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Validation of an Algorithm to Classify Urine Cultures in Family Medicine
Jack Zhang1, Rachael Morkem2, Akshay Rajaram2,3
1Section of Anesthesia, Northern Ontario School of Medicine University, Sudbury, Canada.
Objectives:
Automation of test follow-up offers potential reductions in workload for clinicians. The primary objective of the study was to evaluate the performance of MicrobEx, a regular expression-based algorithm in classifying urine culture reports in primary care.
Methods:
A retrospective validation of MicrobEx was performed using urine culture reports abstracted from a single academic family health team. MicrobEx classifications were compared with labels assigned manually by a human reviewer. Measures of diagnostic performance were calculated.
Results:
MicrobEx achieved 95.3% accuracy, 88.6% sensitivity, and 100% specificity in classifying 1,999 urine culture reports.
Conclusion:
The accuracy of MicrobEx was comparable to its performance in the original development and validation study by Eickelberg. Additional work is required to explore and improve the accuracy of MicrobEx and assess its performance across primary care settings and with more complex urine culture reports.
