Timely detection of modifiable risk factors for postoperative pulmonary complications (PPCs) could inform ventilation strategies that attenuate lung injury. We sought to develop, validate, and internally test machine learning models that use intraoperative respiratory features to predict PPCs. We analysed perioperative data from a cohort comprising patients aged 65 yr and older at an academic medical centre from 2019 to 2023. Two linear and four nonlinear learning models were developed and compared with the current gold-standard risk assessment tool ARISCAT (Assess Respiratory Risk in Surgical Patients in Catalonia Tool). The Shapley additive explanation of artificial intelligence was utilised to interpret feature importance and interactions. Perioperative data were obtained from 10 284 patients who underwent 10 484 operations (mean age [range] 71 [65-98] yr; 42% female). An optimised XGBoost model that used preoperative variables and intraoperative respiratory variables had area under the receiver operating characteristic curves (AUROCs) of 0.878 (0.866-0.891) and 0.881 (0.879-0.883) in the validation and prospective cohorts, respectively. These models outperformed ARISCAT (AUROC: 0.496-0.533). The intraoperative dynamic features of respiratory dynamic system compliance, mechanical power, and driving pressure were identified as key modifiable contributors to PPCs. A simplified model based on XGBoost including 20 variables generated an AUROC of 0.864 (0.852-0.875) in an internal testing cohort. This has been developed into a web-based tool for further external validation (https://aorm.wchscu.cn/). These findings suggest that real-time identification of surgical patients' risk of postoperative pulmonary complications could help personalise intraoperative ventilatory strategies and reduce postoperative pulmonary complications.

Background: The association between anemia and depression has been demonstrated in previous studies, but it's still unclear whether depressive symptoms as a hazard factor for anemia. The findings of a large-scale cross-sectional and longitudinal examination of such an association of among the middle-aged and elderly individuals in China were presented in our study. Methods: The data from China Health and Retirement Longitudinal Study in 2011 and 2015 were evaluated. 10,179 and 5,887 participants were included in cross-sectional and longitudinal study, respectively. According to the World Health Organization, hemoglobin concentrations below 13 g/dL for males and 12 g/dL for females are considered anemia. The research population was separated into two groups based on scores of the 10-item short form of the Center for Epidemiologic Studies Depression Scale (CES-D-10): the group with depressed symptoms had a score of more than 10 points, and the group with depressive disorder had a score of more than 20 points. Multilevel logistic regression analyses were conducted to explore the relationship between anemia and varying degrees of depressive symptoms, utilizing three models based on adjusting for different types of covariates. Results: In our cross-sectional investigation, depression disorders were more likely to link to the occurrence of anemia (OR, 1.34; 95% CI, 1.02–1.77; P = 0.035). Additionally, there seems a linear connection between depression questionnaire scores and hemoglobin concentrations (r = - 0.053, P < 0.001). Depressive symptom was significantly associated with anemia over 4 years of follow-up, and the more intense the depressive symptoms, the greater the danger of anemia (depressive symptoms group: OR, 1.27; 95% CI, 1.02–1.57, P = 0.032; depressive disorder group: OR, 1.59; 95% CI, 1.12–2.25, P = 0.010). Conclusions: Our findings suggest that depression symptoms seem related to anemia in the middle-aged and elderly in China cross-sectionally and longitudinally, and that the risk of anemia increases with the severity of depressive symptoms.

