Predicting Ebola Severity: A Clinical Prioritization Score for Ebola Virus Disease

A Framework for Assessing the Impact of Outbreak Response Immunization Programs

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

Metagenomic surveillance uncovers diverse and novel viral taxa in febrile patients from Nigeria

Effective infectious disease surveillance in high-risk regions is critical for clinical care and pandemic preemption; however, few clinical diagnostics are available for the wide range of potential human pathogens. Here, we conduct unbiased metagenomic sequencing of 593 samples from febrile Nigerian patients collected in three settings: i) population-level surveillance of individuals presenting with symptoms consistent with Lassa Fever (LF); ii) real-time investigations of outbreaks with suspected infectious etiologies; and iii) undiagnosed clinically challenging cases. We identify 13 distinct viruses, including the second and third documented cases of human blood-associated dicistrovirus, and a highly divergent, unclassified dicistrovirus that we name human blood-associated dicistrovirus 2. We show that pegivirus C is a common co-infection in individuals with LF and is associated with lower Lassa viral loads and favorable outcomes. We help uncover the causes of three outbreaks as yellow fever virus, monkeypox virus, and a noninfectious cause, the latter ultimately determined to be pesticide poisoning. We demonstrate that a local, Nigerian-driven metagenomics response to complex public health scenarios generates accurate, real-time differential diagnoses, yielding insights that inform policy.

A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.

Risk Factors for Ebola Virus Persistence in Semen of Survivors in Liberia

BACKGROUND

Long-term persistence of Ebola virus (EBOV) in immunologically privileged sites has been implicated in recent outbreaks of Ebola virus disease (EVD) in Guinea and the Democratic Republic of Congo. This study was designed to understand how the acute course of EVD, convalescence, and host immune and genetic factors may play a role in prolonged viral persistence in semen.

METHODS

A cohort of 131 male EVD survivors in Liberia were enrolled in a case-case study. "Early clearers" were defined as those with 2 consecutive negative EBOV semen test results by real-time reverse-transcription polymerase chain reaction (rRT-PCR) ≥2 weeks apart within 1 year after discharge from the Ebola treatment unit or acute EVD. "Late clearers" had detectable EBOV RNA by rRT-PCR >1 year after discharge from the Ebola treatment unit or acute EVD. Retrospective histories of their EVD clinical course were collected by questionnaire, followed by complete physical examinations and blood work.

RESULTS

Compared with early clearers, late clearers were older (median, 42.5 years; P < .001) and experienced fewer severe clinical symptoms (median 2, P = .006). Late clearers had more lens opacifications (odds ratio, 3.9 [95% confidence interval, 1.1-13.3]; P = .03), after accounting for age, higher total serum immunoglobulin G3 (IgG3) titers (P = .005), and increased expression of the HLA-C*03:04 allele (0.14 [.02-.70]; P = .007).

CONCLUSIONS

Older age, decreased illness severity, elevated total serum IgG3 and HLA-C*03:04 allele expression may be risk factors for the persistence of EBOV in the semen of EVD survivors. EBOV persistence in semen may also be associated with its persistence in other immunologically protected sites, such as the eye.

Development of the PREDS score to predict in-hospital mortality of patients with Ebola virus disease under advanced supportive care: Results from the EVISTA cohort in the Democratic Republic of the Congo

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 Republic of the Congo (DRC) and to develop a simple risk score for predicting in-hospital death, called PREDS.

METHODS

In this prospective cohort (NCT04815175), patients were recruited during the 10^th EVD outbreak in the DRC across three Ebola Treatment Centers (ETCs). Demographic, clinical, biological, virological and treatment data were collected. We evaluated factors known to affect the risk of in-hospital death and applied univariate and multivariate Cox proportional-hazards analyses to derive the risk score in a training dataset. We validated the score in an internal-validation dataset, applying C-statistics as a measure of discrimination.

FINDINGS

Between August 1^st 2018 and December 31^th 2019, 711 patients were enrolled in the study. Regarding supportive care, patients received vasopressive drug (n = 111), blood transfusion (n = 101), oxygen therapy (n = 250) and cardio-pulmonary ultrasound (n = 15). Overall, 323 (45%) patients died before day 28. Six independent prognostic factors were identified (ALT, creatinine, modified NEWS2 score, viral load, age and symptom duration). The final score range from 0 to 13 points, with a good concordance (C = 86.24%) and calibration with the Hosmer-Lemeshow test (p = 0.12).

