Developing an Electronic Hospital Trigger for Bleeding – The Ottawa Hospital ETriggers Project

Abstract

Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital.

Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events.

Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84).

Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population.