Validation of an Electronic Phenotyping Algorithm for Patients With Acute Respiratory Failure
Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine..
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.
DESIGN: This is a cross-sectional observational study to validate our algorithm for phenotyping acute respiratory patients by method of respiratory support. We randomly selected 5% (n = 4,319) from each phenotype for manual validation. We calculated the algorithm performance and generated summary statistics for each phenotype and a priori defined clinical subgroups.
SETTING: Data 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.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Micro- 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.
CONCLUSIONS: The 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:4 |
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| Enthalten in: |
Critical care explorations - 4(2022), 3 vom: 01. März, Seite e0645 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Essay, Patrick [VerfasserIn] |
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| Links: |
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| Themen: |
Algorithms |
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| Anmerkungen: |
Date Revised 25.03.2022 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1097/CCE.0000000000000645 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine. | ||
| 520 | |a 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 | ||
| 520 | |a DESIGN: This is a cross-sectional observational study to validate our algorithm for phenotyping acute respiratory patients by method of respiratory support. We randomly selected 5% (n = 4,319) from each phenotype for manual validation. We calculated the algorithm performance and generated summary statistics for each phenotype and a priori defined clinical subgroups | ||
| 520 | |a SETTING: Data were extracted from a clinical data warehouse containing electronic health record data from 46 ICUs in the southwest United States | ||
| 520 | |a 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 | ||
| 520 | |a INTERVENTIONS: None | ||
| 520 | |a MEASUREMENTS AND MAIN RESULTS: Micro- 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 | ||
| 520 | |a CONCLUSIONS: The 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a algorithms | |
| 650 | 4 | |a computable phenotype | |
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| 700 | 1 | |a Mosier, Jarrod M |e verfasserin |4 aut | |
| 700 | 1 | |a Subbian, Vignesh |e verfasserin |4 aut | |
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