Prediction of Mortality in Adolescents with Cystic Fibrosis

Hulzebos, Erik H. J.1; Bomhof-Roordink, Hanna1,3; van de Weert-van Leeuwen, Pauline B.2; Twisk, Jos W. R.3; Arets, H. G. M.2; van der Ent, Cornelis K.2; Takken, Tim1

Medicine & Science in Sports & Exercise: November 2014 - Volume 46 - Issue 11 - p 2047–2052
doi: 10.1249/MSS.0000000000000344
Clinical Sciences

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 (V˙O2peak/kg%predicted), peak minute ventilation (V˙Epeak), peak V˙E/V˙O2, peak V˙E/V˙CO2, 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 V˙E/V˙O2 (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 V˙E/V˙O2 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.

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

Address for correspondence: Erik H. J. Hulzebos, Ph.D., Child Development and Exercise Centre, Cystic Fibrosis Center, University Medical Center Utrecht, KB 02.056.0, PO Box 85090, 3508 AB Utrecht, the Netherlands; E-mail: h.hulzebos@umcutrecht.nl.

Submitted for publication November 2013.

Accepted for publication March 2014.

Article Outline

Life expectancy in patients with cystic fibrosis (CF) is increasing (34); however, because the predicted median survival is still around 50 yr for individuals with CF born in 2000 (8), it remains an important, clinically relevant outcome. A number of modifiable and nonmodifiable variables, including gender (6,10,15), lung function decline (4,15,17,19,21,26,28,32), number of pulmonary exacerbations (4,19), nutritional status/lower muscle mass (7,11,14,19,21,42), diabetes mellitus (5,19), Burkholderia cepacia colonization (4,19,26), and peak oxygen uptake(V˙O2peak) (21,26,28), have been associated with mortality in patients with CF. Among these variables, several studies have identified percentage of predicted forced expired volume in 1 s (FEV1%predicted) as being one of the best predictors of mortality in adults (15,21,26), children, and adolescents with CF (15,26,28). Other common predictors of mortality in patients with CF are derived from cardiopulmonary exercise testing (CPET) including peak minute ventilation (V˙Epeak), peak ventilatory equivalent ratio for oxygen (V˙E/V˙O2) (21), a marker of ventilatory efficiency, and perhaps the best known predictor from CPET, V˙O2peak.

The debate with regard to the strongest predictor of mortality in CF is ongoing, with two studies suggesting that exercise parameters are not better than FEV1%predicted (21,28) in predicting mortality and another reporting that V˙O2peak%predicted (26) remained in the final model whereas FEV1%predicted did not . To date, a strong focus has been on V˙O2peak as a biomarker of health status, for example, to assess how physical activity (13) or exercise training (16) might positively affect V˙O2peak.Yet, the question of whether other parameters of exercise or a combination of both exercise and nonexercise parameters is also a strong or an even stronger biomarker of health status arises. Therefore, the aim of the current study was to develop a multivariate model to predict mortality using CPET-derived parameters in addition to nutritional status and resting lung function in adolescents with CF. Parameters were dichotomized to enable use in clinical practice.

Back to Top | Article Outline

METHODS

Back to Top | Article Outline

Study design and subjects

A prospective longitudinal cohort study involving adolescent patients with CF was performed. Patients of the Cystic Fibrosis Center, University Medical Center Utrecht (Utrecht, the Netherlands), attended a multidisciplinary examination every year. Pulmonary function tests, CPET, and measurements of inflammation and anthropometrics were routinely performed. Sputum cultures were taken during the entire year preceding the annual examination and were retrospectively evaluated and also recorded in the database. In addition, the database contained information about demographics, decease dates, and CF transmembrane conductance regulator genotype. All the measurements were strictly performed, following the standardized operating procedures of the University Medical CF Center. Only the data of patients who gave an informed consent are registered in the research database, and all these patients were included in this prospective study. Data from one examination, the examination in which they performed the CPET on a bike for the first time, were included in the analyses. The patient database also contained information relating to patient decease dates and dates of lung transplantation (if applicable).

