Predicting risk of Plasmodium vivax microscopy-detected episodes using serological markers in patients with Plasmodium falciparum malaria: a multi-country diagnostic performance evaluation
Journal Title
Journal of Infectious Diseases
Publication Type
Mar 17
Abstract
BACKGROUND: Plasmodium vivax presents a significant obstacle to malaria elimination due to its capacity to form dormant liver-stage hypnozoites that can cause relapses. Universal radical cure, which administers hypnozoite-targeting treatment to patients with P. falciparum malaria living in co-endemic areas, has potential to reduce P. vivax relapses. However, its implementation is hindered by the lack of a diagnostic tool for detecting hypnozoite-carriage. METHODS: P. vivax serological exposure markers (SEMs) were evaluated as a screening tool in P. falciparum patients for predicting risk of P. vivax microscopy-detected episodes over the following 63 days. Analysis was performed using samples from participants in the PRIMA study from Ethiopia, Indonesia, and Bangladesh (NCT03916003). An existing random forest serological classification algorithm was used and evaluated for sensitivity and specificity, then further optimized by re-training a study-specific model. FINDINGS: IgG antibodies were measured in 244 P. falciparum participant samples, of which 22 had a vivax microscopy-detected episode during follow-up. SEMs showed high sensitivity (82%) but low specificity (27%), likely due to sustained antibody responses in moderate-to-high transmission areas or undetected sub-microscopic P. vivax infections during follow-up. A study-specific algorithm increased specificity to 68%, but with a corresponding drop in sensitivity to 68%. INTERPRETATION: Although P. vivax SEMs showed limited suitability for guiding radical cure in P. falciparum patients in this study (where the outcome was microscopy-detected vivax episodes), they could play a valuable role in building community trust and acceptance of universal radical cure when supported by strong communication and implementation strategies.
Publisher
Oxford Academic
Keywords
Plasmodium falciparum; Plasmodium vivax; antibodies; machine-learning algorithm; malaria; relapse; serological exposure markers; serological surveillance; serology; universal radical cure
Research Division(s)
Infection and Global Health
PubMed ID
41846579
Open Access at Publisher's Site
https://doi.org/10.1093/infdis/jiag16
Terms of Use/Rights Notice
Refer to copyright notice on published article.


Creation Date: 2026-03-24 02:09:49
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