Performance Evaluation of a Novel Artificial Intelligence-Assisted Digital Microscopy System for the Routine Analysis of Bone Marrow Aspirates.

Bone marrow aspiration (BMA) smear analysis is essential for diagnosis, treatment, and monitoring of a variety of benign and neoplastic hematological conditions. Currently, this analysis is performed by manual microscopy. We conducted a multicenter study to validate a computational microscopy approach with an artificial intelligence-driven decision support system. A total of 795 BMA specimens (615 Romanowsky-stained and 180 Prussian blue-stained) from patients with neoplastic and other clinical conditions were analyzed, comparing the performance of the Scopio Labs X100 Full Field BMA system (test method) with manual microscopy (reference method). The system provided an average of 1,385 ± 536 (range, 0-3,131) cells per specimen for analysis. An average of 39.98 ± 19.64 fields of view (range, 0-140) per specimen were selected by the system for analysis, of them 87% ± 21% (range, 0%-100%) were accepted by the qualified operators. These regions were included in an average of 17.62 ± 7.24 regions of interest (range, 1-50) per specimen. The efficiency, sensitivity, and specificity for primary and secondary marrow aspirate characteristics (maturation, morphology, and count assessment), as well as overall interuser agreement, were evaluated. The test method showed a high correlation with the reference method for comprehensive BMA evaluation, both on Romanowsky- (90.85% efficiency, 81.61% sensitivity, and 92.88% specificity) and Prussian blue-stained samples (90.0% efficiency, 81.94% sensitivity, and 93.38% specificity). The overall agreement between the test and reference methods for BMA assessment was 91.1%. For repeatability and reproducibility, all standard deviations and coefficients of variation values were below the predefined acceptance criteria both for discrete measurements (coefficient of variation below 20%) and differential measurements (SD below 5%). The high degree of correlation between the digital decision support system and manual microscopy demonstrates the potential of this system to provide a high-quality, accurate digital BMA analysis, expediting expert review and diagnosis of BMA specimens, with practical applications including remote BMA evaluation and possibly new opportunities for the research of normal and neoplastic hematopoiesis.

Clinical value of smear review of flagged samples analyzed with the Sysmex XN hematology analyzer.

OBJECTIVES: A smear review is typically made in flagged differential counts performed with hematology analyzers although the clinical value of such reviews is uncertain. Therefore, we evaluated the differences in differential counts between Sysmex XN-9000 and a smear review in flagged samples. Furthermore, the clinical value of blasts identified was investigated. METHODS: Data on all differential counts performed in a two-year period were identified at two laboratories. In patients with blasts, the electronic health record was reviewed. Agreement between automated and manual differential counts was evaluated by Bland-Altman plots. Concordance between the two methods categorized according to reference intervals was evaluated and adjusted for irrelevant non-concordance caused by random analytical error. RESULTS: In total, 5,500 flagged differential counts were identified from 4,092 patients. A good agreement between the automated and manual differential count was found for all cell types (-0.480 × 109/L to 0.297 × 109/L). The concordance between the two methods was excellent for all cell types, except for monocytes (82 %) where the automated estimates were higher than the manual in 19 % of samples. Blasts were identified in 241 (1 %) of smear reviews. Acute leukemia was diagnosed in 13 (5 %) patients, and only in one patient contributed the detection of blasts to the suspicion of acute leukemia. CONCLUSIONS: Our findings indicate that routine smear review of all flagged samples do not contribute with additional, significant information. After local validation and dialogue with clinical departments, such reviews may potentially be omitted to increase cost-effectiveness and reduce turn-around-time.

Comparison of Sysmex XN-V body fluid mode and deep-learning-based quantification with manual techniques for total nucleated cell count and differential count for equine bronchoalveolar lavage samples.

