Abstract 328: Identification of In-Hospital Cardiac Arrest Using Administrative Billing Codes

Introduction: In-hospital cardiac arrest (IHCA) is experienced by approximately 200,000 patients annually in the US. While individual care teams can readily identify IHCA at the bedside, subsequent event identification for research or quality improvement (QI) purposes is challenging and often relies on the use of administrative billing codes. Prior research has shown that the use of administrative codes from International Classification of Diseases-9 (ICD-9) was both insensitive and nonspecific for identification of IHCA events. However, the performance of this approach using updated ICD-10 codes has not been established.

Hypothesis: ICD-10 codes can be used to identify IHCAs in a QI database of IHCA events with high sensitivity.

Methods: We performed a retrospective cohort study of all adult IHCA patients (

18 years old) at the Hospital of the University of Pennsylvania from 06/2018-03/2022. True IHCA events were confirmed by a research coordinator and a physician through detailed encounter-level chart review of clinical records. We searched for four individual ICD-10 diagnostic codes: ICD I46.2 - cardiac arrest due to underlying cardiac condition; ICD I46.8 - cardiac arrest due to other underlying condition; ICD I46.9 - cardiac arrest, unspecified; and ICD I49.01 - ventricular fibrillation. ICD-identified IHCA was defined as the presence of any of these codes from a hospital stay but not present on hospital arrival.

Results: Of 466 patients with a confirmed IHCA event contained within the QI database, the median age was 65 (54-73), 43% (199/466) were female, and 69% (323/466) had sustained return of spontaneous circulation (ROSC) at the end of their code. The use of billing codes identified 318 patients as having IHCA, corresponding to a sensitivity of 68%. The most used ICD-10 code in this cohort was ICD I46.9 (141/466, 30%).

Conclusions: The use of ICD-10 codes has a low sensitivity for identification of IHCA events. These findings are consistent with previously published work using older ICD iterations and suggests significant limitations with using administrative codes to identify IHCA events. Novel approaches (e.g., natural language processing and machine learning algorithms) to identify IHCA may facilitate more accurate research and QI efforts.