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Probability‐Based Compatibility Curves for Calcium and Phosphates in Parenteral Nutrition Formulations
Author(s) -
Gonyon Thomas,
Carter Phillip W.,
Phillips Gerald,
Owen Heather,
Patel Dipa,
Kotha Priyanka,
Green JohnBruce D.
Publication year - 2014
Publication title -
journal of parenteral and enteral nutrition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.935
H-Index - 98
eISSN - 1941-2444
pISSN - 0148-6071
DOI - 10.1177/0148607113495415
Subject(s) - compatibility (geochemistry) , calcium , logistic regression , phosphate , probabilistic logic , computer science , materials science , mathematics , chemistry , statistics , composite material , artificial intelligence , biochemistry , metallurgy
Background: The information content of the calcium phosphate compatibility curves for adult parenteral nutrition (PN) solutions may benefit from a more sophisticated statistical treatment. Binary logistic regression analyses were evaluated as part of an alternate method for generating formulation compatibility curves. Materials and Methods : A commercial PN solution was challenged with a systematic array of calcium and phosphate concentrations. These formulations were then characterized for particulates by visual inspection, light obscuration, and filtration followed by optical microscopy. Logistic regression analyses of the data were compared with traditional treatments for generating compatibility curves. Results : Assay‐dependent differences were observed in the compatibility curves and associated probability contours; the microscopic method of precipitate detection generated the most robust results. Calcium and phosphate compatibility data generated from small‐volume glass containers reasonably predicted the observed compatibility of clinically relevant flexible containers. Conclusions : The published methods for creating calcium and phosphate compatibility curves via connecting the highest passing or lowest failing calcium concentrations should be augmented or replaced by probability contours of the entire experimental design to determine zones of formulation incompatibilities. We recommend researchers evaluate their data with logistic regression analysis to help build a more comprehensive probabilistic database of compatibility information.