Second, the sequences of single fold, fold switching, and intrinsically disordered proteins can evolve at distinct rates. Third, proteins from these three classes are best predicted using different computational techniques. Finally, late-breaking results suggest that single folders, fold switchers, and IDPs have distinct patterns of residue-residue coevolution. The review closes by discussing high-throughput and medium-throughput experimental approaches that might be used to identify new fold-switching proteins.
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Affiliation(s)
- Devlina Chakravarty
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
| | - Joseph W. Schafer
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
| | - Lauren L. Porter
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of HealthBethesdaMarylandUSA
- Biochemistry and Biophysics Center, National Heart, Lung, and Blood Institute, National Institutes of HealthBethesdaMarylandUSA
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11
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Derry A, Altman RB. COLLAPSE: A representation learning framework for identification and characterization of protein structural sites. Protein Sci 2023; 32:e4541. [PMID: 36519247 PMCID: PMC9847082 DOI: 10.1002/pro.4541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 12/02/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
The identification and characterization of the structural sites which contribute to protein function are crucial for understanding biological mechanisms, evaluating disease risk, and developing targeted therapies. However, the quantity of known protein structures is rapidly outpacing our ability to functionally annotate them. Existing methods for function prediction either do not operate on local sites, suffer from high false positive or false negative rates, or require large site-specific training datasets, necessitating the development of new computational methods for annotating functional sites at scale. We present COLLAPSE (Compressed Latents Learned from Aligned Protein Structural Environments), a framework for learning deep representations of protein sites. COLLAPSE operates directly on the 3D positions of atoms surrounding a site and uses evolutionary relationships between homologous proteins as a self-supervision signal, enabling learned embeddings to implicitly capture structure-function relationships within each site. Our representations generalize across disparate tasks in a transfer learning context, achieving state-of-the-art performance on standardized benchmarks (protein-protein interactions and mutation stability) and on the prediction of functional sites from the Prosite database. We use COLLAPSE to search for similar sites across large protein datasets and to annotate proteins based on a database of known functional sites. These methods demonstrate that COLLAPSE is computationally efficient, tunable, and interpretable, providing a general-purpose platform for computational protein analysis.
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Affiliation(s)
- Alexander Derry
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
| | - Russ B. Altman
- Department of Biomedical Data ScienceStanford UniversityStanfordCaliforniaUSA
- Departments of Bioengineering, Genetics, and MedicineStanford UniversityStanfordCaliforniaUSA
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12
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Song Q, diFlorio‐Alexander RM, Patel SD, Sieberg RT, Margron MJ, Ansari SM, Karagas MR, Mackenzie TA, Hassanpour S. Association between fat-infiltrated axillary lymph nodes on screening mammography and cardiometabolic disease. Obes Sci Pract 2022; 8:757-766. [PMID: 36483128 PMCID: PMC9722459 DOI: 10.1002/osp4.608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 04/08/2022] [Accepted: 04/19/2022] [Indexed: 12/11/2022] Open
Abstract
Objective Ectopic fat deposition within and around organs is a stronger predictor of cardiometabolic disease status than body mass index (BMI). Fat deposition within the lymphatic system is poorly understood. This study examined the association between the prevalence of cardiometabolic disease and ectopic fat deposition within axillary lymph nodes (LNs) visualized on screening mammograms. Methods A cross-sectional study was conducted on 834 women presenting for full-field digital screening mammography. The status of fat-infiltrated LNs was assessed based on the size and morphology of axillary LNs from screening mammograms. The prevalence of cardiometabolic disease was retrieved from the electronic medical records, including type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, high blood glucose, cardiovascular disease, stroke, and non-alcoholic fatty liver disease. Results Fat-infiltrated axillary LNs were associated with a high prevalence of T2DM among all women (adjusted odds ratio: 3.92, 95% CI: [2.40, 6.60], p-value < 0.001) and in subgroups of women with and without obesity. Utilizing the status of fatty LNs improved the classification of T2DM status in addition to age and BMI (1.4% improvement in the area under the receiver operating characteristic curve). Conclusion Fat-infiltrated axillary LNs visualized on screening mammograms were associated with the prevalence of T2DM. If further validated, fat-infiltrated axillary LNs may represent a novel imaging biomarker of T2DM in women undergoing screening mammography.
