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

Bibliographic Data

ID19592116
AuthorsJepsy Yango (0000-0002-1258-7481, National Institute of Biomedical Research, corresponding author), Antoine Oloma Tshomba (0000-0003-2440-8468, National Institute of Biomedical Research, corresponding author), Papy Kwete (0009-0009-8525-0749, National Institute of Biomedical Research, corresponding author), Joule Madinga, Joule Ntwan Madinga (0000-0003-2661-234X, University of Kinshasa, corresponding author), Sabue Mulangu (0000-0002-2722-2637, University of Kinshasa, corresponding author), Placide Mbala-Kingebeni, Placide Mbala‐Kingebeni (0000-0003-1556-3570, University of Kinshasa, corresponding author), Aquiles R Henríquez-Trujillo (0000-0002-3094-4438, Instituut voor Tropische Geneeskunde, corresponding author), Bart K M Jacobs (0000-0002-4677-0911, Instituut voor Tropische Geneeskunde, corresponding author)
EditorsMaría Del Pilar Fernández (0000-0001-8645-2267)
Year2024
Volume4
Issue8
Pagese0003583
Publication date2024-08-26
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePLOS Global Public Health (JOURNAL)
Journal identifiersISSN: 2767-3375 • E-ISSN: 2767-3375
PublisherPublic Library of Science (PLoS) (PUBLISHER)
DOI10.1371/journal.pgph.0003583
PMID39186506
OpenAlexW4401880143
LanguageEN
Citations received1
References cited16

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

Disease · Ebola virus · Generalizability theory · Outbreak · Receiver operating characteristic · Sore throat · Statistics · Triage · COVID-19 epidemiological studies · Disaster Response and Management · Medicine · Viral Infections and Outbreaks Research · Emergency Medicine · Internal Medicine · Surgery · Virology