External validation of multidimensional prognostic indices (ADO, BODEx and DOSE) in a primary care international cohort (PROEPOC/COPD cohort)

初级保健国际队列(PROEPOC/COPD 队列)中多维预后指数(ADO、BODEx 和 DOSE)的外部验证

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作者:Maite Espantoso-Romero, Miguel Román Rodríguez, Ana Duarte-Pérez, Jaime Gonzálvez-Rey, Pedro A Callejas-Cabanillas, Durdica Kasuba Lazic, Berta Anta-Agudo, Pere Torán Monserrat, Rosa Magallon-Botaya, Biljana Gerasimovska Kitanovska, Heidrun Lingner, Radost S Assenova, Claudia Iftode, Francisco Gude-

Background

Due to the heterogeneous and systemic nature of the chronic obstructive pulmonary disease (COPD), the new guidelines are oriented toward individualized attention. Multidimensional scales could facilitate its proper clinical and prognostic assessment, but not all of them were validated in an international primary care cohort, different from the original ones used for model development. Therefore, our main

Discussion

The Research Agenda for General Practice/Family Medicine highlights that the evidence on predictive values of prognostic indices in primary care is scarce. A prospective cohort like that of PROEPOC/COPD provides good opportunities for research into COPD and make communication easier between family practitioners, nursing staff, pneumologists and other professionals, supporting a multi-disciplinary approach to the treatment of these patients.

Methods

Design: External validation of scales, open and prospective cohort study in primary care. Setting: 36 health centres in 6 European high, medium and low income countries. Subjects: 477 patients diagnosed with COPD, captured in clinical visit by their General Practitioner/Nurse. Predictors: Detailed patient history, exacerbations, lung function test and questionnaires at baseline. Outcomes: Exacerbations, all-cause mortality and specific mortality, within 5 years of recruitment. Analysis: Multivariate logistic regression and Cox regression will be used. Possible non-linear effect of the indices will be studied by using Structured Additive Regression models with penalised splines. Subsequently, we will assess different aspects of the regression models: discrimination, calibration and diagnostic precision. Clinical variables modulated in primary care and the interval between exacerbations will be considered and incorporated into the analysis.

Trial registration

ISRCTN52402811 . Date: 15/01/2015. Prospectively registered.

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