Background Viral infections can cause significant morbidity in cystic fibrosis (CF). The current Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic could therefore have a serious impact on the health of people with CF (pwCF). Methods We used the 38-country European Cystic Fibrosis Society Patient Registry (ECFSPR) to collect case data about pwCF and SARS-CoV-2 infection. Results Up to 30 June 2020, 16 countries reported 130 SARS-CoV-2 cases in people with CF, yielding an incidence of 2.70/1000 pwCF. Incidence was higher in lung-transplanted patients (n=23) versus non-transplanted patients (n=107) (8.43 versus 2.36 cases/1000). Incidence was higher in pwCF versus the age-matched general population in the age groups <15, 15-24, and 25-49 years (p<0.001), with similar trends for pwCF with and without lung transplant. Compared to the general population, pwCF (regardless of transplantation status) had significantly higher rates of admission to hospital for all age groups with available data, and higher rates of intensive care, although not statistically significant. Most pwCF recovered (96.2%), however 5 died, of whom 3 were lung transplant recipients. The case fatality rate for pwCF (3.85%, 95% CI: 1.26-8.75) was non-significantly lower than that of the general population (7.46%; p=0.133). Conclusions SARS-CoV-2 infection can result in severe illness and death for pwCF, even for younger patients and especially for lung transplant recipients. PwCF should continue to shield from infection and should be prioritized for vaccination.

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BackgroundThe current lifetable approach to survival estimation is favoured by CF registries. Recognising the limitation of this approach, we examined the utility of a parametric survival model to project birth cohort survival estimates beyond the follow-up period, where short duration of follow-up meant median survival estimates were indeterminable.MethodsParametric models were fitted to observed survivorship data from the US CF Foundation (CFF) Patient Registry 1980–1994 birth cohort. Model-predicted median survival was estimated. The best fitting model was applied to a Cystic Fibrosis Registry of Ireland dataset to allow an evaluation of the model's ability to estimate predicted median survival. This involved a comparison of birth cohort lifetable predicted and observed (Kaplan–Meier) median survival estimates.ResultsA Weibull model with main effects of gender and birth cohort was developed using a US CFF dataset (n=13 115) for which median survival was not directly estimable. Birth cohort lifetable predicted median survival for male and female patients born between 1985 and 1994 and surviving their first birthday was 50.9 and 42.4 years respectively. To evaluate the accuracy of a Weibull model in predicting median survival, a model was developed for the 1980–1984 Cystic Fibrosis Registry of Ireland birth cohort (n=243), which had an observed (Kaplan–Meier) median survival of 27.7 years. Model-predicted median survival estimates were calculated using data censored at different follow-up periods. The estimates converged to the true value as length of follow-up increased.ConclusionsAccurate prognostic information that is clinically critical for care of patients affected by rare, life-limiting disorders can be provided by parametric survival models. Problems associated with short duration of follow-up for recent birth cohorts can be overcome using this approach, providing better opportunities to monitor survival and plan services locally.

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