Surveillance of H. pylori prevalence and antibiotic resistance in the urban Chinese population: a nationwide multi-center study

tient cases from MIMIC-IV, were integrated into a web app developed using Django.Clinicians involved in antimicrobial prescribing were recruited to review these cases and provide IVOS decisions.Results: 5,610 unique stays were used for cross-validation with the prospective dataset containing 547.The model achieved an AU-ROC of 0.77, accuracy of 0.70, true positive rate of 0.61, and false positive rate of 0.21 on the unseen prospective dataset, predicting that 51% of patients could have switched earlier than they did.Minimal differences between the real and predicted switch events correlated with reduced mean patient LOS.A statistically significant (p-value < 0.01) difference in remaining LOS was observed between patients receiving oral versus intravenous treatment until five days of intravenous treatment.Data collection for the case vignette study is ongoing and will be completed before ICID.Preliminary qualitative results indicate that if a user trusts the CDSS, it sees increased engagement during decision-making, and can enable faster prescribing choices.Discussion: This CDSS aims to facilitate early switching when appropriate.Our model's predictions align with current guidelines, suggesting that switching in the first 48 hours is generally inappropriate, but subsequent early IVOS can be determined based on a patient's clinical status.Through case vignettes, we aim to quantify differences in prescribing behaviors between the CDSS and standard care, as well as evaluate user acceptability of the technology.Conclusion: AI-based CDSSs have the potential to support antimicrobial stewardship decision-making, though their impact on prescribing and safety remains unanswered.