Nomogram for subsyndromal delirium after cardiac surgery with cardiopulmonary bypass: a retrospective cohort study

The incidence of subsyndromal delirium (SSD) after cardiac surgery with cardiopulmonary bypass (CPB) is relatively high. In this study, we aimed to analyze the risk factors associated with postoperative SSD and to construct a predictive model to facilitate early identification of SSD, thereby providing an effective clinical prediction tool.

We conducted a retrospective cohort study, including patients who underwent cardiac surgery with CPB at a tertiary hospital in Sichuan Province from January 2023 to March 2024. Clinical nurses performed six daily assessments for SSD by using the Confusion Assessment Method for the Intensive Care Unit until delirium occurred or the patient was discharged from the intensive care unit or died. A total of 994 patients were included and divided into a non-SSD group (n = 832) and an SSD group (n = 162). Univariate and multivariable logistic regression analyses were performed to identify independent risk factors for postoperative SSD. A nomogram was constructed using R software, and the model's discriminative ability was evaluated using the area under the receiver operating characteristic (ROC) curve.

Among the 994 patients who underwent cardiac surgery, the incidence of SSD was 16.30% (n = 162), and 46.30% (n = 52) of these patients subsequently progressed to delirium. Multivariable logistic regression analysis showed that age (OR = 1.039), body mass index (OR = 1.078), the use of pain medication after extubation (OR = 2.918), postoperative stress hyperglycemia (OR = 1.848), a postoperative minimum albumin level < 33.4 g/L (OR = 1.97), and a postoperative minimum hemoglobin level < 90 g/L (OR = 2.284) were independent risk factors for SSD after cardiac surgery. A risk prediction model was constructed based on these variables. The area under the ROC curve was 0.744 (95% CI [0.701-0.787]; p < 0.001), indicating moderate predictive accuracy of the model.

The incidence of SSD after cardiac surgery with CPB was high. The predictive model developed in this study demonstrated good predictive performance and may assist healthcare professionals in the early identification of high-risk patients, enabling timely preventive interventions to reduce the risk of postoperative SSD.