Electroencephalographic Biomarker to Predict Acute Post-operatory Cognitive Dysfunction

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Acute post-operatory cognitive dysfunction states are one of the most important complications in older patients after surgery. Two acute cognitive dysfunctions have been described: postoperative delirium (PD) and postoperative subsyndromal delirium (PSSD). In previous reports, the incidence of PD in...

Acute post-operatory cognitive dysfunction states are one of the most important complications in older patients after surgery. Two acute cognitive dysfunctions have been described: postoperative delirium (PD) and postoperative subsyndromal delirium (PSSD). In previous reports, the incidence of PD in older patients is between 10% to 30%, while PSSD is more frequent 30% to 50%. Patients who develop delirium, both as a complete or incomplete syndrome, have poorer long-term outcomes, such as longer length of hospital stay, institutionalization at discharge, and even higher mortality, and consequently, the human and economic costs significantly increase for the health system. An early diagnostic and prevention of delirium are the key points to decrease the poor long-term outcomes and health costs. The diagnosis requires cognitive testing to elucidate functional patients' status before and after surgery. The need for a biomarker that may predict the occurrence of PD and PSSD and allow the selection of patients who need prevention strategies is a primary research field. Here the research team will use an observational cohort, investigator blinded in two-center with a primary endpoint to validate the relative alpha power ratio as a predictive biomarker of postoperative cognitive dysfunctions. To calculate the sample size, the investigators used values obtained from a previous work in a cohort of 30 patients and decided to compare the prediction ability of MoCA and alpha power ratio. ROC curves and their AUC were used to calculate the prediction ability of MoCA and alpha power ratio. Thus, a sample size of 425 patients was calculated considering an AUC of MoCA = 0.786 and AUC of alpha power = 0.895, a two-tailed test, an alpha error of 0.05 and a power of 0.8 and considering a 25% loss. Investigators consider this study as a pilot validation trial to establish the utility and the capacity of the EEG biomarker for predicting PD and PSSD, the research team aims to include the 25% of the total sample. This yields the need for 106 patients for this preliminary trial.

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