Retrospective Derivation and Validation of a Search Algorithm to Identify Emergent Endotracheal Intubations in the Intensive Care Unit

Summary

Background: The development and validation of automated electronic medical record (EMR) search strategies are important in identifying emergent endotracheal intubations in the intensive care unit (ICU).

Objective: To develop and validate an automated search algorithm (strategy) for emergent endotracheal intubation in the critically ill patient.

Methods: The EMR search algorithm was created through sequential steps with keywords applied to an institutional EMR database. The search strategy was derived retrospectively through a secondary analysis of a 450-patient subset from the 2,684 patients admitted to either a medical or surgical ICU from January 1, 2010, through December 31, 2011. This search algorithm was validated against an additional 450 randomly selected patients. Sensitivity, specificity, and negative and positive predictive values of the automated search algorithm were compared with a manual medical record review (the reference standard) for data extraction of emergent endotracheal intubations. Results: In the derivation subset, the automated electronic note search strategy achieved a sensitivity of 74% (95% CI, 69%-79%) and a specificity of 98% (95% CI, 92%-100%). With refinements in the search algorithm, sensitivity increased to 95% (95% CI, 91%-97%) and specificity decreased to 96% (95% CI, 92%-98%) in this subset. After validation of the algorithm through a separate patient subset, the final reported sensitivity and specificity were 95% (95% CI, 86%-99%) and 100% (95% CI, 98%-100%).

Conclusions: Use of electronic search algorithms allows for correct extraction of emergent endotracheal intubations in the ICU, with high degrees of sensitivity and specificity. Such search algorithms are a reliable alternative to manual chart review for identification of emergent endotracheal intubations.

Keywords

Electronic medical record - emergent - endotracheal intubation - intensive care unit - search algorithm

 

Conflict of Interest

The authors have no conflicts of interest in the research.

• References

• 1 Le Tacon S. et al. Complications of difficult tracheal intubations in a critical care unit.. Ann Fr Anesth Reanim. 2000; 19 (10) 719-724.
• 2 Stauffer JL, Olson DE, Petty TL. Complications and consequences of endotracheal intubation and tracheotomy: a prospective study of 150 critically ill adult patients.. Am J Med 1981; 70 (01) 65-76.
• 3 Griesdale DE. et al. Complications of endotracheal intubation in the critically ill.. Intensive Care Med 2008; 34 (10) 1835-1842.
• 4 Schwartz DE, Matthay MA, Cohen NH. Death and other complications of emergency airway management in critically ill adults: a prospective investigation of 297 tracheal intubations.. Anesthesiology 1995; 82 (02) 367-376.
• 5 Mort TC. Emergency tracheal intubation: complications associated with repeated laryngoscopic attempts.. Anesth Analg 2004; 99 (02) 607-613.
• 6 Clifford L. et al. Electronic health record surveillance algorithms facilitate the detection of transfusion-related pulmonary complications.. Transfusion 2013; 53 (06) 1205-1216.
• 7 Singh B. et al. Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records.. Mayo Clin Proc 2012; 87 (09) 817-824.
• 8 Chute CG, Beck SA, Fisk TB, Mohr DN. The Enterprise Data Trust at Mayo Clinic: a semantically integrated warehouse of biomedical data.. J Am Med Inform Assoc 2010; 17 (02) 131-135.
• 9 Herasevich V. et al. Validation of an electronic surveillance system for acute lung injury.. Intensive Care Med 2009; 35 (06) 1018-1023.
• 10 Alsara A. et al. Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury.. Mayo Clin Proc 2011; 86 (05) 382-388.
• 11 Hsiao CJ, Hing E, Socey TC, Cai B. Electronic health record systems and intent to apply for meaningful use incentives among office-based physician practices: United States, 2001–2011.. NCHS Data Brief 2011; 79: 1-8.
• 12 Bays RA, Kaelin LD. Electronic medical records for the office.. J Vasc Surg 2010; 51 (05) 1302-1308.
• 13 Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records.. N Engl J Med 2010; 363 (06) 501-504.
• 14 Mort TC. The incidence and risk factors for cardiac arrest during emergency tracheal intubation: a justification for incorporating the ASA Guidelines in the remote location.. J Clin Anesth 2004; 16 (07) 508-516.
• 15 Jaber S. et al. Clinical practice and risk factors for immediate complications of endotracheal intubation in the intensive care unit: a prospective, multiple-center study.. Crit Care Med 2006; 34 (09) 2355-2361.
• 16 Simpson GD, Ross MJ, McKeown DW, Ray DC. Tracheal intubation in the critically ill: a multi-centre national study of practice and complications.. Br J Anaesth 2012; 108 (05) 792-799.
• 17 Kutney-Lee A, Kelly D. The effect of hospital electronic health record adoption on nurse-assessed quality of care and patient safety.. J Nurs Adm 2011; 41 (11) 466-472.
• 18 Murff HJ. et al. Automated identification of postoperative complications within an electronic medical record using natural language processing.. JAMA 2011; 306 (08) 848-855.
• 19 Wisniewski MF. et al. Development of a clinical data warehouse for hospital infection control.. J Am Med Inform Assoc 2003; 10 (05) 454-462.