Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit

Nathan Smischney, Venu M. Velagapudi, James A. Onigkeit, Brian W Pickering, Vitaly D Herasevich, Rahul Kashyap

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Background: The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient. Methods. The EMR search algorithm was developed on the basis of several mechanical ventilation parameters, with the final parameter being positive end-expiratory pressure (PEEP), and was applied to a comprehensive institutional EMR database. The search algorithm was derived from a secondary retrospective analysis of a subset of 450 patients from a cohort of 2,684 patients admitted to a medical ICU and a surgical ICU from January 1, 2010, through December 31, 2011. It was then validated in an independent subset of 450 patients from the same period. The overall percent of agreement between our search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using peak inspiratory pressure (PIP) as the reference standard, was compared to assess timing of mechanical ventilation initiation. Results: In the derivation subset, the automated electronic search strategy achieved an 87% (κ = 0.87) perfect agreement, with 94% agreement to within one minute. In validating this search algorithm, perfect agreement was found in 92% (κ = 0.92) of patients, with 99% agreement occurring within one minute. Conclusions: The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.

Original languageEnglish (US)
Article number55
JournalBMC Medical Informatics and Decision Making
Volume14
Issue number1
DOIs
StatePublished - Jun 25 2014

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Artificial Respiration
Intensive Care Units
Electronic Health Records
Patient Care Management
Positive-Pressure Respiration
Critical Care
Critical Illness
Medical Records
Databases
Pressure
Research

Keywords

  • Airway management
  • Electronic health records
  • Intensive care units
  • Mechanical ventilation initiation
  • Search algorithm

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy

Cite this

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title = "Derivation and validation of a search algorithm to retrospectively identify mechanical ventilation initiation in the intensive care unit",
abstract = "Background: The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient. Methods. The EMR search algorithm was developed on the basis of several mechanical ventilation parameters, with the final parameter being positive end-expiratory pressure (PEEP), and was applied to a comprehensive institutional EMR database. The search algorithm was derived from a secondary retrospective analysis of a subset of 450 patients from a cohort of 2,684 patients admitted to a medical ICU and a surgical ICU from January 1, 2010, through December 31, 2011. It was then validated in an independent subset of 450 patients from the same period. The overall percent of agreement between our search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using peak inspiratory pressure (PIP) as the reference standard, was compared to assess timing of mechanical ventilation initiation. Results: In the derivation subset, the automated electronic search strategy achieved an 87{\%} (κ = 0.87) perfect agreement, with 94{\%} agreement to within one minute. In validating this search algorithm, perfect agreement was found in 92{\%} (κ = 0.92) of patients, with 99{\%} agreement occurring within one minute. Conclusions: The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.",
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AU - Smischney, Nathan

AU - Velagapudi, Venu M.

AU - Onigkeit, James A.

AU - Pickering, Brian W

AU - Herasevich, Vitaly D

AU - Kashyap, Rahul

PY - 2014/6/25

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N2 - Background: The development and validation of automated electronic medical record (EMR) search strategies are important for establishing the timing of mechanical ventilation initiation in the intensive care unit (ICU).Thus, we sought to develop and validate an automated EMR search algorithm (strategy) for time zero, the moment of mechanical ventilation initiation in the critically ill patient. Methods. The EMR search algorithm was developed on the basis of several mechanical ventilation parameters, with the final parameter being positive end-expiratory pressure (PEEP), and was applied to a comprehensive institutional EMR database. The search algorithm was derived from a secondary retrospective analysis of a subset of 450 patients from a cohort of 2,684 patients admitted to a medical ICU and a surgical ICU from January 1, 2010, through December 31, 2011. It was then validated in an independent subset of 450 patients from the same period. The overall percent of agreement between our search algorithm and a comprehensive manual medical record review in the derivation and validation subsets, using peak inspiratory pressure (PIP) as the reference standard, was compared to assess timing of mechanical ventilation initiation. Results: In the derivation subset, the automated electronic search strategy achieved an 87% (κ = 0.87) perfect agreement, with 94% agreement to within one minute. In validating this search algorithm, perfect agreement was found in 92% (κ = 0.92) of patients, with 99% agreement occurring within one minute. Conclusions: The use of an electronic search strategy resulted in highly accurate extraction of mechanical ventilation initiation in the ICU. The search algorithm of mechanical ventilation initiation is highly efficient and reliable and can facilitate both clinical research and patient care management in a timely manner.

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