INTERPRETATION

The implementation of advanced supportive care is possible for EVD patients in emergency settings. PREDS is a simple, accurate tool that could help in orienting early advanced care for at-risk patients after external validation.

FUNDING

This study was funded by ALIMA.

Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease

Background

Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus.

Methods

Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014–2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018–2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers.

Findings

Pediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74–0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64–0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77–1.00) and 0.87 (0.74–1.00), respectively.

Conclusion

The novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD.

Although case fatality rates remain high, there are limited data on predicting mortality in children with Ebola Virus Disease (EVD). Furthermore, challenges in predicting EVD outcomes using clinical and laboratory data highlight the need for the development and validation of pediatric predictive models. The novel EVD Prognosis in Children (EPiC) model uses clinical and biochemical information, such as AST and CK, to predict mortality in infected children. While few prognostic models or scoring systems have been developed to predict clinical outcomes of EVD, the majority of them were limited in geographical and temporal scope having been derived using data from one location. As such, the EPiC model is the first externally validated model for the prognosis of pediatric EVD using diverse datasets from geographically and temporally separate outbreaks. This model can be easily applied by bedside clinicians to assess pediatric patients at risk for death and help to allocate resources accordingly.

Modelling of a triage scoring tool for SARS-COV-2 PCR testing in health-care workers: data from the first German COVID-19 Testing Unit in Munich

Background

Numerous scoring tools have been developed for assessing the probability of SARS-COV-2 test positivity, though few being suitable or adapted for outpatient triage of health care workers.

Methods

We retrospectively analysed 3069 patient records of health care workers admitted to the COVID-19 Testing Unit of the Ludwig-Maximilians-Universität of Munich between January 27 and September 30, 2020, for real-time polymerase chain reaction analysis of naso- or oropharyngeal swabs. Variables for a multivariable logistic regression model were collected from self-completed case report forms and selected through stepwise backward selection. Internal validation was conducted by bootstrapping. We then created a weighted point-scoring system from logistic regression coefficients.

Results

4076 (97.12%) negative and 121 (2.88%) positive test results were analysed. The majority were young (mean age: 38.0), female (69.8%) and asymptomatic (67.8%). Characteristics that correlated with PCR-positivity included close-contact professions (physicians, nurses, physiotherapists), flu-like symptoms (e.g., fever, rhinorrhoea, headache), abdominal symptoms (nausea/emesis, abdominal pain, diarrhoea), less days since symptom onset, and contact to a SARS-COV-2 positive index-case. Variables selected for the final model included symptoms (fever, cough, abdominal pain, anosmia/ageusia) and exposures (to SARS-COV-positive individuals and, specifically, to positive patients). Internal validation by bootstrapping yielded a corrected Area Under the Receiver Operating Characteristics Curve of 76.43%. We present sensitivity and specificity at different prediction cut-off points. In a subgroup with further workup, asthma seems to have a protective effect with regard to testing result positivity and measured temperature was found to be less predictive than anamnestic fever.

Conclusions

We consider low threshold testing for health care workers a valuable strategy for infection control and are able to provide an easily applicable triage score for the assessment of the probability of infection in health care workers in case of resource scarcity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07627-5.

Enterotoxigenic Escherichia coli Heat-Stable Toxin and Ebola Virus Delta Peptide: Similarities and Differences

Enterotoxigenic Escherichia coli (ETEC) STb toxin exhibits striking structural similarity to Ebola virus (EBOV) delta peptide. Both ETEC and EBOV delta peptide are enterotoxins. Comparison of the structural and functional similarities and differences of these two toxins illuminates features that are important in induction of pathogenesis by a bacterial and viral pathogen.

The impact of malaria coinfection on Ebola virus disease outcomes: A systematic review and meta-analysis

Introduction

Viral outbreaks present a particular challenge in countries in Africa where there is already a high incidence of other infectious diseases, including malaria which can alter immune responses to secondary infection. Ebola virus disease (EVD) is one such problem; understanding how Plasmodium spp. and Ebolavirus (EBOV) interact is important for future outbreaks.

