Risk Prediction Model

The elderly patients have poorer overall conditions and have lower tolerance to trauma, anesthesia, and surgery. Therefore, the incidence of postoperative complications is relatively higher. In non-cardiac surgeries, approximately 20% of elderly patients experience postoperative complications, and the incidence of postoperative delirium (POD) is 23.8%. This may lead to prolonged hospital stays, increased hospital costs, and affect prognosis and even mortality. The investigators plan to conduct a prospective cohort study by systematically collecting biological samples and clinical information of elderly patients during the perioperative period to explore the possible risk factors and pathogenesis of postoperative delirium and postoperative complications in elderly surgical patients, and to construct a risk prediction model for postoperative complications.

The goal of this three-year mixed-methods observational study with an embedded randomized controlled trial is to develop and validate a frailty risk prediction model and evaluate an artificial intelligence-based voice emotion detection-guided counselling intervention in adults with congenital heart disease (ACHD). The main questions it aims to answer are: Are symptom clusters associated with frailty and psychological outcomes in adults with congenital heart disease? Can symptom clusters and psychosocial factors be used to predict frailty risk over time in ACHD patients? Does an AI-based voice emotion detection-guided counselling intervention improve psychological outcomes, fatigue, and quality of life among high-risk ACHD patients? Researchers will compare ACHD patients receiving AI-based voice emotion detection-guided counselling with those receiving usual care to determine whether the intervention reduces depression, anxiety, sleep disturbance, fatigue, and frailty risk, and improves grit and quality of life. Participants will: Complete longitudinal assessments of symptom clusters, frailty, and psychological status at baseline and follow-up time points Participate in qualitative interviews to explore lived experiences related to symptoms and frailty Receive AI-based voice emotion detection-guided counselling (intervention group only in Year 3)