Development of a target product profile for a point-of-care cardiometabolic device

Abstract Introduction Multi-parameter diagnostic devices can simplify cardiometabolic disease diagnosis. However, existing devices may not be suitable for use in low-resource settings, where the burden of non-communicable diseases is high. Here we describe the development of a target product profile (TPP) for a point-of-care multi-parameter device for detection of biomarkers for cardiovascular disease and metabolic disorders, including diabetes, in primary care settings in low- and middle-income countries (LMICs). Methods A draft TPP developed by an expert group was reviewed through an online survey and semi-structured expert interviews to identify device characteristics requiring refinement. The draft TPP included 41 characteristics with minimal and optimal requirements; characteristics with an agreement level for either requirement of ≤ 85% in either the survey or among interviewees were further discussed by the expert group and amended as appropriate. Results Twenty people responded to the online survey and 18 experts participated in the interviews. Twenty-two characteristics had an agreement level of ≤ 85% in either the online survey or interviews. The final TPP defines the device as intended to be used for basic diagnosis and management of cardiometabolic disorders (lipids, glucose, HbA1c, and creatinine) as minimal requirement, and offering an expanded test menu for wider cardiometabolic disease management as optimal requirement. To be suitable, the device should be intended for level 1 healthcare settings or lower, used by minimally trained healthcare workers and allow testing using self-contained cartridges or strips without the need for additional reagents. Throughput should be one sample at a time in a single or multi-analyte cartridge, or optimally enable testing of several samples and analytes in parallel with random access. Conclusion This TPP will inform developers of cardiometabolic multi-parameter devices for LMIC settings, and will support decision makers in the evaluation of existing and future devices.

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Target Product Profile (TPP) for Chagas Disease Point-of-Care Diagnosis and Assessment of Response to Treatment

PLoS Neglected Tropical Diseases ◽

10.1371/journal.pntd.0003697 ◽

2015 ◽

Vol 9 (6) ◽

pp. e0003697 ◽

Cited By ~ 36

Author(s):

Analía I. Porrás ◽

Zaida E. Yadon ◽

Jaime Altcheh ◽

Constança Britto ◽

Gabriela C. Chaves ◽

...

Keyword(s):

Chagas Disease ◽

Point Of Care ◽

Response To Treatment ◽

Target Product ◽

Target Product Profile ◽

Product Profile

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Target Product Profile for a point-of-care diagnostic test for dermal leishmaniases

Parasite Epidemiology and Control ◽

10.1016/j.parepi.2019.e00103 ◽

2019 ◽

Vol 5 ◽

pp. e00103 ◽

Cited By ~ 5

Author(s):

Israel Cruz ◽

Audrey Albertini ◽

Mady Barbeitas ◽

Byron Arana ◽

Albert Picado ◽

...

Keyword(s):

Diagnostic Test ◽

Point Of Care ◽

Target Product ◽

Target Product Profile ◽

Product Profile

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A target product profile for electronic clinical decision support algorithms combined with point-of-care diagnostic test results to support evidence-based decisions during patient consultations by health workers

10.1101/19008433 ◽

2019 ◽

Author(s):

Karell G. Pellé ◽

Clotilde Rambaud-Althaus ◽

Valérie D’Acremont ◽

Gretchen Moran ◽

Rangarajan Sampath ◽

...

Keyword(s):

Decision Support ◽

Health Workers ◽

Point Of Care ◽

Clinical Decision ◽

Evidence Based ◽

Target Product ◽

Test Results ◽

Target Product Profile ◽

Product Profile ◽

Electronic Clinical Decision Support

ABSTRACTHealth workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point of care diagnostics with evidence-based clinical protocols, can help improve the quality of care, the rational use of resources (humans, diagnostics and medicines) and save patient lives. The development of a target product profile for electronic clinical decision support algorithms (CDSAs) aimed at guiding preventive or curative consultations, and that integrate diagnostic test results will help align developer and implementer processes and specifications with the needs of end-users, in terms of quality, safety, performance and operational functionality. To identify characteristics for a CDSA, experts from academia, research institutions, and industry as well as policy makers with expertise in diagnostic and CDSA development, and implementation in LMICs were convened. Experts discussed the critical characteristics of a draft TPP which was revised and finalised through a Delphi process to facilitate consensus building. Experts were in overwhelming agreement that, given that CDSAs provide patients’ management recommendations, the underlying clinical algorithms should be available in human readable format and evidence-based. Whenever possible, the algorithm output should take into account pre-test disease probabilities, diagnostic likelihood ratios of clinical or laboratory predictors and disease probability thresholds to test and to treat. Validation processes should at a minimum ensure the CDSA are implementing faithfully the evidence-based algorithm they are based on (internal validation through clinical association and analytical validation). Additionally, clinical validation, bringing practice evidence about the impact of the CDSA use on health outcomes, was recognized as a good to have. The CDSAs should be designed to fit within clinic workflows, connectivity challenges and high volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.

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Matching diagnostics development to clinical need: Target product profile development for a point of care test for community-acquired lower respiratory tract infection

PLoS ONE ◽

10.1371/journal.pone.0200531 ◽

2018 ◽

Vol 13 (8) ◽

pp. e0200531 ◽

Cited By ~ 4

Author(s):

Micaela Gal ◽

Nicholas A. Francis ◽

Kerenza Hood ◽

Jorge Villacian ◽

Herman Goossens ◽

...