BACKGROUND: Bronchoalveolar lavage (BAL) is a diagnostic method for the assessment of the lower respiratory airway health status in horses. Differential cell count and sometimes also total nucleated cell count (TNCC) are routinely measured by time-consuming manual methods, while faster automated methods exist. The aims of this study were to compare: 1) the Sysmex XN-V body fluid (BF) mode with the manual techniques for TNCC and two-part differential into mononuclear and polymorphonuclear cells; 2) the Olympus VS200 slide scanner and software generated deep-learning-based algorithm with manual techniques for four-part differential cell count into alveolar macrophages, lymphocytes, neutrophils, and mast cells. The methods were compared in 69 clinical BAL samples. RESULTS: Incorrect gating by the Sysmex BF mode was observed on many scattergrams, therefore all samples were reanalyzed with manually set gates. For the TNCC, a proportional and systematic bias with a correlation of r = 0.79 was seen when comparing the Sysmex BF mode with manual methods. For the two-part differential count, a mild constant and proportional bias and a very small mean difference with moderate limits of agreement with a correlation of r = 0.84 and 0.83 were seen when comparing the Sysmex BF mode with manual methods. The Sysmex BF mode classified significantly more samples as abnormal based on the TNCC and the two-part differential compared to the manual method. When comparing the Olympus VS200 deep-learning-based algorithm with manual methods for the four-part differential cell count, a very small bias in the regression analysis and a very small mean difference in the difference plot, as well as a correlation of r = 0.85 to 0.92 were observed for all four cell categories. The Olympus VS200 deep-learning-based algorithm also showed better precision than manual methods for the four-part differential cell count, especially with an increasing number of analyzed cells. CONCLUSIONS: The Sysmex XN-V BF mode can be used for TNCC and two-part differential count measurements after reanalyzing the samples with manually set gates. The Olympus VS200 deep-learning-based algorithm correlates well with the manual methods, while showing better precision and can be used for a four-part differential cell count.

Enhancing pneumonia prognosis in the emergency department: a novel machine learning approach using complete blood count and differential leukocyte count combined with CURB-65 score.

BACKGROUND: Pneumonia poses a major global health challenge, necessitating accurate severity assessment tools. However, conventional scoring systems such as CURB-65 have inherent limitations. Machine learning (ML) offers a promising approach for prediction. We previously introduced the Blood Culture Prediction Index (BCPI) model, leveraging solely on complete blood count (CBC) and differential leukocyte count (DC), demonstrating its effectiveness in predicting bacteremia. Nevertheless, its potential in assessing pneumonia remains unexplored. Therefore, this study aims to compare the effectiveness of BCPI and CURB-65 in assessing pneumonia severity in an emergency department (ED) setting and develop an integrated ML model to enhance efficiency. METHODS: This retrospective study was conducted at a 3400-bed tertiary medical center in Taiwan. Data from 9,352 patients with pneumonia in the ED between 2019 and 2021 were analyzed in this study. We utilized the BCPI model, which was trained on CBC/DC data, and computed CURB-65 scores for each patient to compare their prognosis prediction capabilities. Subsequently, we developed a novel Cox regression model to predict in-hospital mortality, integrating the BCPI model and CURB-65 scores, aiming to assess whether this integration enhances predictive performance. RESULTS: The predictive performance of the BCPI model and CURB-65 score for the 30-day mortality rate in ED patients and the in-hospital mortality rate among admitted patients was comparable across all risk categories. However, the Cox regression model demonstrated an improved area under the ROC curve (AUC) of 0.713 than that of CURB-65 (0.668) for in-hospital mortality (p<0.001). In the lowest risk group (CURB-65=0), the Cox regression model outperformed CURB-65, with a significantly lower mortality rate (2.9% vs. 7.7%, p<0.001). CONCLUSIONS: The BCPI model, constructed using CBC/DC data and ML techniques, performs comparably to the widely utilized CURB-65 in predicting outcomes for patients with pneumonia in the ED. Furthermore, by integrating the CURB-65 score and BCPI model into a Cox regression model, we demonstrated improved prediction capabilities, particularly for low-risk patients. Given its simple parameters and easy training process, the Cox regression model may be a more effective prediction tool for classifying patients with pneumonia in the emergency room.

Bacteremia detection from complete blood count and differential leukocyte count with machine learning: complementary and competitive with C-reactive protein and procalcitonin tests.

