Praveen Kumar, Parul Arora, Subhadra Sharma, Arti Kapil$, A.K.Mukhopadhyay Departments of Lab Medicine & Microbiology$ All India Institute of Medical Sciences, New Delhi

1. AUTOMATED HEMATOLOGY ANALYZERAUTOMATED HEMATOLOGY ANALYZER
AS A COST EFFECTIVE AID TO SCREENAS A COST EFFECTIVE AID TO SCREEN
AND MONITOR SEPSISAND MONITOR SEPSIS
Praveen Kumar, , ,Parul Arora Subhadra Sharma Arti Kapil$
, . .A K Mukhopadhyay
&Departments of Lab Medicine Microbiology$
,All India Institute of Medical Sciences New Delhi
6th
International Conference of Cost Effective Use of Technology in e-Healthcare 2016 (CEUTH)

2. Introduction

3. Technology in health
care system
Westbrook JI, et al. J Am Med Inform Assoc 2015;22:784–793
Introduction of technology into the lab management
process is the need of the hour…
Assessing the cost versus effectiveness is
critical to determining their value and ultimately its
adoption.
But it is complex & expensive to acquire, implement, and maintain…

4.  An ideal economic evaluation of these
technologies would explicitly measure all direct
and indirect healthcare costs
Early diagnosis is a significant way to achieve
cost effectiveness..!!
Cost effectiveness
Health Serv Res. 1974 Spring; 9(1): 22–32.

5. What brought us to
this study…

6. Bloodstream infection is a major cause of morbidity
& mortality despite the availability of potent antimicrobial
therapy & good supportive care1
Sepsis is the most common cause of death in
hospitalized patients worldwide2
Early diagnosis of bacteremia is extremely important
but remains a diagnostic challenge to reducehigh mortality
rates 3
Bacterial Sepsis
1 Expert Rev Anti Infect Ther. 2005;3(6):915
2
Reinhart et al. Clin Res Cardiol, 95:429-454 (2006)
3
Riedel S et al. J Clin Microbiol.2008;46:1381–1385

7. Rationale for this study
1
Stefan Riedel et al.AJCP,2011 135, 182-189.
2
Pierre RV Lab Med 2002;22:279–97.
Blood culture:
• Low sensitivity
• Easily amenable to
contamination
• Takes at least 48 hrs
to give result
PS examination:
• Labour intensive
• Needs expertise
(observer dependent)
• Only 100-200 cells can
be analysed
Differentiating sepsis from other non-infectious causes
of systemic inflammation is difficult
because
fever and leucocytosis have poor sensitivity and
specificity in many clinical settings
VCS Technology
Rapid analysis of >8000
WBCs
within a minute

8. Technological update

9. VOLUME:
 Impedance generated by displacing isotonic diluent.
 Size of cell
CONDUCTIVITY:
 Alternating current energy penetrates the cell
 Internal structure: chemical composition & nuclear volume.
SCATTER:
 LASER beam scatter from cell
 Cellular granularity, nuclear lobularity and cell surface structure.
COULTER®
3-D VCS
1
Jones Am Clin Lab. 1990;9:18-22 Coulter principle

10. Simultaneous Measurements
gives complete details

11. Blood Culture: gold standard for diagnosing sepsis1
In bacterial sepsis, reactive neutrophils & immature granulocytes
are the predominant WBCs seen on peripheral smear examination
These cells are larger, have less complex nuclear structure and
coarser granules than normal neutrophils
Background for this study
1 Cohen J et al.Springer-Verlag, 1995:29.
Normal Bacterial infection
Study of VCS parameters can yield important
diagnostic information

12. Can automated analyzer cell population data
(CPD) parameters help in early diagnosis of
bacterial sepsis?
Do their values change with initiation of therapy
reflecting response ?
Research Question

13. Study site: Department of Laboratory Medicine,
AlIMS, New Delhi
Study design: Retrospective observational study
Study period: May 2015 – July 2016
Study duration: 15 months
Methodology

14. Inclusion criteria:
Cases: Confirmed Bacterial Blood Culture
positive cases
Controls: Voluntary Healthy donors from
Blood Bank, AIIMS
Methodology

15. Methodology:
Study flow
Blood samples from clinically suspected sepsis cases
received at our Lab for hemogram and blood culture were
screened
Blood Culture positive cases were identified
VCS / CPD parameters of the Blood Culture positive cases
and controls were studied on day 0, day 3 & day 7
Data compiled and entered in Microsoft Excel
Statistical Analysis of data

16. Results

17. No. of individuals enrolled: 134 cases & 100 controls
Results:
Baseline characteristics
Control
(n=100)
Patient samples
(n=134)
p value
Mean
Age
32.9 ± 8.3 32.2 ± 10.6 0.7
M:F 48:52 69:65 0.6
TLC
(x10 9
/l)
7.8 (4-9.5) 11.3 (2-51) 0.001
% Neutro 56.2 ± 13.24 72 ± 17.29 0.001

