|Year : 2018 | Volume
| Issue : 1 | Page : 1-4
Applicability of volume conductivity and scatter parameters for early prediction of dengue virus infection
Harsha Hemant Jaykar, Anjali J Kelkar, NS Mani
Department of Pathology, Bharti Vidyapeeth Medical College, Pune, Maharashtra, India
|Date of Web Publication||22-Mar-2018|
Dr. Anjali J Kelkar
Department of Pathology, Bharti Vidyapeeth Medical College and Hospital, Dhankawadi, Pune - 411 043, Maharashtra
Source of Support: None, Conflict of Interest: None
Introduction: Dengue virus infection is an acute febrile illness endemic in several countries including India. Although the management is empirical, early diagnosis is crucial for timely intervention to reduce the morbidity and mortality. The diagnosis of dengue virus infection mainly depends on serological testing and virus isolation which are costly and time-consuming. A simple blood test like complete blood count (CBC) run on five-part differential cell counter with volume conductivity, and scatter (VCS) data can help in predicting dengue infection without additional cost.
Methods: In the present study, we analyzed VCS parameters of 337 febrile patients over the period of 1 year (January 2016 to December 2016). Out of these, 255 cases were positive for dengue serology and 82 were febrile controls. Comparative analysis of utility of previously published dengue factor, lymph index, and monocyte factor along with the “New Dengue Factor” introduced in the present study.
Results: The “New Dengue Factor” proposed by us gave highest “positive likelihood ratio” for the diagnosis of dengue virus infection. Dengue factor proposed by Sharma et al. was the most “sensitive,” and “lymph index” proposed by Hawaldar et al. was the most specific followed by the “New dengue factor” introduced in the present study.
Conclusion: Factors derived from VCS parameters are useful for early prediction of dengue virus infection without incurring additional cost and thus would contribute to better patient care.
Keywords: Beckman LH 750, complete blood count, dengue, volume conductivity and scatter parameters
|How to cite this article:|
Jaykar HH, Kelkar AJ, Mani N S. Applicability of volume conductivity and scatter parameters for early prediction of dengue virus infection. J Appl Hematol 2018;9:1-4
|How to cite this URL:|
Jaykar HH, Kelkar AJ, Mani N S. Applicability of volume conductivity and scatter parameters for early prediction of dengue virus infection. J Appl Hematol [serial online] 2018 [cited 2019 Jan 23];9:1-4. Available from: http://www.jahjournal.org/text.asp?2018/9/1/1/228332
| Introduction|| |
Dengue virus infection is an acute febrile illness caused by Arbovirus endemic in several countries and emerging as a global health problem with 50–100 million infections occurring each year. There is no specific treatment available, but timely initiation of supportive treatment can reduce the morbidity and mortality of severe cases to <1%. The diagnosis depends mainly on serological testing and virus isolation from the patient's blood. Virus isolation is considered as the “gold standard;” however, it is expensive, time-consuming, and not available as a routine diagnostic test.
Some of the five-part differential automated blood cell analyzers offer cell population data (CPD) in the form of volume conductivity and scatter (VCS) parameters. Volume – volume is measured directly by impedance. Conductivity – reveals information about the internal structure of the Cell. Scatter – the scattered light gives information about cell surface and granularity. Using this VCS technology, various studies have been previously conducted to generate factors which would help in early diagnosis of dengue virus infection.,, In the present study, we have compared the utility of various factors based on VCS parameters and also have proposed a new factor having high positive likelihood ratio.
| Methods|| |
The study was conducted in a tertiary care teaching hospital attached to University Medical College in Western India.
Duration of study – 1 year (from January 2016–December 2016) and majority of cases were in season of Monsoon.
All febrile patients positive for Dengue virus serology (NS1 Antigen and or IgM antibody by rapid solid phase immune-chromatographic method (Dengue Day 1 Test kit By J. Mitra & Co. Pvt. Ltd.). All febrile patients negative for dengue virus serology were included as controls. There were 255 dengue positive cases and 82 febrile controls.
Ethylenediaminetetraacetic acid samples were run on LH 750 automated analyzer (Beckman Coulter Inc., Miami, FL, USA). The CPD collected was mean and standard deviation (SD) values of volume, conductivity, and scatter parameters of lymphocytes and monocytes. Data collected for the statistical analysis to derive “factor” were platelet count, lymphocyte%, monocyte%, VCS parameters of lymphocyte and monocytes.
VCS data was used in the following manner to calculate previously published factors and “ New Dengue Factor” introduced by us.
- Lymph index = LVXLV-SD/LC 
- Dengue factor =0.3–0.00183X (PLT) +0.00619X ([LY%) +0.0335X (LY-SD-C)
- Monocyte factor = % monocyte + MoV-SD 
- New dengue factor = (% monocyte + MoV-SD) + (LVXLV-SD/LC) +PLT
The diagnostic performances of various factors then generated were assessed by receiver operating characteristics (ROC) with calculation of area under curve (AUC) using Medcalc software, version (12). P value was calculated by pair-wise comparison of difference between areas under the curve in two factors.
| Results|| |
All the parameters were retrospectively analyzed. A total of 337 patients were analyzed, out of which 82 patients with negative dengue serology were used for statistical analysis as febrile controls. Moreover, 255 patients with positive dengue serology were considered as cases. Out of 255 cases, 101 had normal platelet counts, that is, more than 150,000/cumm.