BACKGROUND: Biomarkers, such as leukocyte count, C-reactive protein (CRP), and procalcitonin (PCT), have been commonly used to predict the occurrence of life-threatening bacteremia and provide prognostic information, given the need for prompt intervention. However, such diagnosis methods require much time and money. Therefore, we propose a method with a high prediction capability using machine learning (ML) models based on complete blood count (CBC) and differential leukocyte count (DC) and compare its performance with traditional CRP or PCT biomarker methods and those of models incorporating CRP or PCT biomarkers. METHODS: We collected 366,586 daily blood culture (BC) results, of which 350,775 (93.2%), 308,803 (82.1%), and 23,912 (6.4%) cases were issued CBC/DC (CBC/DC group), CRP with CBC/DC (CRP&CBC/DC group), and PCT with CBC/DC (PCT&CBC/DC group), respectively. For the ML methods, conventional logistic regression and random forest models were selected, trained, applied, and validated for each group. Fivefold validation and prediction capability were also evaluated and reported. RESULTS: Overall, the ML methods, such as the random forest model, demonstrated promising performances. When trained with CBC/DC data, it achieved an area under the ROC curve (AUC) of 0.802, which is superior to the prediction conventionally made with CRP/PCT levels (0.699/0.731). Upon evaluating the performance enhanced by incorporating CRP or PCT biomarkers, it reported no substantial AUC increase with the addition of either CRP or PCT to CBC/DC data, which suggests the predicting power and applicability of using only CBC/DC data. Moreover, it showed competitive prognostic capability compared to the PCT test with similar all-cause in-hospital mortality (45.10% vs. 47.40%) and overall median survival time (27 vs. 25 days). CONCLUSIONS: The ML models using only CBC/DC data yielded more accurate bacteremia predictions compared to those by methods using CRP and PCT data and reached similar prognostic performance as by PCT data. Thus, such models are potentially complementary and competitive with traditional CRP and PCT biomarkers for conducting and guiding antibiotic usage.

Automated cell differential count in sputum is feasible and comparable to manual cell count in identifying eosinophilia.

INTRODUCTION: Cell differential count (CDC) of induced sputum is considered the gold standard for inflammatory phenotyping of asthma but is not implemented in routine care due to its heavy time- and staff demands. Digital Cell Morphology is a technique where digital images of cells are captured and presented preclassified as white blood cells (neutrophils, eosinophils, lymphocytes, macrophages, and unidentified) and nonwhite blood cells for review. With this study, we wanted to assess the accuracy of an automated CDC in identifying the key inflammatory cells in induced sputum. METHODS: Sputum from 50 patients with asthma was collected and processed using the standard processing protocol with one drop 20% albumin added to hinder cell smudging. Each slide was counted automatically using the CellaVision DM96 and manually by an experienced lab technician. Sputum was classified as eosinophilic or neutrophilic using 3% and 61% cutoffs, respectively. RESULTS: We found a good agreement using intraclass correlation for all target cells, despite significant differences in the cell count rate. The automated CDC had a sensitivity of 65%, a specificity of 93%, and a kappa-coefficient of 0.61 for identification of sputum eosinophilia. In contrast, the automated CDC had a sensitivity of 29%, a specificity of 100%, and a kappa-coefficient of 0.23 for identification of sputum neutrophilia. CONCLUSION: Automated- and manual cell counts of sputum agree with regards to the key inflammatory cells. The automated cell count had a modest sensitivity but a high specificity for the identification of both neutrophil and eosinophil asthma.

Non-neoplastic respiratory fluid cytology including cell differential counts for interstitial lung disease.

Bronchioloalveolar lavage (BAL) is a non-invasive and well-tolerated procedure that plays a key role in the diagnosis of a variety of non-neoplastic pulmonary diseases, including acute respiratory failure, infection, diffuse parenchymal lung disease (DLPD), paediatric and occupational lung disease, and in the evaluation of the lung allograft. A variety of analytic techniques are commonly performed on BAL fluid, including cytology, cell differential count, microbiology and virology, as well as a number of additional techniques in specific circumstances.

Estimation of numbers of mature and immature neutrophils in blood by a novel, rapid and simple technology.

