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Matthewman J, Andresen K, Suffel A, Lin LY, Schultze A, Tazare J, Bhaskaran K, Williamson E, Costello R, Quint J, Strongman H. Checklist and guidance on creating codelists for routinely collected health data research. NIHR OPEN RESEARCH 2024; 4:20. [PMID: 39345273 PMCID: PMC11437289 DOI: 10.3310/nihropenres.13550.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/17/2024] [Indexed: 10/01/2024]
Abstract
Background Codelists are required to extract meaningful information on characteristics and events from routinely collected health data such as electronic health records. Research using routinely collected health data relies on codelists to define study populations and variables, thus, trustworthy codelists are important. Here, we provide a checklist, in the style of commonly used reporting guidelines, to help researchers adhere to best practice in codelist development and sharing. Methods Based on a literature search and a workshop with researchers experienced in the use of routinely collected health data, we created a set of recommendations that are 1. broadly applicable to different datasets, research questions, and methods of codelist creation; 2. easy to follow, implement and document by an individual researcher, and 3. fit within a step-by-step process. We then formatted these recommendations into a checklist. Results We have created a 10-step checklist, comprising 28 items, with accompanying guidance on each step. The checklist advises on which metadata to provide, how to define a clinical concept, how to identify and evaluate existing codelists, how to create new codelists, and how to review, check, finalise, and publish a created codelist. Conclusions Use of the checklist can reassure researchers that best practice was followed during the development of their codelists, increasing trust in research that relies on these codelists and facilitating wider re-use and adaptation by other researchers.
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Affiliation(s)
- Julian Matthewman
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - Kirsty Andresen
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - Anne Suffel
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - Liang-Yu Lin
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - Anna Schultze
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - John Tazare
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | - Krishnan Bhaskaran
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | | | - Ruth Costello
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
| | | | - Helen Strongman
- London School of Hygiene & Tropical Medicine, London, England, WC1E 7HT, UK
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Elson WH, Jamie G, Wimalaratna R, Forbes A, Leston M, Okusi C, Byford R, Agrawal U, Todkill D, Elliot AJ, Watson C, Zambon M, Morbey R, Lopez Bernal J, Hobbs FR, de Lusignan S. Validation of an acute respiratory infection phenotyping algorithm to support robust computerised medical record-based respiratory sentinel surveillance, England, 2023. Euro Surveill 2024; 29. [PMID: 39212059 DOI: 10.2807/1560-7917.es.2024.29.35.2300682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
IntroductionRespiratory sentinel surveillance systems leveraging computerised medical records (CMR) use phenotyping algorithms to identify cases of interest, such as acute respiratory infection (ARI). The Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) is the English primary care-based sentinel surveillance network.AimThis study describes and validates the RSC's new ARI phenotyping algorithm.MethodsWe developed the phenotyping algorithm using a framework aligned with international interoperability standards. We validated our algorithm by comparing ARI events identified during the 2022/23 influenza season in England through use of both old and new algorithms. We compared clinical codes commonly used for recording ARI.ResultsThe new algorithm identified an additional 860,039 cases and excluded 52,258, resulting in a net increase of 807,781 cases (33.84%) of ARI compared to the old algorithm, with totals of 3,194,224 cases versus 2,386,443 cases. Of the 860,039 newly identified cases, the majority (63.7%) were due to identification of symptom codes suggestive of an ARI diagnosis not detected by the old algorithm. The 52,258 cases incorrectly identified by the old algorithm were due to inadvertent identification of chronic, recurrent, non-infectious and other non-ARI disease.ConclusionWe developed a new ARI phenotyping algorithm that more accurately identifies cases of ARI from the CMR. This will benefit public health by providing more accurate surveillance reports to public health authorities. This new algorithm can serve as a blueprint for other CMR-based surveillance systems wishing to develop similar phenotyping algorithms.
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Affiliation(s)
- William H Elson
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Gavin Jamie
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rashmi Wimalaratna
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Anna Forbes
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- Renal services, Epsom and St. Helier University Hospitals NHS Trust, London, United Kingdom
| | - Meredith Leston
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Cecilia Okusi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rachel Byford
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Utkarsh Agrawal
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Dan Todkill
- Real-time Syndromic Surveillance Team, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | - Alex J Elliot
- Real-time Syndromic Surveillance Team, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | - Conall Watson
- Immunisation and Vaccine-Preventable Diseases Division, United Kingdom Health Security Agency, London, United Kingdom
| | - Maria Zambon
- Reference Microbiology, United Kingdom Health Security Agency, London, United Kingdom
| | - Roger Morbey
- Real-time Syndromic Surveillance Team, United Kingdom Health Security Agency, Birmingham, United Kingdom
| | - Jamie Lopez Bernal
- Immunisation and Vaccine-Preventable Diseases Division, United Kingdom Health Security Agency, London, United Kingdom
| | - Fd Richard Hobbs
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Vadhan JD, Thoppil J, Vasquez O, Suarez A, Bartels B, McDonald S, Courtney DM, Farrar JD, Thakur B. Primary Infection Site as a Predictor of Sepsis Development in Emergency Department Patients. J Emerg Med 2024; 67:e128-e137. [PMID: 38849253 DOI: 10.1016/j.jemermed.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/20/2023] [Accepted: 01/06/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Sepsis is a life-threatening condition but predicting its development and progression remains a challenge. OBJECTIVE This study aimed to assess the impact of infection site on sepsis development among emergency department (ED) patients. METHODS Data were collected from a single-center ED between January 2016 and December 2019. Patient encounters with documented infections, as defined by the Systematized Nomenclature of Medicine-Clinical Terms for upper respiratory tract (URI), lower respiratory tract (LRI), urinary tract (UTI), or skin or soft-tissue infections were included. Primary outcome was the development of sepsis or septic shock, as defined by Sepsis-1/2 criteria. Secondary outcomes included hospital disposition and length of stay, blood and urine culture positivity, antibiotic administration, vasopressor use, in-hospital mortality, and 30-day mortality. Analysis of variance and various different logistic regression approaches were used for analysis with URI used as the reference variable. RESULTS LRI was most associated with sepsis (relative risk ratio [RRR] 5.63; 95% CI 5.07-6.24) and septic shock (RRR 21.2; 95% CI 17.99-24.98) development, as well as hospital admission rates (odds ratio [OR] 8.23; 95% CI 7.41-9.14), intensive care unit admission (OR 4.27; 95% CI 3.84-4.74), in-hospital mortality (OR 6.93; 95% CI 5.60-8.57), and 30-day mortality (OR 7.34; 95% CI 5.86-9.19). UTIs were also associated with sepsis and septic shock development, but to a lesser degree than LRI. CONCLUSIONS Primary infection sites including LRI and UTI were significantly associated with sepsis development, hospitalization, length of stay, and mortality among patients presenting with infections in the ED.
