Publication: Identifying risk factors for the development of sepsis during adult severe malaria
4
Issued Date
2018-07-31
Resource Type
ISSN
14752875
Other identifier(s)
2-s2.0-85051231025
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Malaria Journal. Vol.17, No.1 (2018)
Suggested Citation
Tsi Njim, Arjen Dondorp, Mavuto Mukaka, Eric O. Ohuma Identifying risk factors for the development of sepsis during adult severe malaria. Malaria Journal. Vol.17, No.1 (2018). doi:10.1186/s12936-018-2430-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45994
Research Projects
Organizational Units
Authors
Journal Issue
Thesis
Title
Identifying risk factors for the development of sepsis during adult severe malaria
Author(s)
Abstract
© 2018 The Author(s). Background: Severe falciparum malaria can be compounded by bacterial sepsis, necessitating antibiotics in addition to anti-malarial treatment. The objective of this analysis was to develop a prognostic model to identify patients admitted with severe malaria at higher risk of developing bacterial sepsis. Methods: A retrospective data analysis using trial data from the South East Asian Quinine Artesunate Malaria Trial. Variables correlating with development of clinically defined sepsis were identified by univariable analysis, and subsequently included into a multivariable logistic regression model. Internal validation was performed by bootstrapping. Discrimination and goodness-of-fit were assessed using the area under the curve (AUC) and a calibration plot, respectively. Results: Of the 1187 adults with severe malaria, 86 (7.3%) developed clinical sepsis during admission. Predictors for developing sepsis were: female sex, high blood urea nitrogen, high plasma anion gap, respiratory distress, shock on admission, high parasitaemia, coma and jaundice. The AUC of the model was 0.789, signifying modest differentiation for identifying patients developing sepsis. The model was well-calibrated (Hosmer-Lemeshow Chi squared = 1.02). The 25th percentile of the distribution of risk scores among those who developed sepsis could identify a high-risk group with a sensitivity and specificity of 70.0 and 69.4%, respectively. Conclusions: The proposed model identifies patients with severe malaria at risk of developing clinical sepsis, potentially benefiting from antibiotic treatment in addition to anti-malarials. The model will need further evaluation with more strictly defined bacterial sepsis as outcome measure.