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Target Product Profile for a Diagnostic Assay to Differentiate between Bacterial and Non-Bacterial Infections and Reduce Antimicrobial Overuse in Resource-Limited Settings: An Expert Consensus.

ABSTRACT: Acute fever is one of the most common presenting symptoms globally. In order to reduce the empiric use of antimicrobial drugs and improve outcomes, it is essential to improve diagnostic capabilities. In the absence of microbiology facilities in low-income settings, an assay to distinguish bacterial from non-bacterial causes would be a critical first step. To ensure that patient and market needs are met, the requirements of such a test should be specified in a target product profile (TPP). To identify minimal/optimal characteristics for a bacterial vs. non-bacterial fever test, experts from academia and international organizations with expertise in infectious diseases, diagnostic test development, laboratory medicine, global health, and health economics were convened. Proposed TPPs were reviewed by this working group, and consensus characteristics were defined. The working group defined non-severely ill, non-malaria infected children as the target population for the desired assay. To provide access to the most patients, the test should be deployable to community health centers and informal health settings, and staff should require <2 days of training to perform the assay. Further, given that the aim is to reduce inappropriate antimicrobial use as well as to deliver appropriate treatment for patients with bacterial infections, the group agreed on minimal diagnostic performance requirements of >90% and >80% for sensitivity and specificity, respectively. Other key characteristics, to account for the challenging environment at which the test is targeted, included: i) time-to-result <10 min (but maximally <2 hrs); ii) storage conditions at 0-40°C, ?90% non-condensing humidity with a minimal shelf life of 12 months; iii) operational conditions of 5-40°C, ?90% non-condensing humidity; and iv) minimal sample collection needs (50-100?L, capillary blood). This expert approach to define assay requirements for a bacterial vs. non-bacterial assay should guide product development, and enable targeted and timely efforts by industry partners and academic institutions.

SUBMITTER: Dittrich S

PROVIDER: S-EPMC4999186 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Target Product Profile for a Diagnostic Assay to Differentiate between Bacterial and Non-Bacterial Infections and Reduce Antimicrobial Overuse in Resource-Limited Settings: An Expert Consensus.

Dittrich Sabine S Tadesse Birkneh Tilahun BT Moussy Francis F Chua Arlene A Zorzet Anna A Tängdén Thomas T Dolinger David L DL Page Anne-Laure AL Crump John A JA D'Acremont Valerie V Bassat Quique Q Lubell Yoel Y Newton Paul N PN Heinrich Norbert N Rodwell Timothy J TJ González Iveth J IJ

PloS one 20160825 8

Acute fever is one of the most common presenting symptoms globally. In order to reduce the empiric use of antimicrobial drugs and improve outcomes, it is essential to improve diagnostic capabilities. In the absence of microbiology facilities in low-income settings, an assay to distinguish bacterial from non-bacterial causes would be a critical first step. To ensure that patient and market needs are met, the requirements of such a test should be specified in a target product profile (TPP). To ide ...[more]

PMID: 27559728

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Project description:BackgroundMycetoma is a chronic mutilating disease of the skin and the underlying tissues caused by fungi or bacteria. Although recently included in the list of neglected tropical diseases by the World Health Organization, strategic control and preventive measures are yet to be outlined. Thus, it continues to pose huge public health threat in many tropical and sub-tropical countries. If not detected and managed early, it results into gruesome deformity of the limbs. Its low report and lack of familiarity may predispose patients to misdiagnosis and delayed treatment initiation. More so in situation where diagnostic tools are limited or unavailable, little or no option is left but to clinically diagnose these patients. Therefore, an overview of clinical course of mycetoma, a suggested diagnostic algorithm and proposed use of materials that cover the exposed susceptible parts of the body during labour may assist in the prevention and improvement of its management. Furthermore, early reporting which should be encouraged through formal and informal education and sensitization is strongly suggested.Main textAn overview of the clinical presentation of mycetoma in the early and late phases, clues to distinguish eumycetoma from actinomycetoma in the field and the laboratory, differential diagnosis and a suggested diagnostic algorithm that may be useful in making diagnosis amidst the differential diagnosis of mycetoma is given. Additionally, a proposed preventive measures which may be helpful in the community is also provided. Since treatment is currently based on expert opinion, we encourage active research to establish treatment guideline for it.ConclusionSince delay in visiting health facility results into gruesome complication, early presentation, recognition and initiation of appropriate choice of regimen is helpful in reducing complications. The clinical overview of mycetoma and the suggested algorithm may enhance suspicion and possibly increase recognition of mycetoma in the community and further guide in differentiation of eumycetoma from actinomycetoma. There is an urgent need for research funding for mycetoma, a disease plagued by severe physical disabilities and social stigma leading to isolation.

Project description:PurposeAdequate cancer pain management (CPM) is challenging in resource-limited settings, where current international guideline recommendations are difficult to implement owing to constraints such as inadequate availability and accessibility of opioids, limited awareness of appropriate opioid use among patients and clinicians, and lack of guidance on how to translate the best evidence into clinical practice. The multinational and multidisciplinary CAncer Pain managEment in Resource-limited settings (CAPER) Working Group proposes a two-step initiative to bridge clinical practice gaps in CPM in resource-limited settings.MethodsA thorough review of the literature, a steering committee meeting in February 2017, and post-meeting teleconference discussions contributed to the development of this initiative. As a first step, we developed practical evidence-based CPM algorithms to support healthcare providers (HCPs) in tailoring treatment according to availability of and access to resources. The second part of the initiative proposes a framework to support an effective implementation of the CPM algorithms that includes an educational program, a pilot implementation, and an advocacy plan.ResultsWe developed CPM algorithms for first-line use, breakthrough cancer pain, opioid rotation, and refractory cancer pain based on the National Comprehensive Cancer Network guidelines and expert consensus. Our proposed educational program emphasizes the practical elements and illustrates how HCPs can provide optimal CPM according to evidence-based guidelines despite varied resource limitations. Pilot studies are proposed to demonstrate the effectiveness of the algorithms and the educational program, as well as for providing evidence to support a draft advocacy document, to lobby policymakers to improve availability and accessibility of analgesics in resource-limited settings.ConclusionsThese practical evidence-informed algorithms and the implementation framework represent the first multinational step towards achieving optimal CPM in resource-limited settings.

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Dataset Information

Target Product Profile for a Diagnostic Assay to Differentiate between Bacterial and Non-Bacterial Infections and Reduce Antimicrobial Overuse in Resource-Limited Settings: An Expert Consensus.

Publications

Target Product Profile for a Diagnostic Assay to Differentiate between Bacterial and Non-Bacterial Infections and Reduce Antimicrobial Overuse in Resource-Limited Settings: An Expert Consensus.

Similar Datasets

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