Artificial intelligence
Anna Marielle B. Dy, Daniela Mennickent, E. Castro, A Rodriguez, Juan Araya, Enrique Guzmán‐Gutiérrez, Salvador Pérez, Sandra Fuentes, Manuel J. Barragán, T Arrobas Velilla, Patricia Fernández‐Riejos, Antonio León‐Justel, Lena Jafri, A.A. Farooqui, Joyce Grant, Rodney Gale, Sibtain Ahmed, Hafsa Majid, A Habib Khan, Usmaan Omer, Carlos Moure, Hanna Jansen, Marije van Haeringen, Marelle J. Bouva, A. Heijboer, Róbert de Jonge, Wendy P. J. den Elzen, Eveline Bruinstroop, Chris van der Ploeg, A S Paul van Trotsenburg, Nitash Zwaveling‐Soonawala, Annet M. Bosch, Marga Hoogendoorn, Anita Boelen, Claus Lohman Brasen, Andreas Christensen, Ejler Ejlersen, Jan Erik Madsen, Ivan Brandslund, Gülşen Yılmaz, Serdar Sezer, Aliye Baştuğ, Vivek Kumar Singh, R Gopalan, Ömer Aydos, Bengi Öztürk, Derya Gökçınar, Amine Kamen, Jamie Gramz, Hürrem Bodur, Filiz Akbıyık, Kristin E. Wickstrøm, Christian Prebensen, Alvaro Köhn‐Luque, Per Trygge Kjelland Flemmen, Aleksander Rygh Holten, E. Amundsen, Valeria Vitelli
Abstract
The Jaffe method tests for creatinine based on the formation of a colored complex between creatinine and alkaline picrate but interfering substances can cause errors. The enzymatic method reduces interferences but not all labs adopted it due to costs. This study aims to create a statistical model to correct for Jaffe method interferences and produce results similar to those from the enzymatic method, and make an easy way for labs to adopt it for daily use.