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Disease-specific reference equations for lung function in patients with cystic fibrosis

Kulich M, Rosenfeld M, Campbell J, Kronmal R, Gibson RL, Goss CH, Ramsey B.

Jazyk angličtina Země Spojené státy americké

Perzistentní odkaz   https://www.medvik.cz/link/bmc07506663
E-zdroje Online

NLK Free Medical Journals od 1997-07-01 do Před 1 rokem
Freely Accessible Science Journals od 1997 do Před 1 rokem
ProQuest Central od 2003-02-01 do 2019-09-15
Open Access Digital Library od 1998-01-01
Nursing & Allied Health Database (ProQuest) od 2003-02-01 do 2019-09-15
Health & Medicine (ProQuest) od 2003-02-01 do 2019-09-15
Public Health Database (ProQuest) od 2003-02-01 do 2019-09-15

RATIONALE: Forced expiratory volume in one second (FEV1), an important measure of pulmonary disease severity in patients with cystic fibrosis (CF), is frequently expressed as a percentage of a predicted value derived from a healthy reference population. There are limitations to comparing the lung function of a patient with CF to that of healthy control subjects, and potential advantages to comparing it to that of other patients with CF. OBJECTIVE: To estimate CF-specific percentiles of FEV1 as functions of height, age, and sex. METHODS: We used 287,108 FEV1 observations among more than 21,000 patients with CF in the CF Foundation National Patient Registry between 1994 and 2001. The percentiles were estimated using quantile regression methods. RESULTS: FEV1 percentile "growth grids" are presented, allowing comparison of an individual's FEV1 to that of patients with CF of the same sex, age, and height. Their potential uses in clinical practice and research are illustrated. CONCLUSIONS: CF-specific reference equations allow individual patients' FEV1 to be placed in the context of the distribution of lung function of their peers with CF, and should improve generalizability of CF clinical trials by setting entry criteria that are equitable across sex and age ranges. They may serve as a useful adjunct to conventional reference equations.

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$a Department of Probability and Mathematical Statistics, Charles University, Prague, Czech Republic. kulich@karlin.mff.cuni.cz
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