ArticleCritical care explorations2022
Validation of an Electronic Phenotyping Algorithm for Patients With Acute Respiratory Failure.
Article in Critical care explorations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed, 7 citations in OpenAlex.
- Integrating Health Care Data in an Informatics for Integrating Biology & the Bedside (i2b2) Model Persisted Through Elasticsearch: Design, Implementation, and Evaluation in a French University Hospital.JMIR medical informatics · 2025Article
- How do Clinicians Use Electronic Health Records for Respiratory Support Decisions? A Qualitative Study in Critical Care.ACI open · 2025Article
- Derivation and validation of generalized sepsis-induced acute respiratory failure phenotypes among critically ill patients: a retrospective study.Critical care (London, England) · 2024Article
- Derivation and Validation of Generalized Sepsis-induced Acute Respiratory Failure Phenotypes Among Critically Ill Patients: A Retrospective Study.Research square · 2024Article
- Noninvasive vs invasive respiratory support for patients with acute hypoxemic respiratory failure.PloS one · 2024Observational
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Acute respiratory failure is a common reason for ICU admission and imposes significant strain on patients and the healthcare system. Noninvasive positive-pressure ventilation and high-flow nasal oxygen are increasingly used as an alternative to invasive mechanical ventilation to treat acute respiratory failure. As such, there is a need to accurately cohort patients using large, routinely collected, clinical data to better understand utilization patterns and patient outcomes. The primary objective of this retrospective observational study was to externally validate our computable phenotyping algorithm for patients with acute respiratory failure requiring various sequences of respiratory support in real-world data from a large healthcare delivery network.
designThis is a cross-sectional observational study to validate our algorithm for phenotyping acute respiratory patients by method of respiratory support. We randomly selected 5% (
settingData were extracted from a clinical data warehouse containing electronic health record data from 46 ICUs in the southwest United States. PATIENTS: All adult (≥ 18 yr) patient records requiring any type of oxygen therapy or mechanical ventilation between November 1, 2013, and September 30, 2020, were extracted for the study.
interventionsNone. MEASUREMENTS AND MAIN
resultsMicro- and macroaveraged multiclass specificities of the algorithm were 0.902 and 0.896, respectively. Sensitivity and specificity of phenotypes individually were greater than 0.90 for all phenotypes except for those patients extubated from invasive to noninvasive ventilation. We successfully created clinical subgroups of common illnesses requiring ventilatory support and provide high-level comparison of outcomes.
conclusionsThe electronic phenotyping algorithm is robust and provides a necessary tool for retrospective research for characterizing patients with acute respiratory failure across modalities of respiratory support.
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Registered trials
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