AI Article Synopsis
- Postoperative delirium (POD) is a serious condition that can happen after surgery, but it might be preventable with the right help.
- Researchers created a smart program to predict POD using data from portable EEG machines, which are easier to use than traditional ones.
- In a study with 128 heart surgery patients, they found that the EEG could accurately forecast POD in 86% of cases by looking at certain brain wave patterns before surgery.
Article Abstract
Introduction: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice.
Objective: We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device.
Methods: We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery.
Results: Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93.
Discussion: This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10682064 | PMC |
| http://dx.doi.org/10.3389/fpsyt.2023.1287607 | DOI Listing |
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