Data were directly entered into the database at the time of the patients’ visit. This was done by an independent and well-trained research nurse. One of the researchers (H. B. R.) independently checked the extracted data for completeness, and errors were corrected. In total, data were available for 127 adolescents with CF followed for the entire duration of their care at the CF clinic of the University Medical Center Utrecht. Patients were 11–14 yr old (70 boys and 57 girls) and were free of pulmonary exacerbations at the time of testing (Table 1). All patients, parents, and/or guardians provided a written informed consent for storage and use of their data for scientific purposes, as approved by the ethical board of the University Medical Center Utrecht. Researchers had full access to all relevant patient data and take responsibility for the integrity of the data and the accuracy of the analyses.

Back to Top | Article Outline

Measurements

Body mass index (BMI) was used as a marker of nutritional status and calculated as weight in kilograms divided by the square of height in meters (kg·m−2) (35). All patients underwent two pulmonary function tests (one without and one after inhalation of 800 µg of salbutamol) in the morning to check for reversibility of the airways. Pulmonary function tests were performed before the exercise test. FEV1 was obtained from the best of three maximal expiratory flow–volume curves (Master screen; Cardinal Health, Houten, the Netherlands) and expressed as FEV1%predicted (44) using reference values from the Utrecht data set (18). All FEV1%predicted curves were checked for acceptability and repeatability (20).

In the afternoon, after the lung function tests, all the patients underwent a maximal CPET according to the Godfrey protocol (12). The CPET was performed on the Ergo line, an electronically braked upright cycle ergometer (Ergo line; Cardinal Health, Houten, the Netherlands), which was calibrated annually.

Throughout the test, patients breathed through a face mask (Hans Rudolph, Inc.) connected to a calibrated metabolic cart (Oxycon Pro; Cardinal Health, Houten, the Netherlands). Volume measurements and breath-by-breath respiratory gas analyses were performed with a flow meter (Triple V volume transducer) and gas analyzer for oxygen and carbon dioxide (Oxycon Pro; Cardinal Health, Houten, the Netherlands). V˙O2, carbon dioxide output (V˙CO2), and the RER were automatically calculated using conventional equations. HR and oxygen saturation were measured continuously with a three-lead ECG and pulse oximeter fitted on the index finger, respectively. The pulse oximeter was verified with the ECG HR. Participants’ CPET results were only included for analysis when the exercise was performed to exhaustion, with maximal effort defined (25). We used subjective signs of maximal effort including unsteady biking, sweating, facial flushing, and clear unwillingness to continue despite strong verbal encouragement and objective signs of maximal effort as indicated by an HRmax >180 beats per minute and RER >1.0. The test was terminated when the participant could no longer maintain the minimum required pedaling rate of 50 rpm despite strong verbal encouragement.

Peak aerobic capacity was calculated as the peak oxygen uptake value averaged over the last 30 s of the test and was expressed as V˙O2peak corrected for body weight (V˙O2peak/kg, mL·kg−1·min−1). Similarly, V˙Epeak was defined as the peak V˙E value averaged over the last 30-s period of the test. For all analyses, maximal exercise values were expressed as a percentage of predicted values (V˙O2peak/kg%predicted, V˙Epeak%predicted) using reference values of healthy Dutch adolescents who were tested using the same protocol and equipment (39).

Additional variables of interest from the CPET included the peak V˙E/V˙O2 and peak ventilatory equivalent ratio for carbon dioxide (V˙E/V˙CO2); those were also calculated over the last 30 s of the test. Breathing reserve was included as well and defined as 1 − (V˙Epeak − maximal voluntary ventilation (MVV)). MVV was estimated using (27.7 × FEV1) + (8.8 × FEV1%predicted), which is reported to be the best prediction equation for MVV in patients with CF, age 10–16.7 yr (37).

Patients were followed for the entire duration of their care at the Utrecht CF Center. The date of last contact at the Utrecht CF Center was used as end date when patients transferred to another hospital. Mortality was defined as the date of death or date of lung transplantation because these patients are expected to have died shortly without transplantation.

