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Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK

Voysey, Merryn ; Clemens, Sue Ann Costa ; Madhi, Shabir A ; [et al.]

Elsevier BV ; 2021

In: The Lancet Vol. 397, No. 10269 ( 2021-01), p. 99-111

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In: The Lancet, Elsevier BV, Vol. 397, No. 10269 ( 2021-01), p. 99-111

Type of Medium: Online Resource

ISSN: 0140-6736

URL: Article

DOI: 10.1016/S0140-6736(20)32661-1

RVK:

XA 10000

Language: English

Publisher: Elsevier BV

Publication Date: 2021

detail.hit.zdb_id: 2067452-1

detail.hit.zdb_id: 3306-6

detail.hit.zdb_id: 1476593-7

SSG: 5,21

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Pattern recognition in lymphoid malignancies using CytoGPS and Mercator

Abrams, Zachary B. ; Tally, Dwayne G. ; Zhang, Lin ; [et al.]

Springer Science and Business Media LLC ; 2021

In: BMC Bioinformatics Vol. 22, No. 1 ( 2021-12)

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In: BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 22, No. 1 ( 2021-12)

Abstract: There have been many recent breakthroughs in processing and analyzing large-scale data sets in biomedical informatics. For example, the CytoGPS algorithm has enabled the use of text-based karyotypes by transforming them into a binary model. However, such advances are accompanied by new problems of data sparsity, heterogeneity, and noisiness that are magnified by the large-scale multidimensional nature of the data. To address these problems, we developed the Mercator R package, which processes and visualizes binary biomedical data. We use Mercator to address biomedical questions of cytogenetic patterns relating to lymphoid hematologic malignancies, which include a broad set of leukemias and lymphomas. Karyotype data are one of the most common form of genetic data collected on lymphoid malignancies, because karyotyping is part of the standard of care in these cancers. Results In this paper we combine the analytic power of CytoGPS and Mercator to perform a large-scale multidimensional pattern recognition study on 22,741 karyotype samples in 47 different hematologic malignancies obtained from the public Mitelman database. Conclusion Our findings indicate that Mercator was able to identify both known and novel cytogenetic patterns across different lymphoid malignancies, furthering our understanding of the genetics of these diseases.

Type of Medium: Online Resource

ISSN: 1471-2105

URL: Article

DOI: 10.1186/s12859-021-03992-1

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2021

detail.hit.zdb_id: 2041484-5

SSG: 12

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Single-dose administration and the influence of the timing of the booster dose on immunogenicity and efficacy of ChAdOx1 nCoV-19 (AZD1222) vaccine: a pooled analysis of four randomised trials

Voysey, Merryn ; Costa Clemens, Sue Ann ; Madhi, Shabir A ; [et al.]

Elsevier BV ; 2021

In: The Lancet Vol. 397, No. 10277 ( 2021-03), p. 881-891

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In: The Lancet, Elsevier BV, Vol. 397, No. 10277 ( 2021-03), p. 881-891

Type of Medium: Online Resource

ISSN: 0140-6736

URL: Article

DOI: 10.1016/S0140-6736(21)00432-3

RVK:

XA 10000

Language: English

Publisher: Elsevier BV

Publication Date: 2021

detail.hit.zdb_id: 2067452-1

detail.hit.zdb_id: 3306-6

detail.hit.zdb_id: 1476593-7

SSG: 5,21

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Syrian hamster convalescence from prototype SARS-CoV-2 confers measurable protection against the attenuated disease caused by the Omicron variant

Ryan, Kathryn A. ; Bewley, Kevin R. ; Watson, Robert J. ; [et al.]

