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Pedrera-Jiménez M, García-Barrio N, Frid S, Moner D, Boscá-Tomás D, Lozano-Rubí R, Kalra D, Beale T, Muñoz-Carrero A, Serrano-Balazote P. Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces. J Med Internet Res 2023; 25:e48702. [PMID: 38153779 PMCID: PMC10784985 DOI: 10.2196/48702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/15/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.
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Affiliation(s)
- Miguel Pedrera-Jiménez
- Data Science Unit, Hospital Universitario 12 de Octubre, Madrid, Spain
- ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Santiago Frid
- Medical Informatics Unit, Hospital Clinic de Barcelona, Barcelona, Spain
| | | | | | | | - Dipak Kalra
- The European Institute for Innovation through Health Data, Gent, Belgium
| | | | - Adolfo Muñoz-Carrero
- Telemedicine and Digital Health Research Unit, Instituto de Salud Carlos III, Madrid, Spain
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Sperotto F, Gutiérrez-Sacristán A, Makwana S, Li X, Rofeberg VN, Cai T, Bourgeois FT, Omenn GS, Hanauer DA, Sáez C, Bonzel CL, Bucholz E, Dionne A, Elias MD, García-Barrio N, González TG, Issitt RW, Kernan KF, Laird-Gion J, Maidlow SE, Mandl KD, Ahooyi TM, Moraleda C, Morris M, Moshal KL, Pedrera-Jiménez M, Shah MA, South AM, Spiridou A, Taylor DM, Verdy G, Visweswaran S, Wang X, Xia Z, Zachariasse JM, Newburger JW, Avillach P. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium. EClinicalMedicine 2023; 64:102212. [PMID: 37745025 PMCID: PMC10511777 DOI: 10.1016/j.eclinm.2023.102212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/22/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
Background Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES -1.18 years [95% CI -2.05, -0.32]), had fewer respiratory symptoms (RD -0.15 [95% CI -0.33, -0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD -0.35 [95% CI -0.64, -0.07]), lower lymphocyte count (ES -0.16 × 109/uL [95% CI -0.30, -0.01]), lower C-reactive protein (ES -28.5 mg/L [95% CI -46.3, -10.7]), and lower troponin (ES -0.14 ng/mL [95% CI -0.26, -0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES -1.6 years [95% CI -2.5, -0.8]), had less frequent SIRS (RD -0.18 [95% CI -0.30, -0.05]), lower lymphocyte count (ES -0.39 × 109/uL [95% CI -0.52, -0.25]), lower troponin (ES -0.16 ng/mL [95% CI -0.30, -0.01]) and less frequently received anticoagulation therapy (RD -0.19 [95% CI -0.37, -0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (-1.3 days [95% CI -2.3, -0.4]). Interpretation Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding None.
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Affiliation(s)
- Francesca Sperotto
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Simran Makwana
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Xiudi Li
- Department of Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
| | - Valerie N. Rofeberg
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Gilbert S. Omenn
- Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & Public Health, University of Michigan, 2017 Palmer Commons, Ann Arbor, MI 48109-2218, United States
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, 100-107 NCRC, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Carlos Sáez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politécnica de Valéncia, Camino de Vera S/N, Valencia 46022, Spain
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Emily Bucholz
- Department of Cardiology, Children's Hospital Colorado, University of Colorado Anschutz, 13123 E. 16th Ave, Aurora, CO 80045, United States
| | - Audrey Dionne
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Matthew D. Elias
- Division of Cardiology, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, United States
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Tomás González González
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Kate F. Kernan
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15213, United States
| | - Jessica Laird-Gion
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Sarah E. Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, NCRC Bldg 400, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
| | - Taha Mohseni Ahooyi
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, Roberts Building, 734 Schuylkill Ave, Philadelphia, PA 19146, United States
| | - Cinta Moraleda
- Pediatric Infectious Disease Department, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Mohsin A. Shah
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children’s, Wake Forest University School of Medicine, Medical Center Boulevard, Winston Salem, NC 27157, United States
| | - Anastasia Spiridou
- Data Research, Innovation and Virtual Environments, Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, United States
- The Department of Pediatrics, University of Pennsylvania Perelman Medical School, 3601 Civic Center Blvd, 6032 Colket, Philadelphia, PA 19104, United States
| | - Guillaume Verdy
- IAM Unit, Bordeaux University Hospital, Place amélie rabat Léon, Bordeaux 33076, France
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, 3501 5th Avenue, BST-3 Suite 7014, Pittsburgh, PA 15260, United States
| | - Joany M. Zachariasse
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Jane W. Newburger
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
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Huespe IA, Ferraris A, Lalueza A, Valdez PR, Peroni ML, Cayetti LA, Mirofsky MA, Boietti B, Gómez-Huelgas R, Casas-Rojo JM, Antón-Santos JM, Núñez-Cortés JM, Lumbreras C, Ramos-Rincón JM, Barrio NG, Pedrera-Jiménez M, Martin-Escalante MD, Ruiz FR, Onieva-García MÁ, Toso CR, Risk MR, Klén R, Pollán JA, Gómez-Varela D. COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study. J Med Virol 2023; 95:e28786. [PMID: 37212340 DOI: 10.1002/jmv.28786] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/21/2023] [Accepted: 05/01/2023] [Indexed: 05/23/2023]
Abstract
The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).
