Standardizing the approach to acute undifferentiated febrile illness: Development of an expert consensus-based algorithm for India.

BackgroundAcute Undifferentiated Febrile Illness (AUFI) poses an immense challenge to diagnosis in India because it can be caused by various pathogens. The clinical presentation is often similar across causes, and the number of potential causative organisms continues to increase due to changes in climate and ecology. To counter these challenges, it was necessary that a holistic diagnostic algorithm based on a set of predefined criteria be formulated by the Indian Council of Medical Research (ICMR).MethodsA Core Committee and Expert Committee were formed with members including infectious disease specialists, clinical microbiologists, radiologists, and public health experts who reviewed national and international guidelines, statistics on instances and prevalence, and recommendations from ICMR. A structured sequence of consultations during 2022 and 2024 led to formulation of an expert consensus-based algorithm as per pathogens prioritization. The algorithm considers various factors pertaining to disease diagnosis and treatment.Results and discussionsThe list of priority pathogens includes dengue, chikungunya, influenza, enteric fever, scrub typhus, leptospirosis, malaria, brucellosis, and some unusual causes like CCHF, KFD, melioidosis, and rickettsial infections. The algorithm recommends a structured method for taking a history, lists warning signs requiring hospital admission, and recommends case-specific diagnostic and treatment guidelines for stable and severe patients. Its main objective is to address diagnostic delay, empiric use of antimicrobials, and risk of complications like sepsis and multi-organ dysfunction.ConclusionThe AUFI diagnostic algorithm is a systematic and specific tool that aims at optimizing early pathogen identification and promoting correct decisions with regards to appropriate clinical practice directly affecting patient care. It will also be useful for optimizing surveillance, thereby affecting public health and optimizing patient outcomes.