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Affiliation(s)
- Jason D Vadhan
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Joby Thoppil
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Ofelia Vasquez
- School of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Arlen Suarez
- School of Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Brett Bartels
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Samuel McDonald
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - D Mark Courtney
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - J David Farrar
- Department of Immunology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Bhaskar Thakur
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Department of Family Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
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Jamie G, Elson W, Kar D, Wimalaratna R, Hoang U, Meza-Torres B, Forbes A, Hinton W, Anand S, Ferreira F, Byford R, Ordonez-Mena J, Agrawal U, de Lusignan S. Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation. JAMIA Open 2024; 7:ooae034. [PMID: 38737141 PMCID: PMC11087727 DOI: 10.1093/jamiaopen/ooae034] [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: 01/04/2024] [Revised: 04/02/2024] [Accepted: 05/02/2024] [Indexed: 05/14/2024] Open
Abstract
Objective To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes. Method We curated 3 phenotypes: Type 2 diabetes mellitus (T2DM), excessive alcohol use, and incident influenza-like illness (ILI) using CQL to define clinical and administrative logic. We defined our phenotypes with valuesets, using SNOMED's hierarchy and expression constraint language, and CQL, combining valuesets and adding temporal elements where needed. We compared the count of cases found using PhEMA with our existing approach using convenience datasets. We assessed our new approach against published desiderata for phenotypes. Results The T2DM phenotype could be defined as 2 intensionally defined SNOMED valuesets and a CQL script. It increased the prevalence from 7.2% to 7.3%. Excess alcohol phenotype was defined by valuesets that added qualitative clinical terms to the quantitative conceptual definitions we currently use; this change increased prevalence by 58%, from 1.2% to 1.9%. We created an ILI valueset with SNOMED concepts, adding a temporal element using CQL to differentiate new episodes. This increased the weekly incidence in our convenience sample (weeks 26-38) from 0.95 cases to 1.11 cases per 100 000 people. Conclusions Phenotypes for surveillance and research can be described fully and comprehensibly using CQL and intensional FHIR valuesets. Our use case phenotypes identified a greater number of cases, whilst anticipated from excessive alcohol this was not for our other variable. This may have been due to our use of SNOMED CT hierarchy. Our new process fulfilled a greater number of phenotype desiderata than the one that we had used previously, mostly in the modeling domain. More work is needed to implement that sharing and warehousing domains.
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Affiliation(s)
- Gavin Jamie
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - William Elson
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Debasish Kar
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Rashmi Wimalaratna
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Uy Hoang
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Bernardo Meza-Torres
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Anna Forbes
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - William Hinton
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Sneha Anand
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Filipa Ferreira
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Rachel Byford
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Jose Ordonez-Mena
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Utkarsh Agrawal
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
| | - Simon de Lusignan
- Nuffield Department of Primary Health Care Sciences, University of Oxford, Oxford, OX2 6ED, United Kingdom
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Senior R, Tsai T, Ratliff W, Nadler L, Balu S, Malcolm E, McPeek Hinz E. Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study. JMIR Med Inform 2024; 12:e51274. [PMID: 38836556 DOI: 10.2196/51274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/01/2023] [Accepted: 02/22/2024] [Indexed: 06/06/2024] Open
Abstract
Background The problem list (PL) is a repository of diagnoses for patients' medical conditions and health-related issues. Unfortunately, over time, our PLs have become overloaded with duplications, conflicting entries, and no-longer-valid diagnoses. The lack of a standardized structure for review adds to the challenges of clinical use. Previously, our default electronic health record (EHR) organized the PL primarily via alphabetization, with other options available, for example, organization by clinical systems or priority settings. The system's PL was built with limited groupers, resulting in many diagnoses that were inconsistent with the expected clinical systems or not associated with any clinical systems at all. As a consequence of these limited EHR configuration options, our PL organization has poorly supported clinical use over time, particularly as the number of diagnoses on the PL has increased. Objective We aimed to measure the accuracy of sorting PL diagnoses into PL system groupers based on Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) concept groupers implemented in our EHR. Methods We transformed and developed 21 system- or condition-based groupers, using 1211 SNOMED CT hierarchal concepts refined with Boolean logic, to reorganize the PL in our EHR. To evaluate the clinical utility of our new groupers, we extracted all diagnoses on the PLs from a convenience sample of 50 patients with 3 or more encounters in the previous year. To provide a spectrum of clinical diagnoses, we included patients from all ages and divided them by sex in a deidentified format. Two physicians independently determined whether each diagnosis was correctly attributed to the expected clinical system grouper. Discrepancies were discussed, and if no consensus was reached, they were adjudicated by a third physician. Descriptive statistics and Cohen κ statistics for interrater reliability were calculated. Results Our 50-patient sample had a total of 869 diagnoses (range 4-59; median 12, IQR 9-24). The reviewers initially agreed on 821 system attributions. Of the remaining 48 items, 16 required adjudication with the tie-breaking third physician. The calculated κ statistic was 0.7. The PL groupers appropriately associated diagnoses to the expected clinical system with a sensitivity of 97.6%, a specificity of 58.7%, a positive predictive value of 96.8%, and an F1-score of 0.972. Conclusions We found that PL organization by clinical specialty or condition using SNOMED CT concept groupers accurately reflects clinical systems. Our system groupers were subsequently adopted by our vendor EHR in their foundation system for PL organization.
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Affiliation(s)
- Rashaud Senior
- Duke University Health System, Durham, NC, United States
| | - Timothy Tsai
- Division of Primary Care and Population Health, Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - William Ratliff
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Lisa Nadler
- Duke University Health System, Durham, NC, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Elizabeth Malcolm
- Division of General Internal Medicine, Duke University School of Medicine, Durham, NC, United States
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De Monnin KS, Terian E, Yeary J, Bathon E, Asaro P, Mintz CM, Baumgartner K. Emergency department initiation of pharmacotherapy for alcohol use disorder: A retrospective cohort study. Acad Emerg Med 2024; 31:525-528. [PMID: 37822078 PMCID: PMC11006820 DOI: 10.1111/acem.14819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/19/2023] [Accepted: 10/06/2023] [Indexed: 10/13/2023]
Affiliation(s)
- Karlee S De Monnin
- Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Emily Terian
- Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Julianne Yeary
- Barnes-Jewish Hospital, Charles F. Knight Emergency and Trauma Center, St. Louis, Missouri, USA
| | - Elizabeth Bathon
- Barnes-Jewish Hospital, Charles F. Knight Emergency and Trauma Center, St. Louis, Missouri, USA
| | - Phillip Asaro
- Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Carrie M Mintz
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Kevin Baumgartner
- Department of Emergency Medicine, Division of Medical Toxicology, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Nagori A, Segar MW, Keshvani N, Patel L, Patel KV, Chandra A, Willett D, Pandey A. Prevalence and Predictors of Subclinical Cardiomyopathy in Patients With Type 2 Diabetes in a Health System. J Diabetes Sci Technol 2023:19322968231212219. [PMID: 38063209 DOI: 10.1177/19322968231212219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
INTRODUCTION Diabetic cardiomyopathy (DbCM) is characterized by subclinical abnormalities in cardiac structure/function and is associated with a higher risk of overt heart failure (HF). However, there are limited data on optimal strategies to identify individuals with DbCM in contemporary health systems. The aim of this study was to evaluate the prevalence of DbCM in a health system using existing data from the electronic health record (EHR). METHODS Adult patients with type 2 diabetes mellitus free of cardiovascular disease (CVD) with available data on HF risk in a single-center EHR were included. The presence of DbCM was defined using different definitions: (1) least restrictive: ≥1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); (2) intermediate restrictive: ≥2 echocardiographic abnormalities; (3) most restrictive: 3 echocardiographic abnormalities. DbCM prevalence was compared across age, sex, race, and ethnicity-based subgroups, with differences assessed using the chi-squared test. Adjusted logistic regression models were constructed to evaluate significant predictors of DbCM. RESULTS Among 1921 individuals with type 2 diabetes mellitus, the prevalence of DbCM in the overall cohort was 8.7% and 64.4% in the most and least restrictive definitions, respectively. Across all definitions, older age and Hispanic ethnicity were associated with a higher proportion of DbCM. Females had a higher prevalence than males only in the most restrictive definition. In multivariable-adjusted logistic regression, higher systolic blood pressure, higher creatinine, and longer QRS duration were associated with a higher risk of DbCM across all definitions. CONCLUSIONS In this single-center, EHR cohort, the prevalence of DbCM varies from 9% to 64%, with a higher prevalence with older age and Hispanic ethnicity.