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Affiliation(s)
- Qingyuan Song
- Department of Biomedical Data ScienceDartmouth CollegeLebanonNew HampshireUSA
| | | | - Sohum D. Patel
- Department of RadiologyDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
| | - Ryan T. Sieberg
- Department of RadiologyDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
| | - Michael J. Margron
- Department of RadiologyDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
| | - Saif M. Ansari
- Department of RadiologyDartmouth‐Hitchcock Medical CenterLebanonNew HampshireUSA
| | | | - Todd A. Mackenzie
- Department of Biomedical Data ScienceDartmouth CollegeLebanonNew HampshireUSA
| | - Saeed Hassanpour
- Department of Biomedical Data ScienceDartmouth CollegeLebanonNew HampshireUSA
- Department of EpidemiologyDartmouth CollegeLebanonNew HampshireUSA
- Department of Computer ScienceDartmouth CollegeHanoverNew HampshireUSA
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13
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Woodward AA, Urbanowicz RJ, Naj AC, Moore JH. Genetic heterogeneity: Challenges, impacts, and methods through an associative lens. Genet Epidemiol 2022; 46:555-571. [PMID: 35924480 PMCID: PMC9669229 DOI: 10.1002/gepi.22497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 07/06/2022] [Accepted: 07/19/2022] [Indexed: 01/07/2023]
Abstract
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.
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Affiliation(s)
- Alexa A. Woodward
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ryan J. Urbanowicz
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Adam C. Naj
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
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14
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Tu T, Alba MM, Datta AA, Hong H, Hua B, Jia Y, Khan J, Nguyen P, Niu X, Pammidimukkala P, Slarve I, Tang Q, Xu C, Zhou Y, Stiles BL. Hepatic macrophage mediated immune response in liver steatosis driven carcinogenesis. Front Oncol 2022; 12:958696. [PMID: 36276076 PMCID: PMC9581256 DOI: 10.3389/fonc.2022.958696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/17/2022] [Indexed: 12/02/2022] Open
Abstract
Obesity confers an independent risk for carcinogenesis. Classically viewed as a genetic disease, owing to the discovery of tumor suppressors and oncogenes, genetic events alone are not sufficient to explain the progression and development of cancers. Tumor development is often associated with metabolic and immunological changes. In particular, obesity is found to significantly increase the mortality rate of liver cancer. As its role is not defined, a fundamental question is whether and how metabolic changes drive the development of cancer. In this review, we will dissect the current literature demonstrating that liver lipid dysfunction is a critical component driving the progression of cancer. We will discuss the involvement of inflammation in lipid dysfunction driven liver cancer development with a focus on the involvement of liver macrophages. We will first discuss the association of steatosis with liver cancer. This will be followed with a literature summary demonstrating the importance of inflammation and particularly macrophages in the progression of liver steatosis and highlighting the evidence that macrophages and macrophage produced inflammatory mediators are critical for liver cancer development. We will then discuss the specific inflammatory mediators and their roles in steatosis driven liver cancer development. Finally, we will summarize the molecular pattern (PAMP and DAMP) as well as lipid particle signals that are involved in the activation, infiltration and reprogramming of liver macrophages. We will also discuss some of the therapies that may interfere with lipid metabolism and also affect liver cancer development.
