Algorithm in the Diagnosis of Febrile Illness Using Pathogen-specific Rapid Diagnostic Tests

Area of Science:

• Tropical medicine
• Infectious disease diagnostics
• Mathematical modeling in healthcare

Background:

• Rapid diagnostic tests (RDTs) for acute undifferentiated febrile illnesses are often misused due to a lack of clear guidelines.
• Current diagnostic approaches may lead to inappropriate use of available RDTs.

Purpose of the Study:

• To develop and evaluate mathematical models for optimizing diagnostic algorithms for febrile illnesses.
• To compare the diagnostic accuracy of sequential versus simultaneous testing strategies using RDTs.
• To create a web-based application for visualizing and comparing diagnostic algorithm performance.

Main Methods:

• Utilized prevalence data from India and Cambodia for five common febrile illnesses.
• Incorporated sensitivity and specificity data of pathogen-specific RDTs into a mathematical model.
• Developed a web application to simulate and compare diagnostic algorithms under varying conditions.

Main Results:

• Sequential testing algorithms achieved higher correct identification rates (74% in India, 89% in Cambodia) compared to simultaneous testing (46% in India, 49% in Cambodia).
• Optimal sequential algorithms varied between India and Cambodia due to differences in disease prevalence.
• The web application demonstrated the impact of prevalence and test performance on algorithm outcomes.

Conclusions:

• Simultaneous testing is inappropriate for diagnosing acute febrile illnesses with current RDTs, discouraging unsupervised multiplex testing.
• Adaptive algorithms can enhance diagnostic accuracy and add value to RDTs.
• The developed web application serves as a valuable tool for selecting optimal diagnostic algorithms based on local epidemiological factors and test performance.