Comprehensive assessment and prediction of calcium-phosphorus precipitation in parenteral nutrition using advanced...

Calcium-phosphate precipitation poses a significant risk in parenteral nutrition. Existing compatibility studies are predominantly qualitative with inconsistent safety thresholds. This study aimed to develop a quantitative predictive framework to overcome these limitations.

A novel high-sensitivity real-time precipitation monitoring device was developed. The compatibility of six calcium-phosphorus formulation pairs was systematically evaluated under clinically relevant conditions using a factorial design, generating 15,342 experimental observations. Four data mining models-C5.1, CHAID, Support Vector Machine, and Artificial Neural Network-were constructed and compared.

Analysis established distinct compatibility profiles. Precipitation was exclusive to inorganic phosphate combinations, specifically calcium chloride or calcium gluconate with potassium dihydrogen phosphate, while organic phosphate formulations remained stable within the tested concentration ranges. Six significant predictors were identified: calcium concentration, phosphorus concentration, pH, and temperature as risk factors, alongside 18AA-II and 18AA-V as protective factors. The C5.1 algorithm demonstrated optimal performance, achieving 98.5% predictive accuracy. External validation using 100 independent clinical samples confirmed robust performance (95% accuracy), facilitating the creation of a web-based clinical decision platform.

This study provides a systematic quantitative evaluation of clinically used calcium-phosphorus combinations under clinically relevant conditions. A data-driven framework that links experimental evidence with clinical decision-making and contributes to improved parenteral nutrition safety is established by integrating real-time precipitation monitoring, a large experimental dataset, predictive modeling, and external validation.