Automated cellular indices to identify dengue and malaria and distinguish them from other febrile illnesses

Inroduction:Tropical Febrile illnesses such as Malaria and Dengue are challenging to differentiate clinically as both present with high grade fever, malaise, and other non specific symptoms: both peak in incidence around the monsoon and Post-monsoon period. Also, both may be complicated by thrombocytopenia, progression to shock and disseminated intravascular coagulation. Hematology analyzers like LH 750 use a combination of Velocity-Electrical Conductivity-Light Scatter (VCS) methods to produce complete red blood cell, platelet, and leukocyte analyses. These parameters may be used for screening of Dengue and Malaria and to differentiate it from other febrile illnesses
V- Volume by Voltage Impedence.
C- An estimate of Cytoplasm/Nuclear ratio by radiofrequency Conductivity.
S- An estimate of Cytoplasmic granularity/nuclear complexity by laser light scatter.
A total of 46 parameter will be generated.
Aim And Objectives:
Aim: Use of Automated Cellular Indices to Identify Dengue and Malaria and distinguish them from other febrile illnesses
Objective:
1. To Generate Laboratory cut-off values based on hematological and VCS indices to differentiate Dengue virus infection and Malaria from other Febrile illnesses
2. To find the Sensitivity and Specificity of laboratory cut-off values based on hematological and VCS indices
3. To compare clinical severity of the illness with above cutoff values
Materials and Methods: The present observational study was a hospital based case control study. It was undertaken to study the use of automated cellular indices to identify Dengue and Malaria and distinguish them from other febrile illnesses.
Study Period: The study was done between December 2014 to September 2016.
Result: The present study was a hospital based case control study undertaken to study use of automated cellular indices to identify Dengue and Malaria and distinguish them from other febrile illnesses.
The study revealed the following points as follows:
• The malaria factor cutoff had an AUC of 0.902 to yield a sensitivity of 89.31% and a specificity of 83.12% in malaria group.
• The dengue factor at had an AUC of 0.893 to yield a sensitivity of 88.1% and a specificity of 73.23.12% in dengue group.
• The febrile control vs malaria/dengue factor had an AUC of 0.713 to yield a sensitivity of 82.13% and a specificity of 90.16% in control group.
Conclusion:Leukocyte abnormalities quantitated by automated analyzers successfully identified malaria and dengue and distinguished them from other fevers. These economic discriminate functions can be rapidly calculated by analyzer software programs to generate electronic flags to trigger-specific testing. They could potentially transform diagnostic approaches to tropical febrile illnesses in cost constrained settings.