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A model for predicting life expectancy of children with cystic fibrosis
1Great Ormond Street Children's Hospital NHS Trust, London, UK.
Insights
This study developed a model to predict life expectancy for children with severe cystic fibrosis lung disease. Key predictors of poor prognosis include low lung function and oxygen levels, indicating a need for timely interventions.
Area of Science:
- Pediatric Pulmonology
- Medical Prognostics
Background:
- Severe cystic fibrosis (CF) lung disease significantly impacts pediatric survival.
- Accurate prediction of life expectancy is crucial for managing CF and planning interventions like lung transplantation.
Purpose of the Study:
- To develop and validate a predictive model for life expectancy in children with severe CF lung disease.
- To identify key clinical parameters associated with prognosis in this population.
Main Methods:
- Survival analysis of 181 children with severe CF lung disease assessed for transplantation between 1988-1998.
- Proportional hazards modeling to identify significant predictors of longevity.
Main Results:
- The predictive model incorporates factors such as low height-predicted forced expiratory volume in one second (FEV1), low minimum oxygen saturation (Sa,O2min), high resting heart rate, younger age, female sex, low plasma albumin, and low hemoglobin.
- Model extrapolations indicate a 44% 2-year mortality risk for a 12-year-old male with specific FEV1 and Sa,O2min levels, versus 63% for a female with identical parameters.
Conclusions:
- The developed model offers valuable insights into predicting life expectancy for children with severe CF lung disease.
- This tool can aid in optimizing the timing of lung transplantation for improved patient outcomes.
Abstract:
In this study the authors aimed to produce a model for predicting the life expectancy of children with severe cystic fibrosis (CF) lung disease. The survival of 181 children with severe CF lung disease referred for transplantation assessment 1988-1998 (mean age 11.5 yrs, median survival without transplant 1.9 yrs from date of assessment) were studied. Proportional hazards modelling was used to identify assessment measurements that are of value in predicting longevity. The resultant model included low height predicted forced expiratory volume in one second (FEV1), low minimum oxygen saturation (Sa,O2min) during a 12-min walk, high age adjusted resting heart rate, young age, female sex, low plasma albumin, and low blood haemoglobin as predictors for poor prognosis. Extrapolation from the model suggests that a 12-yr old male child with an FEV1 of 30% pred and a Sa,O2min of 85% has a 44% risk of death within 2 yrs (95% confidence interval (CI) 35-54%), whilst a female child with the same measurements has a 63% risk of death (95% CI 52-73%) within the same period. The model produced may be of value in predicting the life expectancy of children with severe cystic fibrosis lung disease and in optimizing the timing of lung transplantation.