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Affiliation(s)
- Aditya Nagori
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matthew W Segar
- Department of Cardiology, Texas Heart Institute, Houston, TX, USA
| | - Neil Keshvani
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lajjaben Patel
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kershaw V Patel
- Department of Cardiology, Houston Methodist DeBakey Heart and Vascular Center, Houston, TX, USA
| | - Alvin Chandra
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - DuWayne Willett
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Arora P, Elliott JM, Pourkazemi F, Nasseri Pebdani R. Multiple emergency department encounters for acute musculoskeletal presentation with an existing mental health diagnosis. Clin Case Rep 2023; 11:e8010. [PMID: 37900712 PMCID: PMC10603289 DOI: 10.1002/ccr3.8010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/21/2023] [Accepted: 09/18/2023] [Indexed: 10/31/2023] Open
Abstract
Reconceptualising acute Musculoskeletal (MSK) injuries with both stress- and tissue- based factors is required to consider prior influences of mental health disorders on acute persistent MSK pain presentations. This report describes repeated emergency presentations of an individual with acute persistent MSK pain in their twenties living with mental health. Their mental health diagnoses included depression, mood disorders, and anorexia nervosa. This person also had mental health related inpatient admissions that were not captured under the retrospective record review for a large district hospital emergency department using the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) classification system. This case report attempts to demonstrate that improving the understanding of preexisting vulnerabilities and mental health diagnoses may assist with informing healthcare design to develop specialised care pathways for acute injury presentations within triage settings.
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Affiliation(s)
- Priya Arora
- Northern Sydney (Arabanoo) PrecinctSydney School of Health Sciences, Faculty of Medicine and Health, The University of SydneyCamperdownNew South WalesAustralia
- Northern Beaches Community Mental Health Services (NBCMHS)Brookvale Community Health CentreBrookvaleNew South WalesAustralia
| | - James M Elliott
- Northern Sydney (Arabanoo) PrecinctSydney School of Health Sciences, Faculty of Medicine and Health, The University of SydneyCamperdownNew South WalesAustralia
- Royal North Shore Hospital—The Kolling InstituteSt LeonardsNew South WalesAustralia
| | - Fereshteh Pourkazemi
- Central Sydney (Patyegarang) PrecinctSydney School of Health Sciences, Faculty of Medicine and Health, The University of SydneyCamperdownNSWAustralia
| | - Roxanna Nasseri Pebdani
- Central Sydney (Patyegarang) PrecinctSydney School of Health Sciences, Faculty of Medicine and Health, The University of SydneyCamperdownNSWAustralia
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Lokmic-Tomkins Z, Block LJ, Davies S, Reid L, Ronquillo CE, von Gerich H, Peltonen LM. Evaluating the representation of disaster hazards in SNOMED CT: gaps and opportunities. J Am Med Inform Assoc 2023; 30:1762-1772. [PMID: 37558235 PMCID: PMC10586035 DOI: 10.1093/jamia/ocad153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/11/2023] Open
Abstract
OBJECTIVE Climate change, an underlying risk driver of natural disasters, threatens the environmental sustainability, planetary health, and sustainable development goals. Incorporating disaster-related health impacts into electronic health records helps to comprehend their impact on populations, clinicians, and healthcare systems. This study aims to: (1) map the United Nations Office for Disaster Risk Reduction and International Science Council (UNDRR-ISC) Hazard Information Profiles to SNOMED CT International, a clinical terminology used by clinicians, to manage patients and provide healthcare services; and (2) to determine the extent of clinical terminologies available to capture disaster-related events. MATERIALS AND METHODS Concepts related to disasters were extracted from the UNDRR-ISC's Hazard Information Profiles and mapped to a health terminology using a procedural framework for standardized clinical terminology mapping. The mapping process involved evaluating candidate matches and creating a final list of matches to determine concept coverage. RESULTS A total of 226 disaster hazard concepts were identified to adversely impact human health. Chemical and biological disaster hazard concepts had better representation than meteorological, hydrological, extraterrestrial, geohazards, environmental, technical, and societal hazard concepts in SNOMED CT. Heatwave, drought, and geographically unique disaster hazards were not found in SNOMED CT. CONCLUSION To enhance clinical reporting of disaster hazards and climate-sensitive health outcomes, the poorly represented and missing concepts in SNOMED CT must be included. Documenting the impacts of climate change on public health using standardized clinical terminology provides the necessary real time data to capture climate-sensitive outcomes. These data are crucial for building climate-resilient healthcare systems, enhanced public health disaster responses and workflows, tracking individual health outcomes, supporting disaster risk reduction modeling, and aiding in disaster preparedness, response, and recovery efforts.
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Affiliation(s)
- Zerina Lokmic-Tomkins
- School of Nursing and Midwifery, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Melbourne, Victoria, Australia
| | - Lorraine J Block
- School of Nursing, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shauna Davies
- Faculty of Nursing, University of Regina, Regina, Saskatchewan, Canada
| | - Lisa Reid
- College of Nursing and Health Sciences, Flinders University, Bedford Park, South Australia, Australia
| | | | - Hanna von Gerich
- Department of Nursing Science, University of Turku, Turku, Finland
- Turku University Hospital, Turku, Finland
| | - Laura-Maria Peltonen
- Department of Nursing Science, University of Turku, Turku, Finland
- Turku University Hospital, Turku, Finland
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10
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Stellmach C, Sass J, Auber B, Boeker M, Wienker T, Heidel AJ, Benary M, Schumacher S, Ossowski S, Klauschen F, Möller Y, Schmutzler R, Ustjanzew A, Werner P, Tomczak A, Hölter T, Thun S. Creation of a structured molecular genomics report for Germany as a local adaption of HL7's Genomic Reporting Implementation Guide. J Am Med Inform Assoc 2023; 30:1179-1189. [PMID: 37080557 DOI: 10.1093/jamia/ocad061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/22/2023] [Accepted: 03/28/2023] [Indexed: 04/22/2023] Open
Abstract
OBJECTIVE The objective was to develop a dataset definition, information model, and FHIR® specification for key data elements contained in a German molecular genomics (MolGen) report to facilitate genomic and phenotype integration in electronic health records. MATERIALS AND METHODS A dedicated expert group participating in the German Medical Informatics Initiative reviewed information contained in MolGen reports, determined the key elements, and formulated a dataset definition. HL7's Genomics Reporting Implementation Guide (IG) was adopted as a basis for the FHIR® specification which was subjected to a public ballot. In addition, elements in the MolGen dataset were mapped to the fields defined in ISO/TS 20428:2017 standard to evaluate compliance. RESULTS A core dataset of 76 data elements, clustered into 6 categories was created to represent all key information of German MolGen reports. Based on this, a FHIR specification with 16 profiles, 14 derived from HL7®'s Genomics Reporting IG and 2 additional profiles (of the FamilyMemberHistory and RiskAssessment resources), was developed. Five example resource bundles show how our adaptation of an international standard can be used to model MolGen report data that was requested following oncological or rare disease indications. Furthermore, the map of the MolGen report data elements to the fields defined by the ISO/TC 20428:2017 standard, confirmed the presence of the majority of required fields. CONCLUSIONS Our report serves as a template for other research initiatives attempting to create a standard format for unstructured genomic report data. Use of standard formats facilitates integration of genomic data into electronic health records for clinical decision support.
