Dataset Information

Predictive 5-year survivorship model of cystic fibrosis.

ABSTRACT: The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. The authors used information from the Cystic Fibrosis Foundation Patient Registry (CFFPR), which has collected longitudinal data on approximately 90% of cystic fibrosis patients diagnosed in the United States since 1986. They developed multivariate logistic regression models by using data on 5,820 patients randomly selected from 11,630 in the CFFPR in 1993. Models were tested for goodness of fit and were validated for the remaining 5,810 patients for 1993. The validated 5-year survivorship model included age, forced expiratory volume in 1 second as a percentage of predicted normal, gender, weight-for-age z score, pancreatic sufficiency, diabetes mellitus, Staphylococcus aureus infection, Burkerholderia cepacia infection, and annual number of acute pulmonary exacerbations. The model provides insights into the complex nature of cystic fibrosis and supplies a rigorous tool for clinical practice and research.

SUBMITTER: Liou TG

PROVIDER: S-EPMC2198936 | biostudies-literature | 2001 Feb

REPOSITORIES: biostudies-literature

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Predictive 5-year survivorship model of cystic fibrosis.

Liou T G TG Adler F R FR Fitzsimmons S C SC Cahill B C BC Hibbs J R JR Marshall B C BC

American journal of epidemiology 20010201 4

The objective of this study was to create a 5-year survivorship model to identify key clinical features of cystic fibrosis. Such a model could help researchers and clinicians to evaluate therapies, improve the design of prospective studies, monitor practice patterns, counsel individual patients, and determine the best candidates for lung transplantation. The authors used information from the Cystic Fibrosis Foundation Patient Registry (CFFPR), which has collected longitudinal data on approximate ...[more]

PMID: 11207152

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Project description:BackgroundCystic Fibrosis (CF) is characterized by chronically inflamed airways, and inflammation even increases during pulmonary exacerbations. These adverse events have an important influence on the well-being, quality of life, and lung function of patients with CF. Prediction of exacerbations by inflammatory markers in exhaled breath condensate (EBC) combined with early treatment may prevent these pulmonary exacerbations and may improve the prognosis.AimTo investigate the diagnostic accuracy of a set of inflammatory markers in EBC to predict pulmonary exacerbations in children with CF.MethodsIn this one-year prospective observational study, 49 children with CF were included. During study visits with an interval of 2 months, a symptom questionnaire was completed, EBC was collected, and lung function measurements were performed. The acidity of EBC was measured directly after collection. Inflammatory markers interleukin (IL)-6, IL-8, tumor necrosis factor α (TNF-α), and macrophage migration inhibitory factor (MIF) were measured using high sensitivity bead based flow immunoassays. Pulmonary exacerbations were recorded during the study and were defined in two ways. The predictive power of inflammatory markers and the other covariates was assessed using conditionally specified models and a receiver operating characteristic curve (SAS version 9.2). In addition, k-nearest neighbors (KNN) algorithm was applied (SAS version 9.2).ResultsSixty-five percent of the children had one or more exacerbations during the study. The conditionally specified models showed an overall correct prediction rate of 55%. The area under the curve (AUC) was equal to 0.62. The results obtained with the KNN algorithm were very similar.ConclusionAlthough there is some evidence indicating that the predictors outperform random guessing, the general diagnostic accuracy of EBC acidity and the EBC inflammatory markers IL-6, IL-8, TNF-α and MIF is low. At present it is not possible to predict pulmonary exacerbations in children with CF with the chosen biomarkers and the method of EBC analysis. The biochemical measurements of EBC markers should be improved and other techniques should be considered.

Project description:BackgroundFrom the original CftrTgH(neoim)Hgu mutant mouse model with a divergent genetic background (129P2, C57BL/6, MF1) we have generated two inbred CftrTgH(neoim)Hgu mutant strains named CF/1-CftrTgH(neoim)Hgu and CF/3-CftrTgH(neoim)Hgu, which are fertile and show normal growth and lifespan. Initial genome wide scan analysis with microsatellite markers indicated that the two inbred strains differed on the genetic level. In order to further investigate whether these genetic differences have an impact on the disease phenotype of cystic fibrosis we characterised the phenotype of the two inbred strains.ResultsReduced amounts, compared to wild type control animals, of correctly spliced Cftr mRNA were detected in the nasal epithelia, lungs and the intestine of both inbred CftrTgH(neoim)Hgu strains, with higher residual amount observed for CF/1-CftrTgH(neoim)Hgu than CF/3-CftrTgH(neoim)Hgu for every investigated tissue. Accordingly the amounts of wild type Cftr protein in the intestine were 9% for CF/1-CftrTgH(neoim)Hgu and 4% for CF/3-CftrTgH(neoim)Hgu. Unlike the apparent strain and/or tissue specific regulation of Cftr mRNA splicing, short circuit current measurements in the respiratory and intestinal epithelium revealed that both strains have ameliorated the basic defect of cystic fibrosis with a presentation of a normal electrophysiology in both tissues.ConclusionUnlike the outbred CftrTgH(neoim)Hgu insertional mouse model, which displayed the electrophysiological defect in the gastrointestinal and respiratory tracts characteristic of cystic fibrosis, both inbred CftrTgH(neoim)Hgu strains have ameliorated the electrophysiological defect. On the basis of these findings both CF/1-CftrTgH(neoim)Hgu and CF/3-CftrTgH(neoim)Hgu offer an excellent model whereby determination of the minimal levels of protein required for the restoration of the basic defect of cystic fibrosis can be studied, along with the modulating factors which may affect this outcome.

Dataset Information

Predictive 5-year survivorship model of cystic fibrosis.

Publications

Predictive 5-year survivorship model of cystic fibrosis.

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