Automated Identification of Immunocompromised Status in Critically Ill Children.

Abstract

Easy identification of immunocompromised hosts (ICHs) would allow for stratification of culture results based on host type. We utilized antimicrobial stewardship program (ASP) team notes written during handshake stewardship rounds in the pediatric intensive care unit (PICU) as the gold standard for host status; clinical notes from the primary team, medication orders during the encounter, problem list, and billing diagnoses documented prior to the ASP documentation were extracted to develop models that predict host status. We calculated performance for three models based on diagnoses/medications, with and without natural language processing from clinical notes. The susceptibility of pathogens causing bacteremia to commonly used empiric antibiotic regimens was then stratified by host status. We identified 844 antimicrobial episodes from 666 unique patients; 160 (18.9%) were identified as ICHs. We randomly selected 675 initiations (80%) for model training and 169 initiations (20%) for testing. A rule-based model using diagnoses and medications alone yielded a sensitivity of 0.87 (08.6-0.88), specificity of 0.93 (0.92-0.93), and positive predictive value (PPV) of 0.74 (0.73-0.75). Adding clinical notes into XGBoost model led to improved specificity of 0.98 (0.98-0.98) and PPV of 0.9 (0.88-0.91), but with decreased sensitivity 0.77 (0.76-0.79). There were 77 bacteremia episodes during the study period identified and a host-specific visualization was created. An electronic health record-based phenotype based on notes, diagnoses, and medications identifies ICH in the PICU with high specificity.