Back to Top | Article Outline

Statistical analyses

Means ± SD and percentages were used to describe the population. Predictors were dichotomized on the basis of receiver operating characteristic (ROC) curves, with the criterion value used as the cutoff point. An ROC is a graphical plot, which is created by plotting the fraction of true positives out of the total actual positives (true positive rate) versus the fraction of false positives out of the total actual negatives (false positive rate), at various threshold settings. The best possible prediction method would yield a point in the upper left corner or coordinate (0, 1: perfect classification) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). We used the highest points in the upper left corner as cutoff points (highest specificity with the highest sensitivity). The proportional hazards assumption for the dichotomized predictors was tested with a Cox regression analysis with a time-dependent covariate.

First, the Cox proportional hazards model (9) was used to determine whether parameters of exercise capacity, BMI, and FEV1 independently predicted mortality in univariate analyses. The Cox proportional hazards model was then used to determine which combination of parameters was best at predicting mortality. BMI, FEV1%predicted, V˙O2peak/kg%predicted, V˙Epeak%predicted, V˙E/V˙O2, V˙E/V˙CO2, and breathing reserve were included in one model using a backward selection procedure. The model with P values <0.05 for each independent variable was considered the best model.

Next, the predictors from this final model were combined to determine the influence of having no, one, or more risk factors on mortality rate. The different groups (no, one, or more negative scores, depending on the number of predicting variables) were compared and depicted in a Kaplan–Meier curve. A log-rank test was used to assess potential differences between groups, with significance set at P < 0.05. All analyses were performed using SPSS 18.0 for Windows (SPSS Inc., Chicago, IL).

Back to Top | Article Outline

RESULTS

The characteristics of the 127 patients in the study sample are shown in Table 1. Mean follow-up duration was 7.5 ± 2.7 yr (range, 1.5–12.9 yr), during which time, nine patients (7.1%) died and six (4.7%) underwent lung transplantation.

The proportional hazards assumption, which was tested using a Cox regression analysis with a time-dependent covariate, was not violated. Univariate analyses revealed that FEV1%predicted, breathing reserve, BMI, and V˙O2peak/kg%predicted were each a statistically significant predictor of survival (Table 2). From the multivariate analysis, the final prediction model included FEV1%predicted (hazard ratio, 17.13; 95% confidence interval (CI), 3.76–78.06), peak V˙E/V˙O2 (hazard ratio, 5.92; 95% CI, 1.27–27.63), and BMI (hazard ratio, 5.54; 95% CI, 1.82–16.83), as shown in Table 3.

Figure 1 shows the Kaplan–Meier curve for the groups that differ with respect to BMI, FEV1%predicted, and V˙E/V˙O2 risk factor presentation (none vs one vs two vs all three). The differences in mortality rate between the reference group (no risk factor, n = 32 patients) and the groups with two (n = 24 patients) or three risk factors (n = 7 patients) were significant (P < 0.001). In additional analyses, the group with all three risk factors (BMI ≤15.13, FEV1%predicted ≤68.08, and peak V˙E/V˙O2 >34.87) was used as reference group. These adolescents had a substantially higher mortality rate (P < 0.001) when compared with those of adolescents that presented with only one or two of these risk factors.