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Affiliation(s)
- Caroline Stellmach
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julian Sass
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Auber
- Department of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Martin Boeker
- Fakultät für Medizin, Technische Universität München, Munich, Germany
| | - Thomas Wienker
- Emeritus Ropers, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | | | - Manuela Benary
- Core Unit Bioinformatics, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon Schumacher
- Medical Data Integration Center (MeDIC), Universitätsklinikum Köln, Cologne, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Frederick Klauschen
- Institut für Pathologie, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Pathologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
- Berlin Institute for the Foundations of Learning and Data (BIFOLD), Berlin, Germany
| | - Yvonne Möller
- Center for personalized medicine (ZPM), Universitätsklinikum Tübingen, Tübingen, Germany
| | - Rita Schmutzler
- Center Familial Breast and Ovarian Cancer, National Center of Familial Tumor Diseases and Center of Integrated Oncology, Universitätsklinikum Köln, Cologne, Germany
| | - Arsenij Ustjanzew
- Institut für Medizinische, Biometrie, Epidemiologie und Informatik Mainz, Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Mainz, Germany
| | | | - Aurelie Tomczak
- Liver Cancer Centre Heidelberg, Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Thimo Hölter
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sylvia Thun
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health (BIH) at Charité - Universitätsmedizin Berlin, Berlin, Germany
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11
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Vuokko R, Vakkuri A, Palojoki S. Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED CT) Clinical Use Cases in the Context of Electronic Health Record Systems: Systematic Literature Review. JMIR Med Inform 2023; 11:e43750. [PMID: 36745498 PMCID: PMC9941898 DOI: 10.2196/43750] [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: 10/23/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED CT) is a clinical terminology system that provides a standardized and scientifically validated way of representing clinical information captured by clinicians. It can be integrated into electronic health records (EHRs) to increase the possibilities for effective data use and ensure a better quality of documentation that supports continuity of care, thus enabling better quality in the care process. Even though SNOMED CT consists of extensively studied clinical terminology, previous research has repeatedly documented a lack of scientific evidence for SNOMED CT in the form of reported clinical use cases in electronic health record systems. OBJECTIVE The aim of this study was to explore evidence in previous literature reviews of clinical use cases of SNOMED CT integrated into EHR systems or other clinical applications during the last 5 years of continued development. The study sought to identify the main clinical use purposes, use phases, and key clinical benefits documented in SNOMED CT use cases. METHODS The Cochrane review protocol was applied for the study design. The application of the protocol was modified step-by-step to fit the research problem by first defining the search strategy, identifying the articles for the review by isolating the exclusion and inclusion criteria for assessing the search results, and lastly, evaluating and summarizing the review results. RESULTS In total, 17 research articles illustrating SNOMED CT clinical use cases were reviewed. The use purpose of SNOMED CT was documented in all the articles, with the terminology as a standard in EHR being the most common (8/17). The clinical use phase was documented in all the articles. The most common category of use phases was SNOMED CT in development (6/17). Core benefits achieved by applying SNOMED CT in a clinical context were identified by the researchers. These were related to terminology use outcomes, that is, to data quality in general or to enabling a consistent way of indexing, storing, retrieving, and aggregating clinical data (8/17). Additional benefits were linked to the productivity of coding or to advances in the quality and continuity of care. CONCLUSIONS While the SNOMED CT use categories were well supported by previous research, this review demonstrates that further systematic research on clinical use cases is needed to promote the scalability of the review results. To achieve the best out-of-use case reports, more emphasis is suggested on describing the contextual factors, such as the electronic health care system and the use of previous frameworks to enable comparability of results. A lesson to be drawn from our study is that SNOMED CT is essential for structuring clinical data; however, research is needed to gather more evidence of how SNOMED CT benefits clinical care and patient safety.
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Affiliation(s)
- Riikka Vuokko
- Unit for Digitalization and Management, Ministry of Social Affairs and Health, Helsinki, Finland
| | - Anne Vakkuri
- Perioperative, Intensive Care and Pain Medicine, Helsinki University Hospital, Vantaa, Finland
| | - Sari Palojoki
- Unit for Digital Transformation, European Centre for Disease Prevention and Control, Stockholm, Sweden
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12
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Elkheder M, Gonzalez-Izquierdo A, Qummer Ul Arfeen M, Kuan V, Lumbers RT, Denaxas S, Shah AD. Translating and evaluating historic phenotyping algorithms using SNOMED CT. J Am Med Inform Assoc 2023; 30:222-232. [PMID: 36083213 PMCID: PMC9846670 DOI: 10.1093/jamia/ocac158] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/25/2022] [Accepted: 08/30/2022] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE Patient phenotype definitions based on terminologies are required for the computational use of electronic health records. Within UK primary care research databases, such definitions have typically been represented as flat lists of Read terms, but Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) (a widely employed international reference terminology) enables the use of relationships between concepts, which could facilitate the phenotyping process. We implemented SNOMED CT-based phenotyping approaches and investigated their performance in the CPRD Aurum primary care database. MATERIALS AND METHODS We developed SNOMED CT phenotype definitions for 3 exemplar diseases: diabetes mellitus, asthma, and heart failure, using 3 methods: "primary" (primary concept and its descendants), "extended" (primary concept, descendants, and additional relations), and "value set" (based on text searches of term descriptions). We also derived SNOMED CT codelists in a semiautomated manner for 276 disease phenotypes used in a study of health across the lifecourse. Cohorts selected using each codelist were compared to "gold standard" manually curated Read codelists in a sample of 500 000 patients from CPRD Aurum. RESULTS SNOMED CT codelists selected a similar set of patients to Read, with F1 scores exceeding 0.93, and age and sex distributions were similar. The "value set" and "extended" codelists had slightly greater recall but lower precision than "primary" codelists. We were able to represent 257 of the 276 phenotypes by a single concept hierarchy, and for 135 phenotypes, the F1 score was greater than 0.9. CONCLUSIONS SNOMED CT provides an efficient way to define disease phenotypes, resulting in similar patient populations to manually curated codelists.
