Right Ventricular Longitudinal Strain Predicts Low-Cardiac- Output Syndrome After Surgical Aortic Valve Replacement in Patients With Preserved and Mid-range Ejection Fraction

The relationship between pre-operative right ventricular longitudinal strain and low-cardiac-output syndrome after surgical aortic valve replacement

Objectives

This study was performed to investigate the relationship between right ventricular free wall longitudinal strain (RVFWSL) and low cardiac output syndrome (LCOS) after surgical aortic valve replacement (SAVR) and to further explore its association with readmission within 2 years in patients who developed LCOS after SAVR.

Methods

This single-center retrospective observational study involved consecutive patients who underwent SAVR at our hospital from May 2018 to June 2020. Preoperative echocardiography was obtained within 3 days before SAVR. The longitudinal strain of the right ventricle was analyzed using the right ventricle as the main section, and the RVFWSL and right ventricular four-chamber longitudinal strain (RV4CSL) were obtained. The primary observation was the occurrence of LCOS. The secondary prognostic indicators were mainly the readmission rates within 2 years.

Results

In total, 146 patients were finally included in this study. The RVFWSL was significantly lower in the LCOS group than in the No-LCOS group (16.63 ± 2.10) vs. (23.95 ± 6.33), respectively; P < 0.001). The multivariate regression analysis showed that the RVFWSL was associated with LCOS (odds ratio, 1.676; 95% confidence interval, 1.258-2.232; P < 0.001). The receiver operating characteristic curve showed that the cut-off value for RVFWSL to predict LCOS was less than -18.3, with an area under the curve of 0.879, sensitivity of 100%, and specificity of 80.47%. The multivariate regression analysis showed that LCOS was an independent risk factor for readmission within 2 years in patients undergoing SAVR.

Conclusion

Patients with RVFWSL (<-18.3%) may be an increased risker for LCOS after SAVR. The occurrence of LCOS after SAVR is Yong-jian Zhang a risk factor for readmission within 2 years. Right ventricular function monitoring may have some predictive value for the postoperative prognosis in patients undergoing SAVR.

Prediction of low cardiac output syndrome in patients following cardiac surgery using machine learning

Background

This study aimed to develop machine learning models to predict Low Cardiac Output Syndrome (LCOS) in patients following cardiac surgery using machine learning algorithms.

Methods

The clinical data of cardiac surgery patients in Nanjing First Hospital between June 2019 and November 2020 were retrospectively extracted from the electronic medical records. Six conventional machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, extreme gradient boosting and light gradient boosting machine, were employed to construct the LCOS predictive models with all predictive features (full models) and selected predictive features (reduced models). The discrimination of these models was evaluated by the area under the receiver operating characteristic curve (AUC) and the calibration of the models was assessed by the calibration curve. Shapley Additive explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to interpret