Dataset Information

An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study.

ABSTRACT: Underuse or unavailability of spirometry is one of the most important factors causing underdiagnosis of COPD. We reported the development of a COPD prediction model to identify at-risk, undiagnosed COPD patients when spirometry was unavailable. This cross-sectional study enrolled subjects aged ?40 years with respiratory symptoms and a smoking history (?20 pack-years) in a medical center in two separate periods (development and validation cohorts). All subjects completed COPD assessment test (CAT), peak expiratory flow rate (PEFR) measurement, and confirmatory spirometry. A binary logistic model with calibration (Hosmer-Lemeshow test) and discrimination (area under receiver operating characteristic curve [AUROC]) was implemented. Three hundred and one subjects (development cohort) completed the study, including non-COPD (154, 51.2%) and COPD cases (147; stage I, 27.2%; II, 55.8%; III-IV, 17%). Compared with non-COPD and GOLD I cases, GOLD II-IV patients exhibited significantly higher CAT scores and lower lung function, and were considered clinically significant for COPD. Four independent variables (age, smoking pack-years, CAT score, and percent predicted PEFR) were incorporated developing the prediction model, which estimated the COPD probability (P_COPD). This model demonstrated favorable discrimination (AUROC: 0.866/0.828; 95% CI 0.825-0.906/0.751-0.904) and calibration (Hosmer-Lemeshow P?=?0.332/0.668) for the development and validation cohorts, respectively. Bootstrap validation with 1000 replicates yielded an AUROC of 0.866 (95% CI 0.821-0.905). A P_COPD of ?0.65 identified COPD patients with high specificity (90%) and a large proportion (91.4%) of patients with clinically significant COPD (development cohort). Our prediction model can help physicians effectively identify at-risk, undiagnosed COPD patients for further diagnostic evaluation and timely treatment when spirometry is unavailable.

SUBMITTER: Su KC

PROVIDER: S-EPMC6538645 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study.

Su Kang-Cheng KC Ko Hsin-Kuo HK Chou Kun-Ta KT Hsiao Yi-Han YH Su Vincent Yi-Fong VY Perng Diahn-Warng DW Kou Yu Ru YR

NPJ primary care respiratory medicine 20190528 1

Underuse or unavailability of spirometry is one of the most important factors causing underdiagnosis of COPD. We reported the development of a COPD prediction model to identify at-risk, undiagnosed COPD patients when spirometry was unavailable. This cross-sectional study enrolled subjects aged ≥40 years with respiratory symptoms and a smoking history (≥20 pack-years) in a medical center in two separate periods (development and validation cohorts). All subjects completed COPD assessment test (CAT ...[more]

PMID: 31138809

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Project description:BackgroundIn the field of tuberculosis (TB), Community Healthcare Workers (CHWs) have been engaged for advocacy, case detection, and patient support in a wide range of settings. Estimates predict large-scale shortfalls of healthcare workers in low- and middle-income settings by 2030 and strategies are needed to optimize the health workforce to achieve universal availability and accessibility of healthcare. In 2018, the World Health Organization (WHO) published guidelines on best practices for CHW engagement, and identified remaining knowledge gaps. Stop TB Partnership's TB REACH initiative has supported interventions using CHWs to deliver TB care in over 30 countries, and utilized the same primary indicator to measure project impact at the population-level for all TB active case finding projects, which makes the results comparable across multiple settings. This study compiled 10 years of implementation data from the initiative's grantee network to begin to address key knowledge gaps in CHW networks.MethodsWe conducted a cross-sectional study analyzing the TB REACH data repository (n = 123) and primary survey responses (n = 50) of project implementers. We designed a survey based on WHO guidelines to understand projects' practices on CHW recruitment, training, activities, supervision, compensation, and sustainability. We segmented projects by TB notification impact and fitted linear random-effect regression models to identify practices associated with higher changes in notifications.ResultsMost projects employed CHWs for advocacy alongside case finding and holding activities. Model characteristics associated with higher project impact included incorporating e-learning in training and having the prospect of CHWs continuing their responsibilities at the close of a project. Factors that trended towards being associated with higher impact were community-based training, differentiated contracts, and non-monetary incentives.ConclusionIn line with WHO guidelines, our findings emphasize that successful implementation approaches provide CHWs with comprehensive training, continuous supervision, fair compensation, and are integrated within the existing primary healthcare system. However, we encountered a great degree of heterogeneity in CHW engagement models, resulting in few practices clearly associated with higher notifications.

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Dataset Information

An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study.

Publications

An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study.

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