Methods

We conducted a systematic review in PubMed and Web of Science to find peer-reviewed papers with primary data literature to determine 1) prevalence of EBOV/Plasmodium spp. coinfection, 2) effect of EBOV/Plasmodium spp. coinfection on EVD pathology and the immune response, 3) impact of EBOV/Plasmodium spp. coinfection on the outcome of EVD-related mortality. Random effects meta-analyses were conducted with the R package meta to produce overall proportion and effect estimates as well as measure between-study heterogeneity.

Results

From 322 peer-reviewed papers, 17 were included in the qualitative review and nine were included in a meta-analysis. Prevalence of coinfection was between 19% and 72%. One study reported significantly lower coagulatory response biomarkers in coinfected cases but no difference in inflammatory markers. Case fatality rates were similar between EBOV(+)/Pl(+) and EBOV(+)/Pl(-) cases (62.8%, 95% CI 49.3–74.6 and 56.7%, 95% CI 53.2–60.1, respectively), and there was no significant difference in risk of mortality (RR 1.09, 95% CI 0.90–1.31) although heterogeneity between studies was high. One in vivo mouse model laboratory study found no difference in mortality by infection status, but another found prior acute Plasmodium yoeli infection was protective against morbidity and mortality via the IFN-γ signalling pathway.

Conclusion

The literature was inconclusive; studies varied widely and there was little attempt to adjust for confounding variables. Laboratory studies may present the best option to answer how pathogens interact within the body but improvement in data collection and analysis and in diagnostic methods would aid patient studies in the future.

Predictors of clinical deterioration in non-severe patients with COVID-19: a retrospective cohort study

OBJECTIVE

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains pandemic with considerable morbidity and mortality around the world. The aim of this study was to identify the predictors for clinical deterioration in patients with COVID-19 who did not show clinical deterioration upon hospital admission.

METHODS

Two hundred fifty-seven patients with confirmed COVID-19 pneumonia admitted to Guangzhou Eighth People's Hospital between 23 January and 21 March 2020 were retrospectively enrolled. Demographic data, symptoms, laboratory values, comorbidities and treatments were all collected. The study endpoint was clinical deterioration within 20 days from hospital admission. Univariate and multivariable logistic regression methods were used to explore the risk factors associated with clinical deterioration.

RESULTS

A total of 49 (19%) patients showed clinical deterioration after admission. Compared with patients that did not experience clinical deterioration, clinically deteriorated patients had more dyspnea, cough and myalgia (65.3% versus 29.3%) symptoms and more had comorbidities (89.8% versus 36.1%). Clinical and laboratory characteristics at admission that were associated with clinical deterioration included senior age, diabetes, hypertension, myalgia, higher temperature, systolic blood pressure, C-reactive protein (CRP), procalcitonin, activated partial thromboplastin time, aspartate aminotransferase, alanine transaminase, direct bilirubin, plasma creatinine, lymphocytopenia, thrombocytopenia, decreased albumin and bicarbonate concentration. Medical history of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, calcium channel blockers and metformin were also risk factors.

CONCLUSION

The four best predictors for clinical deterioration were CRP, procalcitonin, age and albumin. A "best" multivariable prediction model, resulting from using a variable selection procedure, included senior age, presentation with myalgia, and higher level of CRP and serum creatinine (bias-corrected c-statistic = 0.909). Sensitivity and specificity corresponding to a cut point of CRP ≥18.45 mg/L for predicting clinical deterioration were 85% and 74%, respectively.

Characterization of Ebola Virus Risk to Bedside Providers in an Intensive Care Environment

Background: The 2014–2016 Ebola outbreak in West Africa recapitulated that nosocomial spread of Ebola virus could occur and that health care workers were at particular risk including notable cases in Europe and North America. These instances highlighted the need for centers to better prepare for potential Ebola virus cases; including understanding how the virus spreads and which interventions pose the greatest risk. Methods: We created a fully equipped intensive care unit (ICU), within a Biosafety Level 4 (BSL4) laboratory, and infected multiple sedated non-human primates (NHPs) with Ebola virus. While providing bedside care, we sampled blood, urine, and gastric residuals; as well as buccal, ocular, nasal, rectal, and skin swabs, to assess the risks associated with routine care. We also assessed the physical environment at end-point. Results: Although viral RNA was detectable in blood as early as three days post-infection, it was not detectable in the urine, gastric fluid, or swabs until late-stage disease. While droplet spread and fomite contamination were present on a few of the surfaces that were routinely touched while providing care in the ICU for the infected animal, these may have been abrogated through good routine hygiene practices. Conclusions: Overall this study has helped further our understanding of which procedures may pose the highest risk to healthcare providers and provides temporal evidence of this over the clinical course of disease.

