Development of a Prediction Model for Delirium After Cardiac Surgery Using a Novel Self-Administered Preoperative Cognitive Assessment Tool

Summary

Participation Requirements

Description

Elderly patients undergoing surgery are more vulnerable to adverse postoperative outcomes due to advanced age, frailty, and concomitant medical conditions. Adverse cognitive outcomes such as postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are frequently encountered in old...

Elderly patients undergoing surgery are more vulnerable to adverse postoperative outcomes due to advanced age, frailty, and concomitant medical conditions. Adverse cognitive outcomes such as postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are frequently encountered in older surgical patients, and are associated with increased morbidity and mortality. Since there is no simple and effective way to treat POD and/or POCD once they have occurred, prevention may be the key. Previous research has shown that delirium may be partially prevented using multicomponent risk intervention strategies. These prophylactic measures are best targeted at high-risk individuals. While most risk factors for POD and POCD may be detected in the medical history, clinical examination, or laboratory investigations, some may be missed in the absence of a specific assessment. Pre-existing cognitive impairment in surgical patients is one of the strongest risk factors for further postoperative cognitive decline including POD and POCD. However, it tends to be underdiagnosed, because an objective evaluation of the cognitive performance is time-consuming and usually requires trained personnel. At present, risk prediction models for POD after cardiac surgery either lack internal and external validation, use only brief cognitive assessments, and/or do not include cognitive functions at all. In 2014, we developed a self-administered tablet computer program for the iPad (CogCheck) to assess the patient's individual risk for adverse postoperative cognitive outcomes. User-friendliness of the CogCheck application was tested in 20 cognitively healthy and 13 cognitively impaired volunteers, as well as in 47 patients undergoing surgery. In addition, normative data for the CogCheck tool have been generated in a previous study with 283 healthy volunteers. Respecting the continuing allocation of limited resources, the tool is self-explanatory and concise with a mean (SD) administration time in healthy participants of 21.7 (2.2) minutes. POD occurs predominantly after cardiac surgery, with a reported incidence between 6% and 56%. The aim of this observational study is to validate the use of the CogCheck application as a risk prediction tool for postoperative delirium in patients undergoing cardiac surgery. Data are gathered from patients undergoing cardiac surgery at the University Hospital Basel. By comparing patients with and without POD, we will determine which subtests of the CogCheck tool best predict the occurrence of POD. Based on the final prediction model, risk profiles are created to facilitate the identification of patients that might benefit from preventive measures in the preoperative period.

Tracking Information