Robertson, John L.Senger, Ryan S.Talty, JanineDu, PangSayed-Issa, AmrAvellar, Maggie L.Ngo, Lacy T.Gomez de la Espriella, MarianaFazili, Tasaduq N.Jackson-Akers, Jasmine Y.Guruli, GeorgiOrlando, GiuseppeBussolati, Benedetta2022-08-092022-08-092022-07-011932-6203PONE-D-21-38432 (PII)http://hdl.handle.net/10919/111492We developed and tested a method to detect COVID-19 disease, using urine specimens. The technology is based on Raman spectroscopy and computational analysis. It does not detect SARS-CoV-2 virus or viral components, but rather a urine ‘molecular fingerprint’, representing systemic metabolic, inflammatory, and immunologic reactions to infection. We analyzed voided urine specimens from 46 symptomatic COVID-19 patients with positive real time-polymerase chain reaction (RT-PCR) tests for infection or household contact with test-positive patients. We compared their urine Raman spectra with urine Raman spectra from healthy individuals (n = 185), peritoneal dialysis patients (n = 20), and patients with active bladder cancer (n = 17), collected between 2016–2018 (i.e., pre-COVID-19). We also compared all urine Raman spectra with urine specimens collected from healthy, fully vaccinated volunteers (n = 19) from July to September 2021. Disease severity (primarily respiratory) ranged among mild (n = 25), moderate (n = 14), and severe (n = 7). Seventy percent of patients sought evaluation within 14 days of onset. One severely affected patient was hospitalized, the remainder being managed with home/ambulatory care. Twenty patients had clinical pathology profiling. Seven of 20 patients had mildly elevated serum creatinine values (>0.9 mg/dl; range 0.9–1.34 mg/dl) and 6/7 of these patients also had estimated glomerular filtration rates (eGFR) <90 mL/min/1.73m2 (range 59–84 mL/min/1.73m2). We could not determine if any of these patients had antecedent clinical pathology abnormalities. Our technology (Raman Chemometric Urinalysis—Rametrix®) had an overall prediction accuracy of 97.6% for detecting complex, multimolecular fingerprints in urine associated with COVID-19 disease. The sensitivity of this model for detecting COVID-19 was 90.9%. The specificity was 98.8%, the positive predictive value was 93.0%, and the negative predictive value was 98.4%. In assessing severity, the method showed to be accurate in identifying symptoms as mild, moderate, or severe (random chance = 33%) based on the urine multimolecular fingerprint. Finally, a fingerprint of ‘Long COVID-19’ symptoms (defined as lasting longer than 30 days) was located in urine. Our methods were able to locate the presence of this fingerprint with 70.0% sensitivity and 98.7% specificity in leave-one-out cross-validation analysis. Further validation testing will include sampling more patients, examining correlations of disease severity and/or duration, and employing metabolomic analysis (Gas Chromatography–Mass Spectrometry [GC-MS], High Performance Liquid Chromatography [HPLC]) to identify individual components contributing to COVID-19 molecular fingerprints.application/pdfenCreative Commons Attribution 4.0 InternationalClinical Research4.2 Evaluation of markers and technologies4.1 Discovery and preclinical testing of markers and technologies4 Detection, screening and diagnosisHumansUrinalysisSpectrum Analysis, RamanCOVID-19SARS-CoV-2Article - Refereed2022-08-08https://doi.org/10.1371/journal.pone.027091417Senger, Ryan [0000-0002-2450-6693]Robertson, John [0000-0002-5499-9943]Du, Pang [0000-0003-1365-4831]Gomez, Mariana [0000-0001-8963-9959]358495721932-6203