18. Comparison of Neutrophil VCS
parameters between
patient and control group:
Control
samples
(n=100)
Patients Group
(n=134)
P value
Neutrophils
%
56.2 ± 13.24 72 ± 17.29 0.001
MNV
140.59 ± 7.6 165.43 ± 18.21 0.001
MNC
155.36 ± 4.27 128.9 ± 7.8 0.001
MNS
142.26 ± 6.7 138.58 ± 9.7 0.027

19. Control
samples
(n=100)
Patients Group
(n=134)
p value
Monocytes % 7.8 (4.3-10.6) 7.6 (0.4-45.1) 0.74
MMV 164.54 ± 9.6 179.8 ± 14.16 0.001
MMC 130.6 ± 2.9 110.8 ± 6.3 0.001
MMS 86.4 ± 2.5 91.16 ± 5.3 0.001
Comparison of Monocyte VCS
parameters between patient and
control group:

22. Further Characterization

23. Correlation of VCS
Parameters with Neutro %:
n=134 Mean/ Median* Value p value
Day 0 Day 3 Day 7 Overall
Day
(0 vs 3)
0-7 3-7
NE %
75.31 ±
15.35
74.86 ±
14.18
74.15 ±
16.63
0.7±2 0.77 0.40 0.62
MNV
166.20 ±
15.79
158.20 ±
15.49
155.04 ±
14.47
<0.001 <0.001 <0.001 0.001
MNC
127.57 ±
7.51
130.85 ±
6.65
129.38 ±
7.14
<0.001 <0.01 0.12 0.14
MNS
137.35 ±
11.62
139.70 ±
7.35
140.03 ±
9.30
0.03 0.06 0.03 0.70

24. Correlation of VCS Parameters
with Mono %:
n=134 Mean/ Median* Value p value
Day 0 Day 3 Day 7 Overall
Day
(0 vs 3)
0-7 3-7
Mono %
7.8*
(0.4-33.6)
7.3*
(0.6-25.2)
6.8*
(0.4-28.8)
0.33 0.50 0.03 0.10
MMV
182.01 ±
15.58
178.07 ±
11.65
176.28 ±
10.63
<0.001 0.001 <0.001 0.012
MMC
110.56 ±
6.13
1110.57 ±
7.39
112.94 ±
8.30
0.003 0.12 0.002 0.04
MMS
90.18 ±
5.22
92.16 ±
4.62
93.09 ±
3.73
<0.001 <0.001 <0.001 0.001

25.
Group
s
Cut
off
points
Sensiti
vity
Specific
ity
+LR -LR AUC
Contro
ls vs
Cases
MNV ≥150.1 80.2 95 15.97 0.2121 92.33
MMV ≥168.7 80.60 77.50 3.5821 0.2504 82.97
VCS in Predicting Acute Bacterial
Infection

26. Discussion…

27. Discussion…

28. Comparison with
other studies…
Points Our study
Chaves et
al.
Celik et al.
Bhargava
etal.*
Sample
size
134 culture
positive
69 culture
positive
76 culture
positive
133 neonates
culture positive
MNV
Control
140.59± 7.70 143 ± 4.8 148.4 ±11 -
Parameters
studied
Blood culture,
CBC, VCS
Blood culture,
CBC, VCS
Blood culture,
CBC, VCS, IL-6
and CRP
Blood culture, CBC,
I/T ratio, VCS and
CRP
Mean age
(years)
32 yrs 52 yrs
Neonates
Neonates
Conclusion
Cut-off
(MNV)
MNV >150.1
sensitivity 80 %
specificity 95 %
AUC 92.3 %
MNV ≥150
sensitivity 70%
specificity91%
MNV>157
sensitivity79 %
specificity82 %
AUC 85%
MNV>154.2
sensitivity 95.5%,
specificity 82.1%
AUC 92%
* Bhargava et al. Int. Jnl. Lab. Hem. 2011 33 (Suppl. 1) pg 54

29. VCS Blood Culture
Sample 2-3 ml EDTA 10 ml
Handing Simple
Needs experienced
technologist
Sterile
conditions
None Strict requirement
Cost per sample ~50/- ~200/-
Reporting time 2-5 Minutes 2-3 days
Sensitivity 80 % 73%
Specificity 95% 100 %
Cost effectiveness of this new
technology

30. VCS data can be used as a surrogate indicator of acute
bacterial infection in the modern era of technology
enhancement
MNV with other parameters are reliable indicators in early
diagnosis and for starting presumptive treatment in sepsis
cases
As these data are readily available in automated analyzers,
their use can bring cost effectiveness in health care system
Conclusion

31. Bench to Bedside……
With the advent of technological enhancement, search
for cost effective tools is the need of the hour…
VCS/ CPD can be one of the readily available
markers that can be flagged in analysers
Can be incorporated in e-hospital portal system with
lab interfacing
CPD can be used for early diagnosis, better patient
care & quality improvement

32. Thanks…