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Clinical Trial Summary

MALDI-TOF MS is capable of directly identifying bacteria and fungi in positive blood cultures, which may be beneficial to patient management. Therefore, MALDI-TOF MS is an important new technology that is becoming routine in developed countries. It is currently unknown whether MALDITOF MS improves diagnostics, costs and patient outcomes in developing countries. This study will assess the clinical impact of a MALDITOF MS system (Maldi Biotyper, Bruker, Germany) in the resource constrained setting of Vietnam and at what cost.


Clinical Trial Description

When an eligible specimen from a patient shows pathogen growth, the pathogen identification will be randomized to either MaldiTof or routine diagnostics ('diagnostic pipelines'). Randomization to MaldiTof or routine diagnostics will be 1:1 with stratification by hospital and specimen type (blood vs. other). Isolates grown from all eligible specimens of the same patient will be assigned to the same diagnostic pipeline as the first randomized specimen of that patient.

Allocation to diagnostic arm will be assigned by a web based randomization program. When a pathogen is isolated from a positive eligible specimen, the laboratory technician will log onto the secure randomization program and enter the patient and specimen code. The random diagnostic pipeline allocation will then be generated, informed to the laboratory technician and logged in the study database. In the case of multiple specimens with pathogen growth for a single patient, the unique patient code will trigger the randomization program to generate the same diagnostic arm allocation as the previous sample(s). ;


Study Design

Allocation: Randomized, Intervention Model: Parallel Assignment, Masking: Single Blind (Outcomes Assessor), Primary Purpose: Diagnostic


Related Conditions & MeSH terms


NCT number NCT02306330
Study type Interventional
Source Oxford University Clinical Research Unit, Vietnam
Contact
Status Completed
Phase N/A
Start date December 2014
Completion date January 2016

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