Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study.
The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gol...
| Publicado en: | Brain Injury Vol. 38; no. 13; pp. 1084 - 1093 |
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| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=180329936&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 180329936 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02699052 B6W jtl: Brain Injury issn: 02699052 maglogo: Y pubinfo: dt: 2024 vid: 38 iid: 13 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 180329936 178436957 180329936 180329936 10.1080/02699052.2024.2373920 180329936 ppf: 1084 ppct: 9 formats: tig: atl: Development and validation of an electronic health record-based algorithm for identifying TBI in the VA: A VA Million Veteran Program study. aug: au: Merritt, Victoria C. Chen, Alicia W. Bonzel, Clara-Lea Hong, Chuan Sangar, Rahul Morini Sweet, Sara Sorg, Scott F. Chanfreau-Coffinier, Catherine affil: VA San Diego Healthcare System (VASDHS), San Diego, CA, USA sug: subj: Brain Injuries Diagnosis Electronic Health Records Algorithms Evaluation Veterans United States Department of Veterans Affairs Human Funding Source Male Female Adult Middle Age Aged Comparative Studies Record Review Descriptive Statistics Phenotype Prediction Models Probability Sensitivity and Specificity Predictive Value of Tests Surveys Military Health Veterans Health Services Validation Studies Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: The purpose of this study was to develop and validate an algorithm for identifying Veterans with a history of traumatic brain injury (TBI) in the Veterans Affairs (VA) electronic health record using VA Million Veteran Program (MVP) data. Manual chart review (n = 200) was first used to establish 'gold standard' diagnosis labels for TBI ('Yes TBI' vs. 'No TBI'). To develop our algorithm, we used PheCAP, a semi-supervised pipeline that relied on the chart review diagnosis labels to train and create a prediction model for TBI. Cross-validation was used to train and evaluate the proposed algorithm, 'TBI-PheCAP.' TBI-PheCAP performance was compared to existing TBI algorithms and phenotyping methods, and the final algorithm was run on all MVP participants (n = 702,740) to assign a predicted probability for TBI and a binary classification status choosing specificity = 90%. The TBI-PheCAP algorithm had an area under the receiver operating characteristic curve of 0.92, sensitivity of 84%, and positive predictive value (PPV) of 98% at specificity = 90%. TBI-PheCAP generally performed better than other classification methods, with equivalent or higher sensitivity and PPV than existing rules-based TBI algorithms and MVP TBI-related survey data. Given its strong classification metrics, the TBI-PheCAP algorithm is recommended for use in future population-based TBI research. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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