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Higgins BE, Leonard-Hawkhead B, Azuara-Blanco A. Quality of Reporting Electronic Health Record Data in Glaucoma: A Systematic Literature Review. Ophthalmol Glaucoma 2024:S2589-4196(24)00064-4. [PMID: 38599318 DOI: 10.1016/j.ogla.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 03/05/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
TOPIC Assessing Reporting Standards in Glaucoma Studies Utilizing Electronic Health Records (EHR) CLINICAL RELEVANCE: Glaucoma's significance, underscored by its status as a leading cause of irreversible blindness worldwide, necessitates reliable research findings. This study evaluates adherence to the CODE-EHR Framework in glaucoma studies using EHR, aiming to improve clinical care and patient outcomes. METHODS A systematic review, following PRISMA guidelines (PROSPERO CRD42023430025), identified relevant studies (January 2022-May 2023) in MEDLINE, EMBASE, CINAHL, and Web of Science. Eligible studies, using EHR data from clinical institutions for glaucoma research, were assessed for study design, participant characteristics, EHR data, and sources. Quality appraisal used the CODE-EHR Framework, focusing on data construction, linkage, fitness for purpose, disease and outcome definitions, analysis, and ethics and governance. RESULTS Of 31 identified studies, predominant EHR sources were hospitals and clinical warehouses. Commonly reported elements included age, gender, glaucoma diagnosis, and intraocular pressure. Only 16% fully adhered to CODE-EHR Framework's minimum standards, with none meeting preferred standards. While statistical analysis and ethical considerations were relatively well-addressed, areas such as EHR data management and study design showed room for improvement. Patient and public involvement, and acknowledgment of data linkage processes, data security and storage reporting were often missed. CONCLUSION Adherence to CODE-EHR Framework's standards in EHR-based studies of glaucoma can be improved upon. Standardised reporting of EHR data is essential to ensure the reliability of research, facilitating its translation into clinical practice and improving healthcare decision-making for better patient outcomes.
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Affiliation(s)
- Bethany E Higgins
- Centre for Public Health, Institute of Clinical Science Block A, Royal Victoria Hospital, Belfast BT12 6BA; Optometry and Visual Sciences, School of Health & Psychological Sciences, City, University of London, Northampton Square, London, EC1V 0HB.
| | - Benedict Leonard-Hawkhead
- Centre for Public Health, Institute of Clinical Science Block A, Royal Victoria Hospital, Belfast BT12 6BA
| | - Augusto Azuara-Blanco
- Centre for Public Health, Institute of Clinical Science Block A, Royal Victoria Hospital, Belfast BT12 6BA
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Nealon CL, Halladay CW, Gorman BR, Simpson P, Roncone DP, Canania RL, Anthony SA, Rogers LRS, Leber JN, Dougherty JM, Bailey JNC, Crawford DC, Sullivan JM, Galor A, Wu WC, Greenberg PB, Lass JH, Iyengar SK, Peachey NS. Association Between Fuchs Endothelial Corneal Dystrophy, Diabetes Mellitus, and Multimorbidity. Cornea 2023; 42:1140-1149. [PMID: 37170406 PMCID: PMC10523841 DOI: 10.1097/ico.0000000000003311] [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: 12/09/2022] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
PURPOSE The aim of this study was to assess risk for demographic variables and other health conditions that are associated with Fuchs endothelial corneal dystrophy (FECD). METHODS We developed a FECD case-control algorithm based on structured electronic health record data and confirmed accuracy by individual review of charts at 3 Veterans Affairs (VA) Medical Centers. This algorithm was applied to the Department of VA Million Veteran Program cohort from whom sex, genetic ancestry, comorbidities, diagnostic phecodes, and laboratory values were extracted. Single-variable and multiple variable logistic regression models were used to determine the association of these risk factors with FECD diagnosis. RESULTS Being a FECD case was associated with female sex, European genetic ancestry, and a greater number of comorbidities. Of 1417 diagnostic phecodes evaluated, 213 had a significant association with FECD, falling in both ocular and nonocular conditions, including diabetes mellitus (DM). Five of 69 laboratory values were associated with FECD, with the direction of change for 4 being consistent with DM. Insulin dependency and type 1 DM raised risk to a greater degree than type 2 DM, like other microvascular diabetic complications. CONCLUSIONS Female sex, European ancestry, and multimorbidity increased FECD risk. Endocrine/metabolic clinic encounter codes and altered patterns of laboratory values support DM increasing FECD risk. Our results evoke a threshold model in which the FECD phenotype is intensified by DM and potentially other health conditions that alter corneal physiology. Further studies to better understand the relationship between FECD and DM are indicated and may help identify opportunities for slowing FECD progression.