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Affiliation(s)
- Musaab Elkheder
- Institute of Health Informatics, University College London, London, UK
| | - Arturo Gonzalez-Izquierdo
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK
| | | | - Valerie Kuan
- Institute of Health Informatics, University College London, London, UK
| | - R Thomas Lumbers
- Institute of Health Informatics, University College London, London, UK.,Barts Health NHS Trust, London, UK.,University College London Hospitals NHS Trust, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK.,Health Data Research UK, London, UK.,British Heart Foundation Data Science Centre, London, UK
| | - Anoop D Shah
- Institute of Health Informatics, University College London, London, UK.,University College London Hospitals NHS Trust, London, UK
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13
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Gorski-Steiner I, Bandeen-Roche K, Volk HE, O'Dell S, Schwartz BS. The association of unconventional natural gas development with diagnosis and treatment of internalizing disorders among adolescents in Pennsylvania using electronic health records. ENVIRONMENTAL RESEARCH 2022; 212:113167. [PMID: 35341757 PMCID: PMC9233008 DOI: 10.1016/j.envres.2022.113167] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/01/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Unconventional natural gas development (UNGD) introduces physical and psychosocial hazards into communities, which could contribute to psychosocial stress in adolescents and an increased risk of internalizing disorders, common and impactful health outcomes. OBJECTIVES To evaluate associations between a 180-day composite UNGD activity metric and new onset of internalizing disorders, overall and separately for anxiety and depressive disorders, and effect modification by sex. METHODS We used a nested case-control design from 2008 to 2016 in 38 Pennsylvania counties using electronic health records from adolescent Geisinger subjects. Cases were defined by at least two diagnoses or medication orders indicating new onset of an internalizing disorder, and controls frequency-matched 4:1 on age, sex, and year. To evaluate associations, we used generalized estimating equations, with logit link, robust standard errors, and an exchangeable correlation structure within community. RESULTS We identified 7,974 adolescents (65.9% female, mean age 15.0 years) with new onset internalizing disorders. There were no associations when we used data from the entire study period. When restricted to years with higher UNGD activity (2010-2016), comparing the highest to lowest quartile, UNGD activity was associated (odds ratio [95% confidence level]) with new onset internalizing disorders (1.15 [1.06, 1.25]). Associations were slightly stronger for depressive disorders. Associations were only present in females (p = 0.009). DISCUSSION This is the first epidemiologic study of UNGD in relation to adolescent mental health, an important health outcome in a potentially susceptible group to the environmental and community impacts of UNGD. UNGD activity was associated with new onset internalizing disorders in females in this large sample in an area of active UNGD.
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Affiliation(s)
- Irena Gorski-Steiner
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Karen Bandeen-Roche
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather E Volk
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sean O'Dell
- Department of Psychiatry and Behavioral Health, Geisinger, Danville, PA, USA
| | - Brian S Schwartz
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Population Health Sciences, Geisinger, Danville, PA, USA; Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
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14
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Segar MW, Patel KV, Hellkamp AS, Vaduganathan M, Lokhnygina Y, Green JB, Wan SH, Kolkailah AA, Holman RR, Peterson ED, Kannan V, Willett DL, McGuire DK, Pandey A. Validation of the WATCH-DM and TRS-HF DM Risk Scores to Predict the Risk of Incident Hospitalization for Heart Failure Among Adults With Type 2 Diabetes: A Multicohort Analysis. J Am Heart Assoc 2022; 11:e024094. [PMID: 35656988 PMCID: PMC9238735 DOI: 10.1161/jaha.121.024094] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HFDM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HFDM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HFDM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HFDM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P>0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P<0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P=0.96). TRS-HFDM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HFDM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.
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Affiliation(s)
| | - Kershaw V Patel
- Department of Cardiology Houston Methodist DeBakey Heart and Vascular Center Houston TX
| | - Anne S Hellkamp
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Muthiah Vaduganathan
- Brigham and Women's Hospital Heart and Vascular Center Department of Medicine Harvard Medical School Boston MA
| | - Yuliya Lokhnygina
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Jennifer B Green
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Siu-Hin Wan
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Ahmed A Kolkailah
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Rury R Holman
- Diabetes Trials Unit Radcliffe Department of Medicine University of Oxford Oxford UK
| | - Eric D Peterson
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX.,Parkland Health and Hospital System Dallas TX
| | - Vaishnavi Kannan
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Duwayne L Willett
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
| | - Darren K McGuire
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX.,Parkland Health and Hospital System Dallas TX
| | - Ambarish Pandey
- Division of Cardiology Department of Internal Medicine University of Texas Southwestern Medical Center Dallas TX
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15
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McAdams MC, Xu P, Saleh SN, Li M, Ostrosky-Frid M, Gregg LP, Willett DL, Velasco F, Lehmann CU, Hedayati SS. Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19. Kidney Med 2022; 4:100463. [PMID: 35434597 PMCID: PMC8990440 DOI: 10.1016/j.xkme.2022.100463] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Rationale & Objective Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant. Study Design Longitudinal cohort study. Setting & Participants Hospitalized patients with a positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction result between March 1, 2020, and August 20, 2021 at 19 hospitals in Texas. Exposures Comorbid conditions, baseline laboratory data, inflammatory biomarkers. Outcomes AKI defined by KDIGO (Kidney Disease: Improving Global Outcomes) creatinine criteria. Analytical Approach Three nested models for AKI were built in a development cohort and validated in 2 out-of-time cohorts. Model discrimination and calibration measures were compared among cohorts to assess performance over time. Results Of 10,034 patients, 5,676, 2,917, and 1,441 were in the development, validation 1, and validation 2 cohorts, respectively, of whom 776 (13.7%), 368 (12.6%), and 179 (12.4%) developed AKI, respectively (P = 0.26). Patients in the validation cohort 2 had fewer comorbid conditions and were younger than those in the development cohort or validation cohort 1 (mean age, 54 ± 16.8 years vs 61.4 ± 17.5 and 61.7 ± 17.3 years, respectively, P < 0.001). The validation cohort 2 had higher median high-sensitivity C-reactive protein level (81.7 mg/L) versus the development cohort (74.5 mg/L; P < 0.01) and higher median ferritin level (696 ng/mL) versus both the development cohort (444 ng/mL) and validation cohort 1 (496 ng/mL; P < 0.001). The final model, which added high-sensitivity C-reactive protein, ferritin, and D-dimer levels, had an area under the curve of 0.781 (95% CI, 0.763-0.799). Compared with the development cohort, discrimination by area under the curve (validation 1: 0.785 [0.760-0.810], P = 0.79, and validation 2: 0.754 [0.716-0.795], P = 0.53) and calibration by estimated calibration index (validation 1: 0.116 [0.041-0.281], P = 0.11, and validation 2: 0.081 [0.045-0.295], P = 0.11) showed stable performance over time. Limitations Potential billing and coding bias. Conclusions We developed and externally validated a model to accurately predict AKI in patients with coronavirus disease 2019. The performance of the model withstood changes in practice patterns and virus variants.
