Management of Severe Falciparum Malaria

Severe falciparum malaria demands rapid recognition and aggressive intervention to avert organ failure and death. Clinical presentation often includes high parasitaemia, acute kidney injury, cerebral involvement and metabolic derangements. First-line therapy is intravenous artesunate, which accelerates parasite clearance and reduces mortality compared with quinine. Supportive care encompasses careful fluid management, correction of anaemia and metabolic acidosis, mechanical ventilation for respiratory distress and renal replacement therapy when indicated. Monitoring for post-treatment complications, notably delayed haemolysis, is essential, as is follow-up of haematological and renal parameters. In non-endemic settings, delayed diagnosis poses a risk, highlighting the need for clinician awareness, rapid diagnostics and clear referral pathways. In resource-limited areas, simplified dosing regimens and task-shifting to peripheral health workers are under evaluation. Emerging decision-support tools aim to personalise triage and optimise allocation of critical care resources. Global control also depends on addressing antimalarial drug resistance and ensuring equitable access to quality-assured artesunate.

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Management of Severe Falciparum Malaria publication trend

The graph below shows the total number of articles in management of severe falciparum malaria across all publications each year (not limited to Nature Index journals).

Technical terms

Parasitaemia: The proportion of red blood cells infected by malaria parasites, used to assess disease severity.

Intravenous artesunate: A water-soluble artemisinin derivative administered parenterally as first-line therapy in severe malaria.

Pitting: Spleen-mediated removal of dead parasites from infected red blood cells, leaving the cell intact but altered.

Post-artemisinin delayed haemolysis (PADH): A delayed drop in haemoglobin occurring days to weeks after artesunate treatment, due to prolonged erythrocyte destruction.

Machine learning (ML): Computational techniques that identify patterns in clinical and laboratory data to predict patient outcomes.

References

1. How to manage adult patients with malaria in the non-endemic setting. Clinical Microbiology and Infection (2024).
2. A machine learning approach for early identification of patients with severe imported malaria. Malaria Journal (2024).
3. Severe malaria in Europe: an 8-year multi-centre observational study. Malaria Journal (2017).