Development and validation of a patient-specific model to predict postoperative SIRS in older patients: A two-center study.

Introduction: Postoperative systemic inflammatory response syndrome (SIRS) is common in surgical patients especially in older patients, and the geriatric population with SIRS is more susceptible to sepsis, MODS, and even death. We aimed to develop and validate a model for predicting postoperative SIRS in older patients. Methods: Patients aged ≥65 years who underwent general anesthesia in two centers of Third Affiliated Hospital of Sun Yat-sen University from January 2015 to September 2020 were included. The cohort was divided into training and validation cohorts. A simple nomogram was developed to predict the postoperative SIRS in the training cohort using two logistic regression models and the brute force algorithm. The discriminative performance of this model was determined by area under the receiver operating characteristics curve (AUC). The external validity of the nomogram was assessed in the validation cohort. Results: A total of 5,904 patients spanning from January 2015 to December 2019 were enrolled in the training cohort and 1,105 patients from January 2020 to September 2020 comprised the temporal validation cohort, in which incidence rates of postoperative SIRS were 24.6 and 20.2%, respectively. Six feature variables were identified as valuable predictors to construct the nomogram, with high AUCs (0.800 [0.787, 0.813] and 0.822 [0.790, 0.854]) and relatively balanced sensitivity (0.718 and 0.739) as well as specificity (0.718 and 0.729) in both training and validation cohorts. An online risk calculator was established for clinical application. Conclusion: We developed a patient-specific model that may assist in predicting postoperative SIRS among the aged patients.

Application of a protective sleeve is associated with decreased occupational anxiety during endotracheal intubation: a randomized controlled trial.

BACKGROUND: The high risk of cross-infection during tracheal intubation has caused excessive occupational anxiety for anaesthesiologists amid the novel coronavirus disease 2019 (COVID-19) pandemic. Currently, there is no effective way to attenuate their anxiety in clinical practice. We found that anaesthesiologist with better protective equipment might experience decreased levels of anxiety during intubation. METHODS: In this study, 60 patients who underwent intubation and extubation in the operating room were enrolled, and then randomized 1:1 to either wear protective sleeves (protective sleeve group) or not (control group). Visual analogue scale (VAS) was used to measure the anxiety level of anaesthesiologists during intubation. The respiratory droplets of patients on the sleeve, and the anaesthesiologists' perception including the patient's oral malodour, exertion, satisfaction degree, waist discomfort and shoulder discomfort were recorded. The patients' anxiety, oppressed feelings and hypoxia and postoperative complications were all measured and recorded. RESULTS: Compared with the control group, the anaesthesiologists in protective sleeve group achieved lower anxiety scores and better satisfaction degrees during the process of intubation and extubation (all P < 0.05). Respiratory droplets were observed only on the inner side, but not the external side, of the protective sleeves (P < 0.001). The incidence of the anaesthesiologists' perception of patients' oral malodour was significantly lower in the protective sleeve group (P = 0.02) and no patients developed hypoxemia or intubation-related complications in the protective sleeve group. CONCLUSION: Using protective devices for intubation might eliminate droplet transmission from patients to anaesthesiologists, while also decreasing their anxiety in a controlled operating room environment. TRIAL REGISTRATION: Chinese Clinical Trial. no. ChiCTR2000030705 . Registry at www.chictr.org.cn on 10/03/2020.

An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.

BACKGROUND: Early prediction of acute kidney injury (AKI) after liver transplantation (LT) facilitates timely recognition and intervention. We aimed to build a risk predictor of post-LT AKI via supervised machine learning and visualize the mechanism driving within to assist clinical decision-making. METHODS: Data of 894 cases that underwent liver transplantation from January 2015 to September 2019 were collected, covering demographics, donor characteristics, etiology, peri-operative laboratory results, co-morbidities and medications. The primary outcome was new-onset AKI after LT according to Kidney Disease Improving Global Outcomes guidelines. Predicting performance of five classifiers including logistic regression, support vector machine, random forest, gradient boosting machine (GBM) and adaptive boosting were respectively evaluated by the area under the receiver-operating characteristic curve (AUC), accuracy, F1-score, sensitivity and specificity. Model with the best performance was validated in an independent dataset involving 195 adult LT cases from October 2019 to March 2021. SHapley Additive exPlanations (SHAP) method was applied to evaluate feature importance and explain the predictions made by ML algorithms. RESULTS: 430 AKI cases (55.1%) were diagnosed out of 780 included cases. The GBM model achieved the highest AUC (0.76, CI 0.70 to 0.82), F1-score (0.73, CI 0.66 to 0.79) and sensitivity (0.74, CI 0.66 to 0.8) in the internal validation set, and a comparable AUC (0.75, CI 0.67 to 0.81) in the external validation set. High preoperative indirect bilirubin, low intraoperative urine output, long anesthesia time, low preoperative platelets, and graft steatosis graded NASH CRN 1 and above were revealed by SHAP method the top 5 important variables contributing to the diagnosis of post-LT AKI made by GBM model. CONCLUSIONS: Our GBM-based predictor of post-LT AKI provides a highly interoperable tool across institutions to assist decision-making after LT.