Counting numbers of blood neutrophils is one of the most common laboratory tests in modern clinical medicine. In this report, we have tested the idea that immunoassay of major constituents of mature neutrophils might serve as proxy of cell counting and allow the development of rapid and simple point-of-care tests. The procedure may also allow for the estimate of the state of maturity of the circulating blood cells. Immunoassays for myeloperoxidase (MPO) and lactoferrin (LF) were used to measure the respective protein in whole blood extracts of 275 unselected hospitalized patient and in 51 healthy controls and leukemia patients of which eight were followed before, during and after remission treatment. MPO was correlated to neutrophil counts in the unselected hospitalized population (r = 0.95, p <.0001). Huge variations were seen in whole blood extracts of patients with AML with very high MPO/LF ratios in half of the AML patients and in all three patients with APL. In extracts from patients with ALL no difference was found in the ratio as compared to healthy persons. The monitoring of AML patients during remission treatment showed intriguing patterns one of which suggested the possibility to monitor the myelopoietic activity in the bone marrow during the recovery phase. We show a novel and easy technology to count mature neutrophils in blood and also to monitor myeloid cell maturity in the blood as well as myelopoietic activity in the bone marrow. The technology lends itself to the development of a rapid and simple point-of-care test.

Continuous ambulatory peritoneal dialysis, ascitic and pleural body fluids evaluation with the Mindray BC-6800 hematology analyzer.

BACKGROUND: Accurate evaluation of hematology analyzers is recommended before these devices can be broadly introduced for the routine testing of continuous ambulatory peritoneal dialysis (CAPD), ascitic, and pleural fluids. METHODS: We evaluated the performance of Mindray BC-6800 for white blood cell (WBC) and differential cell count in 50 CAPD, 60 ascitic and 40 pleural compared with manual microscopy. Within-run precision, limit of blank (LoB), limit of detection (LoD), limit of quantitation (LoQ), and carryover were assessed. RESULTS: The Passing-Bablok regression in all fluids showed the following equations: yWBC =1.05x+3.31 (95%CI slope 0.95 to 1.12; intercept -0.25 to 5.52); yMN =0.85x+15.63 (95%CI slope 0.72 to 1.05; intercept -24.18 to 84.47); and yPMN =1.21x+13.37 (95%CI slope 1.03 to 1.35; intercept 4.00 to 32.47) with bias 78 cells/µL. The AUC for clinical PMN cut-off was 0.88 (95%CI: 0.77 to 0.98). In ascitic, pleural, and CAPD fluids the AUC for clinical PMN cut-off were 0.88 (95%CI: 0.63 to 1.00), 0.83 (95%CI: 0.68 to 0.99), and 1.00 (95%CI: 1.00 to 1.00) respectively. CV ranged from 3%-34%. LoB of 3 cell/µL was verified. LoD and LoQ reported the same result (8 cells/µL). Carry over never exceeded 0.05%. CONCLUSION: The effectiveness of BC-6800 to categorize cells from different body fluids was not compromised by the slight positive bias observed. This conclusion is supported by the high AUC and agreement between the automated method and the reference method. The results show that BC-6800 offers rapid, accurate, and reproducible results for clinical management of CAPD, ascitic, and pleural fluids.

Impact of lymphocyte differential count > 15% in BALF on the mortality of patients with acute exacerbation of chronic fibrosing idiopathic interstitial pneumonia.

BACKGROUND: Chronic fibrosing idiopathic interstitial pneumonia (CFIIP) has a potential risk of acute exacerbation (AE). However, the usefulness of cellular analysis of bronchoalveolar lavage fluid (BALF) has never been evaluated. This study aimed to evaluate the impact of the lymphocyte differential count > 15% in BALF on the mortality of patients with AE of CFIIP. METHODS: We retrospectively analysed 37 patients with AE of CFIIP who underwent BAL on admission. Patients were divided into two groups: one group consisting of those with a lymphocyte differential count > 15% and the other consisting of those with a lymphocyte differential count ≤ 15%. We compared the 90-day mortality between the two groups as the primary outcome, using the two-tailed log-rank test. RESULTS: The median follow-up duration was 6.9 months. Twenty-four patients had a lymphocyte differential count > 15%. The 90-day mortality was significantly higher in the group with a lymphocyte differential count ≤ 15% than in the group with a lymphocyte differential count > 15% (long rank test, p = 0.003). In the multivariate analysis a lymphocyte differential count > 15% was shown to be an independent favourable prognostic factor for 90-day mortality (HR: 0.125; 95% CI: 0.0247-0.589; p = 0.009). CONCLUSIONS: A lymphocyte differential count > 15% in BALF may be associated with favourable outcomes in patients with AE of CFIIP.

