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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. [ABSTRACT FROM AUTHOR]

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., (© 2024. The Author(s).)

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., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Background: N-terminal pro-B-type natriuretic peptide (NT-proBNP) is often viewed as an indicator for heart failure. However, the prognostic association and the predictive utility of NT-proBNP for postoperative major adverse cardiovascular events (MACEs) and myocardial injury after noncardiac surgery (MINS) among older patients are unclear., Methods: In this study, we included 5033 patients aged 65 years or older who underwent noncardiac surgery with preoperative NT-proBNP recorded. Logistic regression was adopted to model the associations between preoperative NT-proBNP and the risk of MACEs and MINS. The receiver operating characteristic curve was used to determine the predictive value of NT-proBNP., Results: A total of 5033 patients were enrolled, 63 patients (1.25%) and 525 patients (10.43%) had incident postoperative MACEs and MINS, respectively. Analysis of the receiver operating characteristic curve indicated that the cutoff values of ln (NT-proBNP) for MACEs and MINS were 5.16 (174 pg/mL) and 5.30 (200 pg/mL), respectively. Adding preoperative ln (NT-proBNP) to the Revised Cardiac Risk Index score and the Cardiac and Stroke Risk Model boosted the area under the receiver operating characteristic curves from 0.682 to 0.726 and 0.787 to 0.804, respectively. The inclusion of preoperative NT-proBNP in the prediction models significantly increased the reclassification and discrimination., Conclusions: Increased preoperative NT-proBNP was associated with a higher risk of postoperative MACEs and MINS. The inclusion of NT-proBNP enhances the predictive ability of the preexisting models., (© 2022 JGC All rights reserved; www.jgc301.com.)