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Affiliation(s)
- Taojian Tu
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Mario M. Alba
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Aditi A. Datta
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Handan Hong
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Brittney Hua
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Yunyi Jia
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Jared Khan
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Phillip Nguyen
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Xiatoeng Niu
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Pranav Pammidimukkala
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Ielyzaveta Slarve
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Qi Tang
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Chenxi Xu
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Yiren Zhou
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
| | - Bangyan L. Stiles
- Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Southern California, Los Angeles, CA, United States
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- *Correspondence: Bangyan L. Stiles,
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15
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Fu H, Nicolet D, Mrózek K, Stone RM, Eisfeld A, Byrd JC, Archer KJ. Controlled variable selection in Weibull mixture cure models for high-dimensional data. Stat Med 2022; 41:4340-4366. [PMID: 35792553 PMCID: PMC9545322 DOI: 10.1002/sim.9513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 06/14/2022] [Accepted: 06/19/2022] [Indexed: 12/03/2022]
Abstract
Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control the false discovery rate (FDR) in variable selection. Through extensive simulation studies, our penalized MCMs have been shown to outperform alternative methods on multiple metrics and achieve high statistical power with FDR being controlled. In an acute myeloid leukemia (AML) application with gene expression data, our proposed approach identified 14 genes associated with potential cure and 12 genes with time-to-relapse, which may help inform treatment decisions for AML patients.
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Affiliation(s)
- Han Fu
- Division of BiostatisticsCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
| | - Deedra Nicolet
- Clara D. Bloomfield Center for Leukemia Outcomes ResearchThe Ohio State University Comprehensive Cancer CenterColumbusOhioUSA
- Alliance Statistics and Data Management CenterThe Ohio State University Comprehensive Cancer CenterColumbusOhioUSA
| | - Krzysztof Mrózek
- Clara D. Bloomfield Center for Leukemia Outcomes ResearchThe Ohio State University Comprehensive Cancer CenterColumbusOhioUSA
| | - Richard M. Stone
- Dana‐Farber/Partners CancerHarvard UniversityBostonMassachusettsUSA
| | - Ann‐Kathrin Eisfeld
- Clara D. Bloomfield Center for Leukemia Outcomes ResearchThe Ohio State University Comprehensive Cancer CenterColumbusOhioUSA
| | - John C. Byrd
- Department of Internal MedicineUniversity of CincinnatiCincinnatiOhioUSA
| | - Kellie J. Archer
- Division of BiostatisticsCollege of Public Health, The Ohio State UniversityColumbusOhioUSA
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16
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Jeong E, Nelson SD, Su Y, Malin B, Li L, Chen Y. Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system. Front Pharmacol 2022; 13:938552. [PMID: 35935872 PMCID: PMC9353301 DOI: 10.3389/fphar.2022.938552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 07/01/2022] [Indexed: 11/18/2022] Open
Abstract
Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety. Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs. Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals. Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.
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Affiliation(s)
- Eugene Jeong
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Scott D. Nelson
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yu Su
- Department of Computer Science and Engineering, College of Engineering, the Ohio State University, Columbus, OH, United States
| | - Bradley Malin
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biostatistics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, the Ohio State University, Columbus, OH, United States
| | - You Chen
- Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, United States
- *Correspondence: You Chen,
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17
