Chest
Original ResearchCOPDInterpreting Lung Function Data Using 80% Predicted and Fixed Thresholds Identifies Patients at Increased Risk of Mortality
Section snippets
Materials and Methods
The Third National Health and Nutrition Examination Survey (NHANES) III was conducted from 1988 to 1994 by the National Center for Health Statistics of the Center for Disease Control and Prevention. In this study, a stratified, multistage, clustered probability design was used to select a sample of the US population. Study subjects completed extensive questionnaires in the household and a comprehensive physical examination, including pulmonary function testing, either in the household or at a
Results
The studied cohort consisted of 13,847 subjects representing an estimated 148 million US adults aged ≥ 25 years during 1988 to 1994. By the end of 2006, 3,774 subjects, representing an estimated 27 million (18.2% [weighted percentage]) of the original cohort, died.
The distribution of sex, age, BMI, education level, race/ethnicity, smoking status, comorbid disease, and respiratory disease stage are shown in Table 2, including the actual numbers of studied subjects and the weighted percentage.
Discussion
In this nationally representative data set, subjects classified as normal using LLN criteria but obstructed or restricted using GOLD criteria had a higher risk of mortality in up to 18 years of follow-up. This finding was seen in both the overall cohort and in a subset of subjects aged ≥ 60 years. These results challenge the opinion that persons whose lung function is above the LLN, but < 80% of their predicted values of the FEV1 or FVC are normal with regard to their respiratory status.
Perspective
This article attempts to address some important contrasts in respiratory medicine. The ongoing discussion over what defines abnormal lung function contrasts two strategies and has been featured in a point/counterpoint editorial debate in CHEST.11, 20 On one side is the contention that strict distributions of normal lung function in the population should define abnormal, and on the other side is the contention that clinical outcomes are a better arbiter of defining abnormal. The former results
Acknowledgments
Author contributions: Drs Mannino and Diaz-Guzman had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Dr Mannino: contributed to the development of the analytic concept, data analyses, interpretation of the results, and writing of the manuscript.
Dr Diaz-Guzman: contributed to the interpretation of the results and writing of the manuscript.
Financial/nonfinancial disclosures: The authors have reported to CHEST
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Funding/Support: The authors have reported to CHEST that no funding was received for this study.
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