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This study aimed to identify potential latent profiles of frailty among patients undergoing cardiac surgery, reveal the risk factors associated with these subgroups and understand the nursing needs of patients in different subgroups.
Patients scheduled for cardiac surgery at a tertiary general hospital in Southwest China between August 2022 and June 2023 were recruited using convenience sampling. The instruments included the General Information Questionnaire, the Chinese version of the Tilburg Frailty Indicator, the Hospital Anxiety and Depression Scale, the Mini-Mental State Examination and the Fatigue Severity Scale. Latent profile analysis was performed to identify potential classifications of preoperative frailty. Univariable and multinomial logistic regression analyses were used to determine associated influencing factors.
A total of 261 patients were included, with a preoperative frailty prevalence of 69.7% and a median TFI score of 6 (IQR: 4–8). Latent profile analysis identified three distinct frailty phenotypes: ‘multidimensional low-load frailty’ (29.5%), ‘social high-load frailty’ (8.8%) and ‘physiopsychological complex frailty’ (61.7%). Multinomial logistic regression revealed significant predictors for these profiles: absence of a spouse, younger age and longer disease duration were independently associated with social high-load frailty. Higher fatigue scores increased the likelihood of physiopsychological complex frailty. Conversely, higher cognitive scores were significantly associated with the multidimensional low-load frailty profile.
Preoperative frailty in cardiac surgery patients presents significant heterogeneity. Clinicians should pay particular attention to patients with social high-load frailty and physiopsychological complex frailty. Tailored nursing interventions based on these specific profiles and their associated risk factors are essential to alleviate frailty and improve patient outcomes.
This study adhered to the STROBE guidelines for cross-sectional studies.
Distinct frailty profiles among preoperative cardiac surgery patients were identified. Understanding these profiles enables tailored nursing interventions and potentially optimises postoperative outcomes. Implementing profile-specific care pathways can enhance perioperative patient management.
Patients recovering from cardiac surgery participated in reviewing the comprehensibility of survey questions for latent profiles. Members of a cardiac patient support group provided feedback on the interpretability of the findings.
Delirium is a common complication following cardiac surgery and significantly affects patient prognosis and quality of life. Recently, the application of artificial intelligence (AI) has gained prominence in predicting and assessing the risk of postoperative delirium, showing considerable potential in clinical settings.
This scoping review summarises existing research on AI-based prediction models for post-cardiac surgery delirium and provides insights and recommendations for clinical practice and future research.
Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, eight databases were searched: China National Knowledge Infrastructure, Wanfang Database, China Biomedical Literature Database, Virtual Information Platform, PubMed, Web of Science, Medline, and Embase. Studies meeting the inclusion criteria were screened, and data were extracted on surgery type, delirium assessment tools, predictive factors, and AI-based prediction models. The search covered database inception through January 12, 2025. Two researchers independently conducted the literature review and data analysis.
Ten studies from China, Canada, and Germany involving 11,702 participants were included. The reported incidence of postoperative delirium ranged from 5.56% to 34%. The most commonly used assessment tools were Confusion Assessment Method for the Intensive Care Unit, Diagnostic and Statistical Manual of Mental Disorders-5, and Intensive Care Delirium Screening Checklist. Key predictive factors included age, cardiopulmonary bypass time, cerebrovascular disease, and pain scores. AI-based prediction models were primarily developed using R (6/10, 60%) and Python (4/10, 40%). Model performance, as measured by the area under the curve, ranged from 0.544 to 0.92. Among these models, Random Forest (RF) was the most effective (5/10, 50%), followed by XGBoost (3/10, 30%) and Artificial Neural Networks (2/10, 20%).
AI-based models show promise for predicting postoperative delirium in cardiac surgery patients. Future studies should prioritise integrating these models into clinical workflows, conducting rigorous multicenter external validation, and incorporating dynamic, time-varying perioperative variables to enhance generalizability and clinical utility.
This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
This study did not include patient or public involvement in its design, conduct, or reporting.
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