**Factors Affecting Prognosis and Prediction of Outcome in Cystic Fibrosis Lung Disease**

Cormac McCarthy, Orla O'Carroll, Alessandro N. Franciosi and Noel G. McElvaney

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/60899

#### **Abstract**

Cystic fibrosis (CF) is a multisystem disorder with a significantly shortened life expectancy with the major cause of mortality related to lung disease. Inflammation is seen in the CF airways from a very early age and contributes significantly to symptoms and disease progression. As the condition worsens over time, lung function declines, usually measured by Forced Expiratory Volume in 1 second (FEV1)% predicted, and extra-pulmonary complications often manifest. While the life expectancy in CF is still short, the median age of death and predicted survival age are continually increasing. Therapeutic interventions for CF have improved significantly in the last 20 years and now there are targeted therapies towards specific elements in CF that may impact upon exacerbation frequency, symptoms, and eventually mortality due to lung disease.

As life expectancy in CF increases, the need for predicting prognosis becomes more and more important. Numerous factors affect prognosis in CF and can be used to ultimately predict outcomes. These factors can be constant or dynamic variables ranging from genetic mutation and gender to clinical measurements including pulmonary function and weight. Further variables that affect prognosis in CF include Diabetes Mellitus, sex, and pancreatic insufficiency. Furthermore, genotype is becoming more and more important as novel targeted therapies are developed that may affect survival and improve prognosis.

While prognosis in CF has traditionally been associated with FEV1% predicted, decrements in lung function that are associated with recurrent pulmonary exacerba‐ tions are increasingly important, and these are increasingly common as CF lung

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disease progresses. What drives these pulmonary exacerbations is bacterial colonisa‐ tion, particularly *Pseudomonas aeruginosa*, with early eradication shown to improve prognosis. Nutrition and weight are also very important in CF and low body mass index has been shown to predict poor outcomes. There are several clinical prediction tools in CF, both radiological and clinical and many are too complex to be used routinely in patient care. However, newer tools aimed at predicting outcomes based on readily available objective measure are now available, including the CF-ABLE score.

In this chapter we outline, firstly, how prognosis in CF has changed over the last decade as a result of changes in treatment, better diagnostics, and improved care. Secondly, we describe the effects that genotype, pancreatic status, gender, and diabetic status have upon outcome. Thirdly, this chapter highlights the usefulness and importance of clinical measurements, including lung function, radiology, bacteriolo‐ gy, and blood and sputum biomarkers of disease and inflammation in predicting outcomes and how changes in these parameters influence prognosis. Finally, we summarise the prediction tools that have been utilised in CF to predict survival and how these may be utilised in clinical practice.

In conclusion, the most sensitive way of predicating prognosis currently remains a multifaceted approach, including several markers of disease and the use of all factors and a composite clinical prediction tool is suggested to stratify patient risk.

**Keywords:** Prognosis, prediction, survival, outcome, cystic fibrosis

### **1. Introduction**

Cystic fibrosis (CF) is a multisystem inflammatory condition that is associated with a signifi‐ cantly shortened life span, primarily as a result of the pulmonary manifestations of the disease [1]. For many years pulmonary function measurements have been utilised as the primary surrogate of disease severity, with forced expiratory volume in 1 second (FEV1) used to assess clinical status of both patients and to predict mortality [2, 3]. However, in the last two decades there has been a significant improvement in survival in CF and this subsequently has conse‐ quences on how to treat patients and predict prognosis in this complex condition. With longer life expectancy it is essential to better predict outcome and prognosticate in CF, thus the use of survival or death as an outcome measure has become almost negligible in clinical trials or indeed in studies to predict prognosis. Hence, the development of surrogate markers or disease severity is increasingly important in CF; these range from physiological measurements of lung function, biomarkers, radiological measures, and composite scoring systems and are becoming essential in CF care and development of new drugs. With groundbreaking therapeutic breakthroughs in CF over the last decade, particularly in the modulation of CFTR function [4], the use of surrogate outcomes has become more apparent. This has led to development of new imaging modalities such as flurodeoxyglucose positron emission tomography (FDG-PET) imaging [5-7] and hyperpolarised helium magnetic resonance imaging (He3-MRI) [8], as well as increased use of multiple breath washout (MBW) and lung clearance index (LCI) to assess disease severity [9]. Moreover, there has been huge progress in the research into and devel‐ opment of biomarkers of inflammation in CF [10–12], both systemic and pulmonary inflam‐ mation that correlates with clinical condition and can predict outcome, further highlighting the deeper understating of the pathophysiological changes in CF and the translational research ongoing in this area. Furthermore, the use of new composite scoring systems, taking into account many aspects of this multisystem condition have been developed to aid with the classification of disease severity and predict outcome over a defined period [13].

In this chapter we will outline, firstly, how prognosis in CF has changed over the last decade as a result of changes in treatment, better diagnostics, and improved care. Secondly, the chapter will describe the effects genotype, such as pancreatic status, gender, and diabetic status, have upon outcome. Thirdly, this chapter will highlight the usefulness and importance of clinical measurements, including lung function, radiology, bacteriology, and blood and sputum biomarkers of disease and inflammation in predicting outcomes and how changes in these parameters influence prognosis. Finally, the chapter will summarise the prediction tools that have been created in CF, both clinical and research tools, which utilise measurements of disease and radiological evidence of bronchiectasis to predict survival and how these may be utilised in clinical practice.
