Yuan Cao1, Ming Cheng, Chengjin Hu. 1. Department of Laboratory Medicine , 90th General Hospital of Jinan, Jinan, Shandong, People's Republic of China.
Abstract
BACKGROUND: Automated systems have been broadly used in the counting of particles in urine, while manual microscopic analyses are still required for confirming components of urine sediments, especially pathologic casts and other unknown particles. Good review rules can reduce the number of manual urine microscopy examinations safely, thereby increasing productivity. Although several methods have been proposed,establishment of microscopic review rules for fl ow cytometer remains challenging. METHODS: A total of 3014 urine samples from outpatient and inpatient were examined using UF-1000i flow cytometry,Urisys-2400 dipstick and RS 2003 urine sediment workstation,respectively. Based on the results above, three supervised machine learning methods were employed to construct classifiers for screening urine samples. RESULTS: Here, we propose a novel method for construction of microscopic review rules, termed UrineCART, which was based on a classification and regression tree (CART) method.With a cut-off value of 0.0745 for Urine CART, we obtained a sensitivity of 92.0 % , a specificity of 81.5 % and a total review rate of 32.4 % on an independent test set. Comparisons with the existing methods showed that Urine CART gave the acceptable sensitivity and lower total review rate. CONCLUSIONS: An algorithm based on machine learning methods for review criteria can be achieved via systematic comparison of UF-1000i flow cytometry and microscopy.Using Urine CART, our microscopic review rate can be reduced to around 30 % , while decreasing significant losses in urinalysis.
BACKGROUND: Automated systems have been broadly used in the counting of particles in urine, while manual microscopic analyses are still required for confirming components of urine sediments, especially pathologic casts and other unknown particles. Good review rules can reduce the number of manual urine microscopy examinations safely, thereby increasing productivity. Although several methods have been proposed,establishment of microscopic review rules for fl ow cytometer remains challenging. METHODS: A total of 3014 urine samples from outpatient and inpatient were examined using UF-1000i flow cytometry,Urisys-2400 dipstick and RS 2003 urine sediment workstation,respectively. Based on the results above, three supervised machine learning methods were employed to construct classifiers for screening urine samples. RESULTS: Here, we propose a novel method for construction of microscopic review rules, termed UrineCART, which was based on a classification and regression tree (CART) method.With a cut-off value of 0.0745 for Urine CART, we obtained a sensitivity of 92.0 % , a specificity of 81.5 % and a total review rate of 32.4 % on an independent test set. Comparisons with the existing methods showed that Urine CART gave the acceptable sensitivity and lower total review rate. CONCLUSIONS: An algorithm based on machine learning methods for review criteria can be achieved via systematic comparison of UF-1000i flow cytometry and microscopy.Using Urine CART, our microscopic review rate can be reduced to around 30 % , while decreasing significant losses in urinalysis.
Authors: Ketan Paranjape; Michiel Schinkel; Richard D Hammer; Bo Schouten; R S Nannan Panday; Paul W G Elbers; Mark H H Kramer; Prabath Nanayakkara Journal: Am J Clin Pathol Date: 2021-05-18 Impact factor: 2.493