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ORIGINAL ARTICLE Table of Contents   
Year : 2009  |  Volume : 52  |  Issue : 2  |  Page : 185-188
Diagnosis of acute malaria by laser based cell counter with comparison of conventional and recent techniques in Indian scenario

1 Department of Pathology, Smt NHL Municipal Medical College, Ellisbridge, Ahmedabad - 380 006, Gujarat, India
2 Department of Pathology, Greencross Laboratories, 102, Anil Kunj Complex, Paldi Char Rasta, Ahmedabad - 380 006, Gujarat, India
3 Department of Haematology, Greencross Laboratories, 102, Anil Kunj Complex, Paldi Char Rasta, Ahmedabad - 380 006, Gujarat, India

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Malaria is one of the most important parasitic diseases in humans affecting 103 countries worldwide. Aims: The present study aims to determine the diagnostic utility of cell counter data - hemoglobin, total leukocyte count, platelet count and depolarized laser light (DLL)-based purple-coded events (PCEs) in detection of acute malaria. This is a retrospective study of 523 patient data that came for complete blood count for the first time. Results : One hundred thirty-five of the 523 patients showed microscopic evidence of malaria. Platelet count showed the highest sensitivity of 77.77% (105/135). PCEs (≥1) showed 43.7% (59/135) sensitivity. Conclusions : It is concluded that a low platelet count (<150 × 109/L) is a good hematological parameter for presumptive diagnosis of malaria. If we change the cut-off for PCEs from ≥1 to ≥2, the sensitivity would be 56.29% (76/135) and the specificity would be 94.58% (367/388), respectively. The sensitivity of DLL was low, particularly with a low parasitic index (PI). The number of PCEs does not correlate with the PI. The cut-off number of PCEs in DLL-based malaria detection should be modified in highly endemic areas.

Keywords: Complete blood count, depolarized laser light, malaria, malarial pigment

How to cite this article:
Rathod DA, Patel V, Kaur AA, Patel VD, Patel DD. Diagnosis of acute malaria by laser based cell counter with comparison of conventional and recent techniques in Indian scenario. Indian J Pathol Microbiol 2009;52:185-8

How to cite this URL:
Rathod DA, Patel V, Kaur AA, Patel VD, Patel DD. Diagnosis of acute malaria by laser based cell counter with comparison of conventional and recent techniques in Indian scenario. Indian J Pathol Microbiol [serial online] 2009 [cited 2020 Jul 5];52:185-8. Available from: http://www.ijpmonline.org/text.asp?2009/52/2/185/48912

   Introduction Top

Malaria is one of the most important parasitic diseases in humans with transmission in 103 countries, affecting more than one billion people and causing between one to three million deaths each year. [1] Malaria is a heavy burden on tropical countries, a threat to non-endemic countries and a danger to travelers. In malaria, patient prompt and accurate diagnosis is the key to effective disease management. Clinical diagnosis, the most widely used approach for diagnosis of malaria in the tropics, is unreliable because the clinical presentation of malaria is diverse. Microscopic diagnosis, the established method of laboratory confirmation for malaria, requires technical expertise and repeated smear examinations. It is a valuable technique when performed correctly but unreliable and wasteful when poorly executed. [2] In India, the drug policy for malaria treatment under the national anti-malarial program states that any fever without any other obvious cause may be considered as malaria, investigated and treated accordingly to prevent death and reduce malarial morbidity. [3] With increasing incidence of chloroquine-resistant malaria, early identification and its treatment is essential. Hence, the search for other diagnostic indicators of malaria is very important. A hematological change like progressively increasing anemia, thrombocytopenia and rarely disseminated intravascular coagulopathy has been reported in Plasmodium falciparum malaria. [4],[5] There is an increase in the interest for presumptive diagnosis of acute malaria infection by using routine hematological parameters. In this area, several authors reported laser light depolarization as a useful diagnosis tool. [6],[7] The intracellular malarial pigment in white blood cells (WBCs) can be detected as part of the routine full blood count analysis by DLL, which is part of some Cell-Dyn hematology instruments like 3200, 3700, 3500 and 4000.

