Leukocyte abnormalities quantitated by automated analyzers successfully identified malaria and dengue and distinguished them from other fevers. These economic discriminant functions can be rapidly calculated by analyzer software programs to generate electronic flags to trigger-specific testing. They could potentially transform diagnostic approaches to tropical febrile illnesses in cost-constrained settings.

The diagnosis of myelodysplastic syndromes (MDS) requires a high clinical index of suspicion to prompt bone marrow studies as well as subjective assessment of dysplastic morphology. We sought to determine if data collected by automated hematology analyzers during complete blood count (CBC) analysis might help to identify MDS in a routine clinical setting. We collected CBC parameters (including those for research use only and cell population data) and demographic information in a large (>5,000), unselected sequential cohort of outpatients. The cohort was divided into independent training and test groups to develop and validate a random forest classifier that identifies MDS. The classifier effectively identified MDS and had a receiver operating characteristic area under the curve (AUC) of 0.942. Platelet distribution width and the standard deviation of red blood cell distribution width were the most discriminating variables within the classifier. Additionally, a similar classifier was validated with an additional, independent set of >200 patients from a second institution with an AUC of 0.93. This retrospective study demonstrates the feasibility of identifying MDS in an unselected outpatient population using data routinely collected during CBC analysis with a classifier that has been validated using two independent data sets from different institutions.

Introduction Coronavirus disease 2019 (COVID‐19) is characterized by a high contagiousness requiring isolation measures. At this time, diagnosis is based on the positivity of specific RT‐PCR and/or chest computed tomography scan, which are time‐consuming and may delay diagnosis. Complete blood count (CBC) can potentially contribute to the diagnosis of COVID‐19. We studied whether the analysis of cellular population data (CPD), provided as part of CBC‐Diff analysis by the DxH 800 analyzers (Beckman Coulter), can help to identify SARS‐CoV‐2 infection. Methods Cellular population data of the different leukocyte subpopulations were analyzed in 137 controls, 322 patients with proven COVID‐19 (COVID+), and 285 patients for whom investigations were negative for SARS‐CoV‐2 infection (COVID−). When CPD of COVID+ were different from controls and COVID− patients, we used receiver operating characteristic analysis to test the discriminating capacity of the individual parameters. Using a random forest classifier, we developed the algorithm based on the combination of 4 monocyte CPD to discriminate COVID+ from COVID− patients. This algorithm was tested prospectively in a series of 222 patients referred to the emergency unit. Results Among the 222 patients, 86 were diagnosed as COVID‐19 and 60.5% were correctly identified using the discriminating protocol. Among the 136 COVID− patients, 10.3% were misclassified (specificity 89.7%, sensitivity 60.5%). False negatives were observed mainly in patients with a low inflammatory state whereas false positives were mainly seen in patients with sepsis. Conclusion Consideration of CPD could constitute a first step and potentially aid in the early diagnosis of COVID‐19.

4729 Sepsis and its sequelae remain leading causes of death in critically ill patients. Early identification and subsequently prompt treatment of sepsis can have a major impact on the outcome of septic patients. White Blood Cell (WBC) count, neutrophils count or percentage and percentage of band and immature neutrophils have been included in the diagnostic criteria of sepsis d. Previous studies have shown that morphological changes of leukocytes during sepsis can be quantified using Volume, Conductivity and Scatter (VCS) technology by some hematology analyzers. DxH800 (Beckman Coulter, Fullerton, CA) has the potential to report, in addition to Volume and Conductivity, five extra-laser diffraction angles for each cellular event. The aim of the study was to evaluate the diagnostic accuracy of VCS parameters of leukocytes in detecting sepsis in critically ill adult patients. This prospective observational study involved all consecutive adult patients admitted to a 31-bed medico-surgical department of intensive care (ICU) for more than 4 hours in a 3.5-month period. Diagnosis of sepsis was based on standard definitions. Blood samples were run by DxH800. Complete blood count (CBC) and WBC differential, but not VCS parameters were reported to attending physicians. Of a total of 722 patients admitted during the observation period 422 had WBC differential (and thus VCS parameters) determined at admission, of whom 125 had sepsis Septic and non-septic patients had a median age (upper and lower quartile) of 60 (51-71) and 58 (45-72) years and APACHE II scores of 19 (14-25) and 11 (7-16). VCS parameters had higher Areas Under the Receiver Operating Characteristic Curve (AUC) than WBC or percentage of neutrophils for diagnosis of sepsis at admission (Table 1.).Table 1.DxH800 VCS parameters. Results as median values (upper and lower quartile)VCS parametersSepsis N=125No Sepsis N=317p valueAUC95%CI of AUCMean Volume of Neutrophils (MNVNE)153 (146–163)144 (140–148)<0.0010.780.72–0.83SD of Volume of Neutrophils (SDVNE)20 (18–23)17 (16–18)<0.0010.810.77–0.86Mean Axial Light loss of Neutrophils (MNAL2NE)155 (150–160)148 (144–151)<0.0010.780.73–0.84SD of Axial Light loss of Neutrophils (SDAL2NE)13 (12–16)11 (10–12)<0.0010.800.75–0.84Mean Volume of Monocytes (MNVMO)179 (169–189)163 (159–167)<0.0010.880.84–0.91SD of Volume of Monocytes (SDVMO)24 (21–28)18 (16–20)<0.0010.880.84–0.91SD of Axial Light loss of Monocytes (SDAL2MO)16 (15–20)13 (12–14)<0.0010.850.81–0.89WBC, 103/ml10.9 (7.3–15.3)10.5 (7.8–14.2)NS0.510.44–0.57Percent of Neutrophils, %85 (76–91)80 (68–88)<0.0010.610.55–0.67 On ICU admission, MNVMO had the highest discriminant values in detecting sepsis with sensitivity (Se) of 84%, specificity (Sp) 76%, positive predictive value (PPV)58%, negative predictive value (NPV) 92% and odds ratio (OR) 17 for a cutoff of 168. MNVMO<168 (59% of patients) gave reasonable evidence against sepsis (likelihood ratio (LR) of 0.21, pre- and post-test probability, 0.28 and 0.076,) and MNVMO≥175 (25% of patients) gave reasonable evidence for sepsis (LR of 6.85, pre- and post-test probability, 0.28 and 0.73, respectively). Only 16% of patients, in the intermediate range, had a rather uninformative test result (LR of 1.32). Combining monocyte or neutrophil VCS parameters with CRP slightly increases AUC to 0.91. During the ICU stay, VCS parameters were obtained in 316 initially non-septic patients, of whom 20 became septic. MNVMO < 174 excluded ICU-acquired infection with NPV of 99% and MNVMO > 174 predicted ICU-acquired infection 1.7 days earlier than clinical diagnosis (Se: 85%, Sp 68%, OR 12 and AUC=0.78, 95% CI: 66–90). CONCLUSIONS: This large prospective study in unselected critically ill adult patients demonstrates the diagnostic utility of VCS parameters, especially MNVMO, in detecting and, more importantly, excluding sepsis at admission and during ICU stay. VCS parameters are obtained automatically, routinely, within minutes, requiring neither additional sampling nor additional cost to that of complete blood count thus making their prospects very promising. Disclosures: Pradier: Beckman Coulter: Consultancy, Membership on an entity's Board of Directors or advisory committees.

We suggest to use the combination of MNV and VDW with markers such as CRP and IL-6, and use diagnostic models created by EMMA.