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Khunsriraksakul C, Markus H, Olsen NJ, Carrel L, Jiang B, Liu DJ. Construction and Application of Polygenic Risk Scores in Autoimmune Diseases. Front Immunol 2022; 13:889296. [PMID: 35833142 PMCID: PMC9271862 DOI: 10.3389/fimmu.2022.889296] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with autoimmune diseases and provided unique mechanistic insights and informed novel treatments. These individual genetic variants on their own typically confer a small effect of disease risk with limited predictive power; however, when aggregated (e.g., via polygenic risk score method), they could provide meaningful risk predictions for a myriad of diseases. In this review, we describe the recent advances in GWAS for autoimmune diseases and the practical application of this knowledge to predict an individual’s susceptibility/severity for autoimmune diseases such as systemic lupus erythematosus (SLE) via the polygenic risk score method. We provide an overview of methods for deriving different polygenic risk scores and discuss the strategies to integrate additional information from correlated traits and diverse ancestries. We further advocate for the need to integrate clinical features (e.g., anti-nuclear antibody status) with genetic profiling to better identify patients at high risk of disease susceptibility/severity even before clinical signs or symptoms develop. We conclude by discussing future challenges and opportunities of applying polygenic risk score methods in clinical care.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Havell Markus
- Graduate Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Nancy J. Olsen
- Department of Medicine, Division of Rheumatology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Dajiang J. Liu
- Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, United States
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, United States
- *Correspondence: Dajiang J. Liu,
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18
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Chen VL, Huang Q, Harouaka R, Du Y, Lok AS, Parikh ND, Garmire LX, Wicha MS. A Dual-Filtration System for Single-Cell Sequencing of Circulating Tumor Cells and Clusters in HCC. Hepatol Commun 2022; 6:1482-1491. [PMID: 35068084 PMCID: PMC9134808 DOI: 10.1002/hep4.1900] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/07/2021] [Accepted: 12/17/2021] [Indexed: 12/12/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Identification and sequencing of circulating tumor (CT) cells and clusters may allow for noninvasive molecular characterization of HCC, which is an unmet need, as many patients with HCC do not undergo biopsy. We evaluated CT cells and clusters, collected using a dual-filtration system in patients with HCC. We collected and filtered whole blood from patients with HCC and selected individual CT cells and clusters with a micropipette. Reverse transcription, polymerase chain reaction, and library preparation were performed using a SmartSeq2 protocol, followed by single-cell RNA sequencing (scRNAseq) on an Illumina MiSeq V3 platform. Of the 8 patients recruited, 6 had identifiable CT cells or clusters. Median age was 64 years old; 7 of 8 were male; and 7 of 8 had and Barcelona Clinic Liver Cancer stage C. We performed scRNAseq of 38 CT cells and 33 clusters from these patients. These CT cells and clusters formed two distinct groups. Group 1 had significantly higher expression than group 2 of markers associated with epithelial phenotypes (CDH1 [Cadherin 1], EPCAM [epithelial cell adhesion molecule], ASGR2 [asialoglycoprotein receptor 2], and KRT8 [Keratin 8]), epithelial-mesenchymal transition (VIM [Vimentin]), and stemness (PROM1 [CD133], POU5F1 [POU domain, class 5, transcription factor 1], NOTCH1, STAT3 [signal transducer and activator of transcription 3]) (P < 0.05 for all). Patients with identifiable group 1 cells or clusters had poorer prognosis than those without them (median overall survival 39 vs. 384 days; P = 0.048 by log-rank test). Conclusion: A simple dual-filtration system allows for isolation and sequencing of CT cells and clusters in HCC and may identify cells expressing candidate genes known to be involved in cancer biology. Presence of CT cells/clusters expressing candidate genes is associated with poorer prognosis in advanced-stage HCC.
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Affiliation(s)
- Vincent L. Chen
- Division of Gastroenterology and HepatologyDepartment of Internal MedicineUniversity of MichiganAnn ArborMIUSA
| | - Qianhui Huang
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMIUSA
| | - Ramdane Harouaka
- Division of Hematology and OncologyDepartment of Internal MedicineUniversity of MichiganAnn ArborMIUSA
| | - Yuheng Du
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMIUSA
| | - Anna S. Lok
- Division of Gastroenterology and HepatologyDepartment of Internal MedicineUniversity of MichiganAnn ArborMIUSA
| | - Neehar D. Parikh
- Division of Gastroenterology and HepatologyDepartment of Internal MedicineUniversity of MichiganAnn ArborMIUSA
| | - Lana X. Garmire
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMIUSA
| | - Max S. Wicha
- Division of Hematology and OncologyDepartment of Internal MedicineUniversity of MichiganAnn ArborMIUSA
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19
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Ostropolets A, Li X, Makadia R, Rao G, Rijnbeek PR, Duarte-Salles T, Sena AG, Shaoibi A, Suchard MA, Ryan PB, Prieto-Alhambra D, Hripcsak G. Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases. Front Pharmacol 2022; 13:814198. [PMID: 35559254 PMCID: PMC9087898 DOI: 10.3389/fphar.2022.814198] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/17/2022] [Indexed: 01/01/2023] Open
Abstract
Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.
