Conference Abstract | Volume 8, Abstract ELIC2025154 (Oral 070) | Published: 14 Aug 2025
Jelte Elsinga1,&, Daniel Hernandez2, Anders Boyd3, James Fom2, Elizabeth Chibuzo4, Temmy Sunyoto1
1Médecins Sans Frontières, Luxembourg Operational Research Unit (LuxOR), 2Medecins Sans Frontières, Brussels Operational Centre, Brussels, Belgium, 3Amsterdam University Medical Centre, Amsterdam, Netherlands, 4Alex Ekwueme Federal University Teaching Hospital, Abakaliki, Nigeria
&Corresponding author: Jelte Elsinga, Médecins Sans Frontières, Luxembourg Operational Research Unit (LuxOR), Luxembourg. Email: j.elsinga@amsterdamumc.nl
Received: 20 May 2025, Accepted: 09 Jul 2025, Published: 15 Aug 2025
Domain: Infectious Disease Epidemiology
Keywords: Lassa fever, mortality, artificial intelligence, Nigeria
©Jelte Elsinga et al. Journal of Interventional Epidemiology and Public Health (ISSN: 2664-2824). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Cite this article: Jelte Elsinga et al Clinical presentation and predictors of severity in Lassa fever patients at a tertiary hospital in Abakaliki, Nigeria, 2018-2024. Journal of Interventional Epidemiology and Public Health. 2025;8(ConfProc5):00070. https://doi.org/10.37432/JIEPH-CONFPRO5-00070
Lassa Fever (LF) remains an important public health problem in Nigeria. Timely case detection and treatment of LF is challenging due to incomplete understanding of disease progression. Médecins Sans Frontières (MSF) and AEFUTHA hospital (Abakaliki, Nigeria) treated over 1,700 suspected and 400 confirmed LF cases since 2018. This study aimed to describe the clinical presentation of LF and identify symptoms and biomarkers of disease prognosis and mortality.
All patients with confirmed LF (positive polymerase chain reaction–PCR) presenting in AEFUTHA between 2018 and 2024 were included. Data was extracted from clinical and laboratory records (symptoms and biomarkers: molecular, biochemical and hematological). Data were cleaned and statistically analyzed using Artificial Intelligence (a Co-pilot based model) and regression models. We report here preliminary univariate analysis with mortality as outcome. Ethical clearance was obtained from the relevant review boards.
Preliminary analysis shows a 40.3% mortality rate among confirmed LF cases, with >50% of deathsoccurring within 48 hours of presentation. In the univariate analysis, non-survivors compared to survivors demonstrated significant differences at admission (p< 0.05): older age (median: 36 vs. 29 years), elevated markers of liver damage (median transaminases: ALT 123 vs. 53 U/L and AST 132 vs. 89 U/L), kidney dysfunction (median creatinine 237 vs. 82 μmol/L and urea 11.8 vs. 3.8 mmol/L), higher white blood cell count (median: 11.7 vs. 5.8 cells/μL), increased neutrophil percentage (median: 56% vs. 49%), and decreased monocyte counts (median: 9% vs. 12%).
This study observed a high case fatality rate (40,3%). Most deaths were reported within 48 hours of admission. The elevated biomarkers for liver and kidney damage in non-survivors suggest patients often present at a later stage of the disease when critical organ damage has occurred. This highlights the urgent (research) need for improved early detection strategies for Lassa Fever.
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