Keyword(s):

Tract Infection ◽

Respiratory Tract ◽

Respiratory Tract Infection ◽

Lower Respiratory Tract Infection ◽

Lower Respiratory Tract ◽

Point Of Care ◽

Target Product ◽

Target Product Profile ◽

Point Of Care Test ◽

Product Profile

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Modelling of hypothetical SARS-CoV-2 point-of-care tests on admission to hospital from A&E: rapid cost-effectiveness analysis

Health Technology Assessment ◽

10.3310/hta25210 ◽

2021 ◽

Vol 25 (21) ◽

pp. 1-68

Author(s):

Matt Stevenson ◽

Andrew Metry ◽

Michael Messenger

Keyword(s):

Cost Effectiveness ◽

Patient Outcomes ◽

Point Of Care ◽

Clinical Effectiveness ◽

Decision Makers ◽

Target Product ◽

Target Product Profile ◽

Point Of Care Test ◽

Product Profile ◽

Point Of Care Tests

Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019. At the time of writing (October 2020), the number of cases of COVID-19 had been approaching 38 million and more than 1 million deaths were attributable to it. SARS-CoV-2 appears to be highly transmissible and could rapidly spread in hospital wards. Objective The work undertaken aimed to estimate the clinical effectiveness and cost-effectiveness of viral detection point-of-care tests for detecting SARS-CoV-2 compared with laboratory-based tests. A further objective was to assess occupancy levels in hospital areas, such as waiting bays, before allocation to an appropriate bay. Perspective/setting The perspective was that of the UK NHS in 2020. The setting was a hypothetical hospital with an accident and emergency department. Methods An individual patient model was constructed that simulated the spread of disease and mortality within the hospital and recorded occupancy levels. Thirty-two strategies involving different hypothetical SARS-CoV-2 tests were modelled. Recently published desirable and acceptable target product profiles for SARS-CoV-2 point-of-care tests were modelled. Incremental analyses were undertaken using both incremental cost-effectiveness ratios and net monetary benefits, and key patient outcomes, such as death and intensive care unit care, caused directly by COVID-19 were recorded. Results A SARS-CoV-2 point-of-care test with a desirable target product profile appears to have a relatively small number of infections, a low occupancy level within the waiting bays, and a high net monetary benefit. However, if hospital laboratory testing can produce results in 6 hours, then the benefits of point-of-care tests may be reduced. The acceptable target product profiles performed less well and had lower net monetary benefits than both a laboratory-based test with a 24-hour turnaround time and strategies using data from currently available SARS-CoV-2 point-of-care tests. The desirable and acceptable point-of-care test target product profiles had lower requirement for patients to be in waiting bays before being allocated to an appropriate bay than laboratory-based tests, which may be of high importance in some hospitals. Tests that appeared more cost-effective also had better patient outcomes. Limitations There is considerable uncertainty in the values for key parameters within the model, although calibration was undertaken in an attempt to mitigate this. The example hospital simulated will also not match those of decision-makers deciding on the clinical effectiveness and cost-effectiveness of introducing SARS-CoV-2 point-of-care tests. Given these limitations, the results should be taken as indicative rather than definitive, particularly cost-effectiveness results when the relative cost per SARS-CoV-2 point-of-care test is uncertain. Conclusions Should a SARS-CoV-2 point-of-care test with a desirable target product profile become available, this appears promising, particularly when the reduction on the requirements for waiting bays before allocation to a SARS-CoV-2-infected bay, or a non-SARS-CoV-2-infected bay, is considered. The results produced should be informative to decision-makers who can identify the results most pertinent to their specific circumstances. Future work More accurate results could be obtained when there is more certainty on the diagnostic accuracy of, and the reduction in time to test result associated with, SARS-CoV-2 point-of-care tests, and on the impact of these tests on occupancy of waiting bays and isolation bays. These parameters are currently uncertain. Funding This report was commissioned by the National Institute for Health Research (NIHR) Evidence Synthesis programme as project number 132154. This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 21. See the NIHR Journals Library website for further project information.

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Quality by Design: Concept to Applications

Current Drug Discovery Technologies ◽

10.2174/1570163815666180308142016 ◽

2019 ◽

Vol 16 (3) ◽

pp. 240-250 ◽

Cited By ~ 2

Author(s):

Suryakanta Swain ◽

Rabinarayan Parhi ◽

Bikash Ranjan Jena ◽

Sitty Manohar Babu

Keyword(s):

Quality By Design ◽

Pharmaceutical Products ◽

Assessment Tools ◽

Basic Knowledge ◽

Target Product ◽

Target Product Profile ◽

Critical Material ◽

Novel Approach ◽

Product Profile ◽

Performance Report

Background: Quality by Design (QbD) is associated with a modern, systematic, scientific and novel approach which is concerned with pre-distinct objectives that not only focus on product, process understanding but also lead to process control. It predominantly signifies the design and product improvement and the manufacturing process in order to fulfill the predefined manufactured goods or final products quality characteristics. It is quite essential to identify the desired and required product performance report, such as Target Product Profile, typical Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQA). Methods: This review highlighted the concepts of QbD design space, for critical material attributes (CMAs) as well as the critical process parameters that can totally affect the CQAs within which the process shall be unaffected thus, consistently manufacturing the required product. Risk assessment tools and design of experiments are its prime components. Results: This paper outlines the basic knowledge of QbD, the key elements; steps as well as various tools for QbD implementation in pharmaceutics field are presented briefly. In addition to this, quite a lot of applications of QbD in numerous pharmaceutical related unit operations are discussed and summarized. Conclusion: This article provides a complete data as well as the roadmap for universal implementation and application of QbD for pharmaceutical products.

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