Pretest Symptom Duration and Cycle Threshold Values for Severe Acute Respiratory Syndrome Coronavirus 2 Reverse-Transcription Polymerase Chain Reaction Predict Coronavirus Disease 2019 Mortality

Background

The relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load and patient symptom duration in both in- and outpatients, and the impact of these factors on patient outcomes, are currently unknown. Understanding these associations is important to clinicians caring for patients with coronavirus disease 2019 (COVID-19).

Methods

We conducted an observational study between March 10 and May 30, 2020 at a large quaternary academic medical center in New York City. Patient characteristics, laboratory values, and clinical outcomes were abstracted from the electronic medical records. Of all patients tested for SARS-CoV-2 during this time (N = 16 384), there were 5467 patients with positive tests, 4254 of which had available cycle threshold (Ct) values and were included in further analysis. Univariable and multivariable logistic regression models were used to test associations between Ct values, duration of symptoms before testing, patient characteristics, and mortality. The primary outcome is defined as death or discharge to hospice.

Results

Lower Ct values at diagnosis (ie, higher viral load) were associated with significantly higher mortality among both in- and outpatients. It is interesting to note that patients with a shorter time since the onset of symptoms to testing had a worse prognosis, with those presenting less than 3 days from symptom onset having 2-fold increased odds of death. After adjusting for time since symptom onset and other clinical covariates, Ct values remained a strong predictor of mortality.

Conclusions

Severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction Ct value and duration of symptoms are strongly associated with mortality. These 2 factors add useful information for clinicians to risk stratify patients presenting with COVID-19.

Severity score for predicting in-facility Ebola treatment outcome

PURPOSE

Sierra Leone recorded the highest incidence rate for the 2013-2016 West African Ebola outbreak. In this investigation, we used the medical records of Ebola patients with different sociodemographic and clinical features to determine the factors that are associated with Ebola treatment outcome during the 2013-2016 West African Ebola outbreak in Sierra Leone and constructed a predictive in-facility mortality score.

METHODS

We used the anonymized medical records of 1077 laboratory-confirmed pediatric and adult patients with EVD who received treatment at the 34 Military Hospital and the Police Training School Ebola Treatment Centers in Sierra Leone between the period of June 2014 and April 2015. We later determined the in-facility case fatality rates for Ebola, the odds of dying during Ebola treatment, and later constructed a predictive in-facility mortality score for these patients based on their clinical and sociodemographic characteristics.

RESULTS

We constructed a model that partitioned the study population into three mortality risk groups of equal patient numbers, based on risk scoring: low (score ≤ -5), medium (score -4 to 1), and high-risk group (score ≥ 2). The CFR of patients with EVD belonging to the low- (≤-5), medium (-4 to 1), and high- (≥2) risk groups were 0.56%, 9.75%, and 67.41%, respectively.

CONCLUSIONS

We succeeded in designing an in-facility mortality risk score that reflects EVD clinical severity and can assist in the clinical prioritization of patients with EVD.

Exposure to Ebola Virus and Risk for Infection with Malaria Parasites, Rural Gabon

An association between malaria and risk for death among patients with Ebola virus disease has suggested within-host interactions between Plasmodium falciparum parasites and Ebola virus. To determine whether such an interaction might also influence the probability of acquiring either infection, we used a large snapshot surveillance study from rural Gabon to test if past exposure to Ebola virus is associated with current infection with Plasmodium spp. during nonepidemic conditions. We found a strong positive association, on population and individual levels, between seropositivity for antibodies against Ebola virus and the presence of Plasmodium parasites in the blood. According to a multiple regression model accounting for other key variables, antibodies against Ebola virus emerged as the strongest individual-level risk factor for acquiring malaria. Our results suggest that within-host interactions between malaria parasites and Ebola virus may underlie epidemiologic associations.

