Improving the management of imported malaria

[eng] Malaria continues to be on the podium of the deadliest infectious diseases globally. Although imported malaria presents a different picture than in endemic areas, it remains a considerable source of morbidity and mortality. Early diagnosis is crucial due to the aggressive nature of the disease, but access to microbiological testing can be challenging in some medical settings. In addition, current criteria for severe malaria, based on the WHO criteria for endemic areas (and aimed primarily at detecting severity in children), may lead to incorrect classification in non-endemic regions, especially considering that the population in these areas is usually adult and may not have been previously exposed to the parasite. The hypothesis of this thesis is that the evaluation of new strategies for the early identification and stratification of patients would optimize the management of malaria in non-endemic regions. The objectives were: 1) To develop a machine-learning-based tool to predict the risk of malaria in travelers returning with fever; 2) Describe life-threatening conditions, including deaths and life-saving interventions, as well as the prevalence of co-infections in a cohort of malaria patients seen from 2005 to 2023; 3) Evaluate a modified classification of severe malaria for non-endemic regions, to identify patients at higher risk of developing life-threatening conditions; 4) To identify predictive factors associated with organ failure and death in patients with imported malaria; 5) To identify host biomarkers associated with severity and organ failure in patients with imported malaria.6) To identify parasite biomarkers associated with severity in patients with imported malaria.7) To evaluate the ability of a conventional HRP2/LDH lateral flow immunoassay test to identify patients with severe imported malaria.