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.
Highlights
Development and validation of automated electronic medical record (EMR) search strategies is important in identifying extubation failure in the intensive care unit (ICU)
The automated electronic search strategy achieved a sensitivity of 85% and a specificity of 95% in the derivation subset (Table 1)
On the basis of this finding, the automated electronic note search strategy was refined to arrive at our final automated search algorithm in which the sensitivity was 93% and specificity increased to 100% in the same derivation subset of 100 patients
Summary
Development 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. In the intensive care unit (ICU) patient population, extubation failure is generally defined as requiring endotracheal reintubation within 72 hours of prior extubation [1]. The negative consequences of extubation failure include increased duration of mechanical ventilation, increased ICU length of stay (LOS), increased nosocomial pneumonia, and increased mortality [2,3]. The prevalence of extubation failure ranges from 2 to 25% depending on the population studied and the time frame (24–72 h) included for analysis [4]. These investigators found that by using the electronic search strategies, they were able to achieve a sensitivity and specificity that were greater than 95%
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