Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy.
<h4>Background</h4>The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. Understanding the characteristics involved with lung transplant and surviv...
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0292568 |
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| author | Annalisa V Piccorelli Jerry A Nick |
| author_facet | Annalisa V Piccorelli Jerry A Nick |
| author_sort | Annalisa V Piccorelli |
| collection | DOAJ |
| description | <h4>Background</h4>The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team.<h4>Methods</h4>This study seeks to identify clinical predictors of lung transplant and survival of individuals with CF using 29,847 subjects from 2003-2014 entered in the Cystic Fibrosis Foundation Patient Registry (CFFPR).<h4>Results</h4>Predictors significant (p ≤ 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years. The final Cox regression model predicting time to lung transplant identified these predictors as significant FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive ibuprofen use for at least 4 years. The concordance indices were 0.89 and 0.92, respectively.<h4>Conclusions</h4>The models are translated into nomograms to simplify investigation of how various characteristics relate to lung transplant and survival prognosis individuals with CF not receiving highly effective CFTR modulator therapy. |
| format | Article |
| id | doaj-art-e10b492c8242422b978f2c15f6e84ffe |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
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| spelling | doaj-art-e10b492c8242422b978f2c15f6e84ffe2025-08-20T02:21:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e029256810.1371/journal.pone.0292568Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy.Annalisa V PiccorelliJerry A Nick<h4>Background</h4>The duration of time a person with cystic fibrosis (pwCF) spends on the lung transplant waitlist is dependent on waitlist and post-transplant survival probabilities and can extend up to 2 years. Understanding the characteristics involved with lung transplant and survival prognoses may help guide decision making by the patient, the referring CF Center and the transplant team.<h4>Methods</h4>This study seeks to identify clinical predictors of lung transplant and survival of individuals with CF using 29,847 subjects from 2003-2014 entered in the Cystic Fibrosis Foundation Patient Registry (CFFPR).<h4>Results</h4>Predictors significant (p ≤ 0.05) in the final logistic regression model predicting probability of lung transplant/death were: FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, insurance status, and consecutive ibuprofen use for at least 4 years. The final Cox regression model predicting time to lung transplant identified these predictors as significant FEV1 (% predicted), BMI, age of diagnosis, age, number of pulmonary exacerbations, race, sex, CF-related diabetes (CFRD), corticosteroid use, infections with B. cepacia, P. aeruginosa, S. aureus, MRSA, pancreatic enzyme use, and consecutive ibuprofen use for at least 4 years. The concordance indices were 0.89 and 0.92, respectively.<h4>Conclusions</h4>The models are translated into nomograms to simplify investigation of how various characteristics relate to lung transplant and survival prognosis individuals with CF not receiving highly effective CFTR modulator therapy.https://doi.org/10.1371/journal.pone.0292568 |
| spellingShingle | Annalisa V Piccorelli Jerry A Nick Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. PLoS ONE |
| title | Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. |
| title_full | Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. |
| title_fullStr | Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. |
| title_full_unstemmed | Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. |
| title_short | Modeling cystic fibrosis patient prognosis: Nomograms to predict lung transplantation and survival prior to highly effective modular therapy. |
| title_sort | modeling cystic fibrosis patient prognosis nomograms to predict lung transplantation and survival prior to highly effective modular therapy |
| url | https://doi.org/10.1371/journal.pone.0292568 |
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