Erik H J Hulzebos1, Hanna Bomhof-Roordink, Pauline B van de Weert-van Leeuwen, Jos W R Twisk, H G M Arets, Cornelis K van der Ent, Tim Takken. 1. 1Child Development and Exercise Center, Cystic Fibrosis Center, University Medical Center Utrecht, Utrecht, the NETHERLANDS; 2Department of Pediatric Pulmonology, Cystic Fibrosis Center, University Medical Center Utrecht, Utrecht, the NETHERLANDS; and 3EMGO Institute for Health and Care Research, VU University Medical Center, VU University Amsterdam, Amsterdam, the NETHERLANDS.
Abstract
INTRODUCTION: Lung function, nutritional status, and parameters of exercise capacity are known predictors of mortality in patients with cystic fibrosis (CF). The aim of the current study was to use these important parameters to develop a multivariate model to predict mortality in adolescent patients with CF. METHODS: A total of 127 adolescents with CF (57 girls) with a mean age of 12.7 ± 0.9 yr and a mean percentage of predicted forced expired volume in 1 s (FEV1% predicted) of 77.7% ± 15.6% were included. Cardiopulmonary exercise testing-derived parameters, nutritional status, and resting lung functions were dichotomized according to the criterion value determined using receiver operating characteristic curves. Body mass index (BMI), FEV1%predicted, predicted peak oxygen uptake corrected for body weight (VO2 peak/kg%predicted), peak minute ventilation (VE peak), peak VE/VO2, peak VE/VCO2, and breathing reserve were included in a multivariate model. The Cox proportional hazards model was used to determine the combination of parameters that best predicted mortality and/or lung transplantation. RESULTS: The mean duration of follow-up was 7.5 ± 2.7 yr, during which, nine of the 127 patients (7.1%) died and six (4.7%) underwent lung transplantation. Mortality in this population was best predicted by the model that included FEV1%predicted (hazard ratio, 17.13; 95% confidence interval (CI), 3.76-78.06), peak VE/VO2 (hazard ratio, 5.92; 95% CI, 1.27-27.63), and BMI (hazard ratio, 5.54; 95% CI, 1.82-16.83). CONCLUSIONS: The currently developed model consisting of BMI, FEV1%predicted, and VE/VO2 is a strong predictor of mortality rate in adolescents with CF. This prediction equation may be useful in clinical practice to detect patients with a high risk of mortality and to provide them with additional therapy earlier.
INTRODUCTION: Lung function, nutritional status, and parameters of exercise capacity are known predictors of mortality in patients with cystic fibrosis (CF). The aim of the current study was to use these important parameters to develop a multivariate model to predict mortality in adolescent patients with CF. METHODS: A total of 127 adolescents with CF (57 girls) with a mean age of 12.7 ± 0.9 yr and a mean percentage of predicted forced expired volume in 1 s (FEV1% predicted) of 77.7% ± 15.6% were included. Cardiopulmonary exercise testing-derived parameters, nutritional status, and resting lung functions were dichotomized according to the criterion value determined using receiver operating characteristic curves. Body mass index (BMI), FEV1%predicted, predicted peak oxygen uptake corrected for body weight (VO2 peak/kg%predicted), peak minute ventilation (VE peak), peak VE/VO2, peak VE/VCO2, and breathing reserve were included in a multivariate model. The Cox proportional hazards model was used to determine the combination of parameters that best predicted mortality and/or lung transplantation. RESULTS: The mean duration of follow-up was 7.5 ± 2.7 yr, during which, nine of the 127 patients (7.1%) died and six (4.7%) underwent lung transplantation. Mortality in this population was best predicted by the model that included FEV1%predicted (hazard ratio, 17.13; 95% confidence interval (CI), 3.76-78.06), peak VE/VO2 (hazard ratio, 5.92; 95% CI, 1.27-27.63), and BMI (hazard ratio, 5.54; 95% CI, 1.82-16.83). CONCLUSIONS: The currently developed model consisting of BMI, FEV1%predicted, and VE/VO2 is a strong predictor of mortality rate in adolescents with CF. This prediction equation may be useful in clinical practice to detect patients with a high risk of mortality and to provide them with additional therapy earlier.
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