Ebola Hemorrhagic Shock Syndrome-on-a-Chip

Ebola virus, for which we lack effective countermeasures, causes hemorrhagic fever in humans, with significant case fatality rates. Lack of experimental human models for Ebola hemorrhagic fever is a major obstacle that hinders the development of treatment strategies. Here, we model the Ebola hemorrhagic syndrome in a microvessel-on-a-chip system and demonstrate its applicability to drug studies. Luminal infusion of Ebola virus-like particles leads to albumin leakage from the engineered vessels. The process is mediated by the Rho/ROCK pathway and is associated with cytoskeleton remodeling. Infusion of Ebola glycoprotein (GP_1,2) generates a similar phenotype, indicating the key role of GP_1,2 in this process. Finally, we measured the potency of a recently developed experimental drug FX06 and a novel drug candidate, melatonin, in phenotypic rescue. Our study confirms the effects of FX06 and identifies melatonin as an effective, safe, inexpensive therapeutic option that is worth investigating in animal models and human trials.

A systematic review and meta-analysis of patient data from the West Africa (2013-16) Ebola virus disease epidemic

BACKGROUND

Over 28 000 individuals were infected with Ebola virus during the West Africa (2013-2016) epidemic, yet there has been criticism of the lack of robust clinical descriptions of Ebola virus disease (EVD) illness from that outbreak.

OBJECTIVES

To perform a meta-analysis of published data from the epidemic to describe the clinical presentation, evolution of disease, and predictors of mortality in individuals with EVD. To assess the quality and utility of published data for clinical and public health decision-making.

DATA SOURCES

Primary articles available in PubMed and published between January 2014 and May 2017.

ELIGIBILITY

Studies that sequentially enrolled individuals hospitalized for EVD and that reported acute clinical outcomes.

METHODS

We performed meta-analyses using random-effect models and assessed heterogeneity using the I2 method. We assessed data representativeness by comparing meta-analysis estimates with WHO aggregate data. We examined data utility by examining the availability and compatibility of data sets.

RESULTS

In all, 3653 articles were screened and 34 articles were included, representing 16 independent cohorts of patients (18 overlapping cohorts) and at least 6168 individuals. The pooled estimate for case fatality rate was 51% (95% CI 46%-56%). However, pooling of estimates for clinical presentation, progression, and predictors of mortality in individuals with EVD were hampered by significant heterogeneity, and inadequate data on clinical progression. Our assessment of data quality found that heterogeneity was largely unexplained, and data availability and compatibility were poor.

CONCLUSIONS

We have quantified a missed opportunity to generate reliable estimates of the clinical manifestations of EVD during the West Africa epidemic. Clinical data standards and data capture platforms are urgently needed.

Machine-learning Prognostic Models from the 2014-16 Ebola Outbreak: Data-harmonization Challenges, Validation Strategies, and mHealth Applications

BACKGROUND

Ebola virus disease (EVD) plagues low-resource and difficult-to-access settings. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. However, data incompleteness and lack of interoperability limit model generalizability. This study harmonizes diverse datasets from the 2014-16 EVD epidemic and generates several prognostic models incorporated into the novel Ebola Care Guidelines app that provides informed access to recommended evidence-based guidelines.

METHODS

Multivariate logistic regression was applied to investigate survival outcomes in 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone at various timepoints during 2014-16. We generated a parsimonious model (viral load, age, temperature, bleeding, jaundice, dyspnea, dysphagia, and time-to-presentation) and several fallback models for when these variables are unavailable. All were externally validated against two independent datasets and compared to further models including expert observational wellness assessments. Models were incorporated into an app highlighting the signs/symptoms with the largest contribution to prognosis.

FINDINGS

The parsimonious model approached the predictive power of observational assessments by experienced clinicians (Area-Under-the-Curve, AUC = 0.70-0.79, accuracy = 0.64-0.74) and maintained its performance across subcohorts with different healthcare seeking behaviors. Age and viral load contributed > 5-fold the weighting of other features and including them in a minimal model had a similar AUC, albeit at the cost of specificity.

INTERPRETATION

Clinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable. Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines.