Differential enumeration of subpopulations in concentrated frozen and lyophilized cultures of Lactobacillus delbrueckii ssp. bulgaricus.

Differential enumeration of subpopulations in concentrated frozen and lyophilized cultures of Lactobacillus delbrueckii ssp. bulgaricus ND02 derived from 2 propagation procedures was determined. The subpopulations consisted of 3 categories (physiological states): viable cells capable of forming colonies on agar plates (VC+), viable cells incapable of forming colonies on agar plates (VC-), widely referred to as viable but nonculturable (VBNC) cells, and nonviable or dead cells (NVC). Counts of VC+ were recorded using a conventional plate count procedure. A fluorescent vital staining procedure that discriminates between viable (VC+ and VC-) and NVC cells was used to determine the number of viable and nonviable cells. Both propagation procedures had 2 variables: in procedure (P)1, the propagation medium was rich in yeast extract (4.0%) and the pH was maintained at 5.7; in P2, the medium was devoid of yeast extract and the pH was maintained at 5.1. The results showed that post-propagation operations-concentration of cells by centrifugation and subsequent freezing or lyophilization of cell concentrate-induced different degrees of transience from VC+ to VC- states in cells derived from P1 and P2. Compared with cells derived from P2, cells from P1 were more labile to stress associated with centrifugation, freezing, and lyophilization, as revealed by differential counting.

The effects of vitamin A supplementation with measles vaccine on leucocyte counts and in vitro cytokine production.

As WHO recommends vitamin A supplementation (VAS) at vaccination contacts after age 6 months, many children receive VAS together with measles vaccine (MV). We aimed to investigate the immunological effect of VAS given with MV. Within a randomised placebo-controlled trial investigating the effect on overall mortality of providing VAS with vaccines in Guinea-Bissau, we conducted an immunological sub-study of VAS v. placebo with MV, analysing leucocyte counts, whole blood in vitro cytokine production, vitamin A status and concentration of C-reactive protein (CRP). VAS compared with placebo was associated with an increased frequency of CRP ≥ 5 mg/l (28 v. 12%; P=0·005). Six weeks after supplementation, VAS had significant sex-differential effects on leucocyte, lymphocyte, monocyte and basophil cell counts, decreasing them in males but increasing them in females. Mainly in females, the effect of VAS on cytokine responses differed by previous VAS: in previous VAS recipients, VAS increased the pro-inflammatory and T helper cell type 1 (Th1) cytokine responses, whereas VAS decreased these responses in previously unsupplemented children. In previous VAS recipients, VAS was associated with increased IFN-γ responses to phytohaemagglutinin in females (geometric mean ratio (GMR): 3·97; 95% CI 1·44, 10·90) but not in males (GMR 0·44; 95% CI 0·14, 1·42); the opposite was observed in previously unsupplemented children. Our results corroborate that VAS provided with MV has immunological effects, which may depend on sex and previous VAS. VAS may increase the number of leucocytes, but also repress both the innate and lymphocyte-derived cytokine responses in females, whereas this repression may be opposite if the females have previously received VAS.

Reference intervals for bone marrow cells in juvenile and young adult cats bone marrow cytology.

BACKGROUND: The current bone marrow (BM) reference intervals (RI) are based on a limited number of cats. Age-related changes in BM variables might be important,possibly affecting the interpretation of the results. OBJECTIVES: Establish BM cytologic reference intervals (RIs) and association of age and sex on these findings, in healthy juvenile and young adult cats. METHODS: BM aspirates of cats deemed healthy based on history and clinical, CBC, serum chemistry findings, and negative retrovirus serology were obtained and examined cytologically. The examination included a 1000-nucleated differential cell count and cell morphologic assessment. RIs were calculated using parametric, robust, and nonparametric methods. The cytologic findings were examined for associations with sex and age. RESULTS: The study included 40 cats (females, 22 [55%]; males, 18 [45%]) with a median age of 1.5 years (range 0.5-5). Most calculated RIs were similar to those previously reported. BM plasma cell and monocyte counts were weakly and positively correlated with age (rs, .312 and .373, respectively; P < .05). Metarubricytes were higher infemales (mean, 25.1%; SD, 6.0%) than males (mean, 21.2%; SD, 6.0%; P < .05). CONCLUSIONS: The BM differential cell counts determined in this study can serve as RIs for cats aged 0.5-5 years.

