Infection Manager System (IMS) as a new hemocytometry-based bacteremia detection tool: A diagnostic accuracy study in a malaria-endemic area of Burkina Faso

Annelies Post, Berenger Kaboré, Joel Bognini, Salou Diallo, Palpouguini Lompo, Basile Kam, Natacha Herssens, Fred van Opzeeland, Christa E van der Gaast-de Jongh, Jeroen D Langereis, Marien I de Jonge, Janette Rahamat-Langendoen, Teun Bousema, Heiman Wertheim, Robert W Sauerwein, Halidou Tinto, Jan Jacobs, Quirijn de Mast, Andre J van der Ven, Annelies Post, Berenger Kaboré, Joel Bognini, Salou Diallo, Palpouguini Lompo, Basile Kam, Natacha Herssens, Fred van Opzeeland, Christa E van der Gaast-de Jongh, Jeroen D Langereis, Marien I de Jonge, Janette Rahamat-Langendoen, Teun Bousema, Heiman Wertheim, Robert W Sauerwein, Halidou Tinto, Jan Jacobs, Quirijn de Mast, Andre J van der Ven

Abstract

Background: New hemocytometric parameters can be used to differentiate causes of acute febrile illness (AFI). We evaluated a software algorithm-Infection Manager System (IMS)-which uses hemocytometric data generated by Sysmex hematology analyzers, for its accuracy to detect bacteremia in AFI patients with and without malaria in Burkina Faso. Secondary aims included comparing the accuracy of IMS with C-reactive protein (CRP) and procalcitonin (PCT).

Methods: In a prospective observational study, patients of ≥ three-month-old (range 3 months- 90 years) presenting with AFI were enrolled. IMS, blood culture and malaria diagnostics were done upon inclusion and additional diagnostics on clinical indication. CRP, PCT, viral multiplex PCR on nasopharyngeal swabs and bacterial- and malaria PCR were batch-tested retrospectively. Diagnostic classification was done retrospectively using all available data except IMS, CRP and PCT results.

Findings: A diagnosis was affirmed in 549/914 (60.1%) patients and included malaria (n = 191) bacteremia (n = 69), viral infections (n = 145), and malaria-bacteremia co-infections (n = 47). The overall sensitivity, specificity, and negative predictive value (NPV) of IMS for detection of bacteremia in patients of ≥ 5 years were 97.0% (95% CI: 89.8-99.6), 68.2% (95% CI: 55.6-79.1) and 95.7% (95% CI: 85.5-99.5) respectively, compared to 93.9% (95% CI: 85.2-98.3), 39.4% (95% CI: 27.6-52.2), and 86.7% (95% CI: 69.3-96.2) for CRP at ≥20mg/L. The sensitivity, specificity and NPV of PCT at 0.5 ng/ml were lower at respectively 72.7% (95% CI: 60.4-83.0), 50.0% (95% CI: 37.4-62.6) and 64.7% (95% CI: 50.1-77.6) The diagnostic accuracy of IMS was lower among malaria cases and patients <5 years but remained equal to- or higher than the accuracy of CRP.

Interpretation: IMS is a new diagnostic tool to differentiate causes of AFI. Its high NPV for bacteremia has the potential to improve antibiotic dispensing practices in healthcare facilities with hematology analyzers. Future studies are needed to evaluate whether IMS, combined with malaria diagnostics, may be used to rationalize antimicrobial prescription in malaria endemic areas.

Trial registration: ClinicalTrials.gov (NCT02669823) https://ichgcp.net/clinical-trials-registry/NCT02669823.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Flow chart of inclusion.
Fig 1. Flow chart of inclusion.
Fig 2. Diagnostic accuracy of the IMS…
Fig 2. Diagnostic accuracy of the IMS for bacteremia by age group.
Legend: IMS: Infection Manager System | “Reference” refers to blood culture confirmed cases.
Fig 3
Fig 3
Median (Interquartile range (IQR)) CRP levels among (A) malaria, co-infection, and bacteremia and (B) the different causes of bacteremia, and median (IQR) PCT (C) malaria, co-infection, and bacteremia and (D) the different causes of bacteremia.Legend: the dotted line represents the cut-off values for CRP and PCT respectively.
Fig 4
Fig 4
ROC curves for IMS, CRP and PCT among patients of all ages without (A) and with (B) malaria parasitemia.

