#AI reads Urine# Prediction of COPD exacerbations using urine biomarkers

This paper explores the use of urinary biomarkers and artificial neural networks (ANNs) to predict the risk of exacerbations in chronic obstructive pulmonary disease (COPD). In a retrospective study of 55 patients, researchers first screened 10 biomarkers (such as NGAL, CRP, and fibrinogen) from 35 urinary markers, which effectively distinguished stable and exacerbation phases with area under the curve (AUC) values of 0.84 and 0.81 for accuracy in differentiation. In a prospective study, 105 patients underwent daily home urine testing, and an ANN model was developed using data from 85 of them. The optimized 5-biomarker model predicted exacerbations 7 days before clinical diagnosis, with an AUC of 0.89, 95% sensitivity, and 85% specificity within a 13-day window. Home testing devices achieved over 85% compliance and a 93% operational success rate. The study demonstrates the potential of this method for early warning of exacerbations, though larger-scale research is needed to validate its effectiveness and generalizability.

ERJ Open Res. 2025 Jun 2;11(3):00797-2024. doi: 10.1183/23120541.00797-2024

Artificial neural network risk prediction of COPD exacerbations using urine biomarkers

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/