Rita Palmieri1, Rosanna Falbo2, Fabrizio Cappellini1, Cristina Soldi1, Giuseppe Limonta1, Paolo Brambilla3. 1. University Department of Laboratory Medicine, Hospital of Desio, Via Mazzini 1, 20033 Desio, MB, Italy. 2. University Department of Laboratory Medicine, Hospital of Desio, Via Mazzini 1, 20033 Desio, MB, Italy. Electronic address: rfalbo@asst-monza.it. 3. University Department of Laboratory Medicine, Hospital of Desio, Via Mazzini 1, 20033 Desio, MB, Italy; Department of Experimental Medicine, School of Medicine, University Milano Bicocca, Milano, Italy.
Abstract
BACKGROUND: Fully automated urine analyzers integrated with expert software can help to select samples that need review in routine clinical laboratory. This study aimed to define review rules to be set in the expert software Director for routine urinalysis on the AutionMAX-SediMAX platform. METHODS: A set of 1002 urinalysis data randomly extracted from the daily routine was used. The blind on-screen assessment was used as a reference. The data set was used to optimize the standard rules preset in the software to establish review criteria useful to intercept automated microscopy misidentification and particles suggestive of clinically significant profile. The review rate was calculated. The rules-set was also evaluated for the selection of clinically significant samples. RESULTS: The review rules established were cross-checked between AutionMAX and SediMAX parameters, element reporting by SediMAX and strip results. For the complete rules-set the review rate was 47.6% and the efficiency for clinically significant sample selection was 58%. Finally, on the basis of the review rules an algorithm for routine practice was created. CONCLUSIONS: Review rules applied to the algorithm for routine practice enhance workflow efficiency and optimize sample screening. Revision is not necessary for samples not flagged by the rules.
BACKGROUND: Fully automated urine analyzers integrated with expert software can help to select samples that need review in routine clinical laboratory. This study aimed to define review rules to be set in the expert software Director for routine urinalysis on the AutionMAX-SediMAX platform. METHODS: A set of 1002 urinalysis data randomly extracted from the daily routine was used. The blind on-screen assessment was used as a reference. The data set was used to optimize the standard rules preset in the software to establish review criteria useful to intercept automated microscopy misidentification and particles suggestive of clinically significant profile. The review rate was calculated. The rules-set was also evaluated for the selection of clinically significant samples. RESULTS: The review rules established were cross-checked between AutionMAX and SediMAX parameters, element reporting by SediMAX and strip results. For the complete rules-set the review rate was 47.6% and the efficiency for clinically significant sample selection was 58%. Finally, on the basis of the review rules an algorithm for routine practice was created. CONCLUSIONS: Review rules applied to the algorithm for routine practice enhance workflow efficiency and optimize sample screening. Revision is not necessary for samples not flagged by the rules.