The aims of the present study were to determine the usefulness of laser light depolarization analysis for the diagnosis of acute malaria, to know the sensitivity of different cell counter parameters for presumptive diagnosis of acute malaria and to note any age-, gender- and plasmodium species-related changes in cell counter-based diagnosis of acute malaria.

   Materials and Methods Top

This retrospective study was conducted from data collected from samples analyzed during January 2006 and April 2007. Inclusion or exclusion of a case data for analysis was decided from a detailed record of the patients obtained at the laboratory before sample collection. Data from patients with acute febrile illness and with no previous history of acute fever since the last 3 weeks were included in the present study and analyzed retrospectively. Three milliliters of whole venous blood was collected in a K3 ethylene diammine tetraacetic acid collection tube. The samples were analyzed in a Cell-Dyn 3200 cell counter (Abbott Diagnostics Division, Santa Clara, CA, USA) (software 3.1 AT) for a complete blood count. The two level controls were run every day and the instrument was maintained as per the manufacturer's instructions. The Cell-Dyn 3200 generates WBC differentials based on the different scatter characteristics of laser light. The separation of eosinophils from neutrophils is achieved by utilizing the depolarizing properties of the eosinophilic granules with the help of depolarized laser light (DLL). In a lobularity/granularity plot (lobularity = X axis, measure of 90º scatter; granularity = Y axis, measure of 90º depolarized scatter), eosinophils are depicted as a green-dotted population above a threshold line, which separates them from the other WBC populations. Hemozoin-containing WBCs are detected due to depolarizing properties of the pigment. The appearance of monocytes as purple-coded events (PCEs) in the eosinophil area (green-coded events) is a sign of malaria. The patient with ≥1 PCE in the scatterogram was considered to be positive for malaria. Thick and thin blood smears were stained with Giemsa stain (1:20 dilution). The smears were examined by an experienced pathologist. Peripheral blood smear examination for malarial parasite was taken as a gold standard for the diagnosis of malaria. Samples were regarded as negative after an examination of at least 200 microscopic fields of a thick blood film under oil immersion lens (×100), which showed no parasites. In a positive blood smear for malarial parasite, parasitic index (PI) was derived by quantifying the parasite number per 1000 red blood cells (RBCs) in a thin smear and dividing it by 10 to get it in percentage. As per the standard operative procedure, the results of the microscopic examination were blinded from the laboratory technician and physician performing and interpreting the DLL procedure. The hematological data (hemoglobin [Hb], total leucocyte count [TC], platelet count) of all the patients were collected from the record sheet generated during investigation. The cut-off of 100 g/L for Hb, <4 × 10 9 /L for TC and <150 × 10 9 /L for platelet count were considered. Hematological parameters were considered as variables for statistical analysis using the MedCALC free statistical package to calculate sensitivity, specificity and students't test. A P -value <0.05 was considered to be statistically significant.

   Results Top

As depicted in [Table 1], of the 523 patients, 135 patients were diagnosed as malaria positive by the microscopic method whereas 374 patients were detected as positive for malaria by the DLL method.

Of these 523 patients, 388 patients had acute febrile illness because of non-malarial causes.

[Table 2] shows the comparison of different variables in the malaria and non-malaria group. The Hb and platelet count showed significant differences ( P < 0.0001). TC showed a less significant difference ( P < 0.04) in the same group.

In the present study patient age ranged from 2 to 74 years. Of the 135 positive malarial cases, 73 patients had Plasmodium falciparum and 62 patients had P. vivax malaria. There was no case of mixed infection.

As depicted in [Table 3], among the different hematological parameters and their different combinations, the highest sensitivity of 77.77% was shown by platelet count followed by PCE and platelet + PCE, which showed 62.2% and 43.7% sensitivity, respectively. All other parameters had a poor sensitivity. All parameters showed good specificity except PCE, which showed a specificity of 25.25%. Sensitivity of these parameters in P. falciparum and P. vivax showed no significant difference except in Hb ( P < 0.2 for Hb).

In the present study, as depicted in [Table 4], it was found that the percentage positivity of DLL in malaria detection reduces as the PI goes down. In P. falciparum malaria, it dropped from 73.5% with PI > 1% to 52.3% with PI < 0.1% while in P. vivax malaria, it dropped from 76.9 % with PI > 1% to 55.26% with PI < 0.1%.

In the present study the cut-off of PCEs for diagnosis of acute malaria was ≥1. In the DLL-positive non-malaria group, 92.75% (269/290) patients showed only one PCE. In the DLL-positive malaria-positive group, only 9.52% (8/84) patients showed one PCE. If we take ≥2 as the cut-off for DLL, the sensitivity, specificity, positive predictive value and negative predictive value will be 56.29%, 94.58%, 78.35% and 86.15%, respectively. As depicted in [Table 5], the present study shows that the number of PCEs and PI do not correlate with each other.

   Discussion Top

Hematological parameters such as Hb, TC, platelet count and their different combinations can predict the presence of acute malaria. The present study demonstrates that the low platelet count (<150 × 10 9 /L) has emerged as the strongest predictor of malaria, with a sensitivity of 77.77% and a specificity of 78.66%. Lathia et al. [8] observed a 60% sensitivity for platelet count <150 × 10 9 /L in malaria. Erhart et al. [9] reported that a platelet count less than 150 × 10 9 /L increases the likelihood of malaria by 12-15 times. The suggested mechanisms for thrombocytopenia include disseminated intravascular coagulation or excessive removal of platelets by the reticuloendothelial system. [10] In the present study, anemia (Hb < 100g/L) showed a 38.51% sensitivity and a 79.38% specificity. Lathia et al. [8] reported a 52% sensitivity and a 73% specificity for anemia (Hb < 100g/L) in malaria detection. The difference in sensitivity between these studies may be due to the presence of a less severe anemia and a different severity of malaria at the time presentation. The pathogenesis of anemia in malaria is multifactorial. A complex chain of pathogenetic processes involving mechanical destruction of parasitized RBCs, marrow suppression, ineffective erythropoiesis and accelerated immune destruction of non-parasitized RBCs has been implicated.

In the present study, leucopenia (TC < 4.0 × 10 9 /L) showed an 18.51% sensitivity and a 96.39% specificity for malaria diagnosis. Erhart et al. [9] observed a 27.05% (112/414) sensitivity for leucopenia. Erhart et al. [9] had taken a TC < 5.0 × 10 9 /L as the cut-off for leucopenia. The leucopenia in malaria is due to bone marrow suppression by the malarial parasite. In the present study, P. falciparum and P. vivax malaria do not show any significant difference in terms of sensitivity for hematological parameters like platelet count and leucopenia. Similar observations were made by Erhart et al. [9] but Hb showed a mild difference, with P < 0.7. The difference in Hb may be due to more severe parasitemia at presentation or because of more severe underlying anemia in the P. falciparum group.