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Affiliation(s)
- Ivan A Huespe
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
- Medicine Department, University of Buenos Aires, Buenos Aires, Argentina
| | - Augusto Ferraris
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Antonio Lalueza
- 12 de Octubre University Hospital, Research Institute of Hospital 12 de Octubre (imas+12), Complutense University, Madrid, Spain
| | | | - Maria L Peroni
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Luis A Cayetti
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Matias A Mirofsky
- Hospital Municipal de Agudos "Dr. Leónidas Lucero", Bahía Blanca, Argentina
| | - Bruno Boietti
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - Ricardo Gómez-Huelgas
- Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA), Málaga, Spain
| | | | | | | | - Carlos Lumbreras
- 12 de Octubre University Hospital, Research Institute of Hospital 12 de Octubre (imas+12), Complutense University, Madrid, Spain
| | | | - Noelia G Barrio
- 12 de Octubre University Hospital, Research Institute of Hospital 12 de Octubre (imas+12), Complutense University, Madrid, Spain
| | - Miguel Pedrera-Jiménez
- 12 de Octubre University Hospital, Research Institute of Hospital 12 de Octubre (imas+12), Complutense University, Madrid, Spain
| | | | | | | | - Carlos R Toso
- Medicine Department, University of Buenos Aires, Buenos Aires, Argentina
| | - Marcelo R Risk
- Instituto de Medicina Traslacional e Ingeniería Biomédica (IMTIB), CONICET-HIBA-IUHI, Buenos Aires, Argentina
| | - Riku Klén
- Turku PET Centre, University of Turku and Turku University Hospital, Turku, Finland
| | - Javier A Pollán
- Intensive Care Unit, Italian Hospital of Buenos Aires, Buenos Aires, Argentina
| | - David Gómez-Varela
- Department of Pharmaceutical Sciences, Division of Pharmacology and Toxicology, University of Vienna, Vienna, Austria
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Frid S, Pastor Duran X, Bracons Cucó G, Pedrera-Jiménez M, Serrano-Balazote P, Muñoz Carrero A, Lozano-Rubí R. An Ontology-Based Approach for Consolidating Patient Data Standardized With European Norm/International Organization for Standardization 13606 (EN/ISO 13606) Into Joint Observational Medical Outcomes Partnership (OMOP) Repositories: Description of a Methodology. JMIR Med Inform 2023; 11:e44547. [PMID: 36884279 PMCID: PMC10034609 DOI: 10.2196/44547] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/28/2022] [Accepted: 01/05/2023] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND To discover new knowledge from data, they must be correct and in a consistent format. OntoCR, a clinical repository developed at Hospital Clínic de Barcelona, uses ontologies to represent clinical knowledge and map locally defined variables to health information standards and common data models. OBJECTIVE The aim of the study is to design and implement a scalable methodology based on the dual-model paradigm and the use of ontologies to consolidate clinical data from different organizations in a standardized repository for research purposes without loss of meaning. METHODS First, the relevant clinical variables are defined, and the corresponding European Norm/International Organization for Standardization (EN/ISO) 13606 archetypes are created. Data sources are then identified, and an extract, transform, and load process is carried out. Once the final data set is obtained, the data are transformed to create EN/ISO 13606-normalized electronic health record (EHR) extracts. Afterward, ontologies that represent archetyped concepts and map them to EN/ISO 13606 and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) standards are created and uploaded to OntoCR. Data stored in the extracts are inserted into its corresponding place in the ontology, thus obtaining instantiated patient data in the ontology-based repository. Finally, data can be extracted via SPARQL queries as OMOP CDM-compliant tables. RESULTS Using this methodology, EN/ISO 13606-standardized archetypes that allow for the reuse of clinical information were created, and the knowledge representation of our clinical repository by modeling and mapping ontologies was extended. Furthermore, EN/ISO 13606-compliant EHR extracts of patients (6803), episodes (13,938), diagnosis (190,878), administered medication (222,225), cumulative drug dose (222,225), prescribed medication (351,247), movements between units (47,817), clinical observations (6,736,745), laboratory observations (3,392,873), limitation of life-sustaining treatment (1,298), and procedures (19,861) were created. Since the creation of the application that inserts data from extracts into the ontologies is not yet finished, the queries were tested and the methodology was validated by importing data from a random subset of patients into the ontologies using a locally developed Protégé plugin ("OntoLoad"). In total, 10 OMOP CDM-compliant tables ("Condition_occurrence," 864 records; "Death," 110; "Device_exposure," 56; "Drug_exposure," 5609; "Measurement," 2091; "Observation," 195; "Observation_period," 897; "Person," 922; "Visit_detail," 772; and "Visit_occurrence," 971) were successfully created and populated. CONCLUSIONS This study proposes a methodology for standardizing clinical data, thus allowing its reuse without any changes in the meaning of the modeled concepts. Although this paper focuses on health research, our methodology suggests that the data be initially standardized per EN/ISO 13606 to obtain EHR extracts with a high level of granularity that can be used for any purpose. Ontologies constitute a valuable approach for knowledge representation and standardization of health information in a standard-agnostic manner. With the proposed methodology, institutions can go from local raw data to standardized, semantically interoperable EN/ISO 13606 and OMOP repositories.
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Affiliation(s)
- Santiago Frid
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
| | - Xavier Pastor Duran
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
| | | | | | | | - Adolfo Muñoz Carrero
- Unit of Investigation in Telemedicine and Digital Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Raimundo Lozano-Rubí
- Medical Informatics Unit, Hospital Clínic de Barcelona, Barcelona, Spain
- Clinical Foundations Department, Universitat de Barcelona, Barcelona, Spain
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Quirós-González V, Rubio R, Pulido F, Rial-Crestelo D, Martín-Jurado C, Hernández-Ros MÁ, López-Jiménez EA, Ferrari JM, Caro-Teller JM, Pinar Ó, Pedrera-Jiménez M, García-Barrio N, Serrano P, Bernal JL. Healthcare outcomes in patients with HIV infection at a tertiary hospital during the COVID-19 pandemic. Enferm Infecc Microbiol Clin 2023; 41:149-154. [PMID: 34456409 PMCID: PMC8380489 DOI: 10.1016/j.eimc.2021.07.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/25/2021] [Indexed: 11/02/2022]
Abstract
Background The COVID-19 pandemic has affected the care of patients with other diseases. Difficulty in access to healthcare during these months has been especially relevant for persons with HIV infection (PWH). This study therefore sought to ascertain the clinical outcomes and effectiveness of the measures implemented among PWH in a region with one of the highest incidence rates in Europe. Methods Retrospective, observational, pre-post intervention study to compare the outcomes of PWH attended at a high-complexity healthcare hospital from March to October 2020 and during the same months across the period 2016-2019. The intervention consisted of home drug deliveries and preferential use of non face-to-face consultations. The effectiveness of the measures implemented was determined by reference to the number of emergency visits, hospitalisations, mortality rate, and percentage of PWH with viral load >50 copies, before and after the two pandemic waves. Results A total of 2760 PWH were attended from January 2016 to October 2020. During the pandemic, there was a monthly mean of 106.87 telephone consultations and 2075 home deliveries of medical drugs dispensed to ambulatory patients. No statistically significant differences were found between the rate of admission of patients with COVID-HIV co-infection and that of the remaining patients (1172.76 admissions/100,000 population vs. 1424.29, p = 0.401) or in mortality (11.54% vs. 12.96%, p = 0.939). The percentage of PWH with viral load >50 copies was similar before and after the pandemic (1.20% pre-pandemic vs. 0.51% in 2020, p = 0.078). Conclusion Our results show that the strategies implemented during the first 8 months of the pandemic prevented any deterioration in the control and follow-up parameters routinely used on PWH. Furthermore, they contribute to the debate about how telemedicine and telepharmacy can fit into future healthcare models.