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Affiliation(s)
- Meredith C. McAdams
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Pin Xu
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - Sameh N. Saleh
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - Michael Li
- University of Texas Southwestern College of Medicine, Dallas, TX
| | - Mauricio Ostrosky-Frid
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | - L. Parker Gregg
- Selzman Institute for Kidney Health, Section of Nephrology, Department of Medicine, Baylor College of Medicine, Houston, TX
- Section of Nephrology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX
- Veterans Affairs Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety, Houston, TX
| | - Duwayne L. Willett
- Division of Cardiology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX
| | | | - Christoph U. Lehmann
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, TX
| | - S. Susan Hedayati
- Division of Nephrology, Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX
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16
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Emamekhoo H, Carroll CB, Stietz C, Pier JB, Lavitschke MD, Mulkerin D, Sesto ME, Tevaarwerk AJ. Supporting Structured Data Capture for Patients With Cancer: An Initiative of the University of Wisconsin Carbone Cancer Center Survivorship Program to Improve Capture of Malignant Diagnosis and Cancer Staging Data. JCO Clin Cancer Inform 2022; 6:e2200020. [PMID: 35802837 PMCID: PMC9296185 DOI: 10.1200/cci.22.00020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 04/27/2022] [Accepted: 05/16/2022] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Structured data elements within electronic health records are health-related information that can be entered, stored, and extracted in an organized manner at later time points. Tracking outcomes for cancer survivors is also enabled by structured data. We sought to increase structured data capture within oncology practices at multiple sites sharing the same electronic health records. METHODS Applying engineering approaches and the Plan-Do-Study-Act cycle, we launched dual quality improvement initiatives to ensure that a malignant diagnosis and stage were captured as structured data. Intervention: Close Visit Validation (CVV) requires providers to satisfy certain criteria before closing ambulatory encounters. CVV may be used to track open clinical encounters and chart delinquencies to encourage optimal clinical workflows. We added two cancer-specific required criteria at the time of closing encounters in oncology clinics: (1) the presence of at least one malignant diagnosis on the Problem List and (2) staging all the malignant diagnoses on the Problem List when appropriate. RESULTS Six months before the CVV implementation, the percentage of encounters with a malignant diagnosis on the Problem List at the time of the encounter was 65%, whereas the percentage of encounters with a staged diagnosis was 32%. Three months after cancer-specific CVV implementation, the percentages were 85% and 75%, respectively. Rates had increased to 90% and 88% more than 2 years after implementation. CONCLUSION Oncologist performance improved after the implementation of cancer-specific CVV criteria, with persistently high percentages of relevant malignant diagnoses and cancer stage structured data capture 2 years after the intervention.
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Affiliation(s)
- Hamid Emamekhoo
- University of Wisconsin, Madison, WI
- Carbone Cancer Center, Madison, WI
| | | | | | | | | | | | - Mary E. Sesto
- University of Wisconsin, Madison, WI
- Carbone Cancer Center, Madison, WI
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17
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Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Designing a standardized framework for data integration between zoonotic diseases systems: Towards one health surveillance. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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18
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Carey IM, Banchoff E, Nirmalananthan N, Harris T, DeWilde S, Chaudhry UAR, Cook DG. Prevalence and incidence of neuromuscular conditions in the UK between 2000 and 2019: A retrospective study using primary care data. PLoS One 2021; 16:e0261983. [PMID: 34972157 PMCID: PMC8719665 DOI: 10.1371/journal.pone.0261983] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/14/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In the UK, large-scale electronic primary care datasets can provide up-to-date, accurate epidemiological information on rarer diseases, where specialist diagnoses from hospital discharges and clinic letters are generally well recorded and electronically searchable. Current estimates of the number of people living with neuromuscular disease (NMD) have largely been based on secondary care data sources and lacked direct denominators. OBJECTIVE To estimate trends in the recording of neuromuscular disease in UK primary care between 2000-2019. METHODS The Clinical Practice Research Datalink (CPRD) database was searched electronically to estimate incidence and prevalence rates (per 100,000) for a range of NMDs in each year. To compare trends over time, rates were age standardised to the most recent CPRD population (2019). RESULTS Approximately 13 million patients were actively registered in each year. By 2019, 28,230 active patients had ever received a NMD diagnosis (223.6), which was higher among males (239.0) than females (208.3). The most common classifications were Guillain-Barre syndrome (40.1), myasthenia gravis (33.7), muscular dystrophy (29.5), Charcot-Marie-Tooth (29.5) and inflammatory myopathies (25.0). Since 2000, overall prevalence grew by 63%, with the largest increases seen at older ages (≥65-years). However, overall incidence remained constant, though myasthenia gravis incidence has risen steadily since 2008, while new cases of muscular dystrophy fell over the same period. CONCLUSIONS Lifetime recording of many NMDs on primary care records exceed current estimates of people living with these conditions; these are important data for health service and care planning. Temporal trends suggest this number is steadily increasing, and while this may partially be due to better recording, it cannot be simply explained by new cases, as incidence remained constant. The increase in prevalence among older ages suggests increases in life expectancy among those living with NMDs may have occurred.
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Affiliation(s)
- Iain M. Carey
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
| | - Emma Banchoff
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
| | | | - Tess Harris
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
| | - Stephen DeWilde
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
| | - Umar A. R. Chaudhry
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
| | - Derek G. Cook
- Population Health Research Institute, St George’s, University of London, London, United Kingdom
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19
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Teufel A, Binder H. Clinical Decision Support Systems. Visc Med 2021; 37:491-498. [PMID: 35087899 PMCID: PMC8738909 DOI: 10.1159/000519420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/03/2021] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. SUMMARY AND KEY MESSAGES Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.
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Affiliation(s)
- Andreas Teufel
- Department of Medicine II, Section of Hepatology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health Baden-Württemberg (CPDBW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
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20
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Geographic disparities in new onset of internalizing disorders in Pennsylvania adolescents using electronic health records. Spat Spatiotemporal Epidemiol 2021; 41:100439. [DOI: 10.1016/j.sste.2021.100439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/20/2021] [Accepted: 06/23/2021] [Indexed: 01/04/2023]
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21
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Definition and validation of SNOMED CT subsets using the expression constraint language. J Biomed Inform 2021; 117:103747. [PMID: 33753269 DOI: 10.1016/j.jbi.2021.103747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/05/2021] [Accepted: 03/06/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND SNOMED CT Expression Constraint Language (ECL) is a declarative language developed by SNOMED International for the definition of SNOMED CT Expression Constraints (ECs). ECs are executable expressions that define intensional subsets of clinical meanings by stating constraints over the logic definition of concepts. The execution of an EC on some SNOMED CT substrate yields the intended subset, and it requires an execution engine able to receive an EC as input, execute it, and return the matching concepts. An important issue regarding subsets of clinical concepts is their use in terminology binding between clinical information models and terminologies for defining the set of valid values of codified data. OBJECTIVE To define and implement methods for the simplification, semantic validation and execution of ECs over a graph-oriented SNOMED CT database, and to provide a method for the visual representation of subsets in order to explore, understand and validate its content, as well as to develop an EC execution platform, called SNQuery, which makes use of these methods. METHODS Since SNOMED CT is a directed and acyclic graph, we have used a graph-oriented database to represent the content of SNOMED CT, where the schema and instances are represented as graphs and the data manipulation is expressed by graph-oriented operations. For the execution of ECs over the graph database, it is performed a translation process in which ECs are translated into a set of Cypher Query Language queries. We have defined some EC simplification methods that leverage the logic structure underlying SNOMED CT. The purpose of these methods is to reduce the complexity of ECs and, in turn, its execution time, as well as to validate them from a SNOMED CT Concept Model and logical definition points of view. We also have developed a graphic representation based on the circle packing geometrical concept, which allows validating subsets, as well as pre-defined refsets and the terminology itself. RESULTS We have developed SNQuery, a platform for the definition of intensional subsets of SNOMED CT concepts by means of the execution of ECs over a graph-oriented SNOMED CT database. Additionally, we have incorporated methods for the simplification and semantic validation of ECs, as well as for the visualization of subsets as a mechanism to understand and validate them. SNQuery has been evaluated in terms of EC execution times. CONCLUSION In this paper, we provide methods to simplify, semantically validate and execute ECs over a graph-oriented database. We also offer a method to visualize the intensional subsets obtained by executing ECs to explore, understand and validate them, as well as refsets and the terminology itself. The definition of intensional subsets is useful to bind content between clinical information models and clinical terminologies, which is a necessary step to achieve semantic interoperability between EHR systems.
