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Hamedani AG, Kim DS, Chaitanuwong P, Gonzalez LA, Moss HE, DeLott LB. Validity of Administrative Coding for Nonarteritic Ischemic Optic Neuropathy. J Neuroophthalmol 2024:00041327-990000000-00639. [PMID: 38706093 DOI: 10.1097/wno.0000000000002163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
BACKGROUND Administrative claims have been used to study the incidence and outcomes of nonarteritic ischemic optic neuropathy (NAION), but the validity of International Classification of Diseases (ICD)-10 codes for identifying NAION has not been examined. METHODS We identified patients at 3 academic centers who received ≥1 ICD-10 code for NAION in 2018. We abstracted the final diagnosis from clinical documentation and recorded the number of visits with an NAION diagnosis code. We calculated positive predictive value (PPV) for the overall sample and stratified by subspecialty and the number of diagnosis codes. For patients with ophthalmology or neuro-ophthalmology visit data, we recorded presenting symptoms, examination findings, and laboratory data and calculated PPV relative to case definitions of NAION that incorporated sudden onset of symptoms, optic disc edema, afferent pupillary defect, and other characteristics. RESULTS Among 161 patients, PPV for ≥1 ICD-10 code was 74.5% (95% CI: 67.2%-80.7%). PPV was similar when restricted to patients who had visited an ophthalmologist (75.8%, 95% CI: 68.4%-82.0%) but increased to 86.8% when restricted to those who had visited neuro-ophthalmologists (95% CI: 79.2%-91.9%). Of 113 patients with >1 ICD-10 code and complete examination data, 37 (32.7%) had documented sudden onset, optic disc swelling, and an afferent pupillary defect (95% CI: 24.7%-42.0%). Of the 76 patients who did not meet these criteria, 54 (71.0%) still received a final clinical diagnosis of NAION; for most (41/54, 75.9%), this discrepancy was due to lack of documented optic disc edema. CONCLUSIONS The validity of ICD-10 codes for NAION in administrative claims data is high, particularly when combined with provider specialty.
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Affiliation(s)
- Ali G Hamedani
- Departments of Neurology, Ophthalmology, and Epidemiology (AGH, DK), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Ophthalmology Department (PC), Rajavithi Hospital, Ministry of Public Health, and Department of Ophthalmology, Faculty of Medicine, Rangsit University, Bangkok, Thailand; Department of Ophthalmology (LAG), Ohio State College of Medicine, Ohio State University, Columbus, Ohio; Department of Ophthalmology and Neurology and Neurological Sciences (HEM), Stanford University School of Medicine, Palo Alto, California; Departments of Ophthalmology and Visual Sciences and Neurology (LD), University of Michigan, Ann Arbor, Michigan
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Dai G, Ma Y, Hasler J, Chen J, Carroll RJ. A robust approach for electronic health record-based case-control studies with contaminated case pools. Biometrics 2023; 79:2023-2035. [PMID: 35841231 PMCID: PMC9841064 DOI: 10.1111/biom.13721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 07/11/2022] [Indexed: 01/18/2023]
Abstract
We consider analyses of case-control studies assembled from electronic health records (EHRs) where the pool of cases is contaminated by patients who are ineligible for the study. These ineligible patients, referred to as "false cases," should be excluded from the analyses if known. However, the true outcome status of a patient in the case pool is unknown except in a subset whose size may be arbitrarily small compared to the entire pool. To effectively remove the influence of the false cases on estimating odds ratio parameters defined by a working association model of the logistic form, we propose a general strategy to adaptively impute the unknown case status without requiring a correct phenotyping model to help discern the true and false case statuses. Our method estimates the target parameters as the solution to a set of unbiased estimating equations constructed using all available data. It outperforms existing methods by achieving robustness to mismodeling the relationship between the outcome status and covariates of interest, as well as improved estimation efficiency. We further show that our estimator is root-n-consistent and asymptotically normal. Through extensive simulation studies and analysis of real EHR data, we demonstrate that our method has desirable robustness to possible misspecification of both the association and phenotyping models, along with statistical efficiency superior to the competitors.
