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- Sator, Lea, et al.
(författare)
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Overdiagnosis of COPD in Subjects With Unobstructed Spirometry A BOLD Analysis
- 2019
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Ingår i: Chest. - : Elsevier BV. - 0012-3692 .- 1931-3543. ; 156:2, s. 277-288
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: There are several reports on underdiagnosis of COPD, while little is known about COPD overdiagnosis and overtreatment. We describe the overdiagnosis and the prevalence of spirometrically defined false positive COPD, as well as their relationship with overtreatment across 23 population samples in 20 countries participating in the BOLD Study between 2003 and 2012.METHODS: A false positive diagnosis of COPD was considered when participants reported a doctor's diagnosis of COPD, but postbronchodilator spirometry was unobstructed (FEV1/FVC > LLN). Additional analyses were performed using the fixed ratio criterion (FEV1/FVC < 0.7).RESULTS: Among 16,177 participants, 919 (5.7%) reported a previous medical diagnosis of COPD. Postbronchodilator spirometry was unobstructed in 569 subjects (61.9%): false positive COPD. A similar rate of overdiagnosis was seen when using the fixed ratio criterion (55.3%). In a subgroup analysis excluding participants who reported a diagnosis of "chronic bronchitis" or "emphysema" (n = 220), 37.7% had no airflow limitation. The site-specific prevalence of false positive COPD varied greatly, from 1.9% in low- to middle-income countries to 4.9% in high-income countries. In multivariate analysis, overdiagnosis was more common among women, and was associated with higher education; former and current smoking; the presence of wheeze, cough, and phlegm; and concomitant medical diagnosis of asthma or heart disease. Among the subjects with false positive COPD, 45.7% reported current use of respiratory medication. Excluding patients with reported asthma, 34.4% of those with normal spirometry still used a respiratory medication.CONCLUSIONS: False positive COPD is frequent. This might expose nonobstructed subjects to possible adverse effects of respiratory medication.
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| 3. |
- Studnicka, Michael, et al.
(författare)
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COPD : Should Diagnosis Match Physiology?
- 2020
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Ingår i: Chest. - : Elsevier BV. - 0012-3692 .- 1931-3543. ; 157:2, s. 473-475
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Acharya, Chayan, et al.
(författare)
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A diagnostic tool for population models using non-compartmental analysis : The ncappc package for R
- 2016
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 127, s. 83-93
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Tidskriftsartikel (refereegranskat)abstract
- Background and objective: Non-compartmental analysis (NCA) calculates pharmacokinetic (PK) metrics related to the systemic exposure to a drug following administration, e.g. area under the concentration time curve and peak concentration. We developed a new package in R, called ncappc, to perform (i) a NCA and (ii) simulation-based posterior predictive checks (ppc) for a population PK (PopPK) model using NCA metrics. Methods: The nca feature of ncappc package estimates the NCA metrics by NCA. The ppc feature of ncappc estimates the NCA metrics from multiple sets of simulated concentration time data and compares them with those estimated from the observed data. The diagnostic analysis is performed at the population as well as the individual level. The distribution of the simulated population means of each NCA metric is compared with the corresponding observed population mean. The individual level comparison is performed based on the deviation of the mean of any NCA metric based on simulations for an individual from the corresponding NCA metric obtained from the observed data. The ncappc package also reports the normalized prediction distribution error (NPDE) of the simulated NCA metrics for each individual and their distribution within a population. Results: The ncappc produces two default outputs depending on the type of analysis performed, i.e., NCA and PopPK diagnosis. The PopPK diagnosis feature of ncappc produces 8 sets of graphical outputs to assess the ability of a population model to simulate the concentration time profile of a drug and thereby evaluate model adequacy. In addition, tabular outputs are generated showing the values of the NCA metrics estimated from the observed and the simulated data, along with the deviation, NPDE, regression parameters used to estimate the elimination rate constant and the related population statistics. Conclusions: The ncappc package is a versatile and flexible tool-set written in R that successfully estimates NCA metrics from concentration time data and produces a comprehensive set of graphical and tabular output to summarize the diagnostic results including the model specific outliers. The output is easy to interpret and to use in evaluation of a population PK model. ncappc is freely available on CRAN (http://crantoprojectorg/web/packages/ncappc/index.html/) and GitHub (https://github.comicacha0227/ncappc/).
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