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Grundel B, Bernardeau MA, Langner H, Schmidt C, Böhringer D, Ritter M, Rosenthal P, Grandjean A, Schulz S, Daumke P, Stahl A. [Extraction of features from clinical routine data using text mining]. Ophthalmologe 2021; 118:264-272. [PMID: 32725541 DOI: 10.1007/s00347-020-01177-4] [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: 11/29/2022]
Abstract
BACKGROUND Anti-VEGF drugs are currently used to treat macular diseases. This has led to a wealth of additional data, which could help understand and predict treatment courses; however, this information is usually only available in free text form. OBJECTIVE A retrospective study was designed to analyze how far interpretable information can be obtained from clinical texts by automated extraction. The aim was to assess the suitability of a text mining method that was customized for this purpose. MATERIAL AND METHODS Data on 3683 patients were available, including 40,485 discharge letters. Some of the data of interest, e.g. visual acuity (VA), intraocular pressure (IOP) and accompanying diagnoses, were not only recorded textually but also entered in a database and could thus serve as a gold standard for text analysis. The text was analyzed using the Averbis Health Discovery text mining platform. To optimize the extraction task, rule knowledge and a German language technical vocabulary linked to the international medical terminology standard systematized nomenclature of medicine (SNOMED CT) was manually added. RESULTS The correspondence between extracted data and the structured database entries is described by the F1 value. There was agreement of 94.7% for VA, 98.3% for IOP and 94.7% for the accompanying diagnoses. Manual analysis of noncorresponding cases showed that in 50% text content did not match the database content for various reasons. After an adjustment, F1 values 1-3% above the previously determined values were obtained. CONCLUSION Text mining procedures are very well suited for the considered discharge letter corpus and the problem described in order to extract contents from clinical texts in a structured manner for further evaluation.
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Affiliation(s)
- Bastian Grundel
- Klinik und Poliklinik für Augenheilkunde, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Marc-Antoine Bernardeau
- Klinik und Poliklinik für Augenheilkunde, Universitätsmedizin Greifswald, Greifswald, Deutschland
| | - Holger Langner
- Professur Medieninformatik, Hochschule Mittweida, Mittweida, Deutschland
| | - Christoph Schmidt
- Institute for Visual and Analytic Computing, Universität Rostock, Rostock, Deutschland
| | - Daniel Böhringer
- Klinik für Augenheilkunde, Universitätsklinikum Freiburg, Medizinische Fakultät, Universität Freiburg, Freiburg, Deutschland
| | - Marc Ritter
- Professur Medieninformatik, Hochschule Mittweida, Mittweida, Deutschland
| | - Paul Rosenthal
- Institute for Visual and Analytic Computing, Universität Rostock, Rostock, Deutschland
| | | | | | | | - Andreas Stahl
- Klinik und Poliklinik für Augenheilkunde, Universitätsmedizin Greifswald, Greifswald, Deutschland
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Conway RBN, Armistead MG, Denney MJ, Smith GS. Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases. Appl Clin Inform 2021; 12:82-89. [PMID: 33567463 PMCID: PMC7875675 DOI: 10.1055/s-0040-1722220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background
Though electronic health record (EHR) data have been linked to national and state death registries, such linkages have rarely been validated for an entire hospital system's EHR.
Objectives
The aim of the study is to validate West Virginia University Medicine's (WVU Medicine) linkage of its EHR to three external death registries: the Social Security Death Masterfile (SSDMF), the national death index (NDI), the West Virginia Department of Health and Human Resources (DHHR).
Methods
Probabilistic matching was used to link patients to NDI and deterministic matching for the SSDMF and DHHR vital statistics records (WVDMF). In subanalysis, we used deaths recorded in Epic (
n
= 30,217) to further validate a subset of deaths captured by the SSDMF, NDI, and WVDMF.
Results
Of the deaths captured by the SSDMF, 59.8 and 68.5% were captured by NDI and WVDMF, respectively; for deaths captured by NDI this co-capture rate was 80 and 78%, respectively, for the SSDMF and WVDMF. Kappa statistics were strongest for NDI and WVDMF (61.2%) and NDI and SSDMF (60.6%) and weakest for SSDMF and WVDMF (27.9%). Of deaths recorded in Epic, 84.3, 85.5, and 84.4% were captured by SSDMF, NDI, and WVDMF, respectively. Less than 2% of patients' deaths recorded in Epic were not found in any of the death registries. Finally, approximately 0.2% of “decedents” in any death registry re-emerged in Epic at least 6 months after their death date, a very small percentage and thus further validating the linkages.
Conclusion
NDI had greatest validity in capturing deaths in our EHR. As a similar, though slightly less capture and agreement rate in identifying deaths is observed for SSDMF and state vital statistics records, these registries may be reasonable alternatives to NDI for research and quality assurance studies utilizing entire EHRs from large hospital systems. Investigators should also be aware that there will be a very tiny fraction of “dead” patients re-emerging in the EHR.
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Affiliation(s)
- Rebecca B N Conway
- Department of Community Health, University of Texas Health Science Center at Tyler, Tyler, Texas, United States
| | - Matthew G Armistead
- Department of Biomedical Informatics, West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States
| | - Michael J Denney
- Department of Biomedical Informatics, West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States
| | - Gordon S Smith
- Department of Epidemiology, West Virginia University, Morgantown, West Virginia, United States
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24
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McDonald SA, Medford RJ, Basit MA, Diercks DB, Courtney DM. Derivation With Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients. Acad Emerg Med 2021; 28:206-214. [PMID: 33249683 PMCID: PMC7753649 DOI: 10.1111/acem.14182] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/20/2020] [Accepted: 11/24/2020] [Indexed: 12/19/2022]
Abstract
OBJECTIVES The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19. METHODS All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease-specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient-boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold. RESULTS A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15-0.3) for the gradient-boosted method. CONCLUSION The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X-ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.
