Predicting future lung function decline in cystic fibrosis patients: Statistical methods and clinical connections

To the Editor: We read the article by Rosenfeld and colleagues with great interest and applaud the authors for investigating the predictive value of decline in (observed) lung function on subsequent decline in lung function in patients with cystic fibrosis (CF). As the authors indicated, there is a dearth of literature on this particular research question, although it is of great CF clinical and epidemiological interest. The authors utilized a large data source to examine the predictive value for a variety of spirometric variables; they clearly defined their calculations and statistical approach. We focus on one of the spirometric variables presented in the study, forced expiratory volume in one second of percent predicted (hereafter, FEV1%p); however, the comments may be generalized to the other spirometric variables that the authors examined. The authors calculated a two-point slope for each CF patient over a two-year interval by taking the difference between maximum FEV1%p for a given year of age and the subsequent two-year value. The authors used the magnitude of the estimated Pearson correlation coefficient to quantify the extent towhich reference slopes were predictive of subsequent two-year slopes; these correlations were performed overall and by defined age strata. Correlations between reference slopes and follow-up levels (as opposed to slopes) were also estimated. Contrary to what they had anticipated, the authors found low correlation estimates for associations between reference and subsequent slopes; the authors found moderate correlation between reference slopes and subsequent level (as opposed to slope). The statistical approach and findings raise questions regarding how to best assess the potential prognostic utility of FEV1%p decline. Patient-specific predictions can be made using a selected statistical model or summary measure, such as the two-point FEV1%p slopes used by the authors. Clinicians and researchers in CF have often operationalized rate of decline in lung function as a slope, which intuitively corresponds to rise over run. The authors’ illustrations and plots of median two-year slopes depict nonlinear age-related FEV1%p progression across CF patients. Their results suggest the need to characterize individual rates of decline in terms of derivatives using quantities related to velocity and acceleration. A previous study of the Cystic Fibrosis Foundation Patient Registry revealed similar trends in age-related FEV1%p decline, as well as acceleration and deceleration, using flexible (nonlinear) modeling via semiparametric regression. The two-point slopes provide an easily interpretable approximation to how the population progresses with regard to FEV1%p decline, but statistical models that can incorporate the aforementioned nonlinearity, as well as covariate information (e.g., weight-for-age percentile), between-subject variation and longitudinal correlation, are needed to characterize observed FEV1%p decline in the individual patient and forecast disease progression. Although such models require assumptions, insights may be gained about individualized fluctuations in FEV1%p and predictions, possibly improving the ability to forecast subsequent FEV1%p decline. A previous study of the Danish Cystic Fibrosis Patient Registry, which the authors cited, incorporated stochastic variation in FEV1%p response in the form of model covariance to improve predictive accuracy. The authors’ work provides new epidemiological insight into the population-based predictive utility of observed lung function decline. To gain understanding of how this work could be translated into clinical settings or used to plan clinical trials, it may be helpful to consider