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Affiliation(s)
| | - Xintong Li
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, United States
| | - Gowtham Rao
- Janssen Research and Development, Titusville, NJ, United States
| | - Peter R. Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Talita Duarte-Salles
- Fundacio Institut Universitari per a la Recerca a L’Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Anthony G. Sena
- Janssen Research and Development, Titusville, NJ, United States
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Azza Shaoibi
- Janssen Research and Development, Titusville, NJ, United States
| | - Marc A. Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick B. Ryan
- Columbia University Medical Center, New York, NY, United States
- Janssen Research and Development, Titusville, NJ, United States
| | | | - George Hripcsak
- Columbia University Medical Center, New York, NY, United States
- New York-Presbyterian Hospital, New York, NY, United States
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Rajaraman S, Zamzmi G, Folio LR, Antani S. Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers. Front Genet 2022; 13:864724. [PMID: 35281798 PMCID: PMC8907925 DOI: 10.3389/fgene.2022.864724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 02/10/2022] [Indexed: 11/25/2022] Open
Abstract
Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e., the posterior-anterior (PA), and the anterior-posterior (AP) views for analysis and decision-making. Lateral CXRs which are heretofore not studied help detect clinically suspected pulmonary TB, particularly in children. Further, Vision Transformers (ViTs) with built-in self-attention mechanisms have recently emerged as a viable alternative to the traditional CNNs. Although ViTs demonstrated notable performance in several medical image analysis tasks, potential limitations exist in terms of performance and computational efficiency, between the CNN and ViT models, necessitating a comprehensive analysis to select appropriate models for the problem under study. This study aims to detect TB-consistent findings in lateral CXRs by constructing an ensemble of the CNN and ViT models. Several models are trained on lateral CXR data extracted from two large public collections to transfer modality-specific knowledge and fine-tune them for detecting findings consistent with TB. We observed that the weighted averaging ensemble of the predictions of CNN and ViT models using the optimal weights computed with the Sequential Least-Squares Quadratic Programming method delivered significantly superior performance (MCC: 0.8136, 95% confidence intervals (CI): 0.7394, 0.8878, p < 0.05) compared to the individual models and other ensembles. We also interpreted the decisions of CNN and ViT models using class-selective relevance maps and attention maps, respectively, and combined them to highlight the discriminative image regions contributing to the final output. We observed that (i) the model accuracy is not related to disease region of interest (ROI) localization and (ii) the bitwise-AND of the heatmaps of the top-2-performing models delivered significantly superior ROI localization performance in terms of mean average precision [mAP@(0.1 0.6) = 0.1820, 95% CI: 0.0771,0.2869, p < 0.05], compared to other individual models and ensembles. The code is available at https://github.com/sivaramakrishnan-rajaraman/Ensemble-of-CNN-and-ViT-for-TB-detection-in-lateral-CXR.
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Affiliation(s)
- Sivaramakrishnan Rajaraman
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Sivaramakrishnan Rajaraman,
| | - Ghada Zamzmi
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
| | | | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD, United States
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Baur B, Lee DI, Haag J, Chasman D, Gould M, Roy S. Deciphering the Role of 3D Genome Organization in Breast Cancer Susceptibility. Front Genet 2022; 12:788318. [PMID: 35087569 PMCID: PMC8787344 DOI: 10.3389/fgene.2021.788318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 12/21/2021] [Indexed: 11/25/2022] Open
Abstract
Cancer risk by environmental exposure is modulated by an individual's genetics and age at exposure. This age-specific period of susceptibility is referred to as the "Window of Susceptibility" (WOS). Rats have a similar WOS for developing breast cancer. A previous study in rat identified an age-specific long-range regulatory interaction for the cancer gene, Pappa, that is associated with breast cancer susceptibility. However, the global role of three-dimensional genome organization and downstream gene expression programs in the WOS is not known. Therefore, we generated Hi-C and RNA-seq data in rat mammary epithelial cells within and outside the WOS. To systematically identify higher-order changes in 3D genome organization, we developed NE-MVNMF that combines network enhancement followed by multitask non-negative matrix factorization. We examined three-dimensional genome organization dynamics at the level of individual loops as well as higher-order domains. Differential chromatin interactions tend to be associated with differentially up-regulated genes with the WOS and recapitulate several human SNP-gene interactions associated with breast cancer susceptibility. Our approach identified genomic blocks of regions with greater overall differences in contact count between the two time points when the cluster assignments change and identified genes and pathways implicated in early carcinogenesis and cancer treatment. Our results suggest that WOS-specific changes in 3D genome organization are linked to transcriptional changes that may influence susceptibility to breast cancer.
