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

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

EClinicalMedicine. 2019 Jun 22;11:54-64. doi: 10.1016/j.eclinm.2019.06.003. eCollection 2019 May-Jun.

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

Colubri A1,2,3, Hartley MA4,5, Siakor M6, Wolfman V6, Felix A2, Sesay T7, Shaffer JG8, Garry RF9, Grant DS10, Levine AC6,11, Sabeti PC1,2,3,12.

Author information: 1 Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, USA. 2 Broad Institute of MIT and Harvard, Cambridge, USA. 3 Howard Hughes Medical Institute, Chevy Chase, USA. 4 University of Lausanne, Faculty of Biology and Medicine, Lausanne, Switzerland. 5 GOAL Global, Dublin, Ireland. 6 International Medical Corps, Los Angeles, USA. 7 Ministry of Health and Sanitation, Freetown, Sierra Leone. 8 Tulane University, School of Public Health and Tropical Medicine, New Orleans, USA. 9 Tulane University, Department of Microbiology and Immunology, New Orleans, USA. 10 Viral Hemorrhagic Fever Program, Kenema Government Hospital, Kenema, Sierra Leone. 11 Brown University, Warren Alpert School of Medicine, Providence, USA. 12 Harvard School of Public Health, Boston, USA.

 

Abstract

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.

KEYWORDS: Clinical intuition; Data visualization; Ebola virus disease; Machine learning; Prognostic models; Severity score; Supportive care guidelines; mHealth

PMID: 31312805 PMCID: PMC6610774 DOI: 10.1016/j.eclinm.2019.06.003

Keywords: Ebola; Machine-Learning.

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#Haemostatic Changes in Five #Patients Infected with #Ebola Virus (Viruses, abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Viruses. 2019 Jul 15;11(7). pii: E647. doi: 10.3390/v11070647.

Haemostatic Changes in Five Patients Infected with Ebola Virus.

Smither SJ1, O’Brien LM2, Eastaugh L2, Woolley T3, Lever S2, Fletcher T4, Parmar K5, Hunt BJ5, Watts S2, Kirkman E2.

Author information: 1 Chemical Biological & Radiological Division, Dstl, Porton Down SP4 0JQ, UK. sjsmither@dstl.gov.uk. 2 Chemical Biological & Radiological Division, Dstl, Porton Down SP4 0JQ, UK. 3 Royal Centre for Defence Medicine, Birmingham Research Park, Birmingham B15 2SQ, UK. 4 Liverpool School of Tropical Medicine, Liverpool L3 5QA, UK. 5 St Thomas’ Hospital Thrombosis & Haemophilia Centre & Thrombosis and Vascular Biology Group, London SE1 7EH, UK.

 

Abstract

Knowledge on haemostatic changes in humans infected with Ebola virus is limited due to safety concerns and access to patient samples. Ethical approval was obtained to collect plasma samples from patients in Sierra Leone infected with Ebola virus over time and samples were analysed for clotting time, fibrinogen, and D-dimer levels. Plasma from healthy volunteers was also collected by two methods to determine effect of centrifugation on test results as blood collected in Sierra Leone was not centrifuged. Collecting plasma without centrifugation only affected D-dimer values. Patients with Ebola virus disease had higher PT and APTT and D-dimer values than healthy humans with plasma collected in the same manner. Fibrinogen levels in patients with Ebola virus disease were normal or lower than values measured in healthy people. Clotting times and D-dimer levels were elevated during infection with Ebola virus but return to normal over time in patients that survived and therefore could be considered prognostic. Informative data can be obtained from plasma collected without centrifugation which could improve patient monitoring in hazardous environments.

KEYWORDS: APTT; D-dimers; Ebola virus; PT; clotting; fibrinogen; haemostasis

PMID: 31311112 DOI: 10.3390/v11070647

Keywords: Ebola.

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Direct #evidence of #H7N7 #avian #influenza virus #mutation from low to high virulence on a single #poultry premises during an #outbreak in free range chickens in the #UK, 2008 (Infect Genet Evol., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Infect Genet Evol. 2018 Oct;64:13-31. doi: 10.1016/j.meegid.2018.06.005. Epub 2018 Jun 5.

Direct evidence of H7N7 avian influenza virus mutation from low to high virulence on a single poultry premises during an outbreak in free range chickens in the UK, 2008.

Seekings AH1, Slomka MJ2, Russell C2, Howard WA2, Choudhury B2, Nuñéz A3, Löndt BZ2, Cox W2, Ceeraz V2, Thorén P4, Irvine RM2, Manvell RJ2, Banks J2, Brown IH2.

