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

BackgroundThe 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.MethodsThe 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.ResultsIn 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.ConclusionsThe 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.

[1]  Christopher G. Chute,et al.  The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data , 2010, J. Am. Medical Informatics Assoc..

[2]  S. Jaber,et al.  Clinical practice and risk factors for immediate complications of endotracheal intubation in the intensive care unit: A prospective, multiple-center study* , 2006, Critical care medicine.

[3]  P. Chountas,et al.  Development of a clinical data warehouse , 2004, 2004 IDEAS Workshop on Medical Information Systems: The Digital Hospital (IDEAS-DH'04).

[4]  Vitaly Herasevich,et al.  Validation of an electronic surveillance system for acute lung injury , 2009, Intensive Care Medicine.

[5]  James A. Onigkeit,et al.  Retrospective Derivation and Validation of a Search Algorithm to Identify Emergent Endotracheal Intubations in the Intensive Care Unit , 2013, Applied Clinical Informatics.

[6]  D. Ray,et al.  Tracheal intubation in the critically ill: a multi-centre national study of practice and complications. , 2012, British journal of anaesthesia.

[7]  V. Herasevich,et al.  Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. , 2011, Mayo Clinic proceedings.

[8]  Jonathan A. Zlabek,et al.  Early cost and safety benefits of an inpatient electronic health record , 2011, J. Am. Medical Informatics Assoc..

[9]  Irene Pala,et al.  BMC Medical Informatics and Decision Making , 2014, BMC Medical Informatics and Decision Making.

[10]  G. Isac,et al.  Complications of endotracheal intubation in the critically ill , 2008, Intensive Care Medicine.

[11]  V. Herasevich,et al.  Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. , 2010, Mayo Clinic proceedings.

[12]  J. Stauffer,et al.  Complications and consequences of endotracheal intubation and tracheotomy. A prospective study of 150 critically ill adult patients. , 1981, The American journal of medicine.