External Validation of a Bayesian Network for Sepsis Mortality Prediction

The primary objective of this study is to assess whether a Bayesian network model can retain its predictive power when applied to a different dataset. We implemented a published Bayesian network for predicting sepsis-related mortality and tested it on a dataset distinct from that used to develop the network. In predicting 5-day mortality, our observed performance metrics were slightly lower (AUC = 0.80) than the published values (AUC = 0.85). The model handled missing data effectively, achieving a sensitivity of 0.71 and a ROC AUC of 0.74. The study high-lights the potential in using Bayesian networks for sepsis prediction, especially in settings with limited resources and missing data. Although dataset shift restricts models from seamless adaptation to different datasets, better reporting may allow for more reliable implementation.