References

    1. D’Acremont V, Kilowoko M, Kyungu E, Philipina S, Sangu W, Kahama-Maro J, et al.. Beyond Malaria—Causes of Fever in Outpatient Tanzanian Children. New England Journal of Medicine. 2014;370(9):809–17. 10.1056/NEJMoa1214482 .
    1. Prasad N, Murdoch DR, Reyburn H, Crump JA. Etiology of Severe Febrile Illness in Low- and Middle-Income Countries: A Systematic Review. PLoS ONE. 2015;10(6):e0127962. 10.1371/journal.pone.0127962 PMC4488327.
    1. Edwards MD, Morris GA, Burr SE, Walther M. Evaluating the frequency of bacterial co-infections in children recruited into a malaria pathogenesis study in The Gambia, West Africa using molecular methods. Molecular and cellular probes. 2012;26(4):151–8. Epub 2012/05/03. 10.1016/j.mcp.2012.04.003 .
    1. Scott JA, Berkley JA, Mwangi I, Ochola L, Uyoga S, Macharia A, et al.. Relation between falciparum malaria and bacteraemia in Kenyan children: a population-based, case-control study and a longitudinal study. Lancet. 2011;378(9799):1316–23. Epub 2011/09/10. 10.1016/S0140-6736(11)60888-X
    1. Takem EN, Roca A, Cunnington A. The association between malaria and non-typhoid Salmonella bacteraemia in children in sub-Saharan Africa: a literature review. Malar J. 2014;13:400. Epub 2014/10/15. 10.1186/1475-2875-13-400
    1. Njozi M, Amuri M, Selemani M, Masanja I, Kigahe B, Khatib R, et al.. Predictors of antibiotics co-prescription with antimalarials for patients presenting with fever in rural Tanzania. BMC Public Health. 2013;13(1):1097. 10.1186/1471-2458-13-1097
    1. Laxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, et al.. Antibiotic resistance-the need for global solutions. The Lancet Infectious diseases. 2013;13(12):1057–98. Epub 2013/11/21. 10.1016/S1473-3099(13)70318-9 .
    1. World Health Organisation. World Health Organization Model List of Essential In Vitro Diagnostics. 2018.
    1. Carrol ED, Mankhambo LA, Jeffers G, Parker D, Guiver M, Newland P, et al.. The diagnostic and prognostic accuracy of five markers of serious bacterial infection in Malawian children with signs of severe infection. PloS one. 2009;4(8):e6621–e. 10.1371/journal.pone.0006621 .
    1. Díez-Padrisa N, Bassat Q, Machevo S, Quintó L, Morais L, Nhampossa T, et al.. Procalcitonin and C-Reactive Protein for Invasive Bacterial Pneumonia Diagnosis among Children in Mozambique, a Malaria-Endemic Area. PLOS ONE. 2010;5(10):e13226. 10.1371/journal.pone.0013226
    1. Prodjosoewojo S, Riswari SF, Djauhari H, Kosasih H, van Pelt LJ, Alisjahbana B, et al.. A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia. PLoS neglected tropical diseases. 2019;13(3):e0007183. 10.1371/journal.pntd.0007183
    1. Post A, Kabore B, Reuling IJ, Bognini J, van der Heijden W, Diallo S, et al.. The XN-30 hematology analyzer for rapid sensitive detection of malaria: a diagnostic accuracy study. BMC medicine. 2019;17(1):103. Epub 2019/05/31. 10.1186/s12916-019-1334-5
    1. United Nations Development Program. Human Development Indices and Indicators. 2018.
    1. Derra K, Rouamba E, Kazienga A, Ouedraogo S, Tahita MC, Sorgho H, et al.. Profile: Nanoro health and demographic surveillance system. International journal of epidemiology. 2012;41(5):1293–301. 10.1093/ije/dys159
