Evidence map›Paper›PMID 31539417›Full record

ArticlePloS one2019

A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

Meagan A Bechel, Adam R Pah, Hanyu Shi, Sanjay Mehrotra, Stephen D Persell, Shayna Weiner, Richard G Wunderink, Luís A Nunes Amaral, Curtis H Weiss

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 8 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 3 institutions in 1 country.

Meagan A BechelMedical Scientist Training Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.ORCID 0000-0003-0600-3774
Adam R PahNorthwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States of America.
Hanyu ShiDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, United States of America.
Sanjay MehrotraDepartment of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, United States of America.
Stephen D PersellDivision of General Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
Shayna WeinerMichigan Oncology Quality Consortium, Ann Arbor, MI, United States of America.
Richard G WunderinkDivision of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America.
Luís A Nunes AmaralNorthwestern Institute on Complex Systems, Northwestern University, Evanston, IL, United States of America.
Curtis H WeissDivision of Pulmonary, Critical Care, Allergy, and Immunology, NorthShore University HealthSystem, Evanston, IL, United States of America.ORCID 0000-0002-3102-5273
Northwestern University · USMichigan Cancer Research Consortium · USNorthShore University HealthSystem · US

Funding

Technology CoreU19AI135964 · NIAID · NORTHWESTERN UNIVERSITY AT CHICAGO · PI JUSTIN B. STARREN · 2018 to 2026
$24.7M
MULTIDISCIPLINARY CLINICAL AND TRANSLATIONAL SCIENCE (MCTS) PROGRAM (UL1)UL1RR025741 · NCRR · NORTHWESTERN UNIVERSITY AT CHICAGO · PI GREENLAND, PHILIP · 2008 to 2011
$24.7M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM008152 · NIGMS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ARDEHALI, HOSSEIN · 1987 to 2021
$24.0M
Multidisciplinary Clinical and Translational Science (MCTS) Program (UL1)UL1TR000150 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LLOYD-JONES, DONALD M · 2012 to 2013
$10.1M
Medical Scientist Training ProgramT32GM144295 · NIGMS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI ALAN R HAUSER · 2022 to 2026
$9.1M
A novel data science and network analysis approach to quantifying facilitators and barriers of low tidal volume ventilation in an international consortium of medical centersR01HL140362 · NHLBI · ENDEAVOR HEALTH CLINICAL OPERATIONS · PI WEISS, CURTIS H. · 2018 to 2022
$3.6M
A novel implementation and social network strategy for acute pulmonary illnessesK23HL118139 · NHLBI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI WEISS, CURTIS H. · 2013 to 2017
$781k
NCATS NIH HHS UL1 TR000150NCRR NIH HHS UL1 RR025741NHLBI NIH HHS K23 HL118139NHLBI NIH HHS R01 HL140362NIAID NIH HHS U19 AI135964NIGMS NIH HHS T32 GM008152NIGMS NIH HHS T32 GM144295
6 · The paper itself

Abstract

importanceDespite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS). Physician under-recognition of ARDS is a significant barrier to LTVV use. We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians.

objectiveTo quantify patient and physician factors affecting physicians' tidal volume selection and to build a computational model of physician recognition of ARDS that accounts for these factors. DESIGN, SETTING, AND

participantsIn this cross-sectional study, electronic health record data were collected for 361 ARDS patients and 388 non-ARDS hypoxemic (control) patients in nine adult intensive care units at four hospitals between June 24 and December 31, 2013.

methodsStandardized tidal volumes (mL/kg predicted body weight) were chosen as a proxy for physician decision-making behavior. Using data-science approaches, we quantified the effect of eight factors (six severity of illness, two physician behaviors) on selected standardized tidal volumes in ARDS and control patients. Significant factors were incorporated in computational behavioral models of physician recognition of ARDS.

resultsHypoxemia severity and ARDS documentation in physicians' notes were associated with lower standardized tidal volumes in the ARDS cohort. Greater patient height was associated with lower standardized tidal volumes (which is already normalized for height) in both ARDS and control patients. The recognition model yielded a mean (99% confidence interval) physician recognition of ARDS of 22% (9%-42%) for mild, 34% (19%-49%) for moderate, and 67% (41%-100%) for severe ARDS. CONCLUSIONS AND RELEVANCE: In this study, patient characteristics and physician behaviors were demonstrated to be associated with differences in ventilator management in both ARDS and control patients. Our model of physician ARDS recognition measurement accounts for these clinical variables, providing an electronic approach that moves beyond relying on chart documentation or resource intensive approaches.

Indexed as

Physician-Patient RelationsTidal VolumeAdultAlgorithmsCross-Sectional StudiesElectronic Health RecordsFemaleHumansIntensive Care UnitsMaleModels, TheoreticalResearch DesignRespiration, ArtificialRespiratory Distress Syndrome

Identifiers

PMID31539417
PMCPMC6754155
OpenAlexW2973662759

What OpenQuestion holds

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Registered trials

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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.