Due to special reasons such as severe surgical trauma, high surgical stress, cardiopulmonary bypass, and the need to be transferred to the Cardiothoracic and Vascular Intensive Care Unit after surgery1-3, the incidence of postoperative delirium (POD) following cardiac and great vessel surgery is as high as 26%\~52%4-6. POD refers to an acute neurofunctional disorder that occurs after surgery, mainly characterized by confusion and decreased ability of spatial and temporal orientation7-12. Relevant studies have confirmed that POD significantly prolongs the duration of mechanical ventilation and ICU stay of patients, increases the incidence of complications, and is closely associated with long-term cognitive decline and increased long-term mortality13, which has become a key issue affecting the prognosis of surgical patients. The 2024 Update of the Evidence-Based and Consensus Guidelines on Postoperative Delirium in Adult Patients by the European Society of Anaesthesiology and Intensive Care Medicine clearly points out that POD seriously affects the outcome of the disease, which can lead to prolonged hospital stay, increased mortality, further impairment of physical and cognitive functions, and increased medical costs of patients, so it should attract the attention of clinical medical workers and researchers14. However, it is more noteworthy that although the incidence of delirium after cardiac surgery is high, there is no ideal treatment method. At present, the guidelines only recommend the use of low-dose haloperidol or dexmedetomidine for symptomatic treatment of POD, and the quality of evidence and strength of recommendation are both weak15. At the same time, the guidelines emphasize that more in-depth research on the pathogenesis, existing risk factors, development and treatment of POD should be encouraged16. The guidelines specifically propose that "early identification of patients at high risk of POD is required", emphasizing the important value of early intervention in improving the prognosis of patients. Against this background, clarifying the predictive factors of POD and reducing its incidence have become an urgent and important clinical task.
In terms of research progress, certain breakthroughs have been made in the exploration of POD risk identification and prognosis prediction: many previous studies published in Anesthesiology, BJA and other journals have pointed out that advanced age, male gender, preoperative cognitive impairment, sleep deprivation, frailty and other factors may be risk factors for POD17-20, and some studies have also attempted to construct simple risk assessment models based on these factors21,22. However, on the whole, the current research still has significant limitations, which restrict the repeated verification and further exploration of the research results. On the one hand, the correlation of risk factors is mostly verified by single-center and small-sample studies, lacking the support of large-sample and long-term follow-up data, resulting in fragmented research evidence, which makes it difficult to fully reveal the pathogenesis and prognostic impact chain of postoperative delirium; on the other hand, the lack of standardized norms for research data collection leads to the difficulty in repeated verification of research results, which has also become a core problem restricting the progress of risk identification and prognosis prediction research. The specific reasons mainly include two aspects: ① Ununified assessment tools: There are more than 10 kinds of POD diagnostic tools, including CAM-ICU, ICDSC, DRS-R-98, etc. Different studies adopt different tools, resulting in a difference in incidence rate of up to 2-3 times, making it impossible to compare research results horizontally17-22; ② Scattered data storage: POD-related data (preoperative cognitive assessment, intraoperative monitoring records, postoperative delirium diagnosis, long-term follow-up) are scattered in the hospital's HIS system, LIS system, nursing record system and outpatient follow-up system, lacking integration in a unified database, leading to low efficiency of research data extraction and easy omission of key information23-25.
In response to the above research gaps and clinical needs, this study innovatively adopts a bidirectional cohort study design. By integrating two research methods, namely "retrospective tracing of postoperative information of past cardiac surgery patients" and "prospective collection of postoperative data of cardiac surgery patients", it realizes the construction of a large-sample postoperative follow-up database, thereby establishing a full-chain analysis framework of "exposure - delirium - outcome", and fundamentally solving the core problems of existing studies: At the level of diagnostic tools, based on clinical evidence-based evidence, this study will uniformly adopt the internationally recognized CAM-ICU/3D CAM assessment tools to ensure the consistency of POD diagnostic criteria, eliminate the statistical deviation of incidence rate caused by tool differences, and realize the horizontal comparability of research data; At the level of data integration, this study will cooperate with the hospital's information department to construct a special integrated database for POD in cardiac surgery, systematically connect data from multiple systems such as HIS, LIS, nursing records and outpatient follow-up, and conduct structured collation according to the time dimension of "preoperative - intraoperative - postoperative - long-term" to solve the problem of scattered data storage and improve the efficiency and integrity of data extraction; At the level of long-term follow-up data support, relying on the follow-up management platform of the anesthesiology department, this study will establish a large-sample long-term follow-up cohort to realize the full-cycle data collection from preoperative cognitive baseline to long-term postoperative prognosis, fill the gap of fragmented evidence in existing studies, and fully reveal the pathogenesis and prognostic impact path of POD.
In summary, this study can not only fill the gaps in current POD research in terms of tool unification, data integration and long-term follow-up through standardized design and innovative methods, but also provide direct scientific basis for the clinical formulation of POD risk prediction models and prevention and control strategies. It has important academic and practical value, and is of great significance for promoting the improvement of prognosis of patients in the field of cardiac and great vessel surgery.
