Sepsis remains a leading cause of mortality in intensive care units (ICUs) worldwide, underscoring the urgent need for early detection to improve patient outcomes. While artificial intelligence (AI) models trained on ICU data show promise for sepsis prediction, their clinical utility is frequently hampered by poor generalization under external validation, largely attributable to distribution shifts arising from heterogeneity in data. Prior studies have focused on direct model deployment or conventional transfer learning methods (e.g., fine tuning), yet systematic exploration of alternative strategies and root causes of performance degradation remains limited. In this study, we quantify those distribution shifts across three harmonized adult ICU cohorts: the high resolution HiRID database (Bern University Hospital, Switzerland; 29,698 stays, 2008 to 2019; 6.3% sepsis), MIMIC IV (Beth Israel Deaconess Medical Center, USA; 63,425 stays, 2008 to 2019; 5.2% sepsis), and eICU (208 US hospitals; 123,413 stays, 2014 to 2015; 4.6% sepsis) for a total of 216,536 stays and 10,846 sepsis cases. We then evaluate five deployment strategies across three model architectures (CNN, InceptionTime, LSTM) under four target data regimes: none, small (32000). The strategies are direct generalisation, standard transfer learning (fine tuning / retraining), target training, supervised domain adaptation (DA: MMD or CORAL), and fusion training (merged datasets). Key results demonstrate that fine tuning consistently underperforms across all data sizes (adjusted p 0.05 vs. DA MMD) but lags in nAUPRC. Overall, our results call for moving beyond routine fine tuning: retraining or fusion are preferable in data poor or data rich scenarios, whereas domain adaptation offers the most stable and substantial gains when moderate target data are available.
Tranchellini et al. (Fri,) studied this question.