Evidence map›Paper›PMID 34258315›Full record

ArticleOpen forum infectious diseases2021

Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction.

Alexander Goehler, Tzu-Ming Harry Hsu, Jacqueline A Seiglie, Mark J Siedner, Janet Lo, Virginia Triant, John Hsu, Andrea Foulkes, Ingrid Bassett, Ramin Khorasani and 4 more

Open access · goldAbstract read
In one paragraph

Article in Open forum infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 14% of its field
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.

  1. Pooled it
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  4. The role of diet and nutrition in the management of COPD.European respiratory review : an official journal of the European Respiratory Society · 2023
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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

14 authors at 4 institutions in 1 country.

Alexander GoehlerComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
Tzu-Ming Harry HsuComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
Jacqueline A SeiglieDiabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Mark J SiednerDivision of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Janet LoMetabolism Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Virginia TriantDivision of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
John HsuMongan Institute, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Andrea FoulkesThe Biostatistics Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ingrid BassettDivision of Infectious Diseases, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Ramin KhorasaniCenter for Evidence-Based Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Deborah J WexlerDiabetes Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Peter SzolovitsComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, Massachusetts, USA.
James B MeigsDivision of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Jennifer Manne-GoehlerMedical Practice Evaluation Center, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0001-9295-0035
Harvard University · USBrigham and Women's Hospital · USMassachusetts Institute of Technology · USBroad Institute · US

Funding

TRAINING PROGRAM IN ENDOCRINOLOGY AND DIABETEST32DK007028 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI Karen K Miller · 1986 to 2026
$18.5M
COVID-19 and Acute Medical Care: Impact on Dementia PatientsR01AG062282 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HSU, JOHN · 2018 to 2022
$4.5M
Enhancing cardiovascular risk estimation in individuals aging with HIV/HCV co-infectionR01AG062393 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI TRIANT, VIRGINIA ATHENA · 2019 to 2023
$4.5M
Impact of Integrated Patient-Facing Health IT Tools on Quality and Resource Use for Patients with DiabetesR01DK085070 · NIDDK · KAISER FOUNDATION RESEARCH INSTITUTE · PI REED, MARY · 2010 to 2019
$4.4M
Optimizing Cardiovascular Risk Prediction in HIVR01HL132786 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI D'AGOSTINO, RALPH B, TRIANT, VIRGINIA ATHENA · 2017 to 2020
$3.5M
Methods for integrated analysis of multi-level omics dataR01GM127862 · NIGMS · MOUNT HOLYOKE COLLEGE · PI FOULKES, ANDREA S · 2018 to 2022
$1.7M
Mentoring in patient-oriented research on HIV prevention in the US and South AfricaK24AI141036 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Ingrid Valerie Bassett · 2019 to 2026
$1.1M
NHLBI NIH HHS R01 HL132786NIAID NIH HHS K24 AI141036NIA NIH HHS R01 AG062282NIA NIH HHS R01 AG062393NIDDK NIH HHS R01 DK085070NIDDK NIH HHS T32 DK007028NIGMS NIH HHS R01 GM127862
6 · The paper itself

Abstract

backgroundObesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI).

methodsWe analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm

resultsA total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (

conclusionsHigh VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

Indexed as

artificial intelligenceCOVID-19 diseasevisceral adiposity

Identifiers

PMID34258315
PMCPMC8244656
OpenAlexW3172172340

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.