Evaluation of the Sysmex DI-60 digital morphology analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators.

BACKGROUND: This study aims to evaluate the trueness of the DI-60 Digital Cell Imaging Analyzer on Wright-stained samples with a focus on prevalence-dependent quality indicators for differential blood counts requested from non-hematology wards. METHODS: Two hundred and ninety-nine samples were included into this performance evaluation study at the Department of Laboratory Medicine, Medical University of Vienna. The following aspects were verified: (a) the reliability of automatedly pre-classified differential counts, (b) the concordance of DI-60 counts with manual-microscopic differential counts and (c) the agreement of DI-60 and manual-microscopic results regarding clinically relevant findings. RESULTS: 82.3% of all leukocytes were correctly pre-classified. Cell categories with a low prevalence (eosinophils, basophils, progenitors/precursors) in non-hematological patients presented with a low positive predictive value (PPV), indicating a high frequency of false positives. Comparisons between visually adjusted results of the DI-60 and manual-microscopic differential counts revealed a good concordance for neutrophil and lymphocyte counts. Besides the detection of precursors/progenitors and normoblasts, no relevant systemic errors were detected. However, due to their low prevalence and technical aspects, the detection of basophilia, monocytosis or the presence of precursors/progenitors showed comparably low accuracies (error rates of 7.4%-24.1%). CONCLUSION: The DI-60 system works well for Wright-stained samples collected in the non-hematology ward. Due to the varying prevalence of cell categories found in peripheral blood, a low PPV can be expected with automatic assignment for those cells with low prevalence (e.g., basophils, eosinophils, precursor and progenitor cells, plasma cells). If the pre-test probability of these conditions is increased, manual microscopic processing may be recommended.

Digital Imaging and AI Pre-classification in Hematology.

A leukocyte differential of peripheral blood can be performed using digital imaging coupled with cellular pre-classification by artificial neural networks. Platelet and erythrocyte morphology can be assessed and counts estimated. Systems from a single vendor have been used in clinical practice for several years, with other vendors' systems, in a development. These systems perform comparably to traditional manual optical microscopy, however, it is important to note that they are designed and intended to be operated by a trained morphologist. These systems have several benefits including increased standardization, efficiency, and remote-review capability.

Canine Cerebrospinal Fluid Analysis Using Two New Automated Techniques: The Sysmex XN-V Body Fluid Mode and an Artificial-Intelligence-Based Algorithm.

Cerebrospinal fluid analysis is an important diagnostic test when assessing a neurological canine patient. For this analysis, the total nucleated cell count and differential cell counts are routinely taken, but both involve time-consuming manual methods. To investigate faster automated methods, in this study, the Sysmex XN-V body fluid mode and the deep-learning-based algorithm generated by the Olympus VS200 slide scanner were compared with the manual methods in 161 canine cerebrospinal fluid samples for the total nucleated cell count and in 65 samples with pleocytosis for the differential counts. Following incorrect gating by the Sysmex body fluid mode, all samples were reanalyzed with manually set gates. The Sysmex body fluid mode then showed a mean bias of 15.19 cells/µL for the total nucleated cell count and mean biases of 4.95% and -4.95% for the two-part differential cell count, while the deep-learning-based algorithm showed mean biases of -7.25%, -0.03% and 7.27% for the lymphocytes, neutrophils and monocytoid cells, respectively. Based on our findings, we propose that the automated Sysmex body fluid mode be used to measure the total nucleated cell count in canine cerebrospinal fluid samples after making adjustments to the predefined settings from the manufacturer. However, the two-part differential count of the Sysmex body fluid mode and the deep-learning-based algorithm require some optimization.

Combination of white blood cell count and left shift level real-timely reflects a course of bacterial infection.

