Comparison of the diagnosis of malaria by microscopy, immunochromatography and PCR in endemic areas of Venezuela

• Main Authors: H Rodulfo, M De Donato, R Mora, L González, C.E Contreras
• Format: Article
• Language: English
• Published: Associação Brasileira de Divulgação Científica 2007-04-01
• Series: Brazilian Journal of Medical and Biological Research
• Subjects: Malaria; Molecular diagnosis of malaria; Microscopic diagnosis of malaria; Polymerase chain reaction; OptiMAL
• Online Access: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2007000400012

Whole blood samples (N = 295) were obtained from different locations in Amazonas and Sucre States, in Venezuela. Malaria was diagnosed by microscopy, OptiMAL™ and polymerase chain reaction (PCR), with Plasmodium vivax, P. falciparum, and P. malariae being detected when possible. We identified 93 infections, 66 of which were caused by P. vivax, 26 by P. falciparum, and 1 was a mixed infection. No infection caused by P. malariae was detected. The sensitivity and specificity of each diagnostic method were high: 95.7 and 97.9% for microscopy, 87.0 and 97.9% for OptiMAL, and 98.0 and 100% for PCR, respectively. Most samples (72.2%) showed more than 5000 parasites/µL blood. The sensitivity of the diagnosis by microscopy and OptiMAL decreased with lower parasitemia. All samples showing disagreement among the methods were reevaluated, but the first result was used for the calculations. Parasites were detected in the 6 false-negative samples by microscopy after the second examination. The mixed infection was only detected by PCR, while the other methods diagnosed it as P. falciparum (microscopy) or P. vivax (OptiMAL) infection. Most of the false results obtained with the OptiMAL strip were related to the P. falciparum-specific band, including 3 species misdiagnoses, which could be related to the test itself or to genetic variation of the Venezuelan strains. The use of the microscopic method for malaria detection is recommended for its low cost but is very difficult to implement in large scale, population-based studies; thus, we report here more efficient methods suitable for this purpose.