Abstract
Ebola Virus Disease (EVD) remains a significant public health threat, particularly in sub-Saharan Africa. During the 10th Ebola outbreak in the Democratic Republic of Congo (DRC), the Pamoja Tulinde Maisha clinical trial (PALM-RCT) provided a unique opportunity to evaluate new therapeutic interventions. Despite these advances, limited knowledge exists regarding the dynamic evolution of mortality risk factors in EVD patients. This study aimed to model risk factors associated with mortality using logistic regression on unbalanced panel data from patients enrolled in this trial.We conducted a retrospective secondary analysis of longitudinal data from 617 EVD patients included in the PALM-RCT. Data were collected at five time points: Day0 (admission), Day7, Day14, Day21, and Day28. A binary logistic regression model was applied at each time point to identify significant predictors of mortality. The Hosmer-Lemeshow test was used to assess model calibration and internal validation. At Day0 (admission), six significant predictors of mortality were identified: viral load (RT-PCR cycle threshold value), creatinine, alanine aminotransferase (ALAT), aspartate aminotransferase (ASAT), haemorrhage, shortness of breath, and conjunctivitis. By Day7, five predictors emerged: sodium, ASAT, coma, abdominal pain, and shortness of breath. At Day14, two predictors remained significant: ASAT and mental state changes. No significant predictors were identified at Day21 and Day28. The dynamic nature of these risk factors highlights the importance of continuous monitoring throughout the clinical course of EVD.Our study demonstrates that mortality risk factors in EVD patients evolve over time, suggesting that a dynamic approach to patient monitoring is critical. Early risk factors such as viral load and renal function should guide initial interventions, while neurological symptoms and electrolyte imbalances require attention in later stages. These findings support a personalized approach to EVD management, where clinical care is adjusted based on real-time clinical data to improve patient outcomes.