Back to Top | Article Outline

DISCUSSION

This study examined the potential for a combination of exercise parameters: BMI and lung function to predict mortality in patients with CF between the ages of 11 and 14 yr. We found that the model combining the risk factors BMI, FEV1%predicted, and peak V˙E/V˙O2 was the best predictor of mortality in this group. More specifically, patients with all three risk factors had a significantly higher risk of mortality compared with that of patients presenting with no or one risk factor and patients with two of the aforementioned factors. Our findings are in line with previous studies, which have reported that FEV1%predicted is an important prognostic indicator of survival in CF patients of various ages and health statuses (19,21,26,28,31) including those awaiting lung transplantation (1). Furthermore, FEV1%predicted remained a significant predictor in our multivariate model. BMI, used as an index of nutritional status, is also known as a predictor of mortality in patients referred for lung transplantation (35). A higher BMI has also been associated with an increased probability of survival in CF patients (33). BMI is not the best measurement of body composition; however, its measurement is 1) not dependent on the instrument used (i.e., bioelectrical impedance or dual-energy x-ray absorptiometry), 2) easy to use/perform in clinical practice, and 3) mostly reported in studies who looked at nutritional status in patients with CF. The findings from the present study provide additional support for this conclusion because much like FEV1%predicted, BMI was a significant predictor of survival in both univariate and multivariate analyses. Two reports have shown V˙O2peak%predicted to be a predictor of mortality (21,26), and this is in line with our finding that V˙O2peak/kg%predicted is a significant predictor of mortality with respect to the univariate analyses. This was also shown in a previous study from our population (40), and furthermore, another report showed rate of decline in V˙O2peak/kg to be a significant predictor of mortality (28). Van de Weert-van Leeuwen et al. (40) concluded that a decline in exercise capacity during adolescence was negatively associated with immunoglobulin (Ig) levels and chronic Pseudomonas aeruginosa infection. A lower exercise capacity was associated with a higher mortality rate, steeper decline in pulmonary function, and higher increase in IgG levels with increasing age in adolescents with CF. Because of the strong association between maximal oxygen uptake, corrected for predicted percentage of body mass, IgG levels, and chronic P. aeruginosa infection, we did not use infection and colonization as variables in our model.

Univariate analyses also identified breathing reserve as a significant predictor of mortality, a finding similar to that of Tantisira et al. (38) who reported that at the lactate threshold, breathing reserve was a predictor of waiting list mortality in adult CF patients. To our knowledge, breathing reserve was not assessed as predictor of mortality in a population comparable to our sample.

Peak V˙E/V˙O2 is an indicator of dead space ventilation, an index of ventilatory efficiency and early onset of anaerobic metabolism, which might be increased in patients with poor survival. It is our finding that the ventilatory drive of CF patients is quite normal (they do not overventilate of the amount of exhaled CO2). However, during exercise, they have a greater reliance on CHO oxidation compared with that in healthy controls. CHO oxidation results in a higher CO2 production compared with that produced in fatty acid oxidation. Hence, they exhibit a higher ventilation to exhale this extra amount of produced CO2 (2,24). In addition, patients with CF show a larger VD/VT ratio during exercise, implying larger physiological dead space ventilation (2). This also contributes to a higher V˙E/V˙O2.

Previously, Moorcroft et al. (21) found that an increased peak V˙E/V˙O2 was significantly associated with mortality in adult CF patients; however, this ratio was not a stronger predictor of mortality than FEV1%predicted in their sample. Peak V˙E/V˙O2 was found to be a significant predictor of mortality in our multivariate model, although its association with mortality was not significant based on univariate analyses. Although our findings and those of Moorcroft et al. (21) provide strong support for the importance of CPET parameters, like peak V˙E/V˙O2, in the assessment of health status, this variable is seldom reported in the available literature. In fact, not one of the exercise interventions included in a recent systematic review in children with CF reported peak V˙E/V˙O2 (41). Interestingly, V˙E/V˙O2 has been shown to be trainable in patients with heart failure and inspiratory muscle weakness. It can be improved significantly by adding inspiratory muscle training to aerobic training (43). In line with this, another study has reported that reduction of peak V˙E/V˙O2 during submaximal exercise occurred when exercise was performed by trained muscle groups (30). Although no randomized controlled studies aimed at improving long-term survival in CF have been performed to date, several studies have shown that biomarkers such as nutritional status (11,22,23,29,42), FEV1%predicted (3,17,27,36,41), and V˙O2peak%predicted (3,13,16,27,36,41) can be improved with medical, nutritional, or physical interventions.

The availability of data from a large group of adolescents in the age range 11–14 yr and the long-term follow-up (a mean of 7.5 yr) are among the key strengths of this study. Perhaps, the greatest advantage of the present study is the use of variables that are dichotomized on the basis of ROC curves, a very robust method for developing disease-specific cutoff points for the examined parameters. Ultimately, this dichotomization allows for relatively simple identification of patients in need of additional therapy and/or more intensive follow-up. It must, however, be noted that the associations reported in the present study may be population specific and therefore not directly transferable to other populations with CF (i.e., other age groups, comorbidities). Furthermore, when compared with that in other studies (15,19), the sample size is not that large, and therefore, the power may be somewhat lower. However, because the age range is small, which results in minimal heterogeneity, the power of the prediction increases. On the other hand, this is the largest study focusing on the prediction of mortality using CPET-derived parameters in patients with CF (21,26,28). A few limitations should be considered. We did not analyze all potential confounders, such as habitual physical activity levels, muscle mass and/or function, body habitus, stunting, comorbidities, and effects of other inflammatory markers, such as interleukin 6. Further research in a more heterogeneous population is recommended.

