Evidence map›Paper›PMID 30521765›Full record

SynthesisAnnals of the American Thoracic Society2019

Electronic "Sniffer" Systems to Identify the Acute Respiratory Distress Syndrome.

Max T Wayne, Thomas S Valley, Colin R Cooke, Michael W Sjoding

Abstract readSystematic Review
In one paragraph

Synthesis in Annals of the American Thoracic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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

4 authors.

Max T Wayne1 Department of Internal Medicine.
Thomas S Valley2 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine.
Colin R Cooke2 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine.
Michael W Sjoding2 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine.

Funding

Preventing avoidable deaths from pneumonia by improving use of intensive careK23HL140165 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI VALLEY, THOMAS SEBASTIAN · 2018 to 2022
$915k
Data-Driven Identification of the Acute Respiratory Distress SyndromeK01HL136687 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SJODING, MICHAEL WILLIAM · 2017 to 2021
$862k
NHLBI NIH HHS K01 HL136687NHLBI NIH HHS K23 HL140165NHLBI NIH HHS L30 HL138779
6 · The paper itself

Abstract

backgroundThe acute respiratory distress syndrome (ARDS) results in substantial mortality but remains underdiagnosed in clinical practice. Automated ARDS "sniffer" systems, tools that can automatically analyze electronic medical record data, have been developed to improve recognition of ARDS in clinical practice.

objectivesTo perform a systematic review examining the evidence underlying automated sniffer systems for ARDS detection. DATA SOURCES: MEDLINE and Scopus databases through November 2018 to identify studies of tools using routinely available clinical data to detect patients with ARDS. DATA EXTRACTION: Study design, tool description, and diagnostic performance were extracted by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate each study for risk of bias in four domains: patient selection, index test, reference standard, and study flow and timing. SYNTHESIS: Among 480 studies identified, 9 met inclusion criteria, and they evaluated six unique ARDS sniffer tools. Eight studies had derivation and/or temporal validation designs, with one also evaluating the effects of implementing a tool in clinical practice. A single study performed an external validation of previously published ARDS sniffer tools. Studies reported a wide range of sensitivities (43-98%) and positive predictive values (26-90%) for detection of ARDS. Most studies had potential for high risk of bias identified in their study design, including patient selection (five of nine), reference standard (four of nine), and flow and timing (three of nine). In the single external validation without any perceived risks of biases, the performance of ARDS sniffer tools was worse.

conclusionsSniffer systems developed to detect ARDS had moderate to high predictive value in their derivation cohorts, although most studies had the potential for high risks of bias in study design. Methodological issues may explain some of the variability in tool performance. There remains an ongoing need for robust evaluation of ARDS sniffer systems and their impact on clinical practice. Systematic review registered with PROSPERO (CRD42015026584).

Indexed as

Early DiagnosisElectronic Data ProcessingHumansMedical Records Systems, ComputerizedPrognosisRespiratory Distress Syndromeacute lung injuryacute respiratory distress syndromediagnostic toolidentificationsystematic review

Identifiers

PMID30521765
PMCPMC6441701

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.