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Affiliation(s)
- Guorong Dai
- Department of Statistics and Data Science, School of Management, Fudan University, Shanghai 200433, China
| | - Yanyuan Ma
- Department of Statistics, Pennsylvania State University, University Park, PA 16802, USA
| | - Jill Hasler
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raymond J. Carroll
- Department of Statistics, Texas A&M University, College Station, TX 77843, USA
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Penrod N, Okeh C, Velez Edwards DR, Barnhart K, Senapati S, Verma SS. Leveraging electronic health record data for endometriosis research. Front Digit Health 2023; 5:1150687. [PMID: 37342866 PMCID: PMC10278662 DOI: 10.3389/fdgth.2023.1150687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 05/10/2023] [Indexed: 06/23/2023] Open
Abstract
Endometriosis is a chronic, complex disease for which there are vast disparities in diagnosis and treatment between sociodemographic groups. Clinical presentation of endometriosis can vary from asymptomatic disease-often identified during (in)fertility consultations-to dysmenorrhea and debilitating pelvic pain. Because of this complexity, delayed diagnosis (mean time to diagnosis is 1.7-3.6 years) and misdiagnosis is common. Early and accurate diagnosis of endometriosis remains a research priority for patient advocates and healthcare providers. Electronic health records (EHRs) have been widely adopted as a data source in biomedical research. However, they remain a largely untapped source of data for endometriosis research. EHRs capture diverse, real-world patient populations and care trajectories and can be used to learn patterns of underlying risk factors for endometriosis which, in turn, can be used to inform screening guidelines to help clinicians efficiently and effectively recognize and diagnose the disease in all patient populations reducing inequities in care. Here, we provide an overview of the advantages and limitations of using EHR data to study endometriosis. We describe the prevalence of endometriosis observed in diverse populations from multiple healthcare institutions, examples of variables that can be extracted from EHRs to enhance the accuracy of endometriosis prediction, and opportunities to leverage longitudinal EHR data to improve our understanding of long-term health consequences for all patients.
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Affiliation(s)
- Nadia Penrod
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Chelsea Okeh
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, United States
| | - Digna R. Velez Edwards
- Department of Obstetrics and Gynecology, Vanderbilt University, Nashville, TN, United States
| | - Kurt Barnhart
- Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Suneeta Senapati
- Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Shefali S. Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Philadelphia, PA, United States
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Verspoor K. The Evolution of Clinical Knowledge During COVID-19: Towards a Global Learning Health System. Yearb Med Inform 2021; 30:176-184. [PMID: 34479389 PMCID: PMC8416229 DOI: 10.1055/s-0041-1726503] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVES We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases. METHODS AND RESULTS We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system. CONCLUSIONS The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.
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Affiliation(s)
- Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne VIC 3000 Australia
- Centre for Digital Transformation of Health, The University of Melbourne, Melbourne VIC 3010 Australia
- School of Computing and Information Systems, The University of Melbourne, Melbourne VIC 3010 Australia
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Zhang L, Ma Y, Herman D, Chen J. Testing calibration of phenotyping models using positive-only electronic health record data. Biostatistics 2021; 23:844-859. [PMID: 33616157 DOI: 10.1093/biostatistics/kxab003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 12/31/2020] [Accepted: 01/04/2021] [Indexed: 11/14/2022] Open
Abstract
Validation of phenotyping models using Electronic Health Records (EHRs) data conventionally requires gold-standard case and control labels. The labeling process requires clinical experts to retrospectively review patients' medical charts, therefore is labor intensive and time consuming. For some disease conditions, it is prohibitive to identify the gold-standard controls because routine clinical assessments are performed for selective patients who are deemed to possibly have the condition. To build a model for phenotyping patients in EHRs, the most readily accessible data are often for a cohort consisting of a set of gold-standard cases and a large number of unlabeled patients. Hereby, we propose methods for assessing model calibration and discrimination using such "positive-only" EHR data that does not require gold-standard controls, provided that the labeled cases are representative of all cases. For model calibration, we propose a novel statistic that aggregates differences between model-free and model-based estimated numbers of cases across risk subgroups, which asymptotically follows a Chi-squared distribution. We additionally demonstrate that the calibration slope can also be estimated using such "positive-only" data. We propose consistent estimators for discrimination measures and derive their large sample properties. We demonstrate performances of the proposed methods through extensive simulation studies and apply them to Penn Medicine EHRs to validate two preliminary models for predicting the risk of primary aldosteronism.
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Affiliation(s)
- Lingjiao Zhang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yanyuan Ma
- Department of Statistics, Penn State University, University Park, PA 16802, USA
| | - Daniel Herman
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
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Schnall J, Zhang L, Chen J. Phenotyping issues for exploring electronic health records to design clinical trials. Clin Trials 2020; 17:402-404. [PMID: 32522027 DOI: 10.1177/1740774520931039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
For utilizing electronic health records to help design and conduct clinical trials, an essential first step is to select eligible patients from electronic health records, that is, electronic health record phenotyping. We present two novel statistical methods that can be used in the context of electronic health record phenotyping. One mitigates the requirement for gold-standard control patients in developing phenotyping algorithms, and the other effectively corrects for bias in downstream analysis introduced by study samples contaminated by ineligible subjects.
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Affiliation(s)
- Jill Schnall
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - LingJiao Zhang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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