sTREM-1 and haemoglobin levels predict the survival of critically ill patients with severe and cerebral malaria
8 affiliations
- 1Department of Immunology, Noguchi Memorial Institute for Medical Research, University of Ghana
- 2Université Paris Cité, MERIT, IRD
- 3Sorbonne Université, Maison des Modélisations, Ingénieries et Technologies, SUMMIT
- 4Institut de Recherche Clinique du Benin (IRCB)
- 5Service de pédiatrie, Centre Hospitalo-Universitaire, Suruléré (CHU-Suruléré)
- 6Département de pédiatrie, Hôpital Mère-enfant la lagune (CHUMEL)
- 7Centre National Hospitalier Universitaire Hubert Koutoucou Mega (CNHU-HKM)
- 8Institut Pasteur
Abstract
Severe malaria is a major cause of malaria morbidity and mortality, yet early diagnosis of patients at highest risk of progressing from uncomplicated to severe malaria remains a critical challenge for prompt clinical management. We evaluated plasma levels of sTREM-1, sMIG, and sCD163 to assess their ability to predict mortality in a retrospective, multicenter study involving 220 children with varying degrees of malaria. Using logistic regression, we evaluated the predictive significance of clinical variables and these potential biomarkers of mortality risk in patients with severe malaria. The predictive model integrated clinical variables, such as pre-hospitalisation convulsions and Blantyre Coma Score to assess outcomes in patients with severe malaria syndromes. This stratification identified high-risk (51.60% mortality) and low-risk (0.60% mortality) groups. Integrating plasma sTREM-1 levels significantly enhanced mortality prediction (AUC 0.82) and further achieved optimal predictive performance (AUC 0.86) when combining sTREM-1 with haemoglobin levels, demonstrating superior risk stratification compared to clinical parameters alone. These findings establish plasma sTREM-1 and haemoglobin as robust predictors of mortality in severe malaria, offering a practical biomarker-based approach to identify high-risk patients. This enhanced risk stratification could enable targeted interventions to optimize resource allocation and improve personalized clinical care in malaria-endemic regions where early mortality prediction is essential for reducing disease burden.
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BibTeX
@article{ayanfultorgby2026strem1,
title = {{sTREM-1 and haemoglobin levels predict the survival of critically ill patients with severe and cerebral malaria}},
author = {Ruth Ayanful-Torgby and Jacqueline Milet and Joshua Adjah and Siyun Wang and Bernard Tornyigah and Azizath Moussiliou and Amoussou Annick and Maroufou J. Alao and Gratien Sagbo and Nicaise Tuikue Ndam and Christian Roussilhon and Gregory Nuel and Rachida Tahar},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-61032-5},
url = {https://doi.org/10.1038/s41598-026-61032-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - sTREM-1 and haemoglobin levels predict the survival of critically ill patients with severe and cerebral malaria
AU - Ruth Ayanful-Torgby
AU - Jacqueline Milet
AU - Joshua Adjah
AU - Siyun Wang
AU - Bernard Tornyigah
AU - Azizath Moussiliou
AU - Amoussou Annick
AU - Maroufou J. Alao
AU - Gratien Sagbo
AU - Nicaise Tuikue Ndam
AU - Christian Roussilhon
AU - Gregory Nuel
AU - Rachida Tahar
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-61032-5
UR - https://doi.org/10.1038/s41598-026-61032-5
AB - Severe malaria is a major cause of malaria morbidity and mortality, yet early diagnosis of patients at highest risk of progressing from uncomplicated to severe malaria remains a critical challenge for prompt clinical management. We evaluated plasma levels of sTREM-1, sMIG, and sCD163 to assess their ability to predict mortality in a retrospective, multicenter study involving 220 children with varying degrees of malaria. Using logistic regression, we evaluated the predictive significance of clinical variables and these potential biomarkers of mortality risk in patients with severe malaria. The predictive model integrated clinical variables, such as pre-hospitalisation convulsions and Blantyre Coma Score to assess outcomes in patients with severe malaria syndromes. This stratification identified high-risk (51.60% mortality) and low-risk (0.60% mortality) groups. Integrating plasma sTREM-1 levels significantly enhanced mortality prediction (AUC 0.82) and further achieved optimal predictive performance (AUC 0.86) when combining sTREM-1 with haemoglobin levels, demonstrating superior risk stratification compared to clinical parameters alone. These findings establish plasma sTREM-1 and haemoglobin as robust predictors of mortality in severe malaria, offering a practical biomarker-based approach to identify high-risk patients. This enhanced risk stratification could enable targeted interventions to optimize resource allocation and improve personalized clinical care in malaria-endemic regions where early mortality prediction is essential for reducing disease burden.
SN - 2045-2322
ER -