Author information: 1 Virology Department, Animal and Plant Health Agency (APHA-Weybridge), Addlestone, Surrey KT15 3NB, United Kingdom. Electronic address: amanda.seekings@apha.gsi.gov.uk. 2 Virology Department, Animal and Plant Health Agency (APHA-Weybridge), Addlestone, Surrey KT15 3NB, United Kingdom. 3 Pathology Department, Animal and Plant Health Agency (APHA-Weybridge), Addlestone, Surrey KT15 3NB, United Kingdom. 4 Swedish Agricultural University (SLU), Uppsala, Sweden.

 

Abstract

H5 and H7 subtypes of low pathogenicity avian influenza viruses (LPAIVs) have the potential to evolve into highly pathogenic avian influenza viruses (HPAIVs), causing high mortality in galliforme poultry with substantial economic losses for the poultry industry. This study provides direct evidence of H7N7 LPAIV mutation to HPAIV on a single poultry premises during an outbreak that occurred in June 2008 in free range laying hens in Oxfordshire, UK. We report the first detection of a rare di-basic cleavage site (CS) motif (PEIPKKRGLF), unique to galliformes, that has previously been associated with a LPAIV phenotype. Three distinct HPAIV CS sequences (PEIPKRKKRGLF, PEIPKKKKRGLF and PEIPKKKKKKRGLF) were identified in the infected sheds suggesting molecular evolution at the outbreak premises. Further evidence for H7N7 LPAIV preceding mutation to HPAIV was derived by examining clinical signs, epidemiological descriptions and analysing laboratory results on the timing and proportions of seroconversion and virus shedding at each infected shed on the premises. In addition to describing how the outbreak was diagnosed and managed via statutory laboratory testing, phylogenetic analysis revealed reassortant events during 2006-2008 that suggested likely incursion of a wild bird origin LPAIV precursor to the H7N7 HPAIV outbreak. Identifying a precursor LPAIV is important for understanding the molecular changes and mechanisms involved in the emergence of HPAIV. This information can lead to understanding how and why only some H7 LPAIVs appear to readily mutate to HPAIV.

Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

KEYWORDS: Avian influenza; H7N7; HPAIV; Pathogenicity

PMID: 29883773 DOI: 10.1016/j.meegid.2018.06.005 [Indexed for MEDLINE]

Keywords: Avian Influenza; H7N7; Poultry; UK.

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#Spatiotemporal #Clustering of Middle East Respiratory Syndrome #Coronavirus (#MERS-CoV) Incidence in #Saudi Arabia, 2012-2019 (Int J Environ Res Public Health, abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Int J Environ Res Public Health. 2019 Jul 15;16(14). pii: E2520. doi: 10.3390/ijerph16142520.

Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012-2019.

Al-Ahmadi K1, Alahmadi S2, Al-Zahrani A3.

Author information: 1 King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia. 2 King Abdulaziz City for Science and Technology, P.O. Box 6086, Riyadh 11442, Saudi Arabia. salahmdi@kacst.edu.sa. 3 King Faisal Specialist Hospital and Research Centre, P.O. Box 3354, Riyadh 11211, Saudi Arabia.

 

Abstract

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff’s spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

KEYWORDS: GIS; Middle East respiratory syndrome; Saudi Arabia; coronavirus; epidemiology; outbreak; spatiotemporal cluster

PMID: 31311073 DOI: 10.3390/ijerph16142520

Keywords: MERS-CoV; Saudi Arabia.

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Recalling the #Future: #Immunological #Memory Toward Unpredictable #Influenza Viruses (Front Immunol., abstract)

[Source: US National Library of Medicine, full page: (LINK). Abstract, edited.]

Front Immunol. 2019 Jul 2;10:1400. doi: 10.3389/fimmu.2019.01400. eCollection 2019.

Recalling the Future: Immunological Memory Toward Unpredictable Influenza Viruses.

Auladell M1, Jia X1, Hensen L1, Chua B1,2, Fox A3, Nguyen THO1, Doherty PC1,4, Kedzierska K1.

Author information: 1 Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, VIC, Australia. 2 Research Center for Zoonosis Control, Hokkaido University, Sapporo, Japan. 3 WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia. 4 Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN, United States.

