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Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

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Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015)

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dc.contributor.author Rockenschaub, Patrick es_ES
dc.contributor.author Nguyen, Vincent es_ES
dc.contributor.author Aldridge, Robert W. es_ES
dc.contributor.author Acosta, Dionisio es_ES
dc.contributor.author Garcia-Gomez, Juan M es_ES
dc.contributor.author Sáez Silvestre, Carlos es_ES
dc.date.accessioned 2021-05-20T03:34:45Z
dc.date.available 2021-05-20T03:34:45Z
dc.date.issued 2020-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/166542
dc.description.abstract [EN] Objectives To demonstrate how data-driven variability methods can be used to identify changes in disease recording in two English electronic health records databases between 2001 and 2015. Design Repeated cross-sectional analysis that applied data-driven temporal variability methods to assess month-by-month changes in routinely collected medical data. A measure of difference between months was calculated based on joint distributions of age, gender, socioeconomic status and recorded cardiovascular diseases. Distances between months were used to identify temporal trends in data recording. Setting 400 English primary care practices from the Clinical Practice Research Datalink (CPRD GOLD) and 451 hospital providers from the Hospital Episode Statistics (HES). Main outcomes The proportion of patients (CPRD GOLD) and hospital admissions (HES) with a recorded cardiovascular disease (CPRD GOLD: coronary heart disease, heart failure, peripheral arterial disease, stroke; HES: International Classification of Disease codes I20-I69/G45). Results Both databases showed gradual changes in cardiovascular disease recording between 2001 and 2008. The recorded prevalence of included cardiovascular diseases in CPRD GOLD increased by 47%-62%, which partially reversed after 2008. For hospital records in HES, there was a relative decrease in angina pectoris (-34.4%) and unspecified stroke (-42.3%) over the same time period, with a concomitant increase in chronic coronary heart disease (+14.3%). Multiple abrupt changes in the use of myocardial infarction codes in hospital were found in March/April 2010, 2012 and 2014, possibly linked to updates of clinical coding guidelines. Conclusions Identified temporal variability could be related to potentially non-medical causes such as updated coding guidelines. These artificial changes may introduce temporal correlation among diagnoses inferred from routine data, violating the assumptions of frequently used statistical methods. Temporal variability measures provide an objective and robust technique to identify, and subsequently account for, those changes in electronic health records studies without any prior knowledge of the data collection process. es_ES
dc.description.sponsorship VN is funded by a Public Health England PhD Studentship. RWA is supported by a Wellcome Trust Clinical Research Career Development Fellowship (206602/Z/17/Z). JMGG and CS contributions to this work were partially supported by the MTS4up Spanish project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R), the CrowdHealth H2020-SC1-2016-CNECT project (No. 727560) (JMGG) and the Inadvance H2020-SC1-BHC-2018-2020 project (No. 825750). PR and DA did not receive any direct funding for this project. Access to the Clinical Practice Research Datalink was supported by the UK Economic and Social Research Council (ES/P008321/1). Access to aggregated Hospital Episode Statistics was provided by Public Health England. This work was further supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. es_ES
dc.language Inglés es_ES
dc.publisher BMJ es_ES
dc.relation AEI/DPI2016-80054-R es_ES
dc.relation.ispartof BMJ Open es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015) es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1136/bmjopen-2019-034396 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/727560/EU/Collective wisdom driving public health policies/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//ES%2FP008321%2F1/GB/Preserving Antibiotics through Safe Stewardship: PASS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/825750/EU/Patient-centred pathways of early palliative care, supportive ecosystems and appraisal standard/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/WT/Population and Public Health/206602/Public health data science to investigate and improve migrant health./
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2016-80054-R/ES/BIOMARCADORES DINAMICOS BASADOS EN FIRMAS TISULARES MULTIPARAMETRICAS PARA EL SEGUIMIENTO Y EVALUACION DE LA RESPUESTA A TRATAMIENTO DE PACIENTES CON GLIOBLASTOMA Y CANCER DE/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Rockenschaub, P.; Nguyen, V.; Aldridge, RW.; Acosta, D.; Garcia-Gomez, JM.; Sáez Silvestre, C. (2020). Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015). BMJ Open. 10(2):1-9. https://doi.org/10.1136/bmjopen-2019-034396 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1136/bmjopen-2019-034396 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 9 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2044-6055 es_ES
dc.identifier.pmid 32060159 es_ES
dc.identifier.pmcid PMC7045100 es_ES
dc.relation.pasarela S\435463 es_ES
dc.contributor.funder Wellcome Trust es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.contributor.funder Welsh Government es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Scottish Government es_ES
dc.contributor.funder British Heart Foundation es_ES
dc.contributor.funder Medical Research Council, Reino Unido es_ES
dc.contributor.funder Economic and Social Research Council, Reino Unido es_ES
dc.contributor.funder Engineering and Physical Sciences Research Council, Reino Unido es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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