    1. Guiraud I, Post A, Diallo SN, Lompo P, Maltha J, Thriemer K, et al.. Population-based incidence, seasonality and serotype distribution of invasive salmonellosis among children in Nanoro, rural Burkina Faso. PLOS ONE. 2017;12(7):e0178577. 10.1371/journal.pone.0178577
    1. Maltha J, Guiraud I, Kaboré B, Lompo P, Ley B, Bottieau E, et al.. Frequency of Severe Malaria and Invasive Bacterial Infections among Children Admitted to a Rural Hospital in Burkina Faso. PLoS ONE. 2014;9(2):e89103. 10.1371/journal.pone.0089103 PMC3925230.
    1. Sysmex Europe GmbH. Novel haematological parameters for rapidly monitoring the immune system response. Sysmex white paper Infection/inflammation: Sysmex Europe GmbH, 2017.
    1. Henriot I, Launay E, Boubaya M, Cremet L, Illiaquer M, Caillon H, et al.. New parameters on the hematology analyzer XN-10 (SysmexTM) allow to distinguish childhood bacterial and viral infections. Int J Lab Hematol. 2017;39(1):14–20. Epub 2016/08/31. 10.1111/ijlh.12562 .
    1. Urrechaga E, Bóveda O, Aguirre U, García S, Pulido E. Neutrophil Cell Population Data biomarkers for Acute Bacterial Infection. 2018.
    1. Linssen J, Jennissen V, Hildmann J, Reisinger E, Schindler J, Malchau G, et al.. Identification and quantification of high fluorescence-stained lymphocytes as antibody synthesizing/secreting cells using the automated routine hematology analyzer XE-2100. Cytometry Part B, Clinical cytometry. 2007;72(3):157–66. Epub 2007/02/03. 10.1002/cyto.b.20150 .
    1. Linssen J, Aderhold S, Nierhaus A, Frings D, Kaltschmidt C, Zanker K. Automation and validation of a rapid method to assess neutrophil and monocyte activation by routine fluorescence flow cytometry in vitro. Cytometry Part B, Clinical cytometry. 2008;74(5):295–309. Epub 2008/04/24. 10.1002/cyto.b.20422 .
    1. Wangrangsimakul T, Althaus T, Mukaka M, Kantipong P, Wuthiekanun V, Chierakul W, et al.. Causes of acute undifferentiated fever and the utility of biomarkers in Chiangrai, northern Thailand. PLoS neglected tropical diseases. 2018;12(5):e0006477–e. 10.1371/journal.pntd.0006477 .
    1. Crump JA, Sjolund-Karlsson M, Gordon MA, Parry CM. Epidemiology, Clinical Presentation, Laboratory Diagnosis, Antimicrobial Resistance, and Antimicrobial Management of Invasive Salmonella Infections. Clinical microbiology reviews. 2015;28(4):901–37. Epub 2015/07/17. 10.1128/CMR.00002-15
    1. Connell TG, Rele M, Cowley D, Buttery JP, Curtis N. How reliable is a negative blood culture result? Volume of blood submitted for culture in routine practice in a children’s hospital. Pediatrics. 2007;119(5):891–6. Epub 2007/05/03. 10.1542/peds.2006-0440 .
    1. Vincent JL, Sakr Y, Sprung CL, Ranieri VM, Reinhart K, Gerlach H, et al.. Sepsis in European intensive care units: results of the SOAP study. Critical care medicine. 2006;34(2):344–53. Epub 2006/01/21. 10.1097/01.ccm.0000194725.48928.3a .
    1. WHO/UNICEF joint Statement. Integrated Community Case Management (iCCM). New York: United Nations Children’s Fund, 2012.
    1. World Health Organisation. Gloabl Framework for Development & Stewardship to Combat Antimicrobial Resistance: draft roadmap. World Health Organization,, 2017.

Source: PubMed

3
Abonner