Malarial parasites detoxify the free hem liberated during Hb digestion by converting it into a stable crystalline brown pigment, hemozoin, by means of a parasite-specific enzyme, hem polymerase. Malarial pigment is released from the infected red cells after schizont rupture and is subsequently ingested by the peripheral blood phagocytes. Unlike Hb and free hem, hemozoin can depolarize light, which allows hemozoin-containing neutrophils and monocyte cells to depolarize light. [11] In the present study, DLL-based malaria detection shows a sensitivity of 62.22% (84/135) and a specificity of 25.25% (98/388), with a cut-off of ≥1 PCEs. Scott et al. [11] observed a 97.4% specificity and an 80% sensitivity in South Africa. Mandelow et al. [12] reported a 72% and a 96% sensitivity and specificity, respectively, in South Africa. Hanscheid et al. [13] noticed a 95% sensitivity and an 88% specificity in Portugal. Martinez et al. [14] observed a 72% sensitivity and a 98% specificity in Spain. Grobusch et al. [15] observed an overall sensitivity of 48.6% and a specificity of 96.2% in Germany. The difference in the sensitivity and the specificity of DLL in detecting acute malaria cases in the present study as compared with the above-mentioned studies may be due to many factors like different clearance kinetics in different populations even in highly endemic areas, different analyzers, different versions of software used in a cell counter and different cut-off levels of PCEs. The removal of pigment-containing monocytes is slower (median 216h) than parasitized erythrocytes (median 96h) or pigment-containing neutrophils (median 72h). Hence, most patients with malaria continue to show circulating pigment-containing leucocytes after parasite clearance. In some patients, there may be intracellular persistence for up to 3 weeks after clinical cure and, in murine models, laden macrophage continues to accumulate in the spleen for at least several months after the clearance of peripheral blood parasitemia. [11] It has been suggested that the kinetics of pigment-containing leucocytes may vary between different populations and could be related to the severity of infection and host immunity. [13] It may be difficult to interpret the clinical relevance of a positive PCE result in countries with endemic malaria where frequent infections and subclinical parasitemia are common. It is important to note that there are differences in the depolarization procedure used by different Cell-Dyn systems; some utilize a helium-neon laser (Cell-Dyn3200, Cell-Dyn 3500 and Cell-Dyn 3700) while the Cell-Dyn 4000 has an argon-ion laser. The detection efficiency of PCE with these different laser benches and the pattern types with respect to different Plasmodium species may also be quite different. [16],[17] In the present study, the cell counter Cell-Dyn 3200 with software 3.1AT was used while other authors have used different cell counters like Cell-Dyn 3500, Cell-Dyn 4000 and Cell-Dyn 3700. It should be noted that the instrument and its software were not specifically designed for malaria diagnosis nor did the instrument give an automated count for pigment-containing leucocytes. As Cell-Dyn 3200 analyzes up to 10,000 leucocytes, only a partial number of leucocytes are represented on the scatterogram. The number of PCEs is not equal to the numbers of pigment-containing leucocytes in the peripheral blood. Different software versions are used in the different studies. The improvement in software, which identifies depolarizing red cells, and an option to analyze a larger quantity of cells, in the case of external, i.e. clinical, suspicion of malaria (query malaria option) and of internal suspicion, i.e. detection of a small quantity of PCEs during routine use, with activation of a built-in query malaria alert would increase the sensitivity of diagnosis. Hanscheid et al. [13] used ≥2 as the cut-off for PCEs. In the present study, the cut-off is ≥1. If we change the cut-off for PCEs from ≥1 to ≥2, the sensitivity would be 56.29% (76/135) and the specificity would be 94.58% (367/388), respectively.

In the present study, it was noticed that sensitivity of DLL for malaria detection reduced as the PI reduced. A similar observation was made by Martinez et al. [14] A reduction in the sensitivity was noted with low PI, perhaps due to the presence of younger and fewer circulating parasites, which produced less and were below the analyzer detection limits. [14] In the present study, it was observed that there was no correlation between the number of PCEs and the PI. A similar observation was made by Hanshield et al. [13]

From the present study the following conclusions can be derived:

  1. Low platelet count (<150 × 10 9 /L) is a good hematological parameter for presumptive diagnosis of malaria.
  2. DLL can be helpful in the diagnosis of acute malaria but cannot replace the existing methods of diagnosis. DLL-based malaria diagnosis would be very helpful in screening of malaria, particularly in blood donors. The cut-off of PCEs and software design in DLL-based malaria detection should be modified in endemic areas.
  3. There is no significant difference in the sensitivity and the specificity of hematological parameters in patients of P. vivax and P. falciparum malaria.

   Acknowledgement Top

The authors acknowledge the expert efforts of Dr. Rakesh Raval, Dr. Adarshjeet Singh, Dr. Bipin Patel, Dr. N. Jesalpura, Ms. Bhumika Bharambhatt, Mr.Vaibhav and Mrs. shalini Priyadarshani.

