Business
Publication of confirmation study results
Publication of confirmation study results.
About this update from Renalytix Plc
[{"type":"text","content":"\n \nRNS Number : 5764U Renalytix AI PLC 01 April 2019 \n\nRenalytix AI plc\n(\"RenalytixAI\" or the \"Company\")\n \nStudy demonstrates machine learning can significantly improve prediction \nof rapid kidney function decline in patients with diabetes\n \nRenalytixAI machine learning approach outperformed current standard of care in 1,369 patient study\n \nRenalytix AI plc (AIM: RENX), the developer of artificial intelligence-enabled diagnostics for kidney disease, announces publication of results generated by a confirmation study.\n \nHighlights\n \n· Study demonstrated that combining the Company's sTNFR 1, sTNFR2 and KIM-1 biomarkers with the analysis of data from de-identified electronic health records can significantly improve prediction of rapid kidney function decline (\"RKFD\") compared to widely used approaches \n· A total of 1,369 patients were part of the study, including 871 patients with Type 2 diabetes and 498 patients of African ancestry\n· The study incorporating a \"random forest\" inference approach to machine learning significantly outperformed standard clinical metrics for prediction of patients experiencing RKFD \n· The study also demonstrated a high negative predictive value can be achieved for approximately 1/3 of patients with existing kidney disease who are unlikely to experience RKFD \n \nThe algorithms used in this study are at the core of the Company's AI-enabled diagnostic product, KidneyIntelX™. \n \nThe manuscript, entitled \"Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data\", concludes that for patients with Type 2 diabetes or of African Ancestry with the high-risk APOL1 genotype, a machine learning model, derived from blood biomarkers sTNFR 1, sTNFR2, and KIM1, and the analysis of de-identified data from a patient's electronic health records, significantly improved prediction of RKFD over standard clinical models and models without blood biomarkers. \n \nA rigorous, multi-center clinical validation study has recently been initiated with c. 5,000 patient blood samples and features from patient electronic health records from the Icahn School of Medicine at Mount Sinai, Emory Univers...