Validation of a Risk Assessment Model for Postoperative Delirium Based on Artificial Intelligence

NCT ID: NCT05639348

Last Updated: 2024-12-18

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 993 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-11-21
• Study Completion Date: 2024-06-15

Brief Summary

Postoperative delirium (POD) is a frequent postoperative complication in the elderly, characterised by fluctuating disturbances in attention, awareness, and cognition. Identifying the patients at highest risk of developing POD was the aim of the artificial intelligence (AI)-based algorithm PIPRA. This prospective cohort study is to externally validate the AI-based PIPRA algorithm. The primary endpoint is the performance (AUC) of the PIPRA algorithm in predicting POD. The secondary endpoint is the performance (AUC) of the clinicians in predicting POD (and how it compares with the performance of the PIPRA algorithm).

Detailed Description

Perioperative neurocognitive disorders (PND) include postoperative delirium (POD) and postoperative neurocognitive disorder or postoperative cognitive dysfunction (POCD). POD is recognised as a frequent postoperative complication in the elderly, occurring in 10% to 50% of older patients after major surgical procedures. POD usually occurs in the early postoperative period and is defined as an acute neuropsychiatric disorder. It is characterised by fluctuating disturbances in attention, awareness, and cognition. The American Society of Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on Postoperative Delirium Prevention recommend focusing on identifying those patients at highest risk of developing POD. Identifying these highest risk patients was the aim of the artificial intelligence (AI)-based algorithm PIPRA, which was created based on an individual participant data (IPD) meta-analysis including more than 2500 patients. This risk-prediction algorithm uses standard data (i.e. age, height, weight, history of delirium, cognitive impairment, ASA status, number of medications, preoperative C reactive protein (CRP), surgical risk and laparotomy), which are routinely collected before surgery. PIPRA was internally validated with an area under the curve (AUC) of 0.837 with 95% confidence interval 0.808 to 0.865, when plotting the true positive rate against the false positive rate. The aim of this prospective cohort study is to externally validate the AI-based PIPRA algorithm.

First, the anaesthesiologist in charge will be asked to evaluate, based on his/her experience (quantified in years of anaesthesia practice), the risk for the included patient to develop POD (categorised as low, intermediate, high or very high). Next, an investigator will assess included patents in a systematic and reproductible manner. After surgery, an investigator will visit the patient twice daily from postoperative day 1 to 5 or until hospital discharge (whichever occurs first) to screen for delirium using the 4AT or the ICDSC. The PIPRA score will be calculated separately by the coordinating study centre.

Conditions

Postoperative Delirium (POD)

Keywords

Perioperative neurocognitive disorders (PND); Postoperative cognitive dysfunction (POCD); Acute neuropsychiatric disorder; Cognitive testing; Artificial intelligence (AI)-based algorithm PIPRA; Intensive Care Delirium Screening Checklist (ICDSC); 4 "A" Tests (4AT)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

Data collection on POD for calculation of the PIPRA score

Data collection for presence of POD as diagnosed by the 4 "A" Tests (4AT) or the Intensive Care Delirium Screening Checklist (ICDSC). The collected data will be used to validate the existing PIPRA algorithm and to improve the algorithm and evaluate it in a cross-validation setting. For the model validation the area under the receiver operating characteristics (ROC) curve (AUC) will be computed.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Surgical patients ≥60 years old
• Planned postoperative hospital stay ≥ 2 days
• Consent from patient

Exclusion Criteria

• Preoperative delirium
• Insufficient knowledge in German or French
• Intracranial surgery
• Cardiac surgery
• Surgery within the two previous weeks
• Patient unable to consent

• Minimum Eligible Age: 60 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Innosuisse - Swiss Innovation Agency

OTHER

Sponsor Role: collaborator

University Hospital, Basel, Switzerland

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Luzius Steiner, Prof. Dr. med.

Role: PRINCIPAL_INVESTIGATOR

University Hospital Basel, Department of Anaesthesiology

Locations

University Hospital Basel, Anaesthesiology

Basel, , Switzerland

Hôpitaux Universitaires de Genève, Anesthesiology

Geneva, , Switzerland

Centre Hospitalier Universitaire Vaudois, Anesthesiology

Lausanne, , Switzerland

Countries

Switzerland

Other Identifiers

2022-00990; am21Dell- Kuster

Identifier Type: -

Identifier Source: org_study_id