Development of an Electronic Algorithm to Identify in Real Time Adults Hospitalized With Suspected Community-Acquired Pneumonia. (2nd June 2021) - Jones, George; Amoah, Joe; Klein, Eili Y; Leeman, Hannah; Smith, Aria; Levin, Scott; Milstone, Aaron M; Dzintars, Kathryn; Cosgrove, Sara E; Fabre, Valeria
Abstract: Background: Community-acquired pneumonia (CAP) is a major driver of hospital antibiotic use. Efficient methods to identify patients treated for CAP in real time using the electronic health record (EHR) are needed. Automated identification of these patients could facilitate systematic tracking, intervention, and feedback on CAP-specific metrics such as appropriate antibiotic choice and duration. Methods: Using retrospective data, we identified suspected CAP cases by searching for patients who received CAP antibiotics AND had an admitting International Classification of Diseases, Tenth Revision ( ICD-10 ) code for pneumonia OR chest imaging within 24 hours OR bacterial urinary antigen testing within 48 hours of admission (denominator query). We subsequently explored different structured and natural language processing (NLP)–derived data from the EHR to identify CAP cases. We evaluated combinations of these electronic variables through receiver operating characteristic (ROC) curves to assess which best identified CAP cases compared to cases identified by manual chart review. Exclusion criteria were age <18 years, absolute neutrophil count <500 cells/mm 3, and admission to an oncology unit. Results: Compared to the gold standard of chart review, the area under the ROC curve to detect CAP was 0.63 (95% confidence interval [CI], .55–.72; P <  .01) using structured data (ie, laboratory and vital signs) and 0.83 (95% CI, .77–.90; P <  .01) when NLP-derived data from radiographic reports were included. The sensitivity and specificity of the latter model were 80% and 81%, respectively. Conclusions: Creating an electronic tool that effectively identifies CAP cases in real time is possible, but its accuracy is dependent on NLP-derived radiographic data.