The Dynamics of Disease Progression in Cystic Fibrosis

In cystic fibrosis, statistical models have been more successful in predicting mortality than the time course of clinical status. We develop a system of partial differential equations that simultaneously track mortality and patient status, with all model parameters estimated from the extensive and c...

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Bibliographic Details
Published inPloS one Vol. 11; no. 6; p. e0156752
Main Authors Adler, Frederick R, Liou, Theodore G
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.06.2016
Public Library of Science (PLoS)
Subjects
Bacteria
Bacterial infections
Biology and Life Sciences
Burkholderia
Burkholderia - pathogenicity
Carrier State
Cystic fibrosis
Cystic Fibrosis - microbiology
Cystic Fibrosis - pathology
Cystic Fibrosis - physiopathology
Data analysis
Data processing
Development and progression
Differential equations
Disease Progression
Feedback
Feedback loops
Humans
Lungs
Mathematical models
Medicine and Health Sciences
Methicillin
Mortality
Negative feedback
Parameter estimation
Partial differential equations
Pathogens
People and Places
Physical Sciences
Positive feedback
Pseudomonas aeruginosa
Pseudomonas aeruginosa - pathogenicity
Respiratory function
Staphylococcus aureus
Staphylococcus aureus - pathogenicity
Statistical analysis
Statistical models
Survival
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Summary:In cystic fibrosis, statistical models have been more successful in predicting mortality than the time course of clinical status. We develop a system of partial differential equations that simultaneously track mortality and patient status, with all model parameters estimated from the extensive and carefully maintained database from the Cystic Fibrosis Foundation. Cystic fibrosis is an autosomal recessive disease that leads to loss of lung function, most commonly assessed using the Forced Expiratory Volume in 1 second (FEV1%). This loss results from inflammation secondary to chronic bacterial infections, particularly Pseudomonas aeruginosa, methicillin-sensitive Staphylococcus aureus (MSSA) and members of the virulent Burkholderia complex. The model tracks FEV1% and carriage of these three bacteria over the course of a patient's life. Analysis of patient state changes from year to year reveals four feedback loops: a damaging positive feedback loop between P. aeruginosa carriage and lower FEV1%, negative feedback loops between P. aeruginosa and MSSA and between P. aeruginosa and Burkholderia, and a protective positive feedback loop between MSSA carriage and higher FEV1%. The partial differential equations built from this data analysis accurately capture the life-long progression of the disease, quantify the key role of high annual FEV1% variability in reducing survivorship, the relative unimportance of short-term bacterial interactions for long-term survival, and the potential benefits of eradicating the most harmful bacteria.
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Competing Interests: T.G. Liou is a member of the Editorial Board of CHEST and an ad hoc member of the Clinical Research Study Review Committee of the US Cystic Fibrosis Foundation (CFF); he is a recent past member of the Thoracic Committee of the United Network for Organ Sharing/Organization for the Procurement and Transplantation Network, the CFF Patient Registry Data Use Committee, the REVEAL Steering Committee (sponsored by Actelion Pharmaceuticals Ltd), and the National Institute for Occupational Safety and Health World Trade Center Research Review Committee. He has received grant funding from CFF Therapeutics, Inc.; the NIH/NHLBI; and the Margolis Family Foundation of Utah; and receives funding for studies of therapies from CFF Therapeutics, Inc.; Genentech Inc.; Gilead Sciences, Inc.; Inspire; MPex Pharmaceuticals, Inc.; Savara Pharmaceuticals; and Vertex. He is a consultant for Gehrson Lehman Group, Inc.; Genentech Inc.; and Vertex. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.
Conceived and designed the experiments: FRA TGL. Performed the experiments: FRA. Analyzed the data: FRA TGL. Contributed reagents/materials/analysis tools: FRA TGL. Wrote the paper: FRA TGL.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0156752