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Free Content Automated detection of malaria pigment: feasibility for malaria diagnosing in an area with seasonal malaria in northern Namibia

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Summary Objective 

To evaluate the feasibility of automated malaria detection with the Cell-Dyn® 3700 (Abbott Diagnostics, Santa Clara, CA, USA) haematology analyser for diagnosing malaria in northern Namibia. Methods 

From April to June 2003, all patients with a positive blood smear result and a subset of patients with no suspicion of malaria were included. Blood smear and a venous blood sample (to determine haemoglobin, platelet and malaria pigment levels) were collected from each patient. Malaria pigment test characteristics, correlations with blood parameters and pigment clearance time were calculated. Finally, a subset of blood samples was run twice to evaluate the consistency of test outcome. Results 

Two hundred and eight patients were included. Ninety had a positive blood smear result of which 84 tested positive for malaria pigment and 118 patients had a negative blood smear result of which four tested positive for malaria pigment. Test characteristics as compared with microscopy were as follows: sensitivity 0.93, specificity 0.97, positive predictive value 0.95, negative predictive value 0.95. Rerun of the blood samples resulted in a change of diagnosis in 14%. After 4 weeks, 33% of patients with an initially positive pigment result still tested positive. Malaria pigment was found to be negatively correlated with haemoglobin. Conclusions 

Automated detection of malaria pigment is a useful diagnostic tool in this semi-rural area. In low-risk malaria season, the test can be used for diagnosing malaria because of the high sensitivity. In high-risk malaria season, the test can be used for excluding malaria in case of a negative pigment result because of the high specificity.

Keywords: automated diagnosis; haemozoin; light depolarization; malaria

Document Type: Research Article


Affiliations: 1:  Department of Infectious Diseases, Tropical Medicine and AIDS, Academic Medical Center, Amsterdam, The Netherlands 2:  Department of Obstetrics and Gynaecology, Onandjokwe Lutheran Hospital, Ondangwa, Namibia 3:  Namibia Institute of Pathology, Onandjokwe Lutheran Hospital, Ondangwa, Namibia 4:  Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands

Publication date: 2006-06-01

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