Evidence map›Paper›PMID 33431604›Full record

ArticleJournal of investigative medicine : the official publication of the American Federation for Clinical Research2021

ALLY in fighting COVID-19: magnitude of albumin decline and lymphopenia (ALLY) predict progression to critical disease.

Johanna S van Zyl, Amit Alam, Joost Felius, Ronnie M Youssef, Dipesh Bhakta, Christina Jack, Aayla K Jamil, Shelley A Hall, Göran B Klintmalm, Cedric W Spak and 1 more

Abstract readValidation Study
In one paragraph

Article in Journal of investigative medicine : the official publication of the American Federation for Clinical Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Johanna S van ZylBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0003-4205-9214
Amit AlamBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0002-3040-3957
Joost FeliusBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0001-7200-8755
Ronnie M YoussefCollege of Medicine, Texas A&M Health Science Center, Texas A&M University, Dallas, Texas, USA.ORCID 0000-0002-8460-5810
Dipesh BhaktaCollege of Medicine, Texas A&M Health Science Center, Texas A&M University, Dallas, Texas, USA.ORCID 0000-0001-7512-7514
Christina JackCollege of Medicine, Texas A&M Health Science Center, Texas A&M University, Dallas, Texas, USA.
Aayla K JamilBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0001-9471-3383
Shelley A HallBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0002-5989-2934
Göran B KlintmalmBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.ORCID 0000-0003-0916-6042
Cedric W SpakBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA.
Robert L GottliebBaylor Scott & White Research Institute, Baylor Scott and White Health, Dallas, Texas, USA robert.gottlieb@bswhealth.org.ORCID 0000-0001-8376-8709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic leading to coronavirus disease 2019 (COVID-19) is straining hospitals. Judicious resource allocation is paramount but difficult due to the unpredictable disease course. Once hospitalized, discerning which patients may progress to critical disease would be valuable for resource planning. Medical records were reviewed for consecutive hospitalized patients with COVID-19 in a large healthcare system in Texas. The main outcome was progression to critical disease within 10 days from admission. Albumin trends from admission to 7 days were analyzed using mixed-effects models, and progression to critical disease was modeled by multivariable logistic regression of laboratory results. Risk models were evaluated in an independent group. Of 153 non-critical patients, 28 (18%) progressed to critical disease. The rate of decrease in mean baseline-corrected (

Indexed as

Models, BiologicalAdultAgedCOVID-19Critical IllnessDisease ProgressionFemaleHumansLymphopeniaMaleMiddle AgedPandemicsRisk FactorsSARS-CoV-2Serum Albumin, HumanSeverity of Illness IndexSerum Albumin, HumanadultCOVID-19critical carepneumoniarespiratory distress syndromeriskSARSserum albumin

Identifiers

PMID33431604
PMCPMC7802390

What OpenQuestion holds

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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.