 

Abstract

Persistent and durable immunological memory forms the basis of any successful vaccination protocol. Generation of pre-existing memory B cell and T cell pools is thus the key for maintaining protective immunity to seasonal, pandemic and avian influenza viruses. Long-lived antibody secreting cells (ASCs) are responsible for maintaining antibody levels in peripheral blood. Generated with CD4+ T help after naïve B cell precursors encounter their cognate antigen, the linked processes of differentiation (including Ig class switching) and proliferation also give rise to memory B cells, which then can change rapidly to ASC status after subsequent influenza encounters. Given that influenza viruses evolve rapidly as a consequence of antibody-driven mutational change (antigenic drift), the current influenza vaccines need to be reformulated frequently and annual vaccination is recommended. Without that process of regular renewal, they provide little protection against “drifted” (particularly H3N2) variants and are mainly ineffective when a novel pandemic (2009 A/H1N1 “swine” flu) strain suddenly emerges. Such limitation of antibody-mediated protection might be circumvented, at least in part, by adding a novel vaccine component that promotes cross-reactive CD8+ T cells specific for conserved viral peptides, presented by widely distributed HLA types. Such “memory” cytotoxic T lymphocytes (CTLs) can rapidly be recalled to CTL effector status. Here, we review how B cells and follicular T cells are elicited following influenza vaccination and how they survive into a long-term memory. We describe how CD8+ CTL memory is established following influenza virus infection, and how a robust CTL recall response can lead to more rapid virus elimination by destroying virus-infected cells, and recovery. Exploiting long-term, cross-reactive CTL against the continuously evolving and unpredictable influenza viruses provides a possible mechanism for preventing a disastrous pandemic comparable to the 1918-1919 H1N1 “Spanish flu,” which killed more than 50 million people worldwide.

KEYWORDS: B cells; T cells; immunological memory; influenza; vaccine

PMID: 31312199 PMCID: PMC6614380 DOI: 10.3389/fimmu.2019.01400

Keywords: Influenza A; Seasonal Influenza; Pandemic Influenza; Vaccines; Immunology.

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#Immunogenicity, Lot #Consistency, and Extended #Safety of rVSVΔG-ZEBOV-GP [#Ebola] #Vaccine: A Phase 3 Randomized, Double-Blind, Placebo-Controlled Study in Healthy Adults (J Infect Dis., abstract)

[Source: Journal of Infectious Diseases, full page: (LINK). Abstract, edited.]

Immunogenicity, Lot Consistency, and Extended Safety of rVSVΔG-ZEBOV-GP Vaccine: A Phase 3 Randomized, Double-Blind, Placebo-Controlled Study in Healthy Adults

Scott A Halperin, Rituparna Das, Matthew T Onorato, Kenneth Liu, Jason Martin, Rebecca J Grant-Klein, Rick Nichols, Beth-Ann Coller, Frans A Helmond, Jakub K Simon, V920-012 Study Team

The Journal of Infectious Diseases, jiz241, https://doi.org/10.1093/infdis/jiz241

Published: 18 July 2019

 

Abstract

Background

This double-blind study assessed immunogenicity, lot consistency, and safety of recombinant vesicular stomatitis virus-Zaire Ebola virus envelope glycoprotein vaccine (rVSVΔG-ZEBOV-GP).

Methods

Healthy adults (N = 1197) were randomized 2:2:2:2:1 to receive 1 of 3 consistency lots of rVSVΔG-ZEBOV-GP (2 × 107 plaque-forming units [pfu]), high-dose 1 × 108 pfu, or placebo. Antibody responses pre-/postvaccination (28 days, 6 months; in a subset [n = 566], months 12, 18, and 24) were measured. post hoc analysis of risk factors associated with arthritis following vaccination was performed.

Results

ZEBOV-GP enzyme-linked immunosorbent assay (ELISA) geometric mean titers (GMTs) increased postvaccination in all rVSVΔG-ZEBOV-GP groups by 28 days (>58-fold) and persisted through 24 months. The 3 manufacturing lots demonstrated equivalent immunogenicity at 28 days. Neutralizing antibody GMTs increased by 28 days in all rVSVΔG-ZEBOV-GP groups, peaking at 18 months with no decrease through 24 months. At 28 days, ≥94% of vaccine recipients seroresponded (ZEBOV-GP ELISA, ≥2-fold increase, titer ≥200 EU/mL), with responses persisting at 24 months in ≥91%. Female sex and a history of arthritis were identified as potential risk factors for the development of arthritis postvaccination.

Conclusions

Immune responses to rVSVΔG-ZEBOV-GP persisted to 24 months. Immunogenicity and safety results support continued rVSVΔG-ZEBOV-GP development.

