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<p>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.</p>

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sTREM-1 and haemoglobin levels predict the survival of critically ill patients with severe and cerebral malaria

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Ruth Ayanful-Torgby,
  • Jacqueline Milet,
  • Joshua Adjah,
  • Siyun Wang,
  • Bernard Tornyigah,
  • Azizath Moussiliou,
  • Amoussou Annick,
  • Maroufou J. Alao,
  • Gratien Sagbo,
  • Nicaise Tuikue Ndam,
  • Christian Roussilhon,
  • Gregory Nuel,
  • Rachida Tahar

摘要

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.