A PROGNOSTIC MODEL FOR THE PREDICTION OF SURVIVAL IN CYSTIC-FIBROSIS

Background - The treatment for endstage cystic fibrosis is, where appr opriate, double-lung, heart-lung or, occasionally, heart-lung-liver tr ansplantation. Optimising the timing of transplantation depends upon a n accurate prediction of survival, but while current criteria give som e guidance to this, they are not based upon statistically derived prog nostic models. Methods - Data collected prospectively on 403 patients with cystic fibrosis, recruited between 1969 and 1987 (cohort A), were analysed by log rank and univariate Cox regression analysis to determ ine variables that accurately predict survival. The significant variab les were then subject to time dependent multivariate Cox regression an alysis to generate a prognostic model. The model was validated, within the study population, using split sample testing, and was subsequentl y validated in a further cohort of patients recruited since October 19 88 (cohort B). Results - One hundred and eighty eight (50.4%) of the s tudy cohort died within the study period. Percentage predicted forced expiratory volume in one second (FEV1), percentage predicted forced vi tal capacity (FVC), short stature, high white cell count (WBC), and ch ronic liver disease (as evidenced by the presence of hepatomegaly) wer e negatively correlated with survival. These variables, when combined into a prognostic index, accurately predicted one year survival in the study population and in the cohort recruited since 1988. Conclusion - This prognostic index may prove valuable in predicting prognosis in o ther cohorts with cystic fibrosis and thereby improve the timing of tr ansplantation.