Clinical Trials RegistrationNCT02503202.

Ebola, clinical trial, immunogenicity, rVSVΔG-ZEBOV-GP, vaccine

Issue Section: Major Article

© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Keywords: Ebola; Vaccines.

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#NAIs and #Hospital Length of Stay: A Meta-analysis of Individual Participant Data to Determine #Treatment Effectiveness Among Patients Hospitalized With Nonfatal #H1N1pdm09 Virus Infection (J Infect Dis., abstract)

[Source: Journal of Infectious Diseases, full page: (LINK). Abstract, edited.]

Neuraminidase Inhibitors and Hospital Length of Stay: A Meta-analysis of Individual Participant Data to Determine Treatment Effectiveness Among Patients Hospitalized With Nonfatal 2009 Pandemic Influenza A(H1N1) Virus Infection

Sudhir Venkatesan, Puja R Myles, Kirsty J Bolton, Stella G Muthuri, Tarig Al Khuwaitir, Ashish P Anovadiya, Eduardo Azziz-Baumgartner, Tahar Bajjou, Matteo Bassetti, Bojana Beovic, Barbara Bertisch, Isabelle Bonmarin, Robert Booy, Victor H Borja-Aburto, Heinz Burgmann, Bin Cao, Jordi Carratala, Tserendorj Chinbayar, Catia Cilloniz, Justin T Denholm, Samuel R Dominguez, Pericles A D Duarte, Gal Dubnov-Raz, Sergio Fanella, Zhancheng Gao, Patrick Gérardin, Maddalena Giannella, Sophie Gubbels, Jethro Herberg Anjarath, Lorena Higuera Iglesias, Peter H Hoeger, Xiao Yun Hu, Quazi T Islam, Mirela F Jiménez, Gerben Keijzers, Hossein Khalili, Gabriela Kusznierz, Ilija Kuzman, Eduard Langenegger, Kamran B Lankarani, Yee-Sin Leo, Romina P Libster, Rita Linko, Faris Madanat, Efstratios Maltezos, Abdullah Mamun, Toshie Manabe, Gokhan Metan, Auksė Mickiene, Dragan Mikić, Kristin G I Mohn, Maria E Oliva, Mehpare Ozkan Dhruv, Parekh Mical, Paul Barbara A Rath, Samir Refaey, Alejandro H Rodríguez, Bunyamin Sertogullarindan, Joanna Skręt-Magierło, Ayper Somer, Ewa Talarek, Julian W Tang, Kelvin To Dat Tran, Timothy M Uyeki, Wendy Vaudry, Tjasa Vidmar, Paul Zarogoulidis, PRIDE Consortium Investigators, Jonathan S Nguyen-Van-Tam

The Journal of Infectious Diseases, jiz152, https://doi.org/10.1093/infdis/jiz152

Published: 17 July 2019

 

Abstract

Background

The effect of neuraminidase inhibitor (NAI) treatment on length of stay (LoS) in patients hospitalized with influenza is unclear.

Methods

We conducted a one-stage individual participant data (IPD) meta-analysis exploring the association between NAI treatment and LoS in patients hospitalized with 2009 influenza A(H1N1) virus (A[H1N1]pdm09) infection. Using mixed-effects negative binomial regression and adjusting for the propensity to receive NAI, antibiotic, and corticosteroid treatment, we calculated incidence rate ratios (IRRs) and 95% confidence intervals (CIs). Patients with a LoS of <1 day and those who died while hospitalized were excluded.

Results

We analyzed data on 18 309 patients from 70 clinical centers. After adjustment, NAI treatment initiated at hospitalization was associated with a 19% reduction in the LoS among patients with clinically suspected or laboratory-confirmed influenza A(H1N1)pdm09 infection (IRR, 0.81; 95% CI, .78–.85), compared with later or no initiation of NAI treatment. Similar statistically significant associations were seen in all clinical subgroups. NAI treatment (at any time), compared with no NAI treatment, and NAI treatment initiated <2 days after symptom onset, compared with later or no initiation of NAI treatment, showed mixed patterns of association with the LoS.

Conclusions

When patients hospitalized with influenza are treated with NAIs, treatment initiated on admission, regardless of time since symptom onset, is associated with a reduced LoS, compared with later or no initiation of treatment.

Neuraminidase inhibitors, pandemic influenza, IPD meta-analysis, length of stay, antivirals

Issue Section: Major Article

Keywords: Seasonal Influenza; H1N1pdm09; Antivirals; Oseltamivir.

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