NCT05777187

Mitigation of Postoperative Delirium in High-Risk Patients

Integration of Machine Learning and Clinical Decision Support to Prevent Postoperative Delirium in Patients With Cognitive Impairment

• Status: Terminated
• Phase: N/A
• Study type: Interventional
• Enrollment: 7,412 (actual)
• Sponsor: Icahn School of Medicine at Mount Sinai · Academic / Other
• Sex: All
• Age: 18 Years – 120 Years
• Healthy volunteers: Not accepted

Summary

Among patients with cognitive impairment (CI) that undergo surgery, the risk for developing postoperative delirium (POD) is high (50%) and associated with further morbidity and mortality. Yet, 30-40% of POD cases are preventable with perioperative management. This randomized pragmatic clinical trial aims to assess incidence of POD in adult surgical patients with CI, as well as provider adherence to a set of 12 perioperative best practice recommendations for perioperative management. Electronic health record (EHR) data will be used to identify patients as high risk for developing POD and clinical decision support (CDS) prompts within the EHR will display best practices. Cases will be randomized to either the control group, usual care or the intervention which includes the high-risk alert and best practice prompts.

Conditions

• Post-operative Delirium
• Decision Support Systems, Clinical
• Cognitive Impairment

Interventions

Type	Name	Description
OTHER	Clinical Decision Support	The intervention will consist of clinical decision support alerts in the electronic health record directed towards anesthesiologists caring for patients with preexisting cognitive impairment. This intervention will alert towards delirium risk informed by history of cognitive impairment and promote 12 evidence based best practices during care for perioperative patients.

Timeline

• Start date: 2023-06-20
• Primary completion: 2024-08-31
• Completion: 2024-08-31
• First posted: 2023-03-21
• Last updated: 2026-02-19
• Results posted: 2026-02-19

Locations

1 site across 1 country: United States