In conclusion, this study revealed the importance of using a combination of BMI, FEV1%predicted, and peak V˙E/V˙O2 to predict mortality in adolescents with CF. These findings underline the relevance of performing a CPET in addition to regular lung function assessments as part of the standard of care for CF patients. Taken together, the use of these strong prognostic markers in clinical practice may allow for the development of medical, nutritional, and/or physical interventions targeted at a small group of high-risk adolescents with CF, thereby avoiding burdens of additional unnecessary treatment in the low-risk group and reducing health care costs.

Back to Top | Article Outline

Clinical implications

Physical activity and exercise training play an important role in the clinical management of patients with CF. Exercise training is more common and recognized as an essential part of rehabilitation programs and overall CF care. Regular exercise training is associated with improved aerobic and anaerobic capacity, higher pulmonary function, and enhanced airway mucus clearance. Furthermore, patients with higher aerobic fitness have improved survival. Aerobic and anaerobic training may have different effects, whereas the combination of both has been reported to be beneficial in CF. However, exercise training remains underused and not always incorporated into routine CF management. We propose that an active lifestyle and exercise training are an efficacious part of regular CF patient management. In addition, peak V˙E/V˙O2 may be improved in patients with CF as a result of training. Given its high trainability and relative importance in the prediction of mortality, we recommend that future studies in CF not only take note of the more commonly reported exercise outcomes but also pay special attention to relevant ventilatory variables like peak V˙E/V˙O2.

The authors’ contributions are as follows: study concept and design, E. H. J. Hulzebos, C. K. van der Ent, H. G. M. Arets, and T. Takken; acquisition of data, E. H. J. Hulzebos, H. Bomhof-Roordink., P. B. van de Weert-van Leeuwen, and T. Takken; analysis and interpretation of the data, H. Bomhof-Roordink, J. W. R. Twisk, P. B. van der Weert-van Leeuwen, E. H. J. Hulzebos, and T. Takken; drafting the manuscript, H. Bomhof-Roordink, E. H. J. Hulzebos, and T. Takken; critical revision of the manuscript, E. H. J. Hulzebos, H. G. M. Arets, C. K. van der Ent, J. W. R. Twisk, and T. Takken; statistical analysis, H. Bomhof-Roordink and J. W. R. Twisk; and study supervision, E. H. J. Hulzebos, T. Takken, C. K. van der Ent, and H. G. M. Arets.

E. H. J. Hulzebos and H. Bomhof-Roordink shared first authorship. E. H. J. Hulzebos and T. Takken 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 analyses.

The authors declare no conflicts of interest.

The results of the present study do not constitute endorsement by the American College of Sports Medicine.

