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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Postoperative Delirium (POD)

Intervention / Treatment
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* Other: Data collection on POD for calculation of the PIPRA score


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 60 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Other: 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.|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in 4 'A's Test (4AT) to evaluate presence of POD | 4 'A's Test (4AT) is a bedside medical scale with 4 parameters: Alertness Abbreviated mental test-4 (AMT4) Attention (months backwards test) Acute change or fluctuating course. The score range is 0-12, with scores of 4 or more suggesting possible delirium. Scores of 1-3 suggest possible cognitive impairment. | Twice daily (morning and evening) from postoperative day 1 until postoperative day 5, hospital discharge or a positive diagnosis of POD, whichever occurs first. | 
| Change in Intensive Care Delirium Screening Checklist (ICDSC) to evaluate presence of POD | The ICDSC is an 8-item delirium screening instrument (range: 0-8 points) that evaluates a patient's level of consciousness, inattention, disorientation, hallucinations or delusions, psychomotor activity, inappropriate speech or mood, sleep disturbance and fluctuation of symptoms (no delirium with a score of 0, less than clinical threshold symptoms of delirium with a score of 1-3 and clinical delirium with a score of 4-8). | Twice daily (morning and evening) from postoperative day 1 until postoperative day 5, hospital discharge or a positive diagnosis of POD, whichever occurs first. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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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)