International journal of laboratory hematology最新文献

Introduction: Numerous AI-based systems are being developed to evaluate peripheral blood (PB) smears, but the feasibility of these systems on different smear preparation methods has not been fully understood. In this study, we assessed the impact of different smear preparation methods on the robustness of the deep learning system (DLS).

Methods: We collected 193 PB samples from patients, preparing a pair of smears for each sample using two systems: (1) SP50 smears, prepared by the DLS recommended fully automated slide preparation with double fan drying and staining (May-Grunwald Giemsa, M-G) system using SP50 (Sysmex) and (2) SP1000i smears, prepared by automated smear preparation with single fan drying by SP1000i (Sysmex) and manually stained with M-G. Digital images of PB cells were captured using DI-60 (Sysmex), and the DLS performed cell classification. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the performance of the DLS.

Results: The specificity and NPV for all cell types were 97.4%-100% in both smear sets. The average sensitivity and PPV were 88.9% and 90.1% on SP50 smears, and 87.0% and 83.2% on SP1000i smears, respectively. The lower performance on SP1000i smears was attributed to the intra-lineage misclassification of neutrophil precursors and inter-lineage misclassification of lymphocytes.

Conclusion: The DLS demonstrated consistent performance in specificity and NPV for smears prepared by a system different from the recommended method. Our results suggest that applying an automated smear preparation system optimized for the DLS system may be important.

Introduction: This study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC-80 digital morphology analyzer.

Methods: Digital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT-M1) and estimate factor (PLT-M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT-I and PLT-O), digital morphology analyzer (PLT-M1 and PLT-M2), and flow cytometry (PLT-IRM) were compared.

Results: There was no significant deviation observed before and after verification for both PLT-M1 and PLT-M2 across the analysis range (average bias: -0.845/-0.682, 95% limit of agreement (LOA): -28.675-26.985/-29.420-28.056). When platelet alarms appeared, PLT-M1/PLT-M2 showed the strongest correlation with PLT-IRM than PLT-I with PLT-IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT-M1/PLT-M2 and PLT-IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT-M1 and PLT-M2 is related to the number of RBCs. Compared with PLT-I, PLT-M1/PLT-M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low-value PLT.

Conclusion: The novel platelet count estimation on the MC-80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

Introduction: Specific assays of plasma rivaroxaban level are not always readily available with short turnaround time, which hamper the management of urgent clinical situations. In this study, we aimed to build a predictive formula of plasma rivaroxaban levels from international normalized ratio (INR) value and validated in real world clinical situations.

Methods: Ninety-four patients who were taking rivaroxaban participated in the study. Patients were randomized into testing cohort and validation cohorts. The prediction formula was built from the testing cohort and then validated in validation cohort. The predictive performance was further validated on real-world clinical requests.

Results: The root mean square error (RMSE) of the predictive formula for the testing and validation cohorts were 61.81 and 69.32 ng/mL, respectively. The sensitivity and specificity for the formula to predict the threshold plasma rivaroxaban level of 75 ng/mL were 95% (95% CI: 85.4%-100%) and 87.5% (95% CI: 71.3%-100%), respectively, in real-world clinical situations.

Conclusion: Plasma rivaroxaban level of threshold level of 75 ng/mL can be calculated from prediction formula by INR value with satisfactory accuracy and it can be used to guide the decision for reversal.

Introduction: Aplastic anemia (AA) and hypoplastic myelodysplastic syndrome (MDS-h) are bone marrow failure disease and difficult to distinguish merely by morphological analysis. In this study, we investigated the value of flow cytometry (FCM) in the differential diagnosis of AA and MDS-h.

Methods: We included 822 patients (626 control, 69 AA, 22 MDS-h and 105 dilution patients) from January 2017 to December 2022 for a retrospective study. Bone marrow myeloid progenitor (MP) cell and mature lymphocytes proportions were analyzed by FCM. The ratio of MP cell proportion and mature lymphocytes proportion, MPLR, was calculated. Data were compared by Kruskal-Wallis test. Differential diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. Cutoff value was determined by the maximum Youden index.

Results: Bone marrow MP cell proportion and MPLR of MDS-h patients were higher than AA patients. Mature lymphocytes proportion of MDS-h patients was lower than AA patients. Area under ROC curve (AUC of ROC) of MP cell proportion, MPLR and mature lymphocytes proportion to distinguish AA from MDS-h were 0.992, 0.988, and 0.850, respectively. Moreover, MPLR of dilution patients was higher than AA patients but lower than MDS-h patients. The AUC of ROC curves of MPLR to distinguish MDS-h and AA from dilution were 0.854 and 0.871, respectively.

Conclusion: Bone marrow MP cell proportion and MPLR can effectively discriminate AA from MDS-h with similar differential efficacy, which is higher than mature lymphocytes proportion. Moreover, MPLR can evaluate the quality of bone marrow aspirates, which would interfere with the differential diagnosis.

Introduction: Fibrinolysis is a critical aspect of the hemostatic system, with assessment of fibrinolytic potential being critical to predict bleeding and clotting risk. We describe the method for a novel low-plasma-volume assay of fibrinolytic capacity utilizing the euglobulin fraction (the "modified mini-euglobulin clot lysis assay [ECLA]"), its analytic sensitivity to alterations in key fibrinolytic substrates/regulators, and its initial applications in acute and convalescent disease cohorts.

Methods: The modified mini-ECLA requires 50 μL of plasma, a maximal read time of 3 h (with most results available within 60 min), and is entirely performed in a 96-well microplate. Assay measurements were obtained in a variety of commercial control and deficient plasmas representing clinically relevant hypo- and hyperfibrinolytic states, and in three distinct adolescent cohorts with acute or convalescent illness: critically ill, following endotracheal intubation; acute COVID-19-related illness; and ambulatory, 3 months following a venous thromboembolic event.

Results: In 100% and 75% deficient plasmas, hypofibrinolysis for plasminogen-deficient, fibrinolysis for alpha-2-antiplasmin-deficient, and hyperfibrinolysis for plasminogen activator inhibitor-1-deficient plasmas were observed.

Conclusion: The modified mini-ECLA Clot Lysis Time Ratio ("CLTR") demonstrated moderate-strength correlations with the Clot Formation and Lysis (CloFAL) assay, is analytically sensitive to altered fibrinolytic states in vitro, and correlates with clinical outcomes in preliminarily-studied patient populations.

Introduction: Autologous hematopoietic stem cell transplantation (ASCT) has gained extensive application in the treatment of lymphoma and multiple myeloma (MM). Plenty of studies demonstrate that peripheral blood indicators could be considered potential predictive biomarkers for hematopoietic stem cells (HSCs) collection efficiency, including white blood cell count (WBC), monocyte count (Mono), platelet count (PLT), hematocrit, and hemoglobin levels. Currently, clinically practical predictive models based on these peripheral detection indicators to quickly, conveniently, and accurately predict collection efficiency are lacking.

Methods: In total, 139 patients with MM and lymphoma undergoing mobilization and collection of ASCT were retrospectively studied. The study endpoint was successful collection of autologous HSCs. We analyzed the effects of clinical characteristics and peripheral blood markers on collection success, and screened variables to establish a prediction model. We determined the optimal cutoff value of peripheral blood markers for predicting successful stem cell collection and the clinical value of a multi-marker prediction approach. We also established a prediction model for collection efficacy.

Results: Univariate and multivariate logistic regression analyses showed that the mobilization regimen, Mono, PLT, mononuclear cell count (MNC), and peripheral blood CD34⁺ cell count (PB CD34⁺ counts) were significant predictors of successful collection of peripheral blood stem cells (PBSC). Two predictive models were constructed based on the results of multivariate logistic analyses. Model 1 included the mobilization regimen, Mono, PLT, and MNC, whereas Model 2 included the mobilization regimen, Mono, PLT, MNC, and PB CD34⁺ counts. Receiver operating characteristic (ROC) curve analysis showed that the PB CD34⁺ counts, Model 1, and Model 2 could predict successful HSCs collection, with cutoff values of 26.92 × 106/L, 0.548, and 0.355, respectively. Model 1 could predict successful HSCs collection with a sensitivity of 84.62%, specificity of 75.73%, and area under the curve (AUC) of 0.863. Model 2 could predict successful HSCs collection with a sensitivity of 83.52%, specificity of 94.17%, and AUC of 0.946; thus, it was superior to the PB CD34⁺ counts alone.

Conclusion: Our findings suggest that the combination of the mobilization regimen, Mono, PLT, MNC, and PB CD34⁺ counts before collection has predictive value for the efficacy of autologous HSCs collection in patients with MM and lymphoma. Using models based on these predictive markers may help to avoid over-collection and improve patient outcomes.