ArticleJournal of intensive care medicine
External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.
Article in Journal of intensive care medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic "Sniffer" Systems to Identify the Acute Respiratory Distress Syndrome.Annals of the American Thoracic Society · 2019Pooled it
- A Computable Electronic Health Record ARDS Classifier and the Association Between theCHEST critical care · 2025Article
- Development and External Validation of a Detection Model to Retrospectively Identify Patients With Acute Respiratory Distress Syndrome.Critical care medicine · 2025Article
- RespBERT: A Multi-Site Validation of a Natural Language Processing Algorithm, of Radiology Notes to Identify Acute Respiratory Distress Syndrome (ARDS).IEEE journal of biomedical and health informatics · 2025Article
- Use of pragmatic and explanatory trial designs in acute care research: lessons from COVID-19.The Lancet. Respiratory medicine · 2022Review
- Impact of Clinician Recognition of Acute Respiratory Distress Syndrome on Evidenced-Based Interventions in the Medical ICU.Critical care explorations · 2021Article
- Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.Annals of translational medicine · 2021Article
- Article
- Use of Machine Learning to Screen for Acute Respiratory Distress Syndrome Using Raw Ventilator Waveform Data.Critical care explorations · 2021Article
- External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.Critical care medicine · 2020Article
- Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit.IEEE pervasive computingArticle
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
introductionAutomated electronic sniffers may be useful for early detection of acute respiratory distress syndrome (ARDS) for institution of treatment or clinical trial screening.
methodsIn a prospective cohort of 2929 critically ill patients, we retrospectively applied published sniffer algorithms for automated detection of acute lung injury to assess their utility in diagnosis of ARDS in the first 4 ICU days. Radiographic full-text reports were searched for "edema" OR ("bilateral" AND "infiltrate") and a more detailed algorithm for descriptions consistent with ARDS. Patients were flagged as possible ARDS if a radiograph met search criteria and had a PaO
resultsThirty percent of 2841 patients included in the analysis had gold standard diagnosis of ARDS. The simpler algorithm had sensitivity for ARDS of 78.9%, specificity of 52%, positive predictive value (PPV) of 41%, and negative predictive value (NPV) of 85.3% over the 4-day study period. The more detailed algorithm had sensitivity of 88.2%, specificity of 55.4%, PPV of 45.6%, and NPV of 91.7%. Both algorithms were more sensitive but less specific than clinician suspicion, which had sensitivity of 40.7%, specificity of 94.8%, PPV of 78.2%, and NPV of 77.7%.
conclusionsPublished electronic sniffer algorithms for ARDS may be useful automated screening tools for ARDS and improve on clinical recognition, but they are limited to screening rather than diagnosis because their specificity is poor.
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Registered trials
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