Dataset Information

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia.

ABSTRACT: BACKGROUND:Distinguishing 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 FINDINGS:600 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.

SUBMITTER: Prodjosoewojo S

PROVIDER: S-EPMC6435198 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia.

Prodjosoewojo Susantina S Riswari Silvita F SF Djauhari Hofiya H Kosasih Herman H van Pelt L Joost LJ Alisjahbana Bachti B van der Ven Andre J AJ de Mast Quirijn Q

PLoS neglected tropical diseases 20190314 3

<h4>Background</h4>Distinguishing 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 S ...[more]

PMID: 30870415

Similar Datasets

Project description:BackgroundAn increasing use of point of care diagnostic tests that exclude malaria, coupled with a declining malaria burden in many endemic countries, is highlighting the lack of ability of many health systems to manage other causes of febrile disease. A lack of knowledge of distribution of these pathogens, and a lack of screening and point-of-care diagnostics to identify them, prevents effective management of these generally treatable contributors to disease burden. While prospective data collection is vital, an untapped body of knowledge already exists in the published health literature.MethodsFocusing on the Mekong region of Southeast Asia, published data from 1986 to 2011 was screened to for frequency of isolation of pathogens implicated in aetiology of non-malarial febrile illness. Eligibility criteria included English-language peer-reviewed studies recording major pathogens for which specific management is likely to be warranted. Of 1,252 identified papers, 146 met inclusion criteria and were analyzed and data mapped.ResultsData tended to be clustered around specific areas where research institutions operate, and where resources to conduct studies are greater. The most frequently reported pathogen was dengue virus (n = 70), followed by Orientia tsutsugamushi and Rickettsia species (scrub typhus/murine typhus/spotted fever group n = 58), Leptospira spp. (n = 35), Salmonella enterica serovar Typhi and Paratyphi (enteric fever n = 24), Burkholderia pseudomallei (melioidosis n = 14), and Japanese encephalitis virus (n = 18). Wide tracts with very little published data on aetiology of fever are apparent.Discussion and conclusionsThis mapping demonstrates a very heterogeneous distribution of information on the causes of fever in the Mekong countries. Further directed data collection to address gaps in the evidence-base, and expansion to a global database of pathogen distribution, is readily achievable, and would help define wider priorities for research and development to improve syndromic management of fever, prioritize diagnostic development, and guide empirical therapy.

Project description:BackgroundIn this work, a platform was developed and tested to allow to detect a variety of candidate viral, bacterial and parasitic pathogens, for acute fever of unknown origin. The platform is based on a centrifugal microfluidic cartridge, the LabDisk ("FeverDisk" for the specific application), which integrates all necessary reagents for sample-to-answer analysis and is processed by a compact, point-of-care compatible device.Methodology/principal findingsA sample volume of 200 μL per FeverDisk was used. In situ extraction with pre-stored reagents was achieved by bind-wash-elute chemistry and magnetic particles. Enzymes for the loop-mediated isothermal amplification (LAMP) were pre-stored in lyopellet form providing stability and independence from the cold chain. The total time to result from sample inlet to read out was 2 h. The proof-of-principle was demonstrated in three small-scale feasibility studies: in Dakar, Senegal and Khartoum, Sudan we tested biobanked samples using 29 and 9 disks, respectively; in Reinfeld, Germany we tested spiked samples and analyzed the limit of detection using three bacteria simultaneously spiked in whole blood using 15 disks. Overall during the three studies, the FeverDisk detected dengue virus (different serotypes), chikungunya virus, Plasmodium falciparum, Salmonella enterica Typhi, Salmonella enterica Paratyphi A and Streptococcus pneumoniae.Conclusions/significanceThe FeverDisk proved to be universally applicable as it successfully detected all different types of pathogens as single or co-infections, while it also managed to define the serotype of un-serotyped dengue samples. Thirty-eight FeverDisks at the two African sites provided 59 assay results, out of which 51 (86.4%) were confirmed with reference assay results. The results provide a promising outlook for future implementation of the platform in larger prospective clinical studies for defining its clinical sensitivity and specificity. The technology aims to provide multi-target diagnosis of the origins of fever, which will help fight lethal diseases and the incessant rise of antimicrobial resistance.

Project description:BACKGROUND:Anemia is one of the most impactful nutrient deficiencies in the world and disproportionately affects children in low-resource settings. Point-of-care devices (PoCDs) measuring blood hemoglobin (Hb) are widely used in such settings to screen for anemia due to their low cost, speed, and convenience. Here we present the first iteration of Aptus, a new PoCD which measures Hb and hematocrit (HCT). AIM:To evaluate the accuracy of Aptus and HemoCue® Hb 301 against an automated hematology analyzer (Medonic®) in Gambian children aged 6-35 months and the Aptus' usage in the field. METHODS:Aptus, HemoCue® and Medonic® were compared using venous blood (n = 180), and Aptus and HemoCue® additionally using capillary blood (n = 506). Agreement was estimated using Bland-Altman analysis and Lin's concordance. Usage was assessed by error occurrence and user experience. RESULTS:Mean Hb values in venous blood did not significantly differ between Aptus and HemoCue® (10.44±1.05 vs 10.56±0.93g/dl, p>0.05), but both measured higher Hb concentrations than Medonic® (9.75±0.99g/dl, p<0.0001). Lin's coefficient between Aptus and Medonic® was rc = 0.548, between HemoCue® and Medonic® rc = 0.636. Mean bias between the PoCDs venous measurements was -0.11g/dl with limits of agreement (LoA) -1.63 and 1.40g/dl. The bias was larger for the comparisons between the Medonic® and both Aptus (0.69g/dl, LoA 0.92 and 2.31g/dl) and HemoCue® (0.81g/dl, LoA 0.17 and 1.78g/dl). ROC curves showed an AUC of 0.933 in HemoCue® and 0.799 in Aptus. Capillary Hb was higher with Aptus than HemoCue® (10.33±1.11g/dl vs 10.01±1.07g/dl, p<0.0001). Mean bias was 0.32g/dl with LoA of -1.91 and 2.54g/dl. Aptus' usage proved intuitive, yet time-to-results and cuvettes could be improved. CONCLUSION:Both PoCDs showed a relatively limited bias but large LoA. Aptus and HemoCue® showed similar accuracy, while both overestimated Hb levels. Aptus showed promise, with its operation unimpaired by field conditions as well as being able to show HCT values.

Dataset Information

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia.

Publications

A novel diagnostic algorithm equipped on an automated hematology analyzer to differentiate between common causes of febrile illness in Southeast Asia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure