No Significant Association Between the Transfusion of Small Volumes of Leukocyte-Depleted Red Blood Cells and Mortality Over 7 Years of Follow-up in Patients Undergoing Cardiac Surgery: A Propensity Score Matched Analysis

Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study

Background

Myocardial injury after noncardiac surgery (MINS) is an easily overlooked complication but closely related to postoperative cardiovascular adverse outcomes; therefore, the early diagnosis and prediction are particularly important.

Objective

We aimed to develop and validate an explainable machine learning (ML) model for predicting MINS among older patients undergoing noncardiac surgery.

Methods

The retrospective cohort study included older patients who had noncardiac surgery from 1 northern center and 1 southern center in China. The data sets from center 1 were divided into a training set and an internal validation set. The data set from center 2 was used as an external validation set. Before modeling, the least absolute shrinkage and selection operator and recursive feature elimination methods were used to reduce dimensions of data and select key features from all variables. Prediction models were developed based on the extracted features using several ML algorithms, including category boosting, random forest, logistic regression, naïve Bayes, light gradient boosting machine, extreme gradient boosting, support vector machine, and decision tree. Prediction performance was assessed by the area under the receiver operating characteristic (AUROC) curve as the main evaluation metric to select the best algorithms. The model performance was verified by internal and external validation data sets with the best algorithm and compared to the Revised Cardiac Risk Index. The Shapley Additive Explanations (SHAP) method was applied to calculate values for each feature, representing the contribution to the predicted risk of complication, and generate personalized explanations.

Results

A total of 19,463 eligible patients were included; among those, 12,464 patients in center 1 were included as the training set; 4754 patients in center 1 were included as the internal validation set; and 2245 in center 2 were included as the external validation set. The best-performing model for prediction was the CatBoost algorithm, achieving the highest AUROC of 0.805 (95% CI 0.778-0.831) in the training set, validating with an AUROC of 0.780 in the internal validation set and 0.70 in external validation set. Additionally, CatBoost demonstrated superior performance compared to the Revised Cardiac Risk Index (AUROC 0.636; P<.001). The SHAP values indicated the ranking of the level of importance of each variable, with preoperative serum creatinine concentration, red blood cell distribution width, and age accounting for the top three. The results from the SHAP method can predict events with positive values or nonevents with negative values, providing an explicit explanation of individualized risk predictions.

Conclusions

The ML models can provide a personalized and fairly accurate risk prediction of MINS, and the explainable perspective can help identify potentially modifiable sources of risk at the patient level.

Role of anesthesiologists in managing perioperative anemia

PURPOSE OF REVIEW

Anemia can contribute negatively to a patient's morbidity and mortality. Which treatment options do exist and what role do anesthesiologists play in management of perioperative anemia treatment? This review gives an overview about recent findings.

RECENT FINDINGS

Patient Blood Management and standards for the management and treatment of anemia have been established worldwide. Various logistic settings and approaches are possible. With a special focus on cardiovascular anesthesia, intravenous iron is a therapeutic option in the preoperative setting. Autologous blood salvage is a standard procedure during surgery. Restrictive transfusion triggers in adult cardiac surgery have been shown to be beneficial in the majority of studies. Elderly patients and defined comorbidities might require higher transfusion triggers. Both, intravenous and oral iron increase hemoglobin values when given prior to surgery. Oral iron is effective when given several weeks prior to elective surgery. Erythropoietin is a treatment decision individualized to each patient.

SUMMARY

Within the previous 18 months, important publications have demonstrated the established role of anesthesiologists in managing perioperative anemia. A substantial pillar for anemia treatment is the implementation of Patient Blood Management worldwide.