Medcalc software, version (12) was used for statistical analysis.
As shown in [Table 1], both dengue cases and febrile controls show male preponderance. The age of patients ranges from 4 years to 70 years. Majority patients were in age range of 20–40 years.
[Table 2] shows all factors have higher value of mean in all dengue cases as compared to febrile controls.
|Table 2: Comparison of various factors in dengue cases and febrile controls|
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[Table 3] shows comparison of various factors in prediction of dengue virus infection; highest sensitivity is given by dengue factor (i.e., 69.8) proposed by Sharma et al. Highest specificity is given by lymph index (i.e., 76.8) followed by new dengue factor (i.e., 74) proposed by us.
[Table 4] shows likelihood ratio in predicting dengue infection: highest positive likelihood ratio is given by new dengue factor, and highest negative likelihood ratio is given by lymph index.
In [Table 5], comparison of dengue factor (Sharma et al.) in the present study population and study by Sharma et al. is done.
|Table 5: Comparison of dengue factor (proposed by Sharma et al.) in the present study and study by Sharma et al.|
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In [Table 6], comparison of lymph index in the present study and study by Ranjana H, Sadhana S. is done.
|Table 6: Comparison of lymph index in the present study and study by Ranjana H, Sadhana S.|
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Pair wise comparison of ROC curve is shown in [Graph 1]. Statistically significant difference between AUC was seen in Dengue factor and lymph index( P< 0.05). This difference was not significant (P > 0.05) between pair wise comparison of Dengue factor & monocyte factor, Dengue factor & New Dengue Factor, monocyte factor & Lymph Index, Lymph index & New Dengue Factor. This indicates overlap in the study population tested by these factors. This suggests the population which was positive by dengue factor and lymph index was significantly different whereas other factors showed the overlap in the study population.
| Discussion|| |
In any febrile illness, complete blood count (CBC) is one of the first and basic investigations requested. It would be of immense practical significance if such a simple investigation could generate data to guide us for early prediction of diseases with high morbidity and mortality like dengue virus infection. Early detection of dengue virus infection and differentiation from other febrile illnesses is important for timely management of patients. Many a time, expected findings of leukopenia and thrombocytopenia may be absent in such patients. Thus, there may be delays in requesting confirmatory investigations and starting the treatment.
In a patient with dengue virus infection, changes in the number of lymphocytes, monocytes, and platelet count are accompanied also by morphological changes in the leukocytes. These morphological changes are reflected in the VCS parameters and have been found to be extremely useful by many scientists for early prediction of bacterial infection, viral infection, malaria, and dengue virus infection.,,,,,,, We also have included monocyte VCS parameters in our factor calculation. Reactive lymphocytes are misidentified by cell counters as monocytes in dengue fever; thus, VCS parameters of monocytes also play important role.
Routine diagnostic/confirmatory tests such as antibody detection or virus isolation are expensive, time-consuming, and need expertise and special equipment.
In the present study, we propose “New Dengue Factor” combining monocyte factor, platelet count, and lymph index which gave highest “positive likelihood ratio” for the prediction of dengue virus infection. We also compared various factors studied by different authors using VCS parameters along with new factor proposed by us. We found that dengue factor was the most “sensitive,” and “lymph index” was the most specific followed by the “New dengue factor.”
We thought about using most sensitive and most specific factors and implementing their cutoff in our LH 750 analyzer for prediction of dengue virus infection. However, when we analyzed the cases, we come to know that few cases were missed between these cutoffs.
Ns1 Antigen and IgM testing for Dengue Infection still plays important role. This could be due to
- Variable presentation of Dengue patients in terms of low to normal platelet count and WBC count. these are likely to result in wide varaition in factor results.
- Reactive changes in lymphocyes/ monocytes resulting in abnormal VCS parameters also occur in non Dengue virus infection. As reactive change also occurs in other viral infection which will be reflected in VCS parameters affecting the factor calculations.
However VCS parameters have one great advantage that they are acquired during simple and routine CBC test very quickly without additional cost. and thus can guide the clinician for appropriate further investigation in febrile patients.
[Table 5] shows comparison of Dengue factor result as previously published by Sharma et al and applicability of this factor in our study population. the observed difference in sensitivity and specificty possibly be explained by different study population, difference in sample size and variable presentation of Dengue cases.
[Table 6] shows comparison of Lymph index in study published by Ranjana H, Sadhana S. and in present study population. The specificity observed is similar however sensitivity is higher in study by Ranjana H, Sadhana S. the difference may be due to different sample size and cut off used.
| Conclusion|| |
In Febrile patients with high clinical suspicion of dengue virus infection, VCS parameters are very useful to substantiate clinical diagnosis. In cases without clinical suspicion VCS parameters will help to predict dengue infection. Simple blood test like CBC without incurring extra cost can give this very useful information which will guide the clinician for proper and timely management of patients. This will help in reducing the morbidity and mortality in these cases. To conclude, in areas endemic with dengue fever, performing CBC on blood cell counts with VCS technology is very useful, as shown in this study to substantiate the clinical diagnosis of dengue fever. We recommend repeating this study at a larger scale and different settings to confirm our findings.
We would like to thank Mrs. Varsha Mangesh Subandh for technical help and Mr. Shrivallabh Sane for statistical analysis.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]