Dieter Menne Menne Biomed Consulting Tübingen, Germany http://www.menne-biomed.de dieter.menne@menne-biomed.de

Menne Biomed Consulting Tübingen, Germany https://www.menne-biomed.de

dieter.menne@menne-biomed.de

This is a reboot of R package dmenne/d13cbreath which is no longer maintained.

What it does

  • Reads several file formats of 13C data: IRIS/Wagner (composite and CSV), BreathID and Excel.
  • Fits Beta-Exponential nonlinear curve fits using nls, which gives successful estimates for 90% of PDR curves.
  • Computes population fits with nlme when data from multiple recordings are available, resulting in much more reliable estimates for studies.
  • Computes prior-constrained Bayesian non-linear fit for single records (refactored to package dmenne/breathteststan)
  • Computes Bayesian non-linear population fit with Stan for multiple records (refactored to package dmenne/breathteststan)
  • Includes three data sets of 13C records from the University Hospital of Zürich
  • A comparison of results with nls, nlme and Bayesian Stan.
  • See the example in the documentation of t50BluckCoward for a comparison with published data.

Sponsors and supporters

The software is being developed in cooperation with the Department of Gastroenterology of the University Hospital of Zürich and Claraspital Basel. Thanks to Benjamin Misselwitz, Mark Fox and Werner Schwizer.

How to install

To install the most recent versions of the package, use

devtools::install_github("dmenne/breathtestcore", build_vignettes = TRUE)
# In case you want to use the fancy Stan-based methodes
devtools::install_github("dmenne/breathteststan")
# And here the web app; this is not on CRAN and must be installed from github
devtools::install_github("dmenne/breathtestshiny", build_vignettes = TRUE)

Do not forget to use build_vignettes = TRUE.

Stable version of the packages breathtestcore and breathteststan can also be installed from CRAN.

For an easy installation, use the Docker image dmenne/breathtestshiny

You can run the web app online. No data are stored, but you can download all results and a series of tests for studies.

Usage example

This example is from the documentation of function nlme_fit.

library(breathtestcore)
d = simulate_breathtest_data(n_records = 3, noise = 0.2, seed = 4711)
data = cleanup_data(d$data)
fit = nlme_fit(data)
plot(fit) # calls plot.breathtestfit

For additional examples, see the documentation and the tests in folder tests/testthat of the source package.

Planned

The core fitting functions and the Stan variants are reasonably stable and can be used to analyze your breath test data with R. The Shiny web app with reporting is work in progress; online demo, source code.

Reference:

  • Ghoos, Y. F., B. D. Maes, B. J. Geypens, G. Mys, M. I. Hiele, P. J. Rutgeerts, and G. Vantrappen. 1993. “Measurement of Gastric Emptying Rate of Solids by Means of a Carbon-Labeled Octanoic Acid Breath Test.” Gastroenterology 104 (6). Department of Medicine, University Hospital Gasthuisberg, Belgium.: 1640–7.

  • Maes, B. D., B. J. Geypens, Y. F. Ghoos, M. I. Hiele, and P. J. Rutgeerts. 1998. “13C-Octanoic Acid Breath Test for Gastric Emptying Rate of Solids.” Gastroenterology 114 (4): 856–59.

  • Bluck LJC (2009) Recent advances in the interpretation of the 13C octanoate breath test for gastric emptying. J. Breath Res. 3, https://iopscience.iop.org/1752-7163/3/3/034002/

  • Bluck, LJC, Jackson S, Vlasakakis G, Mander A (2011) Bayesian hierarchical methods to interpret the 13C-octanoic acid breath test for gastric emptying. Digestion 83_96-107.