Evidence map›Paper›PMID 34104894›Full record

ArticleCritical care explorations2021

Development and Validation of ARC, a Model for Anticipating Acute Respiratory Failure in Coronavirus Disease 2019 Patients.

Suchi Saria, Peter Schulam, Brian J Yeh, Daniel Burke, Sean D Mooney, Christine T Fong, Jacob E Sunshine, Dustin R Long, Vikas N O'Reilly-Shah

Open access · goldAbstract read
In one paragraph

Article in Critical care explorations, 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
0.2field-weighted citation impact, top 47% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
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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

9 authors at 2 institutions in 1 country.

Suchi SariaComputer Science and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD.
Peter SchulamBayesian Health, New York, NY.
Brian J YehBayesian Health, New York, NY.
Daniel BurkeBayesian Health, New York, NY.
Sean D MooneyBiomedical Informatics and Medical Education, University of Washington, Seattle, WA.
Christine T FongAnesthesiology and Pain Medicine, University of Washington, Seattle, WA.
Jacob E SunshineAnesthesiology and Pain Medicine, University of Washington, Seattle, WA.
Dustin R LongAnesthesiology and Pain Medicine, University of Washington, Seattle, WA.
Vikas N O'Reilly-ShahAnesthesiology and Pain Medicine, University of Washington, Seattle, WA.
University of Washington · USJohns Hopkins University · US

Funding

Anesthesiology & Perioperative Medicine Training GrantT32GM086270 · NIGMS · UNIVERSITY OF WASHINGTON · PI Tonya M Palermo · 2009 to 2026
$4.3M
Low-Cost Mobile Detection of Opioid Overdose EventsK23DA046686 · NIDA · UNIVERSITY OF WASHINGTON · PI SUNSHINE, JACOB · 2019 to 2022
$761k
NIDA NIH HHS K23 DA046686NIGMS NIH HHS T32 GM086270
6 · The paper itself

Abstract

objectivesTo evaluate factors predictive of clinical progression among coronavirus disease 2019 patients following admission, and whether continuous, automated assessments of patient status may contribute to optimal monitoring and management.

designRetrospective cohort for algorithm training, testing, and validation.

settingEight hospitals across two geographically distinct regions. PATIENTS: Two-thousand fifteen hospitalized coronavirus disease 2019-positive patients.

interventionsNone. MEASUREMENTS AND MAIN

resultsAnticipating Respiratory failure in Coronavirus disease (ARC), a clinically interpretable, continuously monitoring prognostic model of acute respiratory failure in hospitalized coronavirus disease 2019 patients, was developed and validated. An analysis of the most important clinical predictors aligns with key risk factors identified by other investigators but contributes new insights regarding the time at which key factors first begin to exhibit aberrency and distinguishes features predictive of acute respiratory failure in coronavirus disease 2019 versus pneumonia caused by other types of infection. Departing from prior work, ARC was designed to update continuously over time as new observations (vitals and laboratory test results) are recorded in the electronic health record. Validation against data from two geographically distinct health systems showed that the proposed model achieved 75% specificity and 77% sensitivity and predicted acute respiratory failure at a median time of 32 hours prior to onset. Over 80% of true-positive alerts occurred in non-ICU settings.

conclusionsPatients admitted to non-ICU environments with coronavirus disease 2019 are at ongoing risk of clinical progression to severe disease, yet it is challenging to anticipate which patients will develop acute respiratory failure. A continuously monitoring prognostic model has potential to facilitate anticipatory rather than reactive approaches to escalation of care (e.g., earlier initiation of treatments for severe disease or structured monitoring and therapeutic interventions for high-risk patients).

Indexed as

coronavirus disease 2019deterioration monitoringelectronic surveillancepredictive model

Identifiers

PMID34104894
PMCPMC8177871
OpenAlexW3168264587

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.