Back to Top | Article Outline

REFERENCES

1. Belkin RA, Henig NR, Singer LG, et al. Risk factors for death of patients with cystic fibrosis awaiting lung transplantation. Am J Respir Crit Care Med. 2006; 173 (6): 659–66.
2. Bongers BC, Hulzebos EH, Arets BG, Takken T. Validity of the oxygen uptake efficiency slope in children with cystic fibrosis and mild-to-moderate airflow obstruction. Pediatr Exerc Sci. 2012; 24 (1): 129–41.
3. Bradley J, Moran F. Physical training for cystic fibrosis. Cochrane Database Syst Rev. 2002; (2): CD002768.
4. Buzzetti R, Alicandro G, Minicucci L, et al. Validation of a predictive survival model in Italian patients with cystic fibrosis. J Cyst Fibros. 2012; 11 (1): 24–9.
5. Chamnan P, Shine BS, Haworth CS, Bilton D, Adler AI. Diabetes as a determinant of mortality in cystic fibrosis. Diabetes Care. 2010; 33 (2): 311–6.
6. Corey M, Edwards L, Levison H, Knowles M. Longitudinal analysis of pulmonary function decline in patients with cystic fibrosis. J Pediatr. 1997; 131 (6): 809–14.
7. Corey M, McLaughlin FJ, Williams M, Levison H. A comparison of survival, growth, and pulmonary function in patients with cystic fibrosis in Boston and Toronto. J Clin Epidemiol. 1988; 41 (6): 583–91.
8. Dodge JA, Lewis PA, Stanton M, Wilsher J. Cystic fibrosis mortality and survival in the UK: 1947–2003. Eur Respir J. 2007; 29 (3): 522–6.
9. Cox DR. Regression model and life-tables J Roy Statist Soc Ser B Metho. 1972; 34 (2): 187–220.
10. FitzSimmons SC. The changing epidemiology of cystic fibrosis. J Pediatr. 1993; 122 (1): 1–9.
11. Fogarty AW, Britton J, Clayton A, Smyth AR. Are measures of body habitus associated with mortality in cystic fibrosis? Chest. 2012; 142 (3): 712–7.
12. Godfrey S. Exercise testing in assessing children with lung or heart disease. Thorax. 1970; 25 (2): 258.
13. Hebestreit H, Kieser S, Rüdiger S, et al. Physical activity is independently related to aerobic capacity in cystic fibrosis. Eur Respir J. 2006; 28 (4): 734–9.
14. Huang NN, Schidlow DV, Szatrowski TH, et al. Clinical features, survival rate, and prognostic factors in young adults with cystic fibrosis. Am J Med. 1987; 82 (5): 871–9.
15. Kerem E, Reisman J, Corey M, Canny GJ, Levison H. Prediction of mortality in patients with cystic fibrosis. N Engl J Med. 1992; 326 (18): 1187–91.
16. Klijn PH, Oudshoorn A, van der Ent CK, van der NJ, Kimpen JL, Helders PJ. Effects of anaerobic training in children with cystic fibrosis: a randomized controlled study. Chest. 2004; 125 (4): 1299–305.
17. Konstan MW, Wagener JS, Vandevanter DR, et al. Risk factors for rate of decline in FEV1 in adults with cystic fibrosis. J Cyst Fibros. 2012; 11 (5): 405–11.
18. Koopman M, Zanen P, Kruitwagen CL, van der Ent CK, Arets HG. Reference values for paediatric pulmonary function testing: the Utrecht dataset. Respir Med. 2011; 105 (1): 15–23.
19. Liou TG, Adler FR, Fitzsimmons SC, Cahill BC, Hibbs JR, Marshall BC. Predictive 5-year survivorship model of cystic fibrosis. Am J Epidemiol. 2001; 153 (4): 345–52.
20. Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005; 26 (2): 319–38.
21. Moorcroft AJ, Dodd ME, Webb AK. Exercise testing and prognosis in adult cystic fibrosis. Thorax. 1997; 52 (3): 291–3.
22. Moorcroft AJ, Dodd ME, Webb AK. Long-term change in exercise capacity, body mass, and pulmonary function in adults with cystic fibrosis. Chest. 1997; 111 (2): 338–43.
23. Morton AM. Symposium 6: young people, artificial nutrition and transitional care. The nutritional challenges of the young adult with cystic fibrosis: transition. Proc Nutr Soc. 2009; 68 (4): 430–40.
24. Nguyen T, Obeid J, Baker JM, et al. Reduced fat oxidation rates during submaximal exercise in boys with cystic fibrosis. J Cyst Fibros. 2014; 13 (1): 92–8.