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Affiliation(s)
- Samuel A. McDonald
- From the Department of Emergency MedicineUniversity of Texas Southwestern Medical CenterDallasTXUSA
- the Clinical Informatics CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Richard J. Medford
- the Clinical Informatics CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
- the Department of Internal Medicine/Infectious DiseaseUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Mujeeb A. Basit
- the Clinical Informatics CenterUniversity of Texas Southwestern Medical CenterDallasTXUSA
- and the Department of Internal Medicine/CardiologyUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - Deborah B. Diercks
- From the Department of Emergency MedicineUniversity of Texas Southwestern Medical CenterDallasTXUSA
| | - D. Mark Courtney
- From the Department of Emergency MedicineUniversity of Texas Southwestern Medical CenterDallasTXUSA
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Kannan V, Wilkinson KE, Varghese M, Lynch-Medick S, Willett DL, Bosler TA, Chu L, Gates SI, Holbein MEB, Willett MM, Reimold SC, Toto RD. Count me in: using a patient portal to minimize implicit bias in clinical research recruitment. J Am Med Inform Assoc 2021; 26:703-713. [PMID: 31081898 DOI: 10.1093/jamia/ocz038] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 03/04/2019] [Accepted: 03/08/2019] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Determine whether women and men differ in volunteering to join a Research Recruitment Registry when invited to participate via an electronic patient portal without human bias. MATERIALS AND METHODS Under-representation of women and other demographic groups in clinical research studies could be due either to invitation bias (explicit or implicit) during screening and recruitment or by lower rates of deciding to participate when offered. By making an invitation to participate in a Research Recruitment Registry available to all patients accessing our patient portal, regardless of demographics, we sought to remove implicit bias in offering participation and thus independently assess agreement rates. RESULTS Women were represented in the Research Recruitment Registry slightly more than their proportion of all portal users (n = 194 775). Controlling for age, race, ethnicity, portal use, chronic disease burden, and other questionnaire use, women were statistically more likely to agree to join the Registry than men (odds ratio 1.17, 95% CI, 1.12-1.21). In contrast, Black males, Hispanics (of both sexes), and particularly Asians (both sexes) had low participation-to-population ratios; this under-representation persisted in the multivariable regression model. DISCUSSION This supports the view that historical under-representation of women in clinical studies is likely due, at least in part, to implicit bias in offering participation. Distinguishing the mechanism for under-representation could help in designing strategies to improve study representation, leading to more effective evidence-based recommendations. CONCLUSION Patient portals offer an attractive option for minimizing bias and encouraging broader, more representative participation in clinical research.
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Affiliation(s)
- Vaishnavi Kannan
- Information Resources Department, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kathleen E Wilkinson
- Center for Translational Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Mereeja Varghese
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Duwayne L Willett
- Center for Translational Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Teresa A Bosler
- Information Resources Department, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Center for Translational Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ling Chu
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Samantha I Gates
- Information Resources Department, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - M E Blair Holbein
- Center for Translational Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Mallory M Willett
- College of Liberal Arts, University of Texas at Austin, Austin, TX, USA
| | - Sharon C Reimold
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Robert D Toto
- Center for Translational Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Grando A, Sottara D, Singh R, Murcko A, Soni H, Tang T, Idouraine N, Todd M, Mote M, Chern D, Dye C, Whitfield MJ. Pilot evaluation of sensitive data segmentation technology for privacy. Int J Med Inform 2020; 138:104121. [PMID: 32278288 DOI: 10.1016/j.ijmedinf.2020.104121] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 03/12/2020] [Accepted: 03/13/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND Consent2Share (C2S) is an open source software created by the Office of the National Coordinator Data Segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share. METHOD Structured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations. RESULTS From the resulting 1,080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (χ2 (2, N = 584) = 114.74, p = <0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %). CONCLUSIONS There is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.
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Affiliation(s)
- Adela Grando
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.
| | | | - Ripudaman Singh
- School of Computing, Informatics and Decision Systems Engineering, Tempe, AZ, United States
| | - Anita Murcko
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States
| | - Hiral Soni
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States
| | - Tianyu Tang
- University of Arizona, College of Medicine, Tucson, AZ, United States
| | - Nassim Idouraine
- Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States
| | - Michael Todd
- College of Nursing and Health Innovation, Arizona State University, Phoenix, United States
| | - Mike Mote
- Health Current, Phoenix, AZ, United States
| | - Darwyn Chern
- Partners in Recovery, Phoenix, AZ, United States
| | - Christy Dye
- Partners in Recovery, Phoenix, AZ, United States
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Chu L, Kannan V, Basit MA, Schaeflein DJ, Ortuzar AR, Glorioso JF, Buchanan JR, Willett DL. SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets. JMIR Med Inform 2019; 7:e11487. [PMID: 30664458 PMCID: PMC6351992 DOI: 10.2196/11487] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Revised: 11/23/2018] [Accepted: 12/09/2018] [Indexed: 01/19/2023] Open
Abstract
Background Defining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition. Objective The goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness. Methods Starting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians. Results The 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms. Conclusions In the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific decision support promoting guideline adherence for patient benefit.
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Affiliation(s)
- Ling Chu
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Vaishnavi Kannan
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Mujeeb A Basit
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Diane J Schaeflein
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Adolfo R Ortuzar
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jimmie F Glorioso
- University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Joel R Buchanan
- University of Wisconsin School of Medicine and Public Health, Madison, WI, United States
| | - Duwayne L Willett
- University of Texas Southwestern Medical Center, Dallas, TX, United States
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Rector A, Schulz S, Rodrigues JM, Chute CG, Solbrig H. On beyond Gruber: "Ontologies" in today's biomedical information systems and the limits of OWL. J Biomed Inform 2019; 100S:100002. [PMID: 34384571 DOI: 10.1016/j.yjbinx.2019.100002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The word "ontology" was introduced to information systems when only closed-world reasoning systems were available. It was "borrowed" from philosophy, but literal links to its philosophical meaning were explicitly disavowed. Since then, open-world reasoning systems based on description logics have been developed, OWL has become a standard, and philosophical issues have been raised. The result has too often been confusion. The question "What statements are ontological" receives a variety of answers. A clearer vocabulary that is better suited to today's information systems is needed. The project to base ICD-11 on a "Common Ontology" required addressing this confusion. This paper sets out to systematise the lessons of that experience and subsequent discussions. We explore the semantics of open-world and closed-world systems. For specifying knowledge bases and software, we propose "invariants" or, more fully, "the first order invariant part of the background domain knowledge base" as an alternative to the words "ontology" and "ontological." We discuss the role and limitations of OWL and description logics and how they are complementary to closed world systems such as frames and to less formal "knowledge organisation systems". We illustrate why the conventions of classifications such as ICD cannot be formulated directly in OWL, but can be linked to OWL knowledge bases by queries. We contend that while OWL and description logics are major advances for representing invariants and terminologies, they must be combined with other technologies to represent broader background knowledge faithfully. The ICD-11 architecture is one approach. We argue that such hybrid architectures can and should be developed further.
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Affiliation(s)
- Alan Rector
- University of Manchester, School of Computer Science, Kilburn Building, Oxford Road, Manchester M13 9PL, UK.
| | - Stefan Schulz
- Medical University of Graz, Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Auenbruggerpltz 2, 8036 Graz, Austria.
| | - Jean Marie Rodrigues
- Université Jean Monnet Saint Etienne/Université de Lyon, CHU de Saint-Etienne, SSPIM - Bâtiment CIM 42, Chemin de la Marandière, 42023 St Etienne cedex 2, France.
| | - Christopher G Chute
- Johns Hopkins University, School of Medicine, Public Health, and Nursing, 2024 E. Monument Street, Suite 1-202, Baltimore, MD 21205, USA.
| | - Harold Solbrig
- Johns Hopkins University, Institute for Clinical and Translational Research, 2024 E. Monument Street, Suite 1-202, Baltimore, MD 21205, USA.
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