The NoMicro classifier appears accurate for evaluating urine cultures in cases of suspected urinary tract infection in the primary care setting without the need for microscopy, according to a study published in the January/February issue of the Annals of Family Medicine.
Gurpreet Dhanda, M.D., from the University of Kansas Medical Center in Kansas City, and colleagues redesigned a classifier (NoMicro) that does not depend on urine microscopy and retrospectively validated a machine learning prediction model for urine cultures internally (emergency department data set) and externally (primary care data set).
Pathogenic urine culture growing ≥100,000 colony-forming units was the primary outcome, while predictor variables were: age; gender; dipstick urinalysis nitrites, leukocytes, clarity, glucose, protein, and blood; dysuria; abdominal pain; and history of urinary tract infection.
The researchers found that removal of microscopy features did not severely compromise performance under internal validation (receiver operating characteristic area under the curve [ROC-AUC], 0.86 and 0.88 for NoMicro/XGBoost and NeedMicro, respectively). In external validation, excellent performance was also achieved (NoMicro/random forests ROC-AUC, 0.85).