Abstract 16450: Early Identification of High Risk Cardiac Decompensation Phenotypes via Real-time Electronic Health Record Data

Early identification of cardiac decompensation remains critical for improved patient outcomes. Digital phenotypes using real-time electronic health record (EHR) data offer an unbiased method to detect decompensation in at-risk individuals. Phenotypes designed to detect cardiac decompensation and its sequelae were retrospectively evaluated in 108,697 adult patient hospitalizations at a single center from October 2015-August 2018. The 6 phenotypes included hypotension, end organ dysfunction (EOD), hypoperfusion (concomitant hypotension and EOD), escalating vasoactive medication use (vasoactive meds), respiratory decline, and respiratory intervention. Median time from admission to phenotype development was measured in hours. In-hospital mortality and unanticipated ICU transfers were determined across all phenotypes and phenotype combinations. Prevalence and time to detection varied across all six phenotypes (Table 1), with EOD found most frequently (35.7%) and detected earliest (3.4h, IQR 0.9-26.2h). Among individual phenotypes, patients with hypoperfusion had the highest rates of unanticipated ICU transfer (20.62%) and in-hospital mortality (20.99%). Patients meeting at least one phenotype had a 5.90% ICU transfer rate and 5.04% in-hospital mortality rate, compared to 0.62% mortality and 2.19% ICU transfer rates for patients meeting zero phenotypes. Among the 41 measured phenotype combinations, patients meeting all 6 phenotypes had the highest rates of unanticipated ICU transfer (28.75%) and in-hospital mortality (36.45%). Digital phenotypes of decompensation using real-world EHR data identify patients at higher risk of unexpected ICU transfer and in-hospital mortality at early times points in the hospitalization. Further studies will evaluate if implementation of a digital phenotype detection tool can improve care pathways and outcomes.

Duke Scholars

Author Harvey Shi