Comparing the performance of three panels rules of blood smear review criteria on an Italian multicenter evaluation

Abstract

These results presented show that the customization of validation rules is necessary for enhancing the quality of hematological testing and optimizing workflow.

Abnormal scattergrams and cell population data generated by fully automated hematological analyzers: New tools for screening malaria infection?

“…Regarding the potential use of XN-module for screening Malaria The use of this formula was characterized by an AUC of 0.91 (95% CI, 0.89-0.91) for discriminating Malaria cases from controls ( Figure 3A). 19 and, more recently, by Pipitone et al 20 Notably, when the analysis was limited to the 1052: 1047 control and 5 malaria samples only with trophozoites and without characteristic abnormalities of WDF e WNR-scattergrams, the diagnostic performance of XN-MF was found to be considerably improved (AUC, 0.96; 95% CI, 0.95-0.97; P < .001) ( Figure 3B). In these samples, the best diagnostic threshold identified with ROC curve analysis was ≤4.0, and was associated with 1.00 SN (95% CI, 0.47-1.00) and 0.91 SP (95% CI, 089-0.83), respectively.…”

“…Although a single false‐negative result could be identified (1 of 14; 7%), this sample displayed the characteristic abnormalities of WDF e WNR‐scattergrams that have been previously described in Malaria samples. Regarding the 200 false‐positive results (18.5% of all control samples), most of these (171 of 200; 86%) would have then required microscopic revision according to the criteria proposed by the International Consensus Group for Hematology (ICGH) and, more recently, by Pipitone et al Notably, when the analysis was limited to the 1052: 1047 control and 5 malaria samples only with trophozoites and without characteristic abnormalities of WDF e WNR‐scattergrams, the diagnostic performance of XN‐MF was found to be considerably improved (AUC, 0.96; 95% CI, 0.95‐0.97; P < .001) (Figure B). In these samples, the best diagnostic threshold identified with ROC curve analysis was ≤4.0, and was associated with 1.00 SN (95% CI, 0.47‐1.00) and 0.91 SP (95% CI, 089‐0.83), respectively.…”

“…In these samples, the best diagnostic threshold identified with ROC curve analysis was ≤4.0, and was associated with 1.00 SN (95% CI, 0.47‐1.00) and 0.91 SP (95% CI, 089‐0.83), respectively. Regarding the 96 false‐positive results (9.5% of all control samples without characteristic abnormalities of WDF e WNR‐scattergrams), the vast majority of these (90 of 96; 94%) would have then required microscopic revision according to the criteria proposed by the International Consensus Group for Hematology (ICGH) and, more recently, by Pipitone et al…”

Validation of the international consensus group criteria for slide review following automated complete blood count at Jimma Medical Center, Ethiopia

“…The FP rate in this study is much better than previous studies which reported FP rate ranging from 7.2% to 34% [14], [18], [15], [17]. Moreover, the review rate in the present study was found to be comparable with some previous studies which reported a review rate ranging from 29% to 51% [14], [15], [17], [19].…”

Establishment of improved review criteria for hematology analyzers in cancer hospitals

“…The International Consensus Group for Hematology Review (ICGH) has developed the guidelines for action after the introduction of the automated hematology analysis in 2005 7 . Accordingly, medical laboratories in China have achieved a consensus that each laboratory should have a manual slide review criteria for hematology analyzers 8,9 . In 2008, the Chinese consensus group developed 23 guidelines for the Sysmex XE‐2100 hematology analyzer (SYSMEX) 10 .…”