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Developing a novel risk prediction model for severe malarial anemia

  • E. B. Brickley (a1) (a2) (a3), E. Kabyemela (a4), J. D. Kurtis (a5), M. Fried (a1), A. M. Wood (a2) and P. E. Duffy (a1)...
Abstract
Abstract

As a pilot study to investigate whether personalized medicine approaches could have value for the reduction of malaria-related mortality in young children, we evaluated questionnaire and biomarker data collected from the Mother Offspring Malaria Study Project birth cohort (Muheza, Tanzania, 2002–2006) at the time of delivery as potential prognostic markers for pediatric severe malarial anemia. Severe malarial anemia, defined here as a Plasmodium falciparum infection accompanied by hemoglobin levels below 50 g/L, is a key manifestation of life-threatening malaria in high transmission regions. For this study sample, a prediction model incorporating cord blood levels of interleukin-1β provided the strongest discrimination of severe malarial anemia risk with a C-index of 0.77 (95% CI 0.70–0.84), whereas a pragmatic model based on sex, gravidity, transmission season at delivery, and bed net possession yielded a more modest C-index of 0.63 (95% CI 0.54–0.71). Although additional studies, ideally incorporating larger sample sizes and higher event per predictor ratios, are needed to externally validate these prediction models, the findings provide proof of concept that risk score-based screening programs could be developed to avert severe malaria cases in early childhood.

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Copyright
This is a work of the U.S. Government and is not subject to copyright protection in the United States. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited
Corresponding author
*Address for correspondence: P. E. Duffy, Laboratory of Malaria Immunology and Vaccinology, NIAID, NIH, Twinbrook I, Room 1111, 5640 Fishers Lane, Rockville, MD 20852, USA (Email: Patrick.Duffy@nih.gov)
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Global Health, Epidemiology and Genomics
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