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Affiliation(s)
- Cari L. Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Christopher W. Halladay
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, Rhode Island, USA
| | - Bryan R. Gorman
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, Massachusetts
- Booz Allen Hamilton, McLean, Virginia, USA
| | - Piana Simpson
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - David P. Roncone
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | | | - Scott A. Anthony
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | | | - Jenna N. Leber
- Ophthalmology Section, VA Western NY Health Care System, Buffalo, New York, USA
| | | | - Jessica N. Cooke Bailey
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Jack M. Sullivan
- Ophthalmology Section, VA Western NY Health Care System, Buffalo, New York, USA
- Research Service, VA Western NY Health Care System, Buffalo, New York, USA
- Department of Ophthalmology (Ross Eye Institute), University at Buffalo-SUNY, Buffalo, New York, USA
| | - Anat Galor
- Miami Veterans Affairs Medical Center, Miami, Florida, USA
- Bascom Palmer Eye Institute, University of Miami, Miami, Florida, USA
| | - Wen-Chih Wu
- Cardiology Section, Medical Service, Providence VA Medical Center, Providence, Rhode Island, USA
| | - Paul B. Greenberg
- Ophthalmology Section, Providence VA Medical Center, Providence, Rhode Island, USA
- Division of Ophthalmology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | | | - Jonathan H. Lass
- Department of Ophthalmology & Visual Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- University Hospitals Eye Institute, Cleveland, Ohio, USA
| | - Sudha K. Iyengar
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
| | - Neal S. Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, USA
- Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, Ohio, USA
- Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
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Waksmunski AR, Kinzy TG, Cruz LA, Nealon CL, Halladay CW, Anthony SA, Greenberg PB, Sullivan JM, Wu WC, Iyengar SK, Crawford DC, Peachey NS, Cooke Bailey JN. Diversity is key for cross-ancestry transferability of glaucoma genetic risk scores in Hispanic Veterans in the Million Veteran Program. Pac Symp Biocomput 2023; 28:413-424. [PMID: 36540996 PMCID: PMC9997528] [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] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
A major goal of precision medicine is to stratify patients based on their genetic risk for a disease to inform future screening and intervention strategies. For conditions like primary open-angle glaucoma (POAG), the genetic risk architecture is complicated with multiple variants contributing small effects on risk. Following the tepid success of genome-wide association studies for high-effect disease risk variant discovery, genetic risk scores (GRS), which collate effects from multiple genetic variants into a single measure, have shown promise for disease risk stratification. We assessed the application of GRS for POAG risk stratification in Hispanic-descent (HIS) and European-descent (EUR) Veterans in the Million Veteran Program. Unweighted and cross-ancestry meta-weighted GRS were calculated based on 127 genomic variants identified in the most recent report of cross-ancestry POAG meta-analyses. We found that both GRS types were associated with POAG case-control status and performed similarly in HIS and EUR Veterans. This trend was also seen in our subset analysis of HIS Veterans with less than 50% EUR global genetic ancestry. Our findings highlight the importance of evaluating GRS based on known POAG risk variants in different ancestry groups and emphasize the need for more multi-ancestry POAG genetic studies.
