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

Abstract

BackgroundDevelopment and validation of automated electronic medical record (EMR) search strategies is important in identifying extubation failure in the intensive care unit (ICU). We developed and validated an automated search algorithm (strategy) for extubation failure in critically ill patients.MethodsThe EMR search algorithm was created through sequential steps with keywords applied to an institutional EMR database. The search strategy was derived retrospectively through secondary analysis of a 100-patient subset from the 978 patient cohort admitted to a neurological ICU from January 1, 2002, through December 31, 2011(derivation subset). It was, then, validated against an additional 100-patient subset (validation subset). Sensitivity, specificity, 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 extubation failure.ResultsIn the derivation subset of 100 random patients, the initial automated electronic search strategy achieved a sensitivity of 85% (95% CI, 56%-97%) and a specificity of 95% (95% CI, 87%-98%). With refinements in the search algorithm, the final sensitivity was 93% (95% CI, 64%-99%) and specificity increased to 100% (95% CI, 95%-100%) in this subset. In validation of the algorithm through a separate 100 random patient subset, the reported sensitivity and specificity were 94% (95% CI, 69%-99%) and 98% (95% CI, 92%-99%) respectively.ConclusionsUse of electronic search algorithms allows for correct extraction of extubation failure in the ICU, with high degrees of sensitivity and specificity. Such search algorithms are a reliable alternative to manual chart review for identification of extubation failure.

Association of Extubation Failure and Functional Outcomes in Patients with Acute Neurologic Illness

“…We excluded patients who required MV for <48 h. This lends the study to potential selection bias. However, patient intubated for short duration are at minimal risk for extubation failure [25], and previous studies investigating extubation failure have used different cutoffs (12-48 h) to exclude patients who are placed on MV for completely reversible causes [11,35]. Additionally, our study did not control for the type of acute neurologic illness which almost certainly impacts extubation rates and prognosis.…”

“…Details of this search strategy have been described elsewhere [11]. Briefly, data from the Mayo Clinic Life Sciences System [12], an exhaustive database of patient information which has been validated [13,14], was accessed using a Web-based commercial software tool set [Data Discovery and Query Builder (DDQB); International Business Machines Corp] [15].…”

Derivation and validation of a search algorithm to retrospectively identify CRRT initiation in the ECMO patients

“…With the widespread adoption of the use of EHR, electronic search strategies have increased in frequency [6]. Automated search strategies have shown high sensitivity and specificity in recognizing various medical entities [6,7]. These strategies would help to accurately identify any risk factors documented in the EHR, contribute to the utilization of various quality improvement initiatives, reduce the incidence of manual errors, and hence reduce the cost and enhance the effectiveness of medical care.…”

“…Therefore, electronic search strategies provide an added value to highly efficient data extraction within an electronic medical environment and allow rapid and more accurate analysis of large databases. [6,7].…”

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

“…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.…”