Forced Expiratory Volume in 1 Second Variability Helps Identify Patients with Cystic Fibrosis at Risk of Greater Loss of Lung Function

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• 作者：Wayne J. Morgan ; MD ; CM<sup>1sup><sup> ; sup><sup>wmorgan@arc.arizona.edu" class="auth_mail" title="E-mail the corresponding authorsup> ; Donald R. VanDevanter ; PhD<sup>2sup> ; David J. Pasta ; MS<sup>3sup> ; Aimee J. Foreman ; MS<sup>3sup> ; Jeffrey S. Wagener ; MD<sup>4sup> ; Michael W. Konstan ; MD<sup>2sup> ; on behalf of the Scientific Advisory Group ; Wayne Morgan (chair) ; Michael Konstan (co-chair) ; Ted Liou ; Susanna McColley ; Ann McMullen ; Alexandra Quittner ; Warren Regelmann ; Clement Ren ; Margaret Rosenfeld ; Greg Sawicki ; Michael Schechter ; Donald VanDevanter ; Jeffrey Wagener ; Marlyn Woo
• 关键词：%pred ; Percent predicted ; CF ; Cystic fibrosis ; FEV1 ; Forced expiratory volume in 1 second
• 刊名：The Journal of Pediatrics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：169
• 期：Complete
• 页码：116-121.e2
• 全文大小：585 K

文摘

To evaluate several alternative measures of forced expiratory volume in 1 second percent predicted (FEV<sub>1sub> %pred) variability as potential predictors of future FEV<sub>1sub> %pred decline in patients with cystic fibrosis.

sSec_2">Study design

spara0015">We included 13 827 patients age ≥6 years from the Epidemiologic Study of Cystic Fibrosis 1994-2002 with ≥4 FEV<sub>1sub> %pred measurements spanning ≥366 days in both a 2-year baseline period and a 2-year follow-up period. We predicted change from best baseline FEV<sub>1sub> %pred to best follow-up FEV<sub>1sub> %pred and change from baseline to best in the second follow-up year by using multivariable regression stratified by 4 lung-disease stages. We assessed 5 measures of variability (some as deviations from the best and some as deviations from the trend line) both alone and after controlling for demographic and clinical factors and for the slope and level of FEV<sub>1sub> %pred.

sSec_3">Results

spara0020">All 5 measures of FEV<sub>1sub> %pred variability were predictive, but the strongest predictor was median deviation from the best FEV<sub>1sub> %pred in the baseline period. The contribution to explanatory power (R<sup>2sup>) was substantial and exceeded the total contribution of all other factors excluding the FEV<sub>1sub> %pred rate of decline. Adding the other variability measures provided minimal additional value.

sSec_4">Conclusions

spara0025">Median deviation from the best FEV<sub>1sub> %pred is a simple metric that markedly improves prediction of FEV<sub>1sub> %pred decline even after the inclusion of demographic and clinical characteristics and the FEV<sub>1sub> %pred rate of decline. The routine calculation of this variability measure could allow clinicians to better identify patients at risk and therefore in need of increased intervention.