ArticleAnnals of clinical and translational neurology2025
Development of a Disease Model for Predicting Postoperative Delirium Using Combined Blood Biomarkers.
Article in Annals of clinical and translational neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction Models for Postoperative Delirium Among Cancer Patients: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Cognitive Impact of Colorectal Cancer Surgery in Elderly Patients: A Narrative Review.Cancers · 2026Review
- Explainable artificial intelligence in anesthesiology prediction models: bridging the gap from black-box algorithms to clinical decision support.Frontiers in medicine · 2026Review
- Recent advances in postoperative delirium in elderly patients: pathophysiological mechanisms, risk prediction, and therapeutic strategies.Frontiers in neuroscience · 2026Review
- Development and internal validation of a multivariable risk stratification model for preoperative anxiety in surgical patients: a retrospective observational study.Frontiers in medicine · 2026Article
- Low serum triglycerides related to delayed neurocognitive recovery in geriatric oral and maxillofacial surgery patients: A prospective cohort study.Lipids in health and disease · 2025Article
- Influence of different anesthetic depth on postoperative delirium in elderly pre-frail patients undergoing abdominal surgery.Perioperative medicine (London, England) · 2025Article
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
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Authors and funding
8 authors.
Funding
Abstract
objectivePostoperative delirium, a common neurocognitive complication after surgery and anesthesia, requires early detection for potential intervention. Herein, we constructed a multidimensional postoperative delirium risk-prediction model incorporating multiple demographic parameters and blood biomarkers to enhance prediction accuracy.
methodsWe included 555 patients undergoing radical surgery for colorectal cancer. Demographic characteristics and lipid profiles were collected preoperatively, and perioperative anesthesia and surgical conditions were recorded; blood biomarkers were measured before and after surgery. The 3D-CAM scale was used to assess postoperative delirium occurrence within 3 days after surgery. Patients were divided into the postoperative delirium (N = 100) and non-postoperative delirium (N = 455) groups. Based on machine learning, linear and nine non-linear models were developed and compared to select the optimal model. Shapley value-interpretation methods and mediation analysis were used to assess feature importance and interaction.
resultsThe median age of the participants was 65 years (interquartile range: 56-71 years; 57.8% male). Among the 10 machine-learning models, the random forest model performed the best (validation cohort, area under the receiver operating characteristic curve of 0.795 [0.704-0.885]). Lipid profile (total cholesterol, triglycerides, and trimethylamine-N-oxide) levels were identified as key postoperative delirium predictors. Mediation analysis further confirmed mediating effects among total cholesterol, trimethylamine-N-oxide, and postoperative delirium; a nomogram model was developed as a web-based tool for external validation and use by other clinicians.
interpretationBlood biomarkers are crucial in predicting postoperative delirium and aid anesthesiologists in identifying its risks in a timely manner. This model facilitates personalized perioperative management and reduces the occurrence of postoperative delirium.
trial registrationChiCTR2300075723.
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