25. Nixon PA, Orenstein DM. Exercise testing in children. Pediatr Pulmonol. 1988; 5: 107–22.
26. Nixon PA, Orenstein DM, Kelsey SF, Doershuk CF. The prognostic value of exercise testing in patients with cystic fibrosis. N Engl J Med. 1992; 327 (25): 1785–8.
27. Orenstein DM, Franklin BA, Doershuk CF, et al. Exercise conditioning and cardiopulmonary fitness in cystic fibrosis. The effects of a three-month supervised running program. Chest. 1981; 80 (4): 392–8.
28. Pianosi P, Leblanc J, Almudevar A. Peak oxygen uptake and mortality in children with cystic fibrosis. Thorax. 2005; 60 (1): 50–4.
29. Powers SW, Jones JS, Ferguson KS, Piazza-Waggoner C, Daines C, Acton JD. Randomized clinical trial of behavioral and nutrition treatment to improve energy intake and growth in toddlers and preschoolers with cystic fibrosis. Pediatrics. 2005; 116 (6): 1442–50.
30. Rasmussen B, Klausen K, Clausen JP, Trap-Jensen J. Pulmonary ventilation, blood gases, and blood pH after training of the arms or the legs. J Appl Physiol. 1975; 38 (2): 250–6.
31. Sharma R, Florea VG, Bolger AP, et al. Wasting as an independent predictor of mortality in patients with cystic fibrosis. Thorax. 2001; 56 (10): 746–50.
32. Sharples L, Hathaway T, Dennis C, Caine N, Higenbottam T, Wallwork J. Prognosis of patients with cystic fibrosis awaiting heart and lung transplantation. J Heart Lung Transplant. 1993; 12 (4): 669–74.
33. Simmonds NJ, Macneill SJ, Cullinan P, Hodson ME. Cystic fibrosis and survival to 40 years: a case-control study. Eur Respir J. 2010; 36 (6): 1277–83.
34. Slieker MG, Uiterwaal CS, Sinaasappel M, Heijerman HG, van der Laag J, van der Ent CK. Birth prevalence and survival in cystic fibrosis: a national cohort study in the Netherlands. Chest. 2005; 128 (4): 2309–15.
35. Snell GI, Bennetts K, Bartolo J, et al. Body mass index as a predictor of survival in adults with cystic fibrosis referred for lung transplantation. J Heart Lung Transplant. 1998; 17 (11): 1097–103.
36. Stanghelle JK, Skyberg D, Haanaes OC. Eight-year follow-up of pulmonary function and oxygen uptake during exercise in 16-year-old males with cystic fibrosis. Acta Paediatr. 1992; 81 (6–7): 527–31.
37. Stein R, Selvadurai H, Coates A, Wilkes DL, Schneiderman-Walker J, Corey M. Determination of maximal voluntary ventilation in children with cystic fibrosis. Pediatr Pulmonol. 2003; 35 (6): 467–71.
38. Tantisira KG, Systrom DM, Ginns LC. An elevated breathing reserve index at the lactate threshold is a predictor of mortality in patients with cystic fibrosis awaiting lung transplantation. Am J Respir Crit Care Med. 2002; 165 (12): 1629–33.
39. Ten Harkel AD, Takken T. Normal values for cardiopulmonary exercise testing in children. Eur J Cardiovasc Prev Rehabil. 2011; 18 (4): 676–7.
40. Van de Weert-van Leeuwen PB, Slieker MG, Hulzebos HJ, Kruitwagen CL, van der Ent CK, Arets HG. Chronic infection and inflammation affect exercise capacity in cystic fibrosis. Eur Respir J. 2012; 39 (4): 893–8.
41. van Doorn N. Exercise programs for children with cystic fibrosis: a systematic review of randomized controlled trials. Disabil Rehabil. 2010; 32 (1): 41–9.
42. Vieni G, Faraci S, Collura M, et al. Stunting is an independent predictor of mortality in patients with cystic fibrosis. Clin Nutr. 2013; 32 (3): 382–5.
43. Winkelmann ER, Chiappa GR, Lima CO, Viecili PR, Stein R, Ribeiro JP. Addition of inspiratory muscle training to aerobic training improves cardiorespiratory responses to exercise in patients with heart failure and inspiratory muscle weakness. Am Heart J. 2009; 158 (5): 768–7.
44. Zapletal A, Samenek TP. Lung function in children and adolescents: methods and reference values. Cesk Pediatr. 1976; 31 (10): 532–9.
Keywords:

CYSTIC FIBROSIS; MORTALITY; EXERCISE CAPACITY; NUTRITIONAL STATUS; LUNG FUNCTION

© 2014 American College of Sports Medicine