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Automated hematology analyzer as a cost effective aid to screen and 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

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Automated hematology analyzer as a cost effective aid to screen and monitor sepsis

  1. 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. 2. Introduction
  3. 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. 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. 5. What brought us to this study…
  6. 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. 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. 8. Technological update
  9. 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. 10. Simultaneous Measurements gives complete details
  11. 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. 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. 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. 14. Inclusion criteria: Cases: Confirmed Bacterial Blood Culture positive cases Controls: Voluntary Healthy donors from Blood Bank, AIIMS Methodology
  15. 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. 16. Results
  17. 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. 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. 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:
  20. 20. Further Characterization
  21. 21. 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
  22. 22. 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
  23. 23.   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
  24. 24. Discussion…
  25. 25. Discussion…
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. Thanks…

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

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