A Method for Deriving Quasi-healthy Cohorts From Clinical Data.
We evaluated quasi-healthy cohorts (model cohorts), derived from clinical data, to determine how well they simulated control cohorts. Control cohorts comprised individuals extracted from a public checkup database in Japan, under the condition that their values for 3 basic laboratory tests fall withi...
| Publicado en: | Biomedical Informatics Insights no. 10; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2018
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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=134199453&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134199453 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2018 iid: 10 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 134199453 134199453 134199453 10.1177/1178222618777758 134199453 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Method for Deriving Quasi-healthy Cohorts From Clinical Data. aug: au: Irino, Satoshi Kurihara, Yukio affil: Department of Nursing, Ehime Prefectural University of Health Sciences, Tobe-cho, Japan sug: subj: Clinical Data Repository Health Status Control Group Human Databases, Health Japan Diagnosis, Laboratory Reference Values Outpatients Hospitalization Time Factors Age Factors Sex Factors ab: We evaluated quasi-healthy cohorts (model cohorts), derived from clinical data, to determine how well they simulated control cohorts. Control cohorts comprised individuals extracted from a public checkup database in Japan, under the condition that their values for 3 basic laboratory tests fall within specific reference ranges (3Ts condition). Model cohorts comprised outpatients, extracted from a clinical database at a hospital, under the 3Ts condition or under the condition that their values for 4 laboratory tests fall within specific reference ranges (4Ts condition). Because even a patient with a serious illness, such as cancer, may present with normal values on basic laboratory tests, one additional condition was added: the duration (1 or 3 months; 1M or 3M) during which patients were not hospitalized after their first laboratory test. For evaluations, cohorts were specified by age and sex. The 4Ts + 3M condition was the most effective condition, under which model cohorts were used to successfully simulate age-dependent changes and sex differences in laboratory test values for control cohorts. Therefore, by properly setting the conditions for extracting quasi-healthy individuals, we can derive cohorts from clinical data to simulate various types of cohorts. Although some issues with the proposed method remain to be solved, this approach presents new possibilities for using clinical data for cohort studies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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