BACKGROUND: The efficacy of white blood cell (WBC) count and left shift in predicting bacterial infections has been controversial. The aim of this study was to prove that WBC count and left shift reflect a course of bacterial infection. MATERIALS: Six patients in whom the onset of bacterial infection had been determined and successful treatment had been done were selected. Manual 100-cell differential counts were repeated at least every 24 hr. RESULTS: WBC count and left shift divided a course of bacterial infection into five phases. In the first phase of bacterial infection (0-10 hr after the onset), WBC count decreased to fewer than reference range without left shift. In the second phase (about 10-20 hr), low WBC count continued and left shift appeared. In the third phase (one to some days), WBC count increased above reference range with left shift. In the fourth phase (some to several days), high WBC count continued without left shift. In the fifth phase, WBC count went down into reference range without left shift. CONCLUSIONS: A combination of WBC count and left shift real-timely reflected a course of bacterial infection from the onset to healing. And we could judge which bacterial infection is adequately treated or not only by the above two routine laboratory tests.

Machine learning of cell population data, complete blood count, and differential count parameters for early prediction of bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

BACKGROUND: Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD). METHODS: The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples. RESULTS: This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio. CONCLUSIONS: ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

Development and Pilot Testing of a Comprehensive Mobile Application to Assist Cell Count Determination During Peripheral Smear and Bone Marrow Examination.

BACKGROUND: In the modern era of complete blood count analysis, manual differential count is performed whenever 'flags' are generated by an automated hematology analyzer. Traditionally, tally counters with five or eight keys are used for manual differential count. A few mobile applications are available to perform this task; however, the application features and cell representation are limited. OBJECTIVES: The primary objective of our study was to develop an indigenous, comprehensive mobile application to assist with manual blood cell differential count. The secondary objective was to measure the usability of a newly developed application among undergraduate medical students. MATERIALS AND METHODS: A new mobile application was developed using a Java development kit, Version 11.0.13 (Oracle Corporation, Austin, USA) in Android Studio Dolphin (2021.3.1) (Google, California, USA). The application content was validated by three pathologists with more than five years of experience. The app's usability was tested among 60 participants using a validated mHealth App Usability Questionnaire (MAUQ). The questionnaire had 18 items covering three domains: ease of use, interface & satisfaction, and usefulness. RESULTS: The newly developed application supports peripheral smear WBC differential count, platelet count, reticulocyte count, malaria parasite quantification, and bone marrow differential count. During usability testing, the app was easy to use in 95% (57/60) of participants, time-efficient in 91.7% (55/60), and helpful for healthcare practice learning in 96.7% (58/60). The total mean score was 6.11, indicating high usability. CONCLUSION: A comprehensive mobile application to assist manual differential count with adequate cell representation was developed. The mobile application was easy to use, time-efficient, and valuable among the study participants.

Relationship between rotating shift work and white blood cell count, white blood cell differential count, obesity, and metabolic syndrome of nurses.

This study investigated the relationship between working rotating shifts and white blood cell (WBC) count, WBC differential count (DC), obesity, and metabolic syndrome of nurses. Samples were obtained from a total of 720 nurses who had received health checkups at a large teaching hospital in Taiwan. Among these nurses, 500 worked rotating shifts, and the remaining 220 nurses worked fixed daytime shifts. We used binary logistic regression to identify the risk factors of obesity or metabolic syndrome. The results indicate that nurses working rotating shifts had a significantly higher WBC count than those working fixed daytime shifts. WBC count was an independent factor relating to an overly large waist circumference or overly high body mass index (BMI) of nurses. For every 103/µL increase in WBC count, the risk of waist circumference exceeding 90 cm and 80 cm respectively in male and female nurses increased by 46%, and for every 103/µL increase in WBC count, the risk of a BMI 24 kg/m2 in nurses increased by 50%. Similarly, WBC count and work type were independent factors influencing the metabolic syndrome of nurses. For every 103/µL increase in WBC count, the risk of metabolic syndrome occurring in nurses increased by 65%. Nurses working rotating shifts were at a 3.34 times greater risk of having metabolic syndrome than those working fixed daytime shifts. The mean age of our participants was 29.54 years old, and only approximately 4% of the participants had metabolic syndrome. Among these nurses, we found that high WBC was correlated with an overly large waist circumference and overly high BMI. Furthermore, the nurses working rotating shifts may have been at greater risk of metabolic syndrome than those working fixed daytime shifts, and increases in WBC were also associated with the occurrence of metabolic syndrome.