FUNDING

Howard Hughes Medical Institute, US National Institutes of Health, Bill & Melinda Gates Foundation, International Medical Corps, UK Department for International Development, and GOAL Global.

Ebola Virus Disease in the Obstetric Population

The clinical management of Ebola created a significant challenge during the outbreak in West Africa, due to the paucity of previous research conducted into the optimum treatment regimen. That left many centres, to some extent, having to ‘work out’ best practice as they went along, and attempting to conduct real time prospective research. Médecins Sans Frontières (MSF) [1] were the only organization to have provided relatively in depth practical guidance prior to the outbreak and this manual was the basis of further planning between the WHO, national Ministry of Health and Sanitation in Sierra Leone, and other relevant stakeholders. Additionally, guidance changed over the epidemic as experience grew. This chapter will describe four key areas in the management of Ebola in West Africa. Firstly, it outlines the most recent WHO guidance; secondly, it looks back at how Ebola was managed in differing low and high resource settings; thirdly it outlines possible and optimal options for managing complications, paying particular attention to some of the controversies faced; fourthly it describes recent and ongoing studies into potential novel therapies that may shape future practice.

Ebola virus disease

Ebolaviruses are pathogenic agents associated with a severe, potentially fatal, systemic disease in man and great apes. Four species of ebolaviruses have been identified in west or equatorial Africa. Once the more virulent forms enter the human population, transmission occurs primarily through contact with infected body fluids and can result in major epidemics in under-resourced settings. These viruses cause a disease characterised by systemic viral replication, immune suppression, abnormal inflammatory responses, major fluid and electrolyte losses, and high mortality. Despite recent progress on vaccines, and with no licensed prophylaxis or treatment available, case management is essentially supportive with management of severe multiple organ failure resulting from immune-mediated cell damage. The 2013-16 outbreak was classified by WHO as a Public Health Emergency of International Concern, which drew attention to the challenges of diseases caused by infections with ebolaviruses and questioned scientific, clinical, and societal preparation to handle future epidemics.

Surviving Ebola: A historical cohort study of Ebola mortality and survival in Sierra Leone 2014-2015

Background

While a number of predictors for Ebola mortality have been identified, less is known about post-viral symptoms. The identification of acute-illness predictors for post-viral symptoms could allow the selection of patients for more active follow up in the future, and those in whom early interventions may be beneficial in the long term. Studying predictors of both mortality and post-viral symptoms within a single cohort of patients could also further our understanding of the pathophysiology of survivor sequelae.

Methods/Principal findings

We performed a historical cohort study using data collected as part of routine clinical care from an Ebola Treatment Centre (ETC) in Kerry Town, Sierra Leone, in order to identify predictors of mortality and of post-viral symptoms. Variables included as potential predictors were sex, age, date of admission, first recorded viral load at the ETC and symptoms (recorded upon presentation at the ETC). Multivariable logistic regression was used to identify predictors. Of 263 Ebola-confirmed patients admitted between November 2014 and March 2015, 151 (57%) survived to ETC discharge. Viral load was the strongest predictor of mortality (adjusted OR comparing high with low viral load: 84.97, 95% CI 30.87–345.94). We did not find evidence that a high viral load predicted post-viral symptoms (ocular: 1.17, 95% CI 0.35–3.97; musculoskeletal: 1.07, 95% CI 0.28–4.08). Ocular post-viral symptoms were more common in females (2.31, 95% CI 0.98–5.43) and in those who had experienced hiccups during the acute phase (4.73, 95% CI 0.90–24.73).

Conclusions/Significance

These findings may add epidemiological support to the hypothesis that post-viral symptoms have an immune-mediated aspect and may not only be a consequence of high viral load and disease severity.

Enhanced case management can be delivered for patients with EVD in Africa: Experience from a UK military Ebola treatment centre in Sierra Leone

BACKGROUND

Limited data exist describing supportive care management, laboratory abnormalities and outcomes in patients with Ebola virus disease (EVD) in West Africa. We report data which constitute the first description of the provision of enhanced EVD case management protocols in a West African setting.

METHODS

Demographic, clinical and laboratory data were collected by retrospective review of clinical and laboratory records of patients with confirmed EVD admitted between 5 November 2014 and 30 June 2015.

