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Background:
As mortality remains high for patients with Ebola virus disease (EVD) despite new treatment options, the ability to level up the provided supportive care and to predict the risk of death is of major importance. This analysis of the EVISTA cohort aims to describe advanced supportive care provided to EVD patients in the Democratic Republ...
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Context 1
... The training sample to develop the prediction model comprised 279 patients randomly selected (Figure 2). Compared to the two LASSO models, prediction performance was similar and calibration at 28 days was better for the full model (i.e. ...
Context 2
... correlation between points-based risk scores and the Cox model-based risk scores in the validation sample (N = 140) was 93.8%. A plot of the observed survival rates at different time points stratified by predicted risk quartiles ( Figure S2) shows that the model-predicted risk scores discriminate well between patient groups with different survival profiles. The sensitivity analyses indicated that the model had a similar predictive value across the 200 multiple imputed held-out datasets. ...
Context 3
... The training sample to develop the prediction model comprised 279 patients randomly selected (Figure 2). Compared to the two LASSO models, prediction performance was similar and calibration at 28 days was better for the full model (i.e. ...
Context 4
... correlation between points-based risk scores and the Cox model-based risk scores in the validation sample (N = 140) was 93.8%. A plot of the observed survival rates at different time points stratified by predicted risk quartiles ( Figure S2) shows that the model-predicted risk scores discriminate well between patient groups with different survival profiles. The sensitivity analyses indicated that the model had a similar predictive value across the 200 multiple imputed held-out datasets. ...
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Our study aimed to identify risk factors associated with mortality in Ebola patients using binary logistic regression analysis and linear discriminant analysis, and to assess the predictive power of these two methods. Our study was a randomized, double-blind, controlled (observational) clinical trial conducted in 2018 during the 10th Ebola outbreak...
Citations
... Similarly, anorexia, which occurred in 43.4% of our patients, is a common response to systemic infection, potentially exacerbated by gastrointestinal involvement. Headaches and digestive disorders highlight the impact of the virus on the gastrointestinal system, including nausea [16]. The analysis revealed no statistically significant differences in the prevalence of symptoms, except nausea, between patients with Ct-np values <22 and patients with Ct-values ≥22. ...
Background: Hematologic disorders occur frequently in patients with Ebola virus disease (EVD) and are characterized by one or several abnormalities in blood cells, including hemostasis, which is poorly documented. This study described the hematologic abnormalities of Ebola patients and the impact on the outcomes of patients who were admitted with EVD.
... rapid stabilisation of vital organ functions, while considering characteristics of EVD. 3,4,10,19,20,50 In this series, at least four of the 13 EVD patients needed O 2 therapy early during ETC admission and may have benefited from noninvasive respiratory support. Over 70% of Ebola patients treated in Europe or the United States required supplemental O 2 , noninvasive or invasive respiratory support during their illness. ...
... 3,4 Raised creatinine levels are associated with an elevated mortality risk. 4,6,50 Signs of rhabdomyolysis (elevated creatinine kinase), frequently seen in EVD patients, may contribute to renal dysfunction. 3,4 This series emphasises the importance of essential AKI management in contexts without ability to initiate renal replacement therapy ( Figure 2). ...
Background: Experience from the Zaire Ebolavirus epidemic in the eastern Democratic
Republic of the Congo (2018–2020) demonstrates that early initiation of essential critical care
and administration of Zaire Ebolavirus specific monoclonal antibodies may be associated with
improved outcomes among patients with Ebola virus disease (EVD).
Objectives: This series describes 13 EVD patients and 276 patients with suspected EVD treated
during a Zaire Ebolavirus outbreak in Guinea in 2021.
Method: Patients with confirmed or suspected EVD were treated in two Ebola treatment
centres (ETC) in the region of N’zérékoré. Data were reviewed from all patients with suspected
or confirmed EVD hospitalised in these two ETCs during the outbreak (14 February 2021 – 19
June 2021). Ebola-specific monoclonal antibodies, were available 2 weeks after onset of the
outbreak.
Results: Nine of the 13 EVD patients (age range: 22–70 years) survived. The four EVD patients
who died, including one pregnant woman, presented with multi-organ dysfunction and died
within 48 h of admission. All eight patients who received Ebola-specific monoclonal antibodies
survived. Four of the 13 EVD patients were health workers. Improvement of ETC design
facilitated implementation of WHO-recommended ‘optimized supportive care for EVD’. In
this context, pragmatic clinical training was integrated in routine ETC activities. Initial clinical
manifestations of 13 confirmed EVD patients were similar to those of 276 patients with
suspected, but subsequently non confirmed EVD. These patients suffered from other acute
infections (e.g. malaria in 183 of 276 patients; 66%). Five of the 276 patients with suspected
EVD died. One of these five patients had Lassa virus disease and a coronavirus disease 2019
(COVID-19) co-infection.
Conclusion: Multidisciplinary outbreak response teams can rapidly optimise ETC design.