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Affiliation(s)
- Andrea R Waksmunski
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road, Cleveland, OH 44106, USA,
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Waksmunski AR, Kinzy TG, Cruz LA, Nealon CL, Halladay CW, Simpson P, Canania RL, Anthony SA, Roncone DP, Sawicki Rogers L, Leber JN, Dougherty JM, Greenberg PB, Sullivan JM, Wu WC, Iyengar SK, Crawford DC, Peachey NS, Cooke Bailey JN. Glaucoma Genetic Risk Scores in the Million Veteran Program. Ophthalmology 2022; 129:1263-1274. [PMID: 35718050 PMCID: PMC9997524 DOI: 10.1016/j.ophtha.2022.06.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [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: 12/03/2021] [Revised: 06/07/2022] [Accepted: 06/09/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Primary open-angle glaucoma (POAG) is a degenerative eye disease for which early treatment is critical to mitigate visual impairment and irreversible blindness. POAG-associated loci individually confer incremental risk. Genetic risk score(s) (GRS) could enable POAG risk stratification. Despite significantly higher POAG burden among individuals of African ancestry (AFR), GRS are limited in this population. A recent large-scale, multi-ancestry meta-analysis identified 127 POAG-associated loci and calculated cross-ancestry and ancestry-specific effect estimates, including in European ancestry (EUR) and AFR individuals. We assessed the utility of the 127-variant GRS for POAG risk stratification in EUR and AFR Veterans in the Million Veteran Program (MVP). We also explored the association between GRS and documented invasive glaucoma surgery (IGS). DESIGN Cross-sectional study. PARTICIPANTS MVP Veterans with imputed genetic data, including 5830 POAG cases (445 with IGS documented in the electronic health record) and 64 476 controls. METHODS We tested unweighted and weighted GRS of 127 published risk variants in EUR (3382 cases and 58 811 controls) and AFR (2448 cases and 5665 controls) Veterans in the MVP. Weighted GRS were calculated using effect estimates from the most recently published report of cross-ancestry and ancestry-specific meta-analyses. We also evaluated GRS in POAG cases with documented IGS. MAIN OUTCOME MEASURES Performance of 127-variant GRS in EUR and AFR Veterans for POAG risk stratification and association with documented IGS. RESULTS GRS were significantly associated with POAG (P < 5 × 10-5) in both groups; a higher proportion of EUR compared with AFR were consistently categorized in the top GRS decile (21.9%-23.6% and 12.9%-14.5%, respectively). Only GRS weighted by ancestry-specific effect estimates were associated with IGS documentation in AFR cases; all GRS types were associated with IGS in EUR cases. CONCLUSIONS Varied performance of the GRS for POAG risk stratification and documented IGS association in EUR and AFR Veterans highlights (1) the complex risk architecture of POAG, (2) the importance of diverse representation in genomics studies that inform GRS construction and evaluation, and (3) the necessity of expanding diverse POAG-related genomic data so that GRS can equitably aid in screening individuals at high risk of POAG and who may require more aggressive treatment.
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Affiliation(s)
- Andrea R Waksmunski
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Tyler G Kinzy
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio; Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - Lauren A Cruz
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Cari L Nealon
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - Christopher W Halladay
- Center of Innovation in Long Term Services and Supports, Providence VA Medical Center, Providence, Rhode Island
| | - Piana Simpson
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | | | - Scott A Anthony
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - David P Roncone
- Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - Lea Sawicki Rogers
- Ophthalmology Section, VA Western NY Healthcare System, Buffalo, New York
| | - Jenna N Leber
- Ophthalmology Section, VA Western NY Healthcare System, Buffalo, New York
| | | | - Paul B Greenberg
- Ophthalmology Section, Providence VA Medical Center, Providence, Rhode Island; Division of Ophthalmology, Alpert Medical School, Brown University, Providence, Rhode Island
| | - Jack M Sullivan
- Ophthalmology Section, VA Western NY Healthcare System, Buffalo, New York; Research Service, VA Western NY Healthcare System, Buffalo, New York
| | - Wen-Chih Wu
- Cardiology Section, Medical Service, Providence VA Medical Center, Providence, Rhode Island
| | - Sudha K Iyengar
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio; Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - Dana C Crawford
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio; Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio
| | - Neal S Peachey
- Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio; Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, Ohio; Department of Ophthalmology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio
| | - Jessica N Cooke Bailey
- Cleveland Institute for Computational Biology, Case Western Reserve University School of Medicine, Cleveland, Ohio; Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio; Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio.
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