Pulmonary outcome prediction for cystic fibrosis patients

The course of lung disease in cystic fibrosis (CF) is largely characterized by recurrent episodes of pulmonary exacerbations, the number and severity of which influence the final outcome by speeding up the overall deterioration in this disease. The day-to-day success or failure of keeping the disease under control determines for most patients their ultimate fate. Today, a child born with CF can expect to live nearly four decades; however, the duration and quality of that life have been shown to depend heavily on the rate of decline of pulmonary function closely associated with increased morbidity. Even though clinical or phenotypic expression varies largely between individuals and over time, the ability to predict lung function decline as an outcome, using clinical scores or other measures, is often thought to be an important target for improvement of patient management. Historically, identifying and quantifying disease severity in children with CF had already begun in 1958 when Shwachman and Kulczycki presented their first score of general clinical evaluation. Following this, numerous other similar scores have been proposed such as the NIH Prognostic Score, the Bernese Score, Chrispin–Norman Score, the Brasfield Score, and the UFCT Score, comprising one or more evaluation of measurable clinical data such as the chest X-ray being the most common, the relative underweight, and lung function parameters. Although associations between chest X-ray scores and lung function parameters could be established, associations between general clinical scores and progression of lung function were very weak. Arguably, the score with the highest discriminative power so far is the Wisconsin score. Subsequently, validation, power of discrimination, inter-observer and intra-observer reproducibility have been the major targets of evaluation for scoring systems. Even today, it remains difficult to define sensitive, reproducible, and feasible measures for quantifying lung disease in children with CF for each patient’s individual progression, not only for the control of treatment but also for the conduct of clinical trials in this particular population. Also, the available scores served more to characterize disease rather than for prognosis or prediction of future outcomes. In this issue of the journal, using data from the Epidemiologic Study of Cystic Fibrosis (ESCF), VanDevanter et al. present a further developed clinical scoring tool to specifically predict annualized change in FEV1% predicted over the near future for CF patients aged 6–17 years, termed ‘‘pulmonary outcome predictor’’ (POP6–17), as well as FEV1% predicted at 6 years for CF patients aged 2–5 years (POP2–5). One of the strengths of the study lies in the use of the ESCF data set, which encompasses a large number of patients in nearly all the CF Centers in the United States and Canada, making these results generalizable. The study was conducted over a time period of 12 years (1994–2005), and careful consideration appear to have been given to including only patients with interpretable data (e.g., sufficient number of pulmonary function tests, baseline parameters determined at influence the rate of decline of pulmonary function). The large number of subjects also allowed the use of separate subpopulations for validation and testing. The study provides a composite score for predicting rate of decline for an individual patient based on infection history, exacerbations, weight for age percentile, respiratory symptoms, clubbing, and (for the POP6–17) lung function. The scores can be considered a tool for identifying high-risk patients, providing a potential basis