ArticlePLoS neglected tropical diseases2019
A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia.
Article in PLoS neglected tropical diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Procalcitonin as a Candidate Biomarker for Malarial Infection and Severe Malaria: A Meta-Analysis.International journal of environmental research and public health · 2022Pooled it
- Rethinking the Kinetics of Extended WBC Differential Count and Inflammatory Index Marker in Secondary Dengue Infection: Case Report at University Hospital.International medical case reports journal · 2026Article
- Low atypical lymphocyte score as a predictor of non-severe dengue.BMC infectious diseases · 2025Observational
- Role of Extended White Blood Cell Parameters in Distinguishing Acute Febrile Illnesses.Advances in hematology · 2025Article
- Extended Inflammation Parameters (EIP) as Markers of Inflammation in Systemic Sclerosis.International journal of inflammation · 2024Article
- Article
- Accuracy of C-reactive Protein and Procalcitonin for Diagnosing Bacterial Infections Among Subjects With Persistent Fever in the Tropics.Open forum infectious diseases · 2022Article
- Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors.Circulation. Cardiovascular quality and outcomes · 2022Article
- Opportunities and challenges to accurate diagnosis and management of acute febrile illness in adults and adolescents: A review.Acta tropica · 2022Review
- The 2000HIV study: Design, multi-omics methods and participant characteristics.Frontiers in immunology · 2022Article
- Development of Nucleic Acid Lateral Flow Immunoassay for Rapid and Accurate Detection of Chikungunya Virus in Indonesia.Journal of microbiology and biotechnology · 2021Article
- A machine learning PROGRAM to identify COVID-19 and other diseases from hematology data.Future science OA · 2021Article
- Comparison of sPLA2IIA performance with high-sensitive CRP neutrophil percentage PCT and lactate to identify bacterial infection.Scientific reports · 2021Article
- Infection Manager System (IMS) as a new hemocytometry-based bacteremia detection tool: A diagnostic accuracy study in a malaria-endemic area of Burkina Faso.PLoS neglected tropical diseases · 2021Article
- Extended Inflammation Parameters (EIP) as Markers of Immune System Cell Activation in Psoriasis.International journal of inflammation · 2021Article
- Observational
- Previously Derived Host Gene Expression Classifiers Identify Bacterial and Viral Etiologies of Acute Febrile Respiratory Illness in a South Asian Population.Open forum infectious diseases · 2020Article
- C-reactive protein as a potential biomarker for disease progression in dengue: a multi-country observational study.BMC medicine · 2020Observational
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDistinguishing arboviral infections from bacterial causes of febrile illness is of great importance for clinical management. The Infection Manager System (IMS) is a novel diagnostic algorithm equipped on a Sysmex hematology analyzer that evaluates the host response using novel techniques that quantify cellular activation and cell membrane composition. The aim of this study was to train and validate the IMS to differentiate between arboviral and common bacterial infections in Southeast Asia and compare its performance against C-reactive protein (CRP) and procalcitonin (PCT). METHODOLOGY/PRINCIPAL
findings600 adult Indonesian patients with acute febrile illness were enrolled in a prospective cohort study and analyzed using a structured diagnostic protocol. The IMS was first trained on the first 200 patients and subsequently validated using the complete cohort. A definite infectious etiology could be determined in 190 of 463 evaluable patients (41%), including 89 arboviral infections (81 dengue and 8 chikungunya), 94 bacterial infections (26 murine typhus, 16 salmonellosis, 6 leptospirosis and 46 cosmopolitan bacterial infections), 3 concomitant arboviral-bacterial infections, and 4 malaria infections. The IMS detected inflammation in all but two participants. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the IMS for arboviral infections were 69.7%, 97.9%, 96.9%, and 77.3%, respectively, and for bacterial infections 77.7%, 93.3%, 92.4%, and 79.8%. Inflammation remained unclassified in 19.1% and 22.5% of patients with a proven bacterial or arboviral infection. When cases of unclassified inflammation were grouped in the bacterial etiology group, the NPV for bacterial infection was 95.5%. IMS performed comparable to CRP and outperformed PCT in this cohort. CONCLUSIONS/SIGNIFICANCE: The IMS is an automated, easy to use, novel diagnostic tool that allows rapid differentiation between common causes of febrile illness in Southeast Asia.
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