Malaria: the value of the automated depolarization analysis

This retrospective and descriptive study was carried out in the University of Malaya Medical Center (UMMC) from January to September, 2004. This study aimed to evaluate the diagnostic utility of the Cell-Dyn 4000 hematology analyzer's depolarization analysis and to determine the sensitivity and specificity of this technique in the context of malaria diagnosis. A total of 889 cases presenting with pyrexia of unknown origin or clinically suspected of malaria were examined. Sixteen of these blood samples were found to be positive; 12 for P. vivax, 3 for P. malariae, and 1 for P. falciparum by peripheral blood smear as the standard technique for parasite detection and species identification. Demographic characteristics showed that the majority of patients were in the age range of 20-57 with a mean of 35.9 (+/- SD) 11.4 years, and male foreign workers. Of these, 16 positive blood samples were also processed by Cell-Dyne 4000 analyzer in the normal complete blood count (CBC) operational mode. Malaria parasites produce hemozoin, which depolarizes light and this allows the automated detection of malaria during routine complete blood count analysis with the Abbot Cell-Dyn CD4000 instrument. The white blood cell (WBC) differential plots of all malaria positive samples showed abnormal depolarization events in the NEU-EOS and EOS I plots. This was not seen in the negative samples. In 12 patients with P. vivax infection, a cluster pattern in the Neu-EOS and EOS I plots was observed, and appeared color-coded green or black. In 3 patients with P. malariae infection, few random depolarization events in the NEU-EOS and EOS I plots were seen, and appeared color-coded green, black or blue. While in the patient with P. falciparum infection, the sample was color-coded green with a few random purple depolarizing events in the NEU-EOS and EOS I plots. This study confirms that automated depolarization analysis is a highly sensitive and specific method to diagnose whether or not a patient has malaria. This automated approach may prove to be particularly useful in situations where there is little or no clinical suspicion of malaria.