**2. Prognosis in cystic fibrosis**

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

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

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

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

and a composite clinical prediction tool is suggested to stratify patient risk.

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

how these may be utilised in clinical practice.

score.

4 Cystic Fibrosis in the Light of New Research

**1. Introduction**

Life expectancy in patients with cystic fibrosis is in a constant state of change. Whilst there are undoubtedly significant further gains to be made, the improvement in predicted survival in cystic fibrosis sufferers has been a relative success story since its original description as a clinical entity in 1938. Median life expectancy, the time period in which half of a given population will die, has increased from a few months in the 1940s to as high as 41 years old in the current era [14] at present in many countries. The predicted median survival of people born with CF today continues to rise.

Numerous factors have contributed to the changing statistics in CF prognosis. Earlier and more sensitive detection methods, centralised specialist multidisciplinary care and evidence-based research have provided patients, their families, and clinicians with an environment that facilitates the long-term management of this complex multisystem disorder. Identification of increasing numbers of CF genotypes (many of which are characterised by phenotypically milder variants) has also contributed to increasing the CF population and this consequently affects the overall statistics on outcome and prognosis.

What is relevant to a patient diagnosed in infancy with a severe form of CF may not be relevant to a patient diagnosed in middle adulthood. Equally, statistics on survival from one country may not relate accurately to another, not because of difference in treatment alone, but because of differences in the predominating demographics in the two cohorts and methodologies used in assessing outcomes. As such, a "one size fits all" approach to prognosticating is not appropriate. In this chapter, we review the evidence regarding prognosis based on key clinical and demographic parameters, and the biomarkers and prediction tools that may be used to predict outcome.