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Affiliation(s)
- Brittany Baur
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
| | - Da-Inn Lee
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
| | - Jill Haag
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
| | - Michael Gould
- McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, WI, United States
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
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Roark C, Wilson MP, Kubes S, Mayer D, Wiley LK. Assessing the utility and accuracy of ICD10-CM non-traumatic subarachnoid hemorrhage codes for intracranial aneurysm research. Learn Health Syst 2021; 5:e10257. [PMID: 34667877 PMCID: PMC8512725 DOI: 10.1002/lrh2.10257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 12/16/2020] [Accepted: 12/23/2020] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The 10th revision of International Classification of Disease, Clinical Modification (ICD10-CM) increased the number of codes to identify non-traumatic subarachnoid hemorrhage from 1 to 22. ICD10-CM codes are able to specify the location of aneurysms causing subarachnoid hemorrhage (aSAH); however, it is not clear how frequently or accurately these codes are being used in practice. OBJECTIVE To systematically evaluate the usage and accuracy of location-specific ICD10-CM codes for aSAH. METHODS We extracted all uses of ICD10-CM codes for non-traumatic subarachnoid hemorrhage (I60.x) during the first 3 years following the implementation of ICD10-CM from the billing module of the electronic health record (EHR) for UCHealth. For those codes that specified aSAH location (I60.0-I60.6), EHR documentation was reviewed to determine whether there was an active aSAH, any patient history of aSAH, or unruptured intracranial aneurysm/s and the locations of those outcomes. RESULTS Between 1 October 2015 and 30 September 2018, there were 3119 instances of non-traumatic subarachnoid hemorrhage ICD10-CM codes (I60.00-I60.9), of which 297 (9.5%) code instances identified aSAH location (I60.0-I60.6). The usage of location-specific codes increased from 5.7% in 2015 to 11.2% in 2018. These codes accurately identified current aSAH (64%), any patient history of aSAH (84%), and any patient history of intracranial aneurysm (87%). The accuracy of identified outcome location was 53% in current aSAH, 72% for any history of aSAH, and 76% for any history of an intracranial aneurysm. CONCLUSIONS Researchers should use ICD10-CM codes with caution when attempting to detect active aSAH and/or aneurysm location.
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Affiliation(s)
- Christopher Roark
- University of Colorado Anschutz Medical CampusDepartment of NeurosurgeryAuroraColoradoUSA
| | - Melissa P. Wilson
- University of Colorado Anschutz Medical CampusDivision of Biomedical Informatics and Personalized Medicine, Department of MedicineAuroraColoradoUSA
| | - Sheila Kubes
- University of Colorado Anschutz Medical CampusDepartment of NeurosurgeryAuroraColoradoUSA
| | - David Mayer
- University of Colorado Anschutz Medical CampusDivision of Biomedical Informatics and Personalized Medicine, Department of MedicineAuroraColoradoUSA
| | - Laura K. Wiley
- University of Colorado Anschutz Medical CampusDivision of Biomedical Informatics and Personalized Medicine, Department of MedicineAuroraColoradoUSA
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