Algorithm Using a Rapid Diagnostic Test for the Management of Childhood Febrile Diseases. — e-MANIC  

Antimicrobial Resistance Clinical Trial

Official title: Development and Evaluation of an Electronic Algorithm Using a Combination of a Two-step Malaria RDT, and Other Rapid Diagnostic Tools for the Management of Febrile Illness in Children Under 5 Attending Outpatient Facilities in Burkina Faso.

Status Completed

Clinical Trial Summary

In Sub-Saharan Africa (SSA), fever remains a major public health problem in children. The introduction of malaria rapid diagnostic tests (RDTs) in routine healthcare has greatly improved the management of malaria. However, despite the good attitude of healthcare workers to adhere to malaria RDT test results, persisting hrp2antigen and low sensitivity of pLDH RDT negatively affect antimalarials and antibiotics prescriptions practices. This is one of the main causes of antimicrobial resistance (AMR) and inappropriate management of febrile diseases. To improve the diagnosis of febrile diseases and subsequent prescription of antimicrobials, it is hypothesized that the implementation of an algorithm including a two-step malaria RDT PfHRP2/pLDH supported by point-of-care tests (POCTs) for C-reactive protein, oximetry, and bacterial infection such as Group A Streptococcus, and Salmonella/Shigella, will significantly improve the management of febrile diseases and thereby tackling AMR.

To assess the value of the proposed algorithm, an open-label randomized controlled trial with three arms, enrolling febrile children under 5 years is proposed.

• In the control arm, febrile children will benefit from a complete clinical examination. Treatment will be done according to the national guideline.

• In RDTs decisional algorithm (RDT-DA) arm (intervention), the complete clinical examination will be supported by two-step malaria RDT and bacterial infections RDTs. Prescription will be left to the discretion of the healthcare workers.

• In e-algorithm arm (intervention), the complete clinical examination and the outcomes of RDTs (malaria and bacterial infections) will be digitalized. Diagnostic and prescription will be done by the algorithm.

A final follow-up visit (day7) will be scheduled for all participants. Patients will be asked to return to the health facilities in case of no improvement.

Primary study outcomes will be the proportion of curative case and antimicrobial(s) prescribed in each arm. Secondary outcomes include: (i) adherence of healthcare workers to the algorithm; (ii) adherence of parents/guardian to treatment; (iii) accuracy of the algorithm for the diagnostic of malaria.

This project will serve as a path of policy change in the management of febrile diseases and AMR. By relying on existing RDTs available, the implementation of this algorithm will tackle AMR and provide better care. If successful, the project will equip the lead applicant to establish himself as an independent researcher with ability to further build his own research team. The project will also offer training opportunities to young scientists, and further strengthen already existing capacities of the home institute.

Clinical Trial Description

Background In Sub-Saharan Africa (SSA), febrile illnesses remains a major public health problem in children. The introduction of malaria rapid diagnostic tests (RDTs) in routine healthcare has greatly improved the management of malaria. However, despite the good attitude of healthcare workers to adhere to malaria RDT test results, persisting hrp2 antigen and low sensitivity of pLDH RDT negatively affect antimalarials and antibiotics prescriptions practices. These limitations of the RDTs lead to poor management of febrile diseases and antimicrobial resistance (AMR). To improve the diagnosis of these febrile diseases and subsequent prescription of antimicrobials, it is hypothesized that the implementation of an algorithm including a two-step malaria RDT PfHRP2/pLDH supported by point-of-care tests (POCTs) for C-reactive protein, white blood cells (WBC) count, urine dipstick, oximetry, and bacterial infection such as Group A Streptococcus, and Salmonella/Shigella, will significantly improve the management of febrile diseases and thereby tackling AMR.

Methodology To assess the value of the proposed algorithm, an open-label randomized controlled trial with three arms, enrolling febrile children from 2 to 59 months is proposed.

• In the control arm, febrile children will be managed according to the IMCI (integrative management of childhood illnesses) and the guideline of diagnostic and treatment (GDT), which are part of the routine system existing in Burkina Faso. Treatment will be done according to the national guideline.

• In RDTs decisional algorithm (RDT-DA) arm (intervention), the clinical examination based on IMIC will be supported by two-step malaria RDT and bacterial infections RDTs. Prescription will be left to the discretion of the healthcare workers.

• In e-algorithm arm (intervention), artificial intelligence integrating multiple layers of clinical information such as clinical examination, signs/symptoms and medical history, and laboratory information such as outcomes of biomarkers (CRP and WBC) and pathogen specific POCT (malaria and bacterial infections) and oximetry will be developed. The e-algorithm will serve to guide the diagnostic and management of febrile infections in children from 2 to 59 months.

In the 3 arms, the supports of data collection will be digitalized. A final follow-up visit (day7) will be scheduled for all participants. Patients will be asked to come back to the health facilities before scheduled visit if the symptoms persist or health condition worsening.

Study Duration This is estimated to be 36 months from the start of the study. The first 12 months will be reserved for the development of the algorithm (artificial intelligence) of e-Algorithm arm and the study CRFs for RDT-DA and control arms.

Study sites The study site will be the Health District of Nanoro, specifically the health facilities of Siglé and Pella in Burkina Faso.

General objective To evaluate the value of the diagnostic performance of the proposed algorithm combining two-step malaria RDT detecting PfHRP2/pLDH and POCT for the diagnostic of malaria and bacterial infections respectively, in the management of febrile illnesses in children from 2 to 59 months in a process of Universal Health Coverage (UHC).

Specific objectives Primary objective

1. To assess the impact of the proposed strategies on the acute fever management practice at primary health facilities;

2. To assess the impact of the proposed strategies on the antimalarial and antibiotic prescription practices at primary health facilities;

3. To assess in randomized superiority diagnostic trial the curative rate whether the RDTs decisional algorithm (RDT-DA), e-Algorithm or routine system and to determine which one allow to access to quality health cares at primary health facilities.

Secondary objectives

1. To assess the accuracy of the algorithm based on (i) two-step malaria RDT detecting PfHRP2/pLDH for the diagnosis of malaria infection compared to microscopy and polymerase chain reaction (PCR); (ii) and POCT for the diagnostic of bacterial infections compared to bacterial cultures.

2. To assess the adherence of health care workers and parents/guardian to the algorithm proposed;

3. To assess the adherence of the health care workers and parents/guardian to the prescription in RDT-DA, e-Algorithm and control arms;

4. To assess the safety of the algorithm in the management of febrile cases in children presenting in outpatient clinic.

Study Population Study participants (children from 2 to 59 months of age) will be identified during their visit to the study health facilities for simple febrile illnesses. Children from 2 to 59 months years of age (with parents/guardians able to give a consent) attending the health facilities where the study will be carried out and who fulfil the inclusion criteria will be asked to participate in the study and to give written informed consent.

Study outcomes Primary study outcomes will be the proportion of recovered case and antimicrobial(s) prescribed in each arm. Secondary outcomes include: (i) adherence of healthcare workers to the algorithm; (ii) adherence of parents/guardian to treatment; (iii) accuracy of the algorithm for the diagnosis of malaria and others POCT to guide bacterial infections (biomarkers and pathogen-specific POCT).

Sample size Sample size is calculated based on data collected in the consultation book of the health facilities of Sigle and Pella. From these data collected in 2018 (January to December), the proportion of febrile children under 5 years presenting at the health facilities with febrile illnesses and who were prescribed antibiotic was 62,63%. The implementation of the algorithm is expected to reduce this proportion to at least 20%. In this study the sample size is computed to a Bonferroni correction of alpha is performed for multiple comparisons, therefore, an alpha level of 0.017 is chosen. Type II error was controlled at 20% i.e. (β=0.20 or 80% power). Based on these parameters, a sample size of 356 is required per arm. Hence, an overall sample size for the study is estimated at 1068 (3×356). Further assuming that 10% of the individuals will be lost to follow-up, an adjusted sample size of 392 children per arm is required. The final estimated sample size for this study is thus 1176 children.

Statistical analysis An analyses plan of the data will be developed and validated by the participants of the first workshop of the project, prior the recruitment of the first study participant. This analyse plan could be revised according to events and in case of amendment of the protocol. The statistical analysis will be done with an appropriate data analysis software (STATA 14.1. and R). ;

Related Conditions & MeSH terms

• Antimicrobial Resistance

• NCT number: NCT05285657
• Study type: Interventional
• Source: Institut de Recherche en Sciences de la Sante, Burkina Faso
• Status: Completed
• Phase: N/A
• Start date: February 28, 2022
• Completion date: November 30, 2023