Abstract:
Background Perioperative real-world data (RWD), despite its comprehensive capture of patient's information and clinical treatment processes, are seldomly utilized in clinical research.
Objective To construct a widespread IoT resources edifice for perioperative setting (WIRE) to integrate multi-source RWD during the perioperative period, so as to promote the application of intelligent technology in real-world studies.
Methods Based on the HL7 Reference Information Model (RIM) and incorporating characteristics of medical Internet of Things data along with clinical data models, the WIRE system was designed to achieve integration at the data model layer of perioperative medical information. Concurrently, the general methods for real-world studies (RWS) of perioperative intelligent early warning technology based on WIRE were explored, and early warning models for intraoperative hypoxemia and hypotension were constructed, so as to develop a risk early warning system integrated with these models.
Results The perioperative specialty data resource libraries based on WIRE were successfully established at the Sixth Medical Center of Chinese PLA General Hospital and Guangdong Provincial People's Hospital, aggregating data from 6 483 and 27 939 surgical cases, providing ample data resources for the early warning models. The intraoperative hypotension early warning model and the intraoperative hypoxemia prediction models for 3-minute and 5-minute early warnings showed a higher accuracy, recall, and F1 scores compared to the performance of anesthesiologists (P < 0.05). Moreover, the risk early warning system enabled real-time alerting for the potential occurrence of intraoperative complications in surgical patients.
Conclusion WIRE can effectively integrate RWD from the perioperative period of surgical patients, facilitating the development of clinical research and providing practical references for the industrial application of clinical data elements.
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