Public Library of Science (PLoS) ; 2023

In: PLOS Pathogens Vol. 19, No. 4 ( 2023-4-4), p. e1011293-

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In: PLOS Pathogens, Public Library of Science (PLoS), Vol. 19, No. 4 ( 2023-4-4), p. e1011293-

Abstract: The mutation profile of the SARS-CoV-2 Omicron (lineage BA.1) variant posed a concern for naturally acquired and vaccine-induced immunity. We investigated the ability of prior infection with an early SARS-CoV-2 ancestral isolate (Australia/VIC01/2020, VIC01) to protect against disease caused by BA.1. We established that BA.1 infection in naïve Syrian hamsters resulted in a less severe disease than a comparable dose of the ancestral virus, with fewer clinical signs including less weight loss. We present data to show that these clinical observations were almost absent in convalescent hamsters challenged with the same dose of BA.1 50 days after an initial infection with ancestral virus. These data provide evidence that convalescent immunity against ancestral SARS-CoV-2 is protective against BA.1 in the Syrian hamster model of infection. Comparison with published pre-clinical and clinical data supports consistency of the model and its predictive value for the outcome in humans. Further, the ability to detect protection against the less severe disease caused by BA.1 demonstrates continued value of the Syrian hamster model for evaluation of BA.1-specific countermeasures.

Type of Medium: Online Resource

ISSN: 1553-7374

URL: Article

DOI: 10.1371/journal.ppat.1011293

DOI: 10.1371/journal.ppat.1011293.g001

DOI: 10.1371/journal.ppat.1011293.g002

DOI: 10.1371/journal.ppat.1011293.g003

DOI: 10.1371/journal.ppat.1011293.g004

DOI: 10.1371/journal.ppat.1011293.g005

DOI: 10.1371/journal.ppat.1011293.g006

DOI: 10.1371/journal.ppat.1011293.s001

DOI: 10.1371/journal.ppat.1011293.s002

DOI: 10.1371/journal.ppat.1011293.s003

DOI: 10.1371/journal.ppat.1011293.s004

DOI: 10.1371/journal.ppat.1011293.s005

Language: English

Publisher: Public Library of Science (PLoS)

Publication Date: 2023

detail.hit.zdb_id: 2205412-1

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A novel model to label delirium in an intensive care unit from clinician actions

Coombes, Caitlin E. ; Coombes, Kevin R. ; Fareed, Naleef

Springer Science and Business Media LLC ; 2021

In: BMC Medical Informatics and Decision Making Vol. 21, No. 1 ( 2021-03-09)

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In: BMC Medical Informatics and Decision Making, Springer Science and Business Media LLC, Vol. 21, No. 1 ( 2021-03-09)

Abstract: In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. Methods EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. Results Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. Conclusions Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models.

Type of Medium: Online Resource

ISSN: 1472-6947

URL: Article

DOI: 10.1186/s12911-021-01461-6

Language: English

Publisher: Springer Science and Business Media LLC

Publication Date: 2021

detail.hit.zdb_id: 2046490-3

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Mercator: a pipeline for multi-method, unsupervised visualization and distance generation

Abrams, Zachary B. ; Coombes, Caitlin E. ; Li, Suli ; [et al.]

Oxford University Press (OUP) ; 2021

In: Bioinformatics Vol. 37, No. 17 ( 2021-09-09), p. 2780-2781

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In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 17 ( 2021-09-09), p. 2780-2781

Abstract: Unsupervised machine learning provides tools for researchers to uncover latent patterns in large-scale data, based on calculated distances between observations. Methods to visualize high-dimensional data based on these distances can elucidate subtypes and interactions within multi-dimensional and high-throughput data. However, researchers can select from a vast number of distance metrics and visualizations, each with their own strengths and weaknesses. The Mercator R package facilitates selection of a biologically meaningful distance from 10 metrics, together appropriate for binary, categorical and continuous data, and visualization with 5 standard and high-dimensional graphics tools. Mercator provides a user-friendly pipeline for informaticians or biologists to perform unsupervised analyses, from exploratory pattern recognition to production of publication-quality graphics. Availabilityand implementation Mercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html).

