Development of a clinical prediction score for Ebola virus disease screening at triage centers in the Democratic Republic of the Congo.

Autor:	Yango J; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.; Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium., Tshomba AO; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo., Kwete P; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo., Madinga J; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.; Department of Medical Biology, University of Kinshasa, Kinshasa, Democratic Republic of the Congo., Mulangu S; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.; Department of Medical Biology, University of Kinshasa, Kinshasa, Democratic Republic of the Congo., Mbala-Kingebeni P; Department of Epidemiology and Global Health, Institut National de Recherche Biomédicale, Kinshasa, Democratic Republic of the Congo.; Department of Medical Biology, University of Kinshasa, Kinshasa, Democratic Republic of the Congo., Henriquez-Trujillo AR; Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium., Jacobs BKM; Department of Clinical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
Jazyk:	angličtina
Zdroj:	PLOS global public health [PLOS Glob Public Health] 2024 Aug 26; Vol. 4 (8), pp. e0003583. Date of Electronic Publication: 2024 Aug 26 (Print Publication: 2024).
DOI:	10.1371/journal.pgph.0003583
Abstrakt:	The 2018-2020 Ebola virus disease (EVD) outbreak in the Democratic Republic of the Congo (DRC) was the largest since the disease's discovery in 1976. Rapid identification and isolation of EVD patients are crucial during triage. This study aimed to develop a clinical prediction score for EVD using clinical and epidemiological predictors. We conducted a retrospective cross-sectional study using surveillance data from EVD outbreak, collected during routine clinical care at the Ebola Transit Center (ETC) in Beni, DRC, from 2018 to 2020. The Spiegelhalter and Knill-Jones method was used for score development, including potential predictors with an adjusted likelihood ratio above 2 or below 0.50. Validation was performed using a dataset previously published in PLOSOne by Tshomba et al. Among 3725 patients screened, 3698 fulfilled the inclusion criteria, with 571 (15.4%) testing positive for EVD via RT-PCR Test. Seven predictive factors were identified: asthenia, sore throat, conjunctivitis, bleeding gums, hematemesis, contact with a sick person, and contact with a traditional healer. The prediction score achieved an Area under the receiver operating characteristic (AUROC) of 0.764, with 81.4% sensitivity and 53.6% specificity at a -1 cutoff. External validation demonstrated an AUROC of 0.766, with 80.8% sensitivity and 41.4% specificity at the -1 cutoff. Our study developed a screening tool to assess the risk of suspected patients developing EVD and being admitted to ETUs for RT-PCR testing and treatment. External validation results affirmed the model's reliability and generalizability in similar settings, suggesting its potential integration into clinical practice. Given the severity and urgency of EVD as well as the risk nosocomial EVD transmission, it is essential to continuously update these models with real-time data on symptoms, disease progression, patient outcomes and validated RDT during EVD outbreaks. This approach will enhance model accuracy, enabling more precise risk assessments and more effective outbreak management. Competing Interests: The authors have declared that no competing interests exist. (Copyright: © 2024 Yango et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
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