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Affiliation(s)
| | - Rafael Rubio
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Federico Pulido
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - David Rial-Crestelo
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Carlos Martín-Jurado
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - María Ángeles Hernández-Ros
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | | | | | - Óscar Pinar
- Servicio de Farmacia, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | | | - Pablo Serrano
- Dirección de Planificación, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - José Luis Bernal
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
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6
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Quirós-González V, Rubio R, Pulido F, Rial-Crestelo D, Martín-Jurado C, Hernández-Ros MÁ, López-Jiménez EA, Ferrari JM, Caro-Teller JM, Pinar Ó, Pedrera-Jiménez M, García-Barrio N, Serrano P, Bernal JL. Healthcare outcomes in patients with HIV infection at a tertiary hospital during the COVID-19 pandemic. Enferm Infecc Microbiol Clin (Engl Ed) 2023; 41:149-154. [PMID: 36870732 PMCID: PMC9977934 DOI: 10.1016/j.eimce.2021.07.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 07/25/2021] [Indexed: 03/05/2023]
Abstract
BACKGROUND The COVID-19 pandemic has affected the care of patients with other diseases. Difficulty in access to healthcare during these months has been especially relevant for persons with HIV infection (PWH). This study therefore sought to ascertain the clinical outcomes and effectiveness of the measures implemented among PWH in a region with one of the highest incidence rates in Europe. METHODS Retrospective, observational, pre-post intervention study to compare the outcomes of PWH attended at a high-complexity healthcare hospital from March to October 2020 and during the same months across the period 2016-2019. The intervention consisted of home drug deliveries and preferential use of non face-to-face consultations. The effectiveness of the measures implemented was determined by reference to the number of emergency visits, hospitalisations, mortality rate, and percentage of PWH with viral load >50copies, before and after the two pandemic waves. RESULTS A total of 2760 PWH were attended from January 2016 to October 2020. During the pandemic, there was a monthly mean of 106.87 telephone consultations and 2075 home deliveries of medical drugs dispensed to ambulatory patients. No statistically significant differences were found between the rate of admission of patients with COVID-HIV co-infection and that of the remaining patients (1172.76 admissions/100,000 population vs. 1424.29, p=0.401) or in mortality (11.54% vs. 12.96%, p=0.939). The percentage of PWH with viral load >50copies was similar before and after the pandemic (1.20% pre-pandemic vs. 0.51% in 2020, p=0.078). CONCLUSION Our results show that the strategies implemented during the first 8 months of the pandemic prevented any deterioration in the control and follow-up parameters routinely used on PWH. Furthermore, they contribute to the debate about how telemedicine and telepharmacy can fit into future healthcare models.
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Affiliation(s)
| | - Rafael Rubio
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Federico Pulido
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - David Rial-Crestelo
- Unidad de Infección VIH, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Carlos Martín-Jurado
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - María Ángeles Hernández-Ros
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | | | | | - Óscar Pinar
- Servicio de Farmacia, Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | | | - Pablo Serrano
- Dirección de Planificación, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - José Luis Bernal
- Servicio de Análisis de Información y Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain
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7
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Pedrera-Jiménez M, García-Barrio N, Rubio-Mayo P, Tato-Gómez A, Cruz-Bermúdez JL, Bernal-Sobrino JL, Muñoz-Carrero A, Serrano-Balazote P. TransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse. Methods Inf Med 2022; 61:e89-e102. [PMID: 36220109 PMCID: PMC9788916 DOI: 10.1055/s-0042-1757763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND During the COVID-19 pandemic, several methodologies were designed for obtaining electronic health record (EHR)-derived datasets for research. These processes are often based on black boxes, on which clinical researchers are unaware of how the data were recorded, extracted, and transformed. In order to solve this, it is essential that extract, transform, and load (ETL) processes are based on transparent, homogeneous, and formal methodologies, making them understandable, reproducible, and auditable. OBJECTIVES This study aims to design and implement a methodology, according with FAIR Principles, for building ETL processes (focused on data extraction, selection, and transformation) for EHR reuse in a transparent and flexible manner, applicable to any clinical condition and health care organization. METHODS The proposed methodology comprises four stages: (1) analysis of secondary use models and identification of data operations, based on internationally used clinical repositories, case report forms, and aggregated datasets; (2) modeling and formalization of data operations, through the paradigm of the Detailed Clinical Models; (3) agnostic development of data operations, selecting SQL and R as programming languages; and (4) automation of the ETL instantiation, building a formal configuration file with XML. RESULTS First, four international projects were analyzed to identify 17 operations, necessary to obtain datasets according to the specifications of these projects from the EHR. With this, each of the data operations was formalized, using the ISO 13606 reference model, specifying the valid data types as arguments, inputs and outputs, and their cardinality. Then, an agnostic catalog of data was developed through data-oriented programming languages previously selected. Finally, an automated ETL instantiation process was built from an ETL configuration file formally defined. CONCLUSIONS This study has provided a transparent and flexible solution to the difficulty of making the processes for obtaining EHR-derived data for secondary use understandable, auditable, and reproducible. Moreover, the abstraction carried out in this study means that any previous EHR reuse methodology can incorporate these results into them.