Type of Medium: Online Resource

ISSN: 1367-4803 , 1367-4811

URL: Article

DOI: 10.1093/bioinformatics/btab037

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2021

detail.hit.zdb_id: 1468345-3

SSG: 12

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Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learning

Coombes, Caitlin E. ; Abrams, Zachary B. ; Nakayiza, Samantha ; [et al.]

F1000 Research Ltd ; 2020

In: F1000Research Vol. 9 ( 2020-10-1), p. 1186-

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In: F1000Research, F1000 Research Ltd, Vol. 9 ( 2020-10-1), p. 1186-

Type of Medium: Online Resource

ISSN: 2046-1402

URL: Journal

URL: Article

DOI: 10.12688/f1000research

DOI: 10.12688/f1000research.25877.1

Language: English

Publisher: F1000 Research Ltd

Publication Date: 2020

detail.hit.zdb_id: 2699932-8

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Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learning

Coombes, Caitlin E. ; Abrams, Zachary B. ; Nakayiza, Samantha ; [et al.]

F1000 Research Ltd ; 2021

In: F1000Research Vol. 9 ( 2021-3-5), p. 1186-

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In: F1000Research, F1000 Research Ltd, Vol. 9 ( 2021-3-5), p. 1186-

Type of Medium: Online Resource

ISSN: 2046-1402

URL: Journal

URL: Article

DOI: 10.12688/f1000research

DOI: 10.12688/f1000research.25877.2

Language: English

Publisher: F1000 Research Ltd

Publication Date: 2021

detail.hit.zdb_id: 2699932-8

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Simulation-derived best practices for clustering clinical data

Coombes, Caitlin E. ; Liu, Xin ; Abrams, Zachary B. ; [et al.]

Elsevier BV ; 2021

In: Journal of Biomedical Informatics Vol. 118 ( 2021-06), p. 103788-

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In: Journal of Biomedical Informatics, Elsevier BV, Vol. 118 ( 2021-06), p. 103788-

Type of Medium: Online Resource

ISSN: 1532-0464

URL: Article

DOI: 10.1016/j.jbi.2021.103788

Language: English

Publisher: Elsevier BV

Publication Date: 2021

detail.hit.zdb_id: 2057141-0

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Sequences of Events from the Electronic Medical Record and the Onset of Infection

Coombes, Caitlin E. ; Coombes, Kevin R. ; Fareed, Naleef

Wiley ; 2022

In: Chemistry & Biodiversity Vol. 19, No. 11 ( 2022-11)

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In: Chemistry & Biodiversity, Wiley, Vol. 19, No. 11 ( 2022-11)

Abstract: We present a novel model of time‐series analysis to learn from electronic health record (EHR) data when infection occurred in the intensive care unit (ICU) by translating methods from proteomics and Bayesian statistics. Using 48,536 patients hospitalized in an ICU, we describe each hospital course as an ‘alphabet’ of 23 physician actions (‘events’) in temporal order. We analyze these as k ‐mers of length 3–12 events and apply a Bayesian model of (cumulative) relative risk (RR). The log2‐transformed RR (median=0.248, mean=0.226) supported the conclusion that the events selected were individually associated with increased risk of infection. Selecting from all possible cutoffs of maximum gain (MG), MG 〉 0.0244 predicts administration of antibiotics with PPV 82.0 %, NPV 44.4 %, and AUC 0.706. Our approach holds value for retrospective analysis of other clinical syndromes for which time‐of‐onset is critical to analysis but poorly marked in EHRs, including delirium and decompensation.

Type of Medium: Online Resource

ISSN: 1612-1872 , 1612-1880

URL: Issue

URL: Article

DOI: 10.1002/cbdv.v19.11

DOI: 10.1002/cbdv.202200657

Language: English

Publisher: Wiley

Publication Date: 2022

detail.hit.zdb_id: 2139001-0

SSG: 12

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