The utility of basic blood counts, WBC histogram and C-reactive protein in detecting malaria.

BACKGROUND: Hematology analyzers display abnormal parameters during malaria infection providing insightful information for suspecting and assessing malaria infection. The goal of this study is to demonstrate the potential of a three-part differential hematology analyzer to assess malaria, provide information about the parasitemia, and discuss the importance of combining C-reactive protein (CRP) with hematology parameters to obtain further information about the malaria infection. METHODS: The present study shows the results of a case-control study during the monsoon season of years 2018 and 2019 in Mumbai, India. The study considers 1008 non-malaria febrile cases, 209 P. vivax and 31 P. falciparum positive malaria samples, five cases of mixed P. vivax and P. falciparum infection, and three co-infection cases of P. vivax and dengue. Raw data from the three-part analyzer LC-667G CRP (HORIBA) and the corresponding microscopic findings (golden standard for diagnosis of malaria) were obtained for each sample. RESULTS: The medians of platelet counts (PLT) were 102.5, 109.0, and 223.0 × 103/µL, while CRP medians were 67.4, 81.4 and 10.4 mg/L in P. vivax, P. falciparum and control groups respectively (p < 0.001 in Mann-Whitney U tests between malaria and control groups). Compared with negative samples, platelets counting less than 161.5 × 103/µL were observed on malaria patients (OR 19.12, 95% CI 11.89-30.75). Especially in P. vivax cases, an abnormal peak was frequently observed in the white blood cells (WBC) histogram around the 37fL channel. The events counted around that channel showed a linear correlation with the counting of red blood cells infected predominantly with larger parasitic forms. Parameters like CRP (rs = 0.325, p < 0.001), WBC (rs = 0.285, p < 0.001) and PLT (rs = - 0.303, p < 0.001) were correlated with the parasitemia of P. vivax samples. Between the malaria and dengue groups, the highest area under the receiver operating characteristic curve was observed on CRP (0.867, CRP ≥ 26.85 mg/L). CONCLUSIONS: A three-part differential hematology analyzer has the potential to not only trigger malaria diagnosis confirmation but also assess the severity of the infection when CRP is considered.

Performance evaluation of machine learning-based infectious screening flags on the HORIBA Medical Yumizen H550 Haematology Analyzer for vivax malaria and dengue fever.

BACKGROUND: Automated detection of malaria and dengue infection has been actively researched for more than two decades. Although many improvements have been achieved, these solutions remain too expensive for most laboratories and clinics in developing countries. The low range HORIBA Medical Haematology Analyzer, Yumizen H550, now provides dedicated flags 'vivax malaria' and 'dengue fever' in routine blood testing, developed through machine learning methods, to be used as a screening tool for malaria and dengue fever in endemic areas. This study sought to evaluate the effectiveness of these flags under real clinical conditions. METHODS: A total of 1420 samples were tested using the Yumizen H550 Haematology Analyzer, including 1339 samples from febrile patients among whom 202 were infected with malaria parasites (Plasmodium vivax only: 182, Plasmodium falciparum only: 18, both: 2), 210 were from febrile dengue infected patients, 3 were from afebrile dengue infected patients and 78 were samples from healthy controls, in an outpatient laboratory clinic in Mumbai, India. Microscopic examination was carried out as the confirmatory reference method for detection of malarial parasite, species identification and assessing parasitaemia based on different stages of parasite life cycle. Rapid diagnostic malarial antigen tests were used for additional confirmation. For dengue infection, NS1 antigen detection by ELISA was used as a diagnostic marker. RESULTS: For the automated vivax malaria flag, the original manufacturer's cut off yielded a sensitivity and specificity of 65.2% and 98.9% respectively with the ROC AUC of 0.9. After optimization of cut-off value, flag performance improved to 72% for sensitivity and 97.9% specificity. Additionally it demonstrated a positive correlation with increasing levels of parasitaemia. For the automated dengue fever flag it yielded a ROC AUC of 0.82 with 79.3% sensitivity and 71.5% specificity. CONCLUSIONS: The results demonstrate a possibility of the effective use of automated infectious flags for screening vivax malaria and dengue infection in a clinical setting.

Automated and simplified identification of normal and abnormal plasma cells in Multiple Myeloma by flow cytometry.

BACKGROUND: Multiple myeloma (MM) is an incurable disease characterized by clonal plasma cell (PC) proliferation within the bone marrow (BM). Next-generation flow cytometry has become the reference tool to follow minimal residual disease (MRD). We developed a new simpler and cheaper flow cytometry method to analyze bone marrow samples in patients with MM. METHODS: To identify and characterize abnormal PCs, we designed a simple panel composed of anti-CD38, antikappa, and antilambda light chain antibodies, combined with two antibody pools with the same fluorophore (anti-CD19 and anti-CD27 for the negative pool and anti-CD56, anti-CD117, and anti-CD200 antibodies for the positive pool). We also developed dedicated software for the automated identification of malignant PCs and MRD assessment. We then compared PC identification with our simple antibody panel and with the larger antibody panel routinely used at Montpellier University Hospital Center in 52 patients with MM (M-CHU cohort). RESULTS: Results for total PC detection (r2  = 0.9965; P < 0.001; n = 52) and malignant PC detection (r2  = 0.9486; P < 0.001; n = 38) obtained with the two panels were significantly correlated. Moreover, comparison of the results obtained by automated detection with our software and by manual gating analysis in 80 BM samples (38 from the M-CHU cohort and 42 patients from another MM cohort) showed strong correlation for both total and malignant PC selection (respectively, r2  = 0.936; P < 0.001 and r2  = 0.9505; P < 0.001). CONCLUSIONS: Our simple and automated strategy for MRD assessment in MM could help increasing reproducibility and productivity without compromising sensitivity and specificity, while decreasing the test cost. © 2017 International Clinical Cytometry Society.

CD24, CD27, CD36 and CD302 gene expression for outcome prediction in patients with multiple myeloma.

Multiple myeloma (MM) is a B cell neoplasia characterized by clonal plasma cell (PC) proliferation. Minimal residual disease monitoring by multi-parameter flow cytometry is a powerful tool for predicting treatment efficacy and MM outcome. In this study, we compared CD antigens expression between normal and malignant plasma cells to identify new potential markers to discriminate normal from malignant plasma cells, new potential therapeutic targets for monoclonal-based treatments and new prognostic factors. Nine genes were significantly overexpressed and 16 were significantly downregulated in MMC compared with BMPC (ratio ≥2; FDR CD24, CD27, CD36 and CD302) was associated with a prognostic value in two independent cohorts of patients with MM (HM cohort and TT2 cohort, n=345). The expression level of these four genes was then used to develop a CD gene risk score that classified patients in two groups with different survival (P = 2.06E-6) in the HM training cohort. The prognostic value of the CD gene risk score was validated in two independent cohorts of patients with MM (TT2 cohort and HOVON65/GMMGHD4 cohort, n=282 patients). The CD gene risk score remained a prognostic factor that separated patients in two groups with significantly different overall survival also when using publicly available data from a cohort of relapsing patients treated with bortezomib (n=188). In conclusion, the CD gene risk score allows identifying high risk patients with MM based on CD24, CD27, CD36 and CD302 expression and could represent a powerful tool for simple outcome prediction in MM.