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Affiliation(s)
- Miguel Pedrera-Jiménez
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain,ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain,Address for correspondence Miguel Pedrera-Jiménez, Eng, MSc Health Informatics DepartmentHospital Universitario 12 de Octubre, Av. de Córdoba, s/n, 28041 MadridSpain
| | - Noelia García-Barrio
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Paula Rubio-Mayo
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Alberto Tato-Gómez
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Juan Luis Cruz-Bermúdez
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
| | - José Luis Bernal-Sobrino
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
| | | | - Pablo Serrano-Balazote
- Data Science Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre, Madrid, Spain
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8
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Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T. SurvMaximin: Robust federated approach to transporting survival risk prediction models. J Biomed Inform 2022; 134:104176. [PMID: 36007785 PMCID: PMC9707637 DOI: 10.1016/j.jbi.2022.104176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 08/15/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVE For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.
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Affiliation(s)
- Xuan Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xin Xiong
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | - Rui Duan
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Meghan R Hutch
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Romain Griffier
- IAM unit, Bordeaux University Hospital, Bordeaux, France; INSERM Bordeaux Population Health ERIAS TEAM, ERIAS - Inserm U1219 BPH, Bordeaux, France
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems, Singapore
| | - Gilbert S Omenn
- Depts of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, Public Health University of Michigan, Ann Arbor, MI, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center
| | | | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | | | | | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Valentina Tibollo
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zijian Guo
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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9
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Sanz A, Ayala R, Hernández G, Lopez N, Gil-Alos D, Gil R, Colmenares R, Carreño-Tarragona G, Sánchez-Pina J, Alonso RA, García-Barrio N, Pérez-Rey D, Meloni L, Calbacho M, Cruz-Rojo J, Pedrera-Jiménez M, Serrano-Balazote P, de la Cruz J, Martínez-López J. Outcomes and patterns of treatment in chronic myeloid leukemia, a global perspective based on a real-world data global network. Blood Cancer J 2022; 12:94. [PMID: 35750670 PMCID: PMC9232604 DOI: 10.1038/s41408-022-00692-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/29/2022] [Accepted: 06/07/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- A Sanz
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - R Ayala
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - G Hernández
- Biomedical Informatics Group, Universidad Politécnica de Madrid, Madrid, Spain
| | - N Lopez
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - D Gil-Alos
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - R Gil
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - R Colmenares
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - G Carreño-Tarragona
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - J Sánchez-Pina
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - R A Alonso
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - N García-Barrio
- Data Science Group, Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
| | - D Pérez-Rey
- Biomedical Informatics Group, Universidad Politécnica de Madrid, Madrid, Spain
| | - L Meloni
- TriNetX, LLC, Cambridge, MA, USA
| | - M Calbacho
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - J Cruz-Rojo
- Data Science Group, Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
| | - M Pedrera-Jiménez
- Data Science Group, Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
| | - P Serrano-Balazote
- Data Science Group, Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
| | - J de la Cruz
- Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
| | - J Martínez-López
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain.