   References Top

1.WHO. WHO Expert Committee on malaria; Twentieth report 1998. Geneva, Switzerland: 2000.  Back to cited text no. 1    
2.WHO. New Perspectives. Malaria diagnosis: Report of a joint WHO/USAID consultation, 25-27 October 1999. Geneva, Switzerland: WHO; 2000.  Back to cited text no. 2    
3.National Anti Malaria Program. New Delhi: Directorate of NAMP, Ministry of health and Family Welfare; 2002.  Back to cited text no. 3    
4.Kakar A, Bhoi S, Prakash V, Kakar S. Profound thrombocytopenia in Plasmodium vivax malaria. Diagn Microbial Infect Dis 1999;35:243-4.  Back to cited text no. 4    
5.Krishnan A, Karnad DR. Severe Falciparum malaria: An important cause of multiple organ failure in India intensive care unit patients. Crit Care Med 2003;31:2278-84.  Back to cited text no. 5    
6.Ben-Ezra J, St. Louis M, Reily RS. Automated malarial detection with the Abboott Cell-Dyn 4000 hematology analyzer. Lab Haematol 2001;17:61-4.  Back to cited text no. 6    
7.Jones KN, Mascia B, Waggoner-Fountain L, Pearson RD. Diagnosis by automated blood analyzer. Clin Infect Dis 2001;33:1944-5.  Back to cited text no. 7    
8.Lathia TB, Joshi R. Can hematological parameters discriminate malaria from nonmalarious acute febrile illness in the tropics? Indian J Med Sci 2004;6:239-44.  Back to cited text no. 8    
9.Erhart LM, Yingyuen K, Chuanak N, Buathong N, Laoboonchai A, Miller RS, et al . Hematological and clinical indices of malaria in a semi-immune population of western Thailand. Am J Trop Med Hyg 2004;7:8-14.  Back to cited text no. 9    
10.Beale PJ, Cormack JD, Oldrey TB. Thrombocytopenia in malaria with IgM changes. Br Med J 1972;1:345-9.  Back to cited text no. 10    
11.Scott CS, van Zyl D, Ho E, Meyersfeld D, Ruivo L, Mendelow BV, et al . Automated detection of malaria associated intra leucocytic by cell-Dyn CD4000 depolarization analysis. Clin Lab Haematol 2003;25:77-86.  Back to cited text no. 11    
12.Mendelow BV, Lyons C, Nhlangothi P, Tana M, Munster M, Wypkema E, et al . Automated malaria detection by depolarization of laser light. Br J Haematol 1999;104:499-503.  Back to cited text no. 12    
13.Hδnscheid T, Melo-Cristino J, Pinto BG. Automated detection of malaria pigment in while blood cells for the diagnosis of malaria in Portugal. Am J Trop Med Hyg 2001;64:290-2.  Back to cited text no. 13    
14.Padial MM, Subirats M, Puente S, Lago M, Crespo S, Palacios G, et al . Sensitivity of laser light depolarization analysis for detection of malaria in blood samples. J Med Microbiol 2005;54:449-52.  Back to cited text no. 14    
15.Grobusch MP, Hδnscheid T, Krδmer B, Neukammer J, May J, Seybold J, et al . Sensitivity of haemozoin detection by automated flow cytometry in non- and semi-immune malaria patients. Cytometry B Clin Cytom 2003;55B:46-51.  Back to cited text no. 15    
16.Scott CS, van Zyl D, Ho E, Meyersfeld D, Ruivo L, Mendelow BV, et al . Automated detection of malaria-associated intraleucocytic by Cell-DynCD4000 depolarization analysis. Clin Lab Haematol 2003;25:77-86.  Back to cited text no. 16    
17.Fawzi ZO, Fakhro NA, Nabhan RA, Alloueche A, Scott CS. Differences in automated depolarisation patterns of Plasmodium falciparum and P. vivax malaria infections defined with the Cell-Dyn® CD4000 haematology analyser. Trans R Soc Trop Med Hyg 2003;97:71-9.  Back to cited text no. 17    

Correspondence Address:
Dinesh A Rathod
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0377-4929.48912

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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]

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