RESULTS

A total of 44 EVD patients were admitted (median age 37 years (range 17-63), 32/44 healthcare workers), and excluding those evacuated, the case fatality rate was 49% (95% CI 33%-65%). No pregnant women were admitted. At admission 9/44 had stage 1 disease (fever and constitutional symptoms only), 12/44 had stage 2 disease (presence of diarrhoea and/or vomiting) and 23/44 had stage 3 disease (presence of diarrhoea and/or vomiting with organ failure), with case fatality rates of 11% (95% CI 1%-58%), 27% (95% CI 6%-61%), and 70% (95% CI 47%-87%) respectively (p = 0.009). Haemorrhage occurred in 17/41 (41%) patients. The majority (21/40) of patients had hypokalaemia with hyperkalaemia occurring in 12/40 patients. Acute kidney injury (AKI) occurred in 20/40 patients, with 14/20 (70%, 95% CI 46%-88%) dying, compared to 5/20 (25%, 95% CI 9%-49%) dying who did not have AKI (p = 0.01). Ebola virus (EBOV) PCR cycle threshold value at baseline was mean 20.3 (SD 4.3) in fatal cases and 24.8 (SD 5.5) in survivors (p = 0.007). Mean national early warning score (NEWS) at admission was 5.5 (SD 4.4) in fatal cases and 3.0 (SD 1.9) in survivors (p = 0.02). Central venous catheters were placed in 37/41 patients and intravenous fluid administered to 40/41 patients (median duration of 5 days). Faecal management systems were inserted in 21/41 patients, urinary catheters placed in 27/41 and blood component therapy administered to 20/41 patients.

CONCLUSIONS

EVD is commonly associated life-threatening electrolyte imbalance and organ dysfunction. We believe that the enhanced levels of protocolized care, scale and range of medical interventions we report, offer a blueprint for the future management of EVD in resource-limited settings.

Management of Ebola Virus Disease in Children

The West African outbreak of 2013 to 2016 was the largest Ebola epidemic in history. With tens of thousands of patients treated during this outbreak, much was learned about how to optimize clinical care for children with Ebola. In anticipation of inevitable future outbreaks, a firsthand summary of the major aspects of pediatric Ebola case management in austere settings is presented. Emphasis is on early and aggressive critical care, including fluid resuscitation, electrolyte repletion, antimicrobial therapy, and nutritional supplementation.

Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring

Ebola virus (EBOV) causes zoonotic viral infection with a potential risk of global spread and a highly fatal effect on humans. Till date, no drug has gotten market approval for the treatment of Ebola virus disease (EVD), and this perhaps allows the use of both experimental and computational approaches in the antiviral drug discovery process. The main target of potential vaccines that are recently undergoing clinical trials is trimeric glycoprotein (GP) of the EBOV and its exact crystal structure was used in this structure based virtual screening study, with the aid of consensus scoring to select three possible hit compounds from about 36 million compounds in MCULE's database. Amongst these three compounds, (5R)-5-[[5-(4-chlorophenyl)-1,2,4-oxadiazol-3-yl]methyl]-N-[(4-methoxyphenyl)methyl]-4,5-dihydroisoxazole-3-carboxamide (SC-2, C₂₁H₁₉ClN₄O₄) showed good features with respect to drug likeness, ligand efficiency metrics, solubility, absorption and distribution properties and non-carcinogenicity to emerge as the most promising compound that can be optimized to lead compound against the GP EBOV. The binding mode showed that SC-2 is well embedded within the trimeric chains of the GP EBOV with molecular interactions with some amino acids. The SC-2 hit compound, upon its optimization to lead, might be a good potential candidate with efficacy against the EBOV pathogen and subsequently receive necessary approval to be used as antiviral drug for the treatment of EVD.

Recent advances in vaccine development against Ebola threat as bioweapon

With the increasing rate of Ebola virus appearance, with multiple natural outbreaks of Ebola hemorrhagic fever, it is worthy of consideration as bioweapon by anti-national groups. Further, with the non-availability of the vaccines against Ebola virus, concerns about the public health emerge. In this regard, this review summarizes the structure, genetics and potential of Ebola virus to be used as a bioweapon. We highlight the recent advances in the treatment strategies and vaccine development against Ebola virus. The understanding of these aspects might lead to effective treatment practices which can be applied during the future outbreaks of Ebola.