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10
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Hong C, Zhang HG, L'Yi S, Weber G, Avillach P, Tan BWQ, Gutiérrez-Sacristán A, Bonzel CL, Palmer NP, Malovini A, Tibollo V, Luo Y, Hutch MR, Liu M, Bourgeois F, Bellazzi R, Chiovato L, Sanz Vidorreta FJ, Le TT, Wang X, Yuan W, Neuraz A, Benoit V, Moal B, Morris M, Hanauer DA, Maidlow S, Wagholikar K, Murphy S, Estiri H, Makoudjou A, Tippmann P, Klann J, Follett RW, Gehlenborg N, Omenn GS, Xia Z, Dagliati A, Visweswaran S, Patel LP, Mowery DL, Schriver ER, Samayamuthu MJ, Kavuluru R, Lozano-Zahonero S, Zöller D, Tan ALM, Tan BWL, Ngiam KY, Holmes JH, Schubert P, Cho K, Ho YL, Beaulieu-Jones BK, Pedrera-Jiménez M, García-Barrio N, Serrano-Balazote P, Kohane I, South A, Brat GA, Cai T. Changes in laboratory value improvement and mortality rates over the course of the pandemic: an international retrospective cohort study of hospitalised patients infected with SARS-CoV-2. BMJ Open 2022; 12:e057725. [PMID: 35738646 PMCID: PMC9226470 DOI: 10.1136/bmjopen-2021-057725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 06/12/2022] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVE To assess changes in international mortality rates and laboratory recovery rates during hospitalisation for patients hospitalised with SARS-CoV-2 between the first wave (1 March to 30 June 2020) and the second wave (1 July 2020 to 31 January 2021) of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS This is a retrospective cohort study of 83 178 hospitalised patients admitted between 7 days before or 14 days after PCR-confirmed SARS-CoV-2 infection within the Consortium for Clinical Characterization of COVID-19 by Electronic Health Record, an international multihealthcare system collaborative of 288 hospitals in the USA and Europe. The laboratory recovery rates and mortality rates over time were compared between the two waves of the pandemic. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome was all-cause mortality rate within 28 days after hospitalisation stratified by predicted low, medium and high mortality risk at baseline. The secondary outcome was the average rate of change in laboratory values during the first week of hospitalisation. RESULTS Baseline Charlson Comorbidity Index and laboratory values at admission were not significantly different between the first and second waves. The improvement in laboratory values over time was faster in the second wave compared with the first. The average C reactive protein rate of change was -4.72 mg/dL vs -4.14 mg/dL per day (p=0.05). The mortality rates within each risk category significantly decreased over time, with the most substantial decrease in the high-risk group (42.3% in March-April 2020 vs 30.8% in November 2020 to January 2021, p<0.001) and a moderate decrease in the intermediate-risk group (21.5% in March-April 2020 vs 14.3% in November 2020 to January 2021, p<0.001). CONCLUSIONS Admission profiles of patients hospitalised with SARS-CoV-2 infection did not differ greatly between the first and second waves of the pandemic, but there were notable differences in laboratory improvement rates during hospitalisation. Mortality risks among patients with similar risk profiles decreased over the course of the pandemic. The improvement in laboratory values and mortality risk was consistent across multiple countries.
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Affiliation(s)
- Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois, USA
| | - Molei Liu
- Department of Biostatistics, Harvard University T H Chan School of Public Health, Boston, Massachusetts, USA
| | - Florence Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Lombardia, Italy
| | | | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hopital Universitaire Necker-Enfants Malades, Paris, Île-de-France, France
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Sarah Maidlow
- MICHR Informatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kavishwar Wagholikar
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shawn Murphy
- Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Medical Center-University of Freiburg, Freiburg, Baden-Württemberg, Germany
| | - Jeffery Klann
- Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, Los Angeles, California, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Gilbert S Omenn
- Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Kansas, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA
| | | | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, University of Freiburg Faculty of Medicine, Freiburg, Baden-Württemberg, Germany
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Kee Yuan Ngiam
- Department of Surgery, National University Hospital, Singapore
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, USA
| | | | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Pablo Serrano-Balazote
- Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Comunidad de Madrid, Spain
| | - Isaac Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Andrew South
- Department of Pediatrics, Section of Nephrology, Wake Forest University, Winston Salem, North Carolina, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - T Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
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11
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Klén R, Purohit D, Gómez-Huelgas R, Casas-Rojo JM, Antón-Santos JM, Núñez-Cortés JM, Lumbreras C, Ramos-Rincón JM, García Barrio N, Pedrera-Jiménez M, Lalueza Blanco A, Martin-Escalante MD, Rivas-Ruiz F, Onieva-García MÁ, Young P, Ramirez JI, Titto Omonte EE, Gross Artega R, Canales Beltrán MT, Valdez PR, Pugliese F, Castagna R, Huespe IA, Boietti B, Pollan JA, Funke N, Leiding B, Gómez-Varela D. Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study. eLife 2022; 11:e75985. [PMID: 35579324 PMCID: PMC9129872 DOI: 10.7554/elife.75985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/24/2022] [Indexed: 11/29/2022] Open
Abstract
New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.