Trained clinical teams can provide WHO-recommended optimised supportive care, including
safe administration of Ebola-specific monoclonal antibodies. Pragmatic training in essential
critical care can be integrated in routine ETC activities.
Contribution: This article describes clinical realities associated with implementation of WHOrecommended
standards of ‘optimized supportive care’ and administration of Ebola virus
specific treatments. In this context, the importance of essential design principles of ETCs is
underlined, which allow continuous visual contact and verbal interaction of health workers
and families with their patients. Elements that may contribute to further quality of care
improvements for patients with confirmed or suspected EVD are discussed.
In order to prevent the re-emergence of an epidemic, predicting its trend while gaining insight into the intrinsic factors affecting it is a key issue in urban governance. Traditional SIR-like compartment models provide insight into the explanatory parameters of an outbreak, and the vast majority of existing deep learning models can predict the course of an outbreak well, but neither performs well in the other’s domain. Simultaneously, studying the commonalities and diversities in the causes of outbreaks among different countrywide regions is also a way to interrupt outbreaks. To address the issues of outbreak intrinsic relationships and prediction, we propose the Neural Compartmental Ordinary Differential Equations (NeuralCODE) model to study the relationship between population movements and outbreak development in different regions. Furthermore, to incorporate the commonalities and diversities in causes among different regions into the prediction and intrinsic inquiry problem, we propose an AutoML framework. Our results found that simply using the NeuralCODE algorithm could obtain better prediction and insight capabilities within different regions. With the introduction of AutoML, it became possible to explore the factors inherent in the epidemic’s development across regions and further improve the original algorithm’s predictive performance.
Ebola virus disease kills more than half of people infected. Since the disease is transmitted via close human contact, identifying individuals at the highest risk of developing the disease is possible on the basis of the type of contact (correlated with viral exposure). Different candidates for post-exposure prophylaxis (PEP; ie, vaccines, antivirals, and monoclonal antibodies) each have their specific benefits and limitations, which we discuss in this Viewpoint. Approved monoclonal antibodies have been found to reduce mortality in people with Ebola virus disease. As monoclonal antibodies act swiftly by directly targeting the virus, they are promising candidates for targeted PEP in contacts at high risk of developing disease. This intervention could save lives, halt viral transmission, and, ultimately, help curtail outbreak propagation. We explore how a strategic integration of monoclonal antibodies and vaccines as PEP could provide both immediate and long-term protection against Ebola virus disease, highlighting ongoing clinical research that aims to refine this approach, and discuss the transformative potential of a successful PEP strategy to help control viral haemorrhagic fever outbreaks.
Recent Ebola outbreaks underscore the importance of continuous prevention and disease control efforts. Authorized vaccines include Merck’s Ervebo (rVSV-ZEBOV) and Johnson & Johnson’s two-dose combination (Ad26.ZEBOV/MVA-BN-Filo). Here, in a five-year follow-up of the PREVAC randomized trial (NCT02876328), we report the results of the immunology ancillary study of the trial. The primary endpoint is to evaluate long-term memory T-cell responses induced by three vaccine regimens: Ad26–MVA, rVSV, and rVSV–booster. Polyfunctional EBOV-specific CD4⁺ T-cell responses increase after Ad26 priming and are further boosted by MVA, whereas minimal responses are observed in the rVSV groups, declining after one year. In-vitro expansion for eight days show sustained EBOV-specific T-cell responses for up to 60 months post-prime vaccination with both Ad26-MVA and rVSV, with no decline. Cytokine production analysis identify shared biomarkers between the Ad26-MVA and rVSV groups. In secondary endpoint, we observed an elevation of pro-inflammatory cytokines at Day 7 in the rVSV group. Finally, we establish a correlation between EBOV-specific T-cell responses and anti-EBOV IgG responses. Our findings can guide booster vaccination recommendations and help identify populations likely to benefit from revaccination.
Background
Although multiple prognostic models for Ebola Virus Disease (EVD) mortality exist, few incorporate biomarkers and none has used longitudinal point-of-care (POC) serum testing throughout Ebola Treatment Center (ETC) care.
Methods
This retrospective study evaluated adult EVD patients during the tenth outbreak in the Democratic Republic of Congo. Ebola virus RT-PCR cycle threshold (Ct) and POC serum biomarker values were collected throughout ETC treatment. Four iterative prognostic mortality machine learning models were created. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days one and two (D1,2), three and four (D3,4) and five and six (D5,6) associated with mortality, were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided time-period specific Area Under Curve (AUC) with 95% confidence intervals (CIs).
Results
Of 310 EVD-positive cases, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium, and low albumin during D1,2, elevated c-reactive protein (CRP), BUN, and potassium during D3,4, and elevated CRP and BUN during D5,6. The AUC substantially improved with each iteration: base model 0.74 (95% CI 0.69–0.80), D1,2 0.84 (95% CI 0.73–0.94), D3,4 0.94 (95% CI 0.88–1.0), and D5,6 0.96 (95% CI 0.90–1.0).
Conclusions
This is the first study to utilize iterative POC biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to six days into patient care.
