Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study.

BACKGROUND: Postoperative delirium is a common complication in older patients, with poor long-term outcomes. This study aimed to investigate risk factors and develop a predictive model for postoperative delirium in older patients after major abdominal surgery. METHODS: This study retrospectively recruited 7577 patients aged ≥ 65 years who underwent major abdominal surgery between January 2014 and December 2018 in a single hospital in Beijing, China. Patients were divided into a training cohort (n = 5303) and a validation cohort (n = 2224) for univariate and multivariate logistic regression analyses and to build a nomogram. Data were collected for 43 perioperative variables, including demographics, medical history, preoperative laboratory results, imaging, and anesthesia information. RESULTS: Age, chronic obstructive pulmonary disease, white blood cell count, glucose, total protein, creatinine, emergency surgery, and anesthesia time were associated with postoperative delirium in multivariate analysis. We developed a nomogram based on the above 8 variables. The nomogram achieved areas under the curve of 0.731 and 0.735 for the training and validation cohorts, respectively. The discriminatory ability of the nomogram was further assessed by dividing the cases into three risk groups (low-risk, nomogram score < 175; medium-risk, nomogram score 175~199; high-risk, nomogram score > 199; P < 0.001). Decision curve analysis revealed that the nomogram provided a good net clinical benefit. CONCLUSIONS: We developed a nomogram that could predict postoperative delirium with high accuracy and stability in older patients after major abdominal surgery.

Gut microbiota and cognitive performance: A bidirectional two-sample Mendelian randomization.

PURPOSE: Previous studies have suggested a potential association between gut microbiota and neurological and psychiatric disorders. However, the causal relationship between gut microbiota and cognitive performance remains uncertain. METHODS: A two-sample Mendelian randomization (MR) study used SNPs linked to gut microbiota (n = 18,340) and cognitive performance (n = 257,841) from recent GWAS data. Inverse-variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode were employed. Heterogeneity was assessed via Cochran's Q test for IVW. Results were shown with funnel plots. Outliers were detected through leave-one-out method. MR-PRESSO and MR-Egger intercept tests were conducted to address horizontal pleiotropy influence. LIMITATIONS: Limited to European populations, generic level, and potential confounding factors. RESULTS: IVW analysis revealed detrimental effects on cognitive perfmance associated with the presence of genus Blautia (P = 0.013, 0.966[0.940-0.993]), Catenibacterium (P = 0.035, 0.977[0.956-0.998]), Oxalobacter (P = 0.043, 0.979[0.960-0.999]). Roseburia (P < 0.001, 0.935[0.906-0.965]), in particular, remained strongly negatively associated with cognitive performance after Bonferroni correction. Conversely, families including Bacteroidaceae (P = 0.043, 1.040[1.001-1.081]), Rikenellaceae (P = 0.047, 1.026[1.000-1.053]), along with genera including Paraprevotella (P = 0.044, 1.020[1.001-1.039]), Ruminococcus torques group (P = 0.016, 1.062[1.011-1.115]), Bacteroides (P = 0.043, 1.040[1.001-1.081]), Dialister (P = 0.027, 1.039[1.004-1.074]), Paraprevotella (P = 0.044, 1.020[1.001-1.039]) and Ruminococcaceae UCG003 (P = 0.007, 1.040[1.011-1.070]) had a protective effect on cognitive performance. CONCLUSIONS: Our results suggest that interventions targeting specific gut microbiota may offer a promising avenue for improving cognitive function in diseased populations. The practical application of these findings has the potential to enhance cognitive performance, thereby improving overall quality of life.

Risk factors prediction of 6-month mortality after noncardiac surgery of older patients in China: a multicentre retrospective cohort study.

BACKGROUND: Identifying the risk factors associated with perioperative mortality is crucial, particularly in older patients. Predicting 6-month mortality risk in older patients based on large datasets can assist patients and surgeons in perioperative clinical decision-making. This study aimed to develop a risk prediction model of mortality within 6 months after noncardiac surgery using the clinical data from 11 894 older patients in China. MATERIALS AND METHODS: A multicentre, retrospective cohort study was conducted in 20 tertiary hospitals. The authors retrospectively included 11 894 patients (aged ≥65 years) who underwent noncardiac surgery between April 2020 and April 2022. The least absolute shrinkage and selection operator model based on linear regression was used to analyse and select risk factors, and various machine learning methods were used to build predictive models of 6-month mortality. RESULTS: The authors predicted 12 preoperative risk factors associated with 6-month mortality in older patients after noncardiac surgery. Including laboratory-associated risk factors such as mononuclear cell ratio and total blood cholesterol level, etc. Also including medical history associated risk factors such as stroke, history of chronic diseases, etc. By using a random forest model, the authors constructed a predictive model with a satisfactory accuracy (area under the receiver operating characteristic curve=0.97). CONCLUSION: The authors identified 12 preoperative risk factors associated with 6-month mortality in noncardiac surgery older patients. These preoperative risk factors may provide evidence for a comprehensive preoperative anaesthesia assessment as well as necessary information for clinical decision-making by anaesthesiologists.

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study.

AIMS: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. METHOD: This was a retrospective study of perioperative medical data from patients undergoing non-cardiac and non-neurology surgery over 65 years old from January 2014 to August 2019. Forty-six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and precision. RESULTS: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765-0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. CONCLUSIONS: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model.