Exploring thirst incidence and risk factors in patients undergoing general anesthesia after extubation based on ERAS principles: a cross sectional study.

BACKGROUND: This study aims to comprehend the levels of dry mouth and thirst in patients after general anesthesia, and to identify the factors influencing them. METHODS: The study included all patients transferred to the Post Anesthesia Care Unit (PACU) at the Second Affiliated Hospital of Dalian Medical University between August 2021 and November 2021 after undergoing general anesthesia. A thirst numeric rating scale was utilized to conduct surveys, enabling the assessment of thirst incidence and intensity. Statistical analysis was performed to explore patient thirst levels and the associated factors. RESULTS: The study revealed a thirst incidence rate of 50.8%. Among the thirst intensity ratings, 71.4% of patients experienced mild thirst, 23.0% reported moderate thirst, and 5.6% expressed severe thirst. Single-factor statistical analysis of potential risk factors among the enrolled cases indicated that gender, history of coronary heart disease, surgical duration, intraoperative fluid volume, intraoperative blood loss, intraoperative urine output, and different surgical departments were linked to post-anesthetic thirst in patients undergoing general anesthesia. Multifactorial Logistic regression analysis highlighted age, gender, history of coronary heart disease, fasting duration, and intraoperative fluid volume as independent risk factors for post-anesthetic thirst in patients undergoing general anesthesia. Moreover, age, gender, history of coronary heart disease, and intraoperative fluid volume were also identified as risk factors for varying degrees of thirst. CONCLUSION: The incidence and intensity of post-anesthetic thirst after general anesthesia are relatively high. Their occurrence is closely associated with age, gender, history of coronary heart disease, fasting duration, and intraoperative fluid volume.

An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.

BACKGROUND: Off-pump coronary artery bypass grafting-associated acute kidney injury (OPCAB-AKI) is related to 30-day perioperative mortality. Existing mathematical models cannot be applied to help clinicians make early diagnosis and intervention decisions. OBJECTIVES: This study used an interpretable machine learning method to establish and screen an optimized OPCAB-AKI prediction model. MATERIAL AND METHODS: Clinical data of 1110 patients who underwent OPCAB in the Department of Cardiac Surgery of General Hospital of Northern Theater Command (Shenyang, China) from January 2018 to December 2020 were collected retrospectively. Four machine learning models were used, including logistic regression (LR), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost). The SHapley Additive exPlanation (SHAP) tool was used for explanatory analysis of the black-box model. The mean absolute value of the characteristic SHAP parameter was defined and sorted. The correlation between the characteristic parameters and OPCAB-AKI was determined based on the SHAP value. A quantitative analysis of a single characteristic and an interaction analysis of multiple characteristics were carried out for the main risk factors. RESULTS: The RF prediction model had the best performance, with an area under the curve (AUC) of 0.90, a precision rate of 0.80, an accuracy rate of 0.83, a recall rate of 0.74, and an F1 score of 0.78 for positive samples. The interpretation analysis of the SHAP model results showed that intraoperative urine volume contributed to the greatest extent to the RF model, and other parameters included intraoperative sufentanil dosage, intraoperative dexmedetomidine dosage, cyclic variation coefficient during the induction period, intraoperative hypotension duration, age, preoperative baseline serum creatinine, body mass index (BMI), and Acute Physiology, Age and Chronic Health Evaluation (APACHE) II score. CONCLUSIONS: The model constructed by the RF ensemble learning algorithm predicted OPCAB-AKI, and indicators such as intraoperative urine volume were closely related to OPCAB-AKI.

Machine learning model-based risk prediction of severe complications after off-pump coronary artery bypass grafting.

BACKGROUND: Compared with coronary artery bypass grafting (CABG) under cardiopulmonary bypass, off-pump coronary artery bypass (OPCAB) is minimally invasive and reduces the risk of intraoperative blood transfusion and acute kidney injury. Nonetheless, OPCAB-related complications still pose a threat. Machine learning technology can analyze a large number of clinical data, establish risk prediction models and help clinicians make early and correct clinical decisions. OBJECTIVES: Risk prediction models are available for mortality and morbidity after cardiac surgery, but they are not specific to OPCAB. This study aimed to develop a predictive model of severe complications after OPCAB, based on machine learning. MATERIAL AND METHODS: Anesthesia records of OPCAB from the General Hospital of the Northern Theater Command (Shenyang, China) collected between January 1, 2019, and June 15, 2020, were analyzed. The endpoint of the study was the occurrence of serious complications after OPCAB (postoperative unplanned intra-aortic balloon pump, secondary surgery and death). The features entered into the models were as follows: intraoperative ventricular fibrillation, number of saphenous vein grafts, nerve block (NeB), venous oxygen saturation (SvO2), skin incision-bypass time, and hypertension. A total of 8 machine learning algorithms were tested: logistic regression analysis (LRA), k-nearest neighbor (KNN), naïve Bayes (NB), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical features gradient boosting (CatBoost). RESULTS: Among the 506 patients found in the records, 27 met the endpoint. The highest area under the curve (AUC) value was achieved with the XGBoost model (AUC = 0.94), and the lowest with the SVM model (AUC = 0.75). The highest and lowest accuracy were observed with the XGBoost and NB models, respectively, while the highest and lowest precision were achieved using the SVM and NB models, respectively. Based on the receiver operating characteristic (ROC) curves, the XGBoost model was selected as the most useful in this study. CONCLUSIONS: This study suggests using the XGBoost model to predict the risk of complications after OPCAB.