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

Abstract

KeywordsElectronic medical record, emergent, endotracheal intubation, intensive care unit, search algorithm SummaryBackground: 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.

Retrospective Derivation and Validation of an Automated Electronic Search Algorithm to Identify Post operative Cardiovascular and Thromboembolic Complications

“…Comparison of the two methodologies demonstrated that the sensitivity and specificity of the automated search algorithm could approach 100% in many instances. These findings further corroborate previously published studies demonstrating that the use of automated search strategies produced very accurate results that were agreeable with those produced by manual review and data extraction methods [2,4,6,7]. Additionally, this study demonstrates that this methodology can be a reliable alternative to manual chart review for research pertaining to postoperative complications.…”

“…A commercially available, web-based query-building tool called Data Discovery and Query Builder (DDQB) was utilized for interrogation of data files contained within the databases listed above (International Business Machines, Corp). The DDQB tool allows clinicians and researchers without extensive programming knowledge to build complex queries based on Boolean logic and free-text searches [4,6]. These tools were used to search through patient records in the EMR to identify deep venous thrombosis, pulmonary embolism, or myocardial infarction within 30 days of the lower extremity orthopedic procedure.…”

“…For instance, search algorithms have been successfully developed for extracting information on Charlson comorbidities [2], initiation of emergent intubations in the intensive care unit (ICU) [4], extubation time in the ICU [5], risk factors for acute lung injury [6], and chronic co-morbidity phenotypes from the EMR and Genomics studies [7]. These studies have all demonstrated that electronic searches can achieve sensitivities and specificities greater than 90% when compared to manual search efforts.…”

Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit

“…Recently, automated search strategies have been used to identify certain elements in a patient’s EMR [6,7]. Recently, Smischney et al [7] developed and validated automated electronic search strategies to identify emergent intubations from EMRs.…”

Derivation and validation of an automated electronic search algorithm to identify patients at risk for obstructive sleep apnea

“…Our results add credence to the use of computerized searches of EHRs of large patient cohorts to extract clinical variables, processes, and outcomes of interest. (2,3,(12)(13)(14) The increased perioperative risk that OSA poses for surgical patients is well recognized, (4,5) and the condition has become the topic of clinical management guidelines that call for increased vigilance of affected patients. (11,15) Despite the risk, OSA may present with vague symptoms (eg, increased daytime sleepiness), and population studies have suggested that most (approximately 90%) of OSA cases are undiagnosed, (7) including OSA of surgical patients.…”