Background: Postoperative major adverse cardiovascular events (MACEs) account for more than one-third of perioperative deaths. Geriatric patients are more vulnerable to postoperative MACEs than younger patients. Identifying high-risk patients in advance can help with clinical decision making and improve prognosis. This study aimed to develop a machine learning model for the preoperative prediction of postoperative MACEs in geriatric patients.Methods: We collected patients’ clinical data and laboratory tests prospectively. All patients over 65 years who underwent surgeries in West China Hospital of Sichuan University from June 25, 2019 to June 29, 2020 were included. Models based on extreme gradient boosting (XGB), gradient boosting machine, random forest, support vector machine, and Elastic Net logistic regression were trained. The models’ performance was compared according to area under the precision-recall curve (AUPRC), area under the receiver operating characteristic curve (AUROC) and Brier score. To minimize the influence of clinical intervention, we trained the model based on undersampling set. Variables with little contribution were excluded to simplify the model for ensuring the ease of use in clinical settings.Results: We enrolled 5705 geriatric patients into the final dataset. Of those patients, 171 (3.0%) developed postoperative MACEs within 30 days after surgery. The XGB model outperformed other machine learning models with AUPRC of 0.404(95% confidence interval [CI]: 0.219–0.589), AUROC of 0.870(95%CI: 0.786–0.938) and Brier score of 0.024(95% CI: 0.016–0.032). Model trained on undersampling set showed improved performance with AUPRC of 0.511(95% CI: 0.344–0.667,p < 0.001), AUROC of 0.912(95% CI: 0.847–0.962,p < 0.001) and Brier score of 0.020 (95% CI: 0.013–0.028,p < 0.001). After removing variables with little contribution, the undersampling model showed comparable predictive accuracy with AUPRC of 0.507(95% CI: 0.338–0.669,p = 0.36), AUROC of 0.896(95%CI: 0.826–0.953,p < 0.001) and Brier score of 0.020(95% CI: 0.013–0.028,p = 0.20).Conclusions: In this prospective study, we developed machine learning models for preoperative prediction of postoperative MACEs in geriatric patients. The XGB model showed the best performance. Undersampling method achieved further improvement of model performance.Trial registration: The protocol of this study was registered atwww.chictr.org.cn(15/08/2019, ChiCTR1900025160)

Background: Major adverse cardiovascular events (MACEs) within 30 days following noncardiac surgery are prognostically relevant. Accurate prediction of risk and modifiable risk factors for postoperative MACEs is critical for surgical planning and patient outcomes. We aimed to develop and validate an accurate and easy-to-use machine learning model for predicting postoperative MACEs in geriatric patients undergoing noncardiac surgery. Materials and methods: The cohort study was conducted at an academic medical center between June 2019 and February 2023. The outcome was postoperative MACEs within 30 days after surgery. Significant predictors were selected using permutation-shuffling. Ten machine learning models were established and compared with the Revised Cardiac Risk Index (RCRI). The SHapley Additive exPlanations algorithm was used to interpret the models. Results: Of the 18,395 patients included, 354 (1.92%) experienced postoperative MACEs. Eighteen predictors were included in model development. The AutoGluon model outperformed other models and the RCRI with an AUROC of 0.884 (95% CI: 0.878–0.890), an accuracy of 0.976 (95% CI: 0.973–0.978), and a Brier score of 0.023 (95% CI: 0.020–0.026). In interpretability analyses, the hemoglobin level was the most important predictor. We identified the relationships between predictors and postoperative MACEs and interaction effects between some predictors. The AutoGluon model has been deployed as a web-based tool for further external validation (https://huggingface.co/spaces/MDC2J/Predicting_postoperative_MACEs). Conclusion: In this prospective study, the AutoGluon model could accurately predict MACEs after noncardiac surgery in geriatric patients, outperforming existing models and the RCRI. Subsequent interpretability analysis can provide insight into how our model works and help personalize surgical strategies.

Aim: Postoperative pulmonary complications (PPCs) can increase the risk of postoperative mortality, and the geriatric population has high incidence of PPCs. Early identification of high-risk geriatric patients is of great value for clinical decision making and prognosis improvement. Existing prediction models are based purely on structured data, and they lack predictive accuracy in geriatric patients. We aimed to develop and validate a deep neural network model based on combined natural language data and structured data for improving the prediction of PPCs in geriatric patients.Methods: We consecutively enrolled patients aged ≥65 years who underwent surgery under general anesthesia at seven hospitals in China. Data from the West China Hospital of Sichuan University were used as the derivation dataset, and a deep neural network model was developed based on combined natural language data and structured data. Data from the six other hospitals were combined for external validation.Results: The derivation dataset included 12,240 geriatric patients, and 1949(15.9%) patients developed PPCs. Our deep neural network model outperformed other machine learning models with an area under the precision-recall curve (AUPRC) of 0.657(95% confidence interval [CI], 0.655–0.658) and an area under the receiver operating characteristic curve (AUROC) of 0.884(95% CI, 0.883–0.885). The external dataset included 7579 patients, and 776(10.2%) patients developed PPCs. In external validation, the AUPRC was 0.632(95%CI, 0.632–0.633) and the AUROC was 0.889(95%CI, 0.888–0.889).Conclusions: This study indicated that the deep neural network model based on combined natural language data and structured data could improve the prediction of PPCs in geriatric patients.