Predictive 5-year survivorship model of cystic fibrosis

Abstract

The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. The authors used information from the Cystic Fibrosis Foundation Patient Registry (CFFPR), which has collected longitudinal data on approximately 90% of cystic fibrosis patients diagnosed in the United States since 1986. They developed multivariate logistic regression models by using data on 5,820 patients randomly selected from 11,630 in the CFFPR in 1993. Models were tested for goodness of fit and were validated for the remaining 5,810 patients for 1993. The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. The model provides insights into the complex nature of cystic fibrosis and supplies a rigorous tool for clinical practice and research.

FIGURE 1

Graphic representation of application of the Hosmer-Lemeshow test (31) to validate a 5-year logistic regression model of cystic fibrosis, United States, 1993. The difference in predictive ability between the two models shown was statistically significant (p < 0.0001). A. Comparison of actual and predicted 5-year survival for patients in the validation set (table 2). The percentage of patients predicted to survive was calculated directly by using the new model (table 3). A “perfect” fit of predictions to actual outcomes would produce the dashed line shown, with slope 1 and intercept 0. The smoothed-curve fit of our model (44, 45) closely approximated this ideal. B. Comparison of actual and predicted percentage of survival using a 5-year logistic regression model based on age, gender, and forced expiratory volume in 1 second as a percentage of predicted normal (15). This model did not identify any patients with the lowest chances of survival, so the curve abruptly ends when the percentage predicted to survive drops below approximately 30%. The model was inaccurate when the percentage predicted to survive 5 years within the group was less than 60%, as shown by the large deviation of the model curve from ideal (dashed line).