Objectives: Postoperative delirium is common, costly, and associated with long-term morbidity and increased mortality. We conducted a cohort study to assess the contribution of cardiopulmonary bypass to the development of postoperative delirium by means of algorithm-based data processing.
Methods: A database was compiled from three datasets of patients who underwent cardiac surgery between 2014 and 2019: Intensive care unit discharge files, cardiopulmonary bypass protocols, and medical quality management records. Following data extraction and structuring using novel algorithms, missing data was imputed. Ten independent imputations were analyzed by multiple logistic regression with stepwise deletion of factors to arrive at a minimal adequate model.
Results: Postoperative delirium was diagnosed in 456/3163 patients (14.4%). In addition to known demographic risk factors and comorbidities like male sex, age, carotid disease, acute kidney failure and diabetes mellitus, cardiopulmonary parameters like total blood volume at the cardiopulmonary bypass (AOR 1.001; CI 1.1001 to 1.002) were independent predictors of postoperative delirium. Higher values of the minimal blood flow were associated with a lower risk of postoperative delirium (AOR 0.993; CI 0.988 to 0.997). Flow rates at least 30% above target did emerge in the minimal adequate model as a potential risk factor, but the confidence interval suggested a lack of statistical significance (AOR 1.819; 95% CI: 0.955 to 3.463).
Conclusions: Cardiopulmonary bypass data processing proved to be a useful tool for obtaining compact information to better identify the roles of individual operational states. Strict adherence to perfusion limits along with tighter control of blood flow and acid-base balance during cardiopulmonary bypass may help to further decrease the risk of postoperative delirium.
Keywords: algorithm-based data processing; cardiac surgery; cardiopulmonary bypass; postoperative delirium; target flow deviations.
