Evidence map›Paper›PMID 35308982›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021

Impact of Clinical and Genomic Factors on COVID-19 Disease Severity.

Sanjoy Dey, Aritra Bose, Subrata Saha, Prithwish Chakraborty, Mohamed Ghalwash, Aldo Guzm X E N-Sáenz, Filippo Utro, Kenney Ng, Jianying Hu, Laxmi Parida and 1 more

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Sanjoy DeyCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.
Aritra BoseComputational Genomics, IBM Research, Yorktown Heights, NY, USA.
Subrata SahaColumbia University Irving Medical Center, Columbia University, NY, USA.
Prithwish ChakrabortyCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.
Mohamed GhalwashCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.
Aldo Guzm X E N-SáenzComputational Genomics, IBM Research, Yorktown Heights, NY, USA.
Filippo UtroComputational Genomics, IBM Research, Yorktown Heights, NY, USA.
Kenney NgCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.
Jianying HuCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.
Laxmi ParidaComputational Genomics, IBM Research, Yorktown Heights, NY, USA.
Daby SowCenter for Computational Health, IBM Research, Yorktown Heights, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To date, there have been 180 million confirmed cases of COVID-19, with more than 3.8 million deaths, reported to WHO worldwide. In this paper we address the problem of understanding the host genome's influence, in concert with clinical variables, on the severity of COVID-19 manifestation in the patient. Leveraging positive-unlabeled machine learning algorithms coupled with RubricOE, a state-of-the-art genomic analysis framework, on UK BioBank data we extract novel insights on the complex interplay. The algorithm is also sensitive enough to detect the changing influence of the emergent B.1.1.7 SARS-CoV-2 (alpha) variant on disease severity, and, changing treatment protocols. The genomic component also implicates biological pathways that can help in understanding the disease etiology. Our work demonstrates that it is possible to build a robust and sensitive model despite significant bias, noise and incompleteness in both clinical and genomic data by a careful interleaving of clinical and genomic methodologies.

Indexed as

COVID-19SARS-CoV-2GenomicsHumansMachine LearningSeverity of Illness Index

Identifiers

PMID35308982
PMCPMC8861728

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.