U-Shaped Association Between Intraoperative Net Fluid Balance and Risk of Postoperative Recurrent Atrial Tachyarrhythmia Among Patients Undergoing the Cryo-Maze Procedure: An Observational Study

Diagnosis and Treatment of New-Onset Perioperative Atrial Fibrillation

This article reviews medical and surgical risk factors for developing atrial fibrillation (AF), the most common sustained dysrhythmia in the United States. Evidence for assessment and management of patients with AF, including AF newly identified in the preoperative clinic, immediately preoperatively, intraoperatively, and unstable AF, is presented. A stepwise approach to guide anesthetic decision-making in the assessment of newly identified preoperative AF is proposed. Anesthetic considerations, including the potential impacts of anesthetic and vasopressor selection, and current evidence related to rate control and rhythm control via pharmacologic or electrical cardioversion as well as anticoagulation strategies are discussed.

Machine learning-based analysis of risk factors for atrial fibrillation recurrence after Cox-Maze IV procedure in patients with atrial fibrillation and chronic valvular disease: A retrospective cohort study with a control group

Objectives

To evaluate the efficacy of the Cox-Maze IV procedure (CMP-IV) in combination with valve surgery in patients with both atrial fibrillation (AF) and valvular disease and use machine learning algorithms to identify potential risk factors of AF recurrence.

Methods

A total of 1,026 patients with AF and valvular disease from two hospitals were included in the study. 555 patients received the CMP-IV procedure in addition to valve surgery and left atrial appendage ligation (CMP-IV group), while 471 patients only received valve surgery and left atrial appendage ligation (Non-CMP-IV group). Kaplan-Meier analysis was used to calculate the sinus rhythm maintenance rate. 58 variables were selected as variables for each group and 10 machine learning models were developed respectively. The performance of the models was evaluated using five-fold cross-validation and metrics including F1 score, accuracy, precision, and recall. The four best-performing models for each group were selected for further analysis, including feature importance evaluation and SHAP analysis.

Results

The 5-year sinus rhythm maintenance rate in the CMP-IV group was 82.13% (95% CI: 78.51%, 85.93%), while in the Non-CMP-IV group, it was 13.40% (95% CI: 10.44%, 17.20%). The eXtreme Gradient Boosting (XGBoost), LightGBM, Category Boosting (CatBoost) and Random Fores (RF) models performed the best in the CMP-IV group, with area under the curve (AUC) values of 0.768 (95% CI: 0.742, 0.786), 0.766 (95% CI: 0.744, 0.792), 0.762 (95% CI: 0.723, 0.801), and 0.732 (95% CI: 0.701, 0.763), respectively. In the Non-CMP-IV group, the LightGBM, XGBoost, CatBoost and RF models performed the best, with AUC values of 0.738 (95% CI: 0.699, 0.777), 0.732 (95% CI: 0.694, 0.770), 0.724 (95% CI: 0.668, 0.789), and 0.716 (95% CI: 0.656, 0.774), respectively. Analysis of feature importance and SHAP revealed that duration of AF, preoperative left ventricular ejection fraction, postoperative heart rhythm, preoperative neutrophil-lymphocyte ratio, preoperative left atrial diameter and heart rate were significant factors in AF recurrence.

Conclusion

CMP-IV is effective in treating AF and multiple machine learning models were successfully developed, and several risk factors were identified for AF recurrence, which may aid clinical decision-making and optimize the individual surgical management of AF.

Prolonged cardiovascular pharmacological support and fluid management after cardiac surgery

OBJECTIVE

To identify potentially modifiable risk factors related to prolonged cardiovascular pharmacological support after weaning from cardiopulmonary bypass (CPB).

METHODS

This is a secondary analysis of two prospective cohort study in a specialized cardiac surgery institution in adult patients undergoing cardiac surgery with the use of CPB between August 2016 and July 2017. Prolonged cardiovascular pharmacological support was defined by the need for at least one vasopressor or one inotropic agent 24 hours after separation from CPB. Risk factors were identified among baseline characteristics and peri-operative events through multivariable logistic regression.

RESULTS

A total of 247 patients were included and 98 (39.7%) developed prolonged pharmacological support. In multivariable analysis, left ventricular ejection fraction ≤ 30% (OR 9.52, 95% confidence interval (CI) 1.14; 79.25), elevated systolic pulmonary artery pressure (sPAP) > 30 and ≤ 55 mmHg (moderate) (OR 2.52, CI 1.15; 5.52) and sPAP > 55 mmHg (severe) (OR 8.12, CI 2.54; 26.03), as well as cumulative fluid balance in the first 24 hours after surgery (OR 1.76, CI 1.32; 2.33) were independently associated with the development of prolonged pharmacological support.

CONCLUSIONS

Prolonged cardiovascular pharmacological support is frequent after cardiac surgery on CPB. Severe LV systolic dysfunction, preoperative pulmonary hypertension and postoperative fluid overload are risk factors. Further studies are required to explore if those risk factors could be modified or not.

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

BACKGROUND

Revision endoscopic sinus surgery (ESS) is often considered for chronic rhinosinusitis (CRS) if maximal conservative treatment and baseline ESS prove insufficient. Emerging research outlines the risk factors of revision ESS. However, accurately predicting revision ESS at the individual level remains uncertain. This study aims to examine the prediction accuracy of revision ESS and to identify the effects of risk factors at the individual level.

METHODS

We collected demographic and clinical variables from the electronic health records of 767 surgical CRS patients ≥16 years of age. Revision ESS was performed on 111 (14.5%) patients. The prediction accuracy of revision ESS was examined by training and validating different machine learning models, while the effects of variables were analysed using the Shapley values and partial dependence plots.

RESULTS

The logistic regression, gradient boosting and random forest classifiers performed similarly in predicting revision ESS. Area under the receiving operating characteristic curve (AUROC) values were 0.744, 0.741 and 0.730, respectively, using data collected from the baseline visit until six months after baseline ESS. The length of time during which data were collected improved the prediction performance. For data collection times of 0, 3, 6 and 12 months after baseline ESS, AUROC values for the logistic regression were 0.682, 0.715, 0.744 and 0.784, respectively. The number of visits before or after baseline ESS, the number of days from the baseline visit to the baseline ESS, patient age, CRS with nasal polyps (CRSwNP), asthma, non-steroidal anti-inflammatory drug exacerbated respiratory disease and immunodeficiency or suspicion of it all associated with revision ESS. Patient age and number of visits before baseline ESS carried non-linear effects for predictions.

CONCLUSIONS

Intelligent data analysis identified important predictors of revision ESS at the individual level, such as the frequency of clinical visits, patient age, Type 2 high diseases and immunodeficiency or a suspicion of it.