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Affiliation(s)
- Riku Klén
- Turku PET Centre, University of Turku and Turku University HospitalTurkuFinland
| | - Disha Purohit
- Max Planck Institute of Experimental MedicineGöttingenGermany
| | - Ricardo Gómez-Huelgas
- Internal Medicine Department, Regional University Hospital of Málaga, Biomedical Research Institute of Málaga (IBIMA), University of Málaga (UMA)MálagaSpain
| | | | | | | | - Carlos Lumbreras
- Internal Medicine Department, 12 de Octubre University HospitalMadridSpain
| | - José Manuel Ramos-Rincón
- Internal Medicine Department, General University Hospital of Alicante, Alicante Institute for 22 Health and Biomedical Research (ISABIAL)AlicanteSpain
| | | | | | | | | | | | | | - Pablo Young
- Hospital Británico of Buenos AiresBuenos AiresArgentina
| | | | | | | | | | | | | | | | - Ivan A Huespe
- Hospital Italiano de Buenos AiresBuenos AiresArgentina
| | - Bruno Boietti
- Hospital Italiano de Buenos AiresBuenos AiresArgentina
| | | | - Nico Funke
- Max Planck Institute for Experimental MedicineGöttingenGermany
| | - Benjamin Leiding
- Institute for Software and Systems Engineering at TU ClausthalClausthalGermany
| | - David Gómez-Varela
- Max Planck Institute for Experimental MedicineGöttingenGermany
- Systems Biology of Pain, Division of Pharmacology & Toxicology, Department of Pharmaceutical Sciences, University of ViennaViennaAustria
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12
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Martinez-Lopez J, Hernandez-Ibarburu G, Alonso R, Sanchez-Pina JM, Zamanillo I, Lopez-Muñoz N, Iñiguez R, Cuellar C, Calbacho M, Paciello ML, Ayala R, García-Barrio N, Perez-Rey D, Meloni L, Cruz J, Pedrera-Jiménez M, Serrano-Balazote P, de la Cruz J. Impact of COVID-19 in patients with multiple myeloma based on a global data network. Blood Cancer J 2021; 11:198. [PMID: 34893583 PMCID: PMC8661359 DOI: 10.1038/s41408-021-00588-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/18/2021] [Accepted: 11/23/2021] [Indexed: 12/13/2022] Open
Abstract
The COVID-19 pandemic has represented a major cause of morbidity/mortality worldwide, overstressing health systems. Multiple myeloma (MM) patients show an increased risk for infections and they are expected to be particularly vulnerable to SARS-CoV-2 infection. Here we have obtained a comprehensive picture of the impact of COVID-19 in MM patients on a local and a global scale using a federated data research network (TriNetX) that provided access to Electronic Medical Records (EMR) from Health Care Organizations (HCO) all over the world. Through propensity score matched analyses we found that the number of new diagnoses of MM was reduced in 2020 compared to 2019 (RR 0.86, 95%CI 0.76-0.96) and the survival of newly diagnosed MM cases decreased similarly (HR 0.61, 0.38-0.81). MM patients showed higher risk of SARS-CoV-2 infection (RR 2.09, 1.58-2.76) and a higher excess mortality in 2020 (difference in excess mortality 9%, 4.4-13.2) than non-MM patients. By interrogating large EMR datasets from HCO in Europe and globally, we confirmed that MM patients have been more severely impacted by COVID-19 pandemic than non-MM patients. This study highlights the necessity of extending preventive measures worlwide to protect vulnerable patients from SARS-CoV-2 infection by promoting social distancing and an intensive vaccination strategies.
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Affiliation(s)
- J Martinez-Lopez
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain.
| | | | - R Alonso
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - J M Sanchez-Pina
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - I Zamanillo
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - N Lopez-Muñoz
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - Rodrigo Iñiguez
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - C Cuellar
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - M Calbacho
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - M L Paciello
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | - R Ayala
- Hematology Department, Hospital 12 de Octubre, Complutense University, CNIO, Madrid, Spain
| | | | - D Perez-Rey
- Biomedical Informatics Group, Universidad Politécnica de Madrid, Madrid, Spain
| | - L Meloni
- TriNetX, LLC, Cambridge, MA, USA
| | - J Cruz
- Data Science Group, Hospital 12 de Octubre, Madrid, Spain
| | | | | | - J de la Cruz
- Research Institute imas12, Hospital 12 de Octubre, Madrid, Spain
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13
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Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Daniar M, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. J Med Internet Res 2021; 23:e22219. [PMID: 33600347 PMCID: PMC7927948 DOI: 10.2196/22219] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/14/2020] [Accepted: 01/10/2021] [Indexed: 12/13/2022] Open
Abstract
Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Bruce J Aronow
- Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,ICS Maugeri, Pavia, Italy
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Marzyeh Ghassemi
- Department of Computer Science and Medicine, University of Toronto, Toronto, ON, Canada
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jeffrey G Klann
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Mohamad Daniar
- Clinical Research Informatics, Boston Children's Hospital, Boston, MA, United States
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Necker-Enfant Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.,Centre de Recherche des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, National University of Singapore, Singapore, Singapore
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, United States
| | - Bradley W Taylor
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Carlo Torti
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Andrew K Vallejos
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kavishwar B Wagholikar
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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14
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Pedrera-Jiménez M, García-Barrio N, Cruz-Rojo J, Terriza-Torres AI, López-Jiménez EA, Calvo-Boyero F, Jiménez-Cerezo MJ, Blanco-Martínez AJ, Roig-Domínguez G, Cruz-Bermúdez JL, Bernal-Sobrino JL, Serrano-Balazote P, Muñoz-Carrero A. Obtaining EHR-derived datasets for COVID-19 research within a short time: a flexible methodology based on Detailed Clinical Models. J Biomed Inform 2021; 115:103697. [PMID: 33548541 PMCID: PMC7857038 DOI: 10.1016/j.jbi.2021.103697] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/18/2020] [Accepted: 02/01/2021] [Indexed: 10/27/2022]
Abstract
BACKGROUND COVID-19 ranks as the single largest health incident worldwide in decades. In such a scenario, electronic health records (EHRs) should provide a timely response to healthcare needs and to data uses that go beyond direct medical care and are known as secondary uses, which include biomedical research. However, it is usual for each data analysis initiative to define its own information model in line with its requirements. These specifications share clinical concepts, but differ in format and recording criteria, something that creates data entry redundancy in multiple electronic data capture systems (EDCs) with the consequent investment of effort and time by the organization. OBJECTIVE This study sought to design and implement a flexible methodology based on detailed clinical models (DCM), which would enable EHRs generated in a tertiary hospital to be effectively reused without loss of meaning and within a short time. MATERIAL AND METHODS The proposed methodology comprises four stages: (1) specification of an initial set of relevant variables for COVID-19; (2) modeling and formalization of clinical concepts using ISO 13606 standard and SNOMED CT and LOINC terminologies; (3) definition of transformation rules to generate secondary use models from standardized EHRs and development of them using R language; and (4) implementation and validation of the methodology through the generation of the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC-WHO) COVID-19 case report form. This process has been implemented into a 1300-bed tertiary Hospital for a cohort of 4489 patients hospitalized from 25 February 2020 to 10 September 2020. RESULTS An initial and expandable set of relevant concepts for COVID-19 was identified, modeled and formalized using ISO-13606 standard and SNOMED CT and LOINC terminologies. Similarly, an algorithm was designed and implemented with R and then applied to process EHRs in accordance with standardized concepts, transforming them into secondary use models. Lastly, these resources were applied to obtain a data extract conforming to the ISARIC-WHO COVID-19 case report form, without requiring manual data collection. The methodology allowed obtaining the observation domain of this model with a coverage of over 85% of patients in the majority of concepts. CONCLUSION This study has furnished a solution to the difficulty of rapidly and efficiently obtaining EHR-derived data for secondary use in COVID-19, capable of adapting to changes in data specifications and applicable to other organizations and other health conditions. The conclusion to be drawn from this initial validation is that this DCM-based methodology allows the effective reuse of EHRs generated in a tertiary Hospital during COVID-19 pandemic, with no additional effort or time for the organization and with a greater data scope than that yielded by conventional manual data collection process in ad-hoc EDCs.
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Affiliation(s)
- Miguel Pedrera-Jiménez
- Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, 28041 Madrid, Spain; ETSI Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
| | | | - Jaime Cruz-Rojo
- Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, 28041 Madrid, Spain.
| | | | | | | | | | | | | | | | | | | | - Adolfo Muñoz-Carrero
- Digital Health Research Dept., Instituto de Salud Carlos III, Av. de Monforte de Lemos, 5, 28029 Madrid, Spain.
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