SynthesisAnnals of the American Thoracic Society2019
Electronic "Sniffer" Systems to Identify the Acute Respiratory Distress Syndrome.
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.
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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.
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.
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Who cites it
9 citing papers in PubMed.
- Improving retrospective ARDS case-finding using a simple 72-h physiologic persistence rule.Intensive care medicine experimental · 2026Article
- An Electronic Health Record-Based Classifier for Moderate-to-Severe Acute Respiratory Distress Syndrome With Persistent Hypoxemia.Critical care medicine · 2026Observational
- Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.BMJ health & care informatics · 2023Article
- An alert tool to promote lung protective ventilation for possible acute respiratory distress syndrome.JAMIA open · 2022Article
- Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition.Critical care explorations · 2021Article
- Deep learning to detect acute respiratory distress syndrome on chest radiographs: a retrospective study with external validation.The Lancet. Digital health · 2021Article
- Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.Critical care explorations · 2021Article
- Identifying Clinical Research Priorities in Adult Pulmonary and Critical Care. NHLBI Working Group Report.American journal of respiratory and critical care medicine · 2020Article
- Performance Measure Development, Use, and Measurement of Effectiveness Using the Guideline on Mechanical Ventilation in Acute Respiratory Distress Syndrome. An Official American Thoracic Society Workshop Report.Annals of the American Thoracic Society · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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).
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