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Improved survival prediction from lung function data in a large population sample
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DOI：10.1016/j.rmed.2008.09.016.png)
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Studies relating tung function to survival commonly express lung function impairment as a percent of predicted but this retains age, height and sex bias. We have studied alternative methods of expressing forced expiratory volume in 1 s (FEV1) for predicting all cause and airway related lung disease mortality in the Copenhagen City Heart Study data. Cox regression models were derived for survival over 25 years in 13,900 subjects. Age on entry, sex, smoking status, body mass index, previous myocardial infarction and diabetes were putative predictors together with FEV1 either as raw data, standardised by powers of height (FEV1/ht(n)), as percent of predicted (FEV1PP) or as standardised residuals (FEV1SR). Quintiles of FEV1/ht(2) were better at predicting all, cause mortality in multivariate models than FEV1PP and FEV1SR, with the hazard ratio (HR) for the worst quintiles being 2.8, 2.0 and 2.1 respectively. Cut levels of lung function were used to categorise impairment and the HR for multivariate prediction of all cause and airway related lung disease mortality were 10 and 2044 respectively for the worst category of FEV1/ht(2) compared to 5 and 194 respectively for the worst category of FEV1PP. In univariate predictions of all cause mortality the HR for FEV1/ht(2) categories was 2-4 times higher than those for FEV1PP and 3-10 times higher for airway related tung disease mortality. We conclude that FEV1/ht(2) is superior to FEV1PP for predicting survival. in a general population and this method of expressing FEV1 impairment best reflects hazard for subsequent death. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Lung function
FEV1
Survival
Epidemiology
Cox regression
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Optimum expression of adult lung function based on all-cause mortality: Results from the Reykjavik study.png)
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