Our study aimed to evaluate the diagnostic performance of point-of-care nitrite and leukocyte esterase (LE) dipsticks in the diagnosis of suspected urinary tract infection (UTI) in infants <6 months (young infants) versus older children. The secondary objectives were to study the dipstick efficacy in children with congenital anomalies of the kidney and urinary tract (CAKUT) versus those without CAKUT; in children with simple UTI versus complicated UTI; and to evaluate the clinico-microbiological profile of children presenting with UTI. In this prospective observational study, cases with suspected UTI were enrolled from pediatric emergency or outpatient departments. Urine was collected for performing the urine dipstick and culture. Descriptive data regarding CAKUT, age, gender, etc., were recorded in a predesigned pro forma. We screened 506 children with suspected UTI, of whom 221 had urine culture positive. Approximately 38.4% of the children with UTI had underlying CAKUT, while 7.6% had renal scars. The most common CAKUT was vesicoureteric reflux (VUR). About 12 patients (2.3%) were known to have CAKUT at the time of enrollment in the study. In infants <6 months, LE dipstick had sensitivity 92%, specificity 89.7%, positive predictive value (PPV) 86.7%, negative predictive value (NPV) 93.8%, likelihood ratio (LR) + 8.9, LR- 0.09. In infants <6 months, nitrite dipstick had sensitivity 38%, specificity 97%, PPV 90.4%, NPV 68%, LR+ 12.6 and LR-0.63. In the age group 6 months to 12 years, the efficacy was better for both dipsticks. In age group more than 6 months to 12 years, LE dipstick had sensitivity 96.4%, specificity 95.8%, PPV 94.8 %, NPV 97.2%, LR+ 22.9, LR- 0.04. In age group more than six months to 12 years, nitrite dipstick had sensitivity 94.7%, specificity 99.5%, PPV 99.3%, NPV 96%, LR+ 189.4, and LR-0.05.
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http://dx.doi.org/10.4103/1319-2442.336765 | DOI Listing |
Ann Lab Med
December 2024
Department of Laboratory Medicine, Chungnam National University School of Medicine, Daejeon, Korea.
Background: Urinalysis, an essential diagnostic tool, faces challenges in terms of standardization and accuracy. The use of artificial intelligence (AI) with mobile technology can potentially solve these challenges. Therefore, we investigated the effectiveness and accuracy of an AI-based program in automatically interpreting urine test strips using mobile phone cameras, an approach that may revolutionize point-of-care testing.
View Article and Find Full Text PDFHeliyon
September 2024
Graduate Institute of Automation and Control, National Taiwan University of Science and Technology, Taipei, Taiwan.
Most urine test strips are intended to enable the general population to rapidly and easily diagnose potential renal disorders. It is semi-quantitative in nature, and although the procedure is straightforward, certain factors will affect the judgmental outcomes. This study describes rapid and accurate quantification of twelve urine test strip parameters: leukocytes, nitrite, urobilinogen, protein, pH, occult blood, specific gravity, ketone, bilirubin, glucose, microalbumin, and creatinine using a micro-electromechanical system (MEMS)-based spectrophotometer, known as a spectrochip.
View Article and Find Full Text PDFIndian J Microbiol
September 2024
Vivosens, Inc., 44 Tehama Street, Suite 409, San Francisco, CA 94105 USA.
Urinary tract infections (UTIs) are prevalent bacterial infections globally, posing significant challenges due to their frequency, recurrence, and antibiotic resistance. This review delves into the advancements in UTI diagnostics over the past decade, particularly focusing on the development of biosensor technologies. The emergence of biosensors, including microfluidic, optical, electrochemical, immunosensors, and nanotechnology-based sensors, offers enhanced diagnostic accuracy, reduced healthcare costs.
View Article and Find Full Text PDFSupport Care Cancer
September 2024
Laboratory of Oral Pathology, School of Dentistry, Universidade Federal de Goiás, Avenida Universitária Esquina Com 1ª Avenida, S/N. Setor Universitário, Goiânia, Goiás, CEP 74605-220, Brazil.
Scand J Prim Health Care
August 2024
Department of Family Medicine, CAPHRI, Maastricht University, Maastricht, The Netherlands.
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