Sepsis and cancer are some of the causes of morbidity and mortality in hospitals. Prompt detection and administration of the appropriate drug targeting the correct causative agent increases the chance of patient survival. The study presents an optical method supported by machine learning for discriminating urinary tract infections from an infection capable of causing urosepsis and urinary changes suggestive of bladder cancer. The method comprises spectra of spectroscopy measurement of patients' urine samples with: urinary tract infection, urosepsis and bladder cancer. To provide reliable classification of results assistance of 27 algorithms were tested. We proved that is possible to obtain up to 95% accuracy of the measurement method with the use of machine learning. The method was validated on urine samples from 93 patients. The advantages of the proposed solution are the simplicity of the sensor, mobility, versatility, and low cost of the test.
Urinary tract infections (UTIs) are prevalent clinical conditions that, if untreated, can progress to urosepsis, a potentially fatal systemic infection. Timely detection and accurate assessment are critical for effective intervention. This presentation will show the integration of Liquid Chromatography-Mass Spectrometry (LC-MS)-based metabolomics and proteomics to advance our comprehension of UTI and urosepsis. Emphasis is placed on biomarker discovery and the development of a Point-of-Care (PoC) device for urosepsis assessment using urine samples.
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