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Parag Dharap1, Sébastien Raimbault2, Sylvie Arnavielhe3, Gérard Dray4, Stefan Janaqi4, Michel Plantié4, Pierre Jean4, Vincent Derozier4, Shubham Rastogi5
1Dr Dharap’s Diagnostic Centre, Mumbai, India, 2Dept. of Innovation, HORIBA Medical, Montpellier, France, 3Kyomed, Montpellier, France, 4LGI2P/EMA, Nimes, France, 5HORIBA Medical, New Delhi, India
Blood cell counter screening for the presence of malaria has been investigated for >20 years. Efficiency reported has varied by methodology and study design. Several manufacturers have introduced malaria flagging on high range instruments with reports claiming sensitivity and specificity >80%. However the economic reality is malaria endemic areas typically utilize low and medium range instruments. We applied contemporary computer machine-learning techniques to develop flagging algorithms for detection of malaria using two low to middle range HORIBA Medical blood counters. The validation included Dengue fever patients, which have similar clinical symptomatology as malaria.
290 blood specimens were serially analyzed the following instruments and testing modes: Microsemi CRP (CBC 3 part diff + CRP modes) and Pentra XLR (5 part diff or fluorescent reticulocyte analysis modes). Specimen selection used three diagnostic categories confirmed by antigen testing: healthy, malaria positive, dengue fever positive. Malaria confirmed cases were further speciated into P. vivax (N=103) and P. falciparum (N=29) and microscopically scored the predominate development stage (schizoints, ring or amoeboid). Normal (N=70) and dengue cases (N=87) were considered to be malaria negative samples for determination of sensitivity, specificity, negative and positive predictive values, correct classification rate and positive and negative likelihood ratios. Data-mining techniques were used to identify those instrument parameters that showed significant differences between malaria from normal in a training set of 550 cases (232 malaria positive) and the flagging algorithms developed included weighting of parameters showing higher discriminating power. The number of parameters selected from those tested for the malaria suspect flag varied by instrument (Microsemi CRP, 59 of 886; PXLR DIFF, 412 of 25,994; PXLR Retic, 402 of 26,395) with the datamining and machine-learning techniques. Samples were analyzed with instrument software integrating the malaria suspect flag with results compared to the confirmatory testing.
Malaria is one of the most common communicable diseases in the world, affecting the populations in all tropical regions. Malaria is a life threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. About 3.2 billion people – almost half of the world’s population are at risk of malaria. Malaria is the most common cause of morbidity in Africa. Malaria is a major public health problem with WHO estimates of 207 million cases of malaria occurred globally in 2012 and 6,270,000 deaths.
Several hematology blood analyzers have reported the ability to detect the presence of malaria infection using various parameters with varying efficiency. Abbott analyzers utilized light depolarization and nucleic acid fluorescent dyes (1-3), Beckman Coulter instruments utilized off-line algorithms developed from cell positional or size parameters (4-6) and Sysmex likewise has utilized cell size parameters in the white blood cell matrix and changes in the fluorescence of red cell or reticulocyte matrix for their malaria flag (7-12, copied as well by Mindray). In common to all these methods of malaria detection, either performed as an automated flag or by user interpretation of scattergram patterns, is that less than four parameters are utilized in the detection of blood changes associated with malaria infection.
HORIBA Medical chose to develop an automated malaria suspect flag using datamining techniques to examine all parameters generated by the Microsemi CRP, Pentra XL and Pentra XLR instruments to determine those most useful in distinguishing malaria from both healthy and non-malarial infections. Using these contemporary machine-learning techniques applied to over 500 patient specimens including not only malaria positive and healthy cases, but also a group of patients with dengue fever, which has a clinical presentation similar to malaria (same signs and symptoms). From these data files 412 of 25,994 parameters for the Pentra XL and Pentra XLR in CBC+Diff mode, 402 of 26,395 for the Pentra XLR in modified Retic mode, and 59 of 886 variables for the Microsemi CRP were identified as having discriminatory power for malaria. From these useful parameters a flagging algorithm was developed and utilized in this validation study.
290 patient samples, selected from diagnostic groups of normal, malaria positive or dengue fever positive, were included in the study with each sample run in duplicate. Rapid antigen diagnostic tests manufactured by SD – Bio Standard Diagnostics Pvt Ltd; Gurgaon, India were used to screen for Plasmodium falciparum (histidine rich protein II), Plasmodium vivax (Plasmodium lactate dehydrogenase), and dengue fever (NS1 Antigen &/ IgM Antibody).
All malaria cases were confirmed by microscopy and scored for the predominant life cycle forms (trophozoites, schizoints, gametocytes).
Blood samples were analyzed in duplicate on a HORIBA Medical Pentra XLR instrument (CBC 5 part differential counts and thiazole orange based reticulocyte analysis) using both the CBC DIFF mode and again with a modified (decreased) fluorescence gain Retic modes and on a Microsemi CRP instrument (CBC 3 part differential counts and whole blood C-reactive protein) in both CBC+Diff mode or CBC+Diff+CRP modes.
Results statistically analyzed for the efficiency, sensitivity, specificity, positive Youden J index, correctness of classification, positive predictive value and negative predictive value of the malaria suspect flag compared to the usual laboratory methods for malaria confirmation.
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