Lawanga Ontshick Leader, Yango Jepsy, Mubiala Yaya Ange, Tshiani Mbaya Olivier, Madinga Twan Joule, Nsengi Ntamabyaliro Jean-Michel, Ali Rosine, Lupola Patrick Mutombo, Bukweli Joseph-Desiré, Muchanga Sifa Marie-Joelle, Lutete Gaston Tona, Kiangebeni Placide Mbala, Mulangu Sabue, Makengo Matendo Rostin Mabela
Department of Epidemiology and Global Health, National Institute of Biomedical Research, Kinshasa, Democratic Republic of Congo.
Department of Mathematics, Statistics and Computer Science, University of Kinshasa, Kinshasa, Democratic Republic of Congo.
PLOS Glob Public Health. 2025 Jul 11;5(7):e0004901. doi: 10.1371/journal.pgph.0004901. eCollection 2025.
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.
埃博拉病毒病（EVD）仍然是一个重大的公共卫生威胁，特别是在撒哈拉以南非洲地区。在刚果民主共和国（DRC）的第10次埃博拉疫情期间，“Pamoja Tulinde Maisha”临床试验（PALM-RCT）提供了一个评估新治疗干预措施的独特机会。尽管有这些进展，但关于埃博拉病毒病患者死亡风险因素的动态演变，人们了解有限。本研究旨在利用该试验中入组患者的不平衡面板数据，通过逻辑回归对与死亡相关的风险因素进行建模。
我们对PALM-RCT中纳入的617例埃博拉病毒病患者的纵向数据进行了回顾性二次分析。在五个时间点收集数据：第0天（入院时）、第7天、第14天、第21天和第28天。在每个时间点应用二元逻辑回归模型来确定死亡的显著预测因素。使用Hosmer-Lemeshow检验评估模型校准和内部验证。在第0天（入院时），确定了六个死亡的显著预测因素：病毒载量（逆转录聚合酶链反应循环阈值）、肌酐、丙氨酸转氨酶（ALAT）、天冬氨酸转氨酶（ASAT）、出血、呼吸急促和结膜炎。到第7天，出现了五个预测因素：钠、ASAT、昏迷、腹痛和呼吸急促。在第14天，两个预测因素仍然显著：ASAT和精神状态变化。在第21天和第28天未发现显著的预测因素。这些风险因素的动态性质凸显了在埃博拉病毒病整个临床过程中持续监测的重要性。
我们的研究表明，埃博拉病毒病患者的死亡风险因素随时间演变，这表明对患者进行动态监测的方法至关重要。早期风险因素如病毒载量和肾功能应指导初始干预，而神经症状和电解质失衡在后期需要关注。这些发现支持了针对埃博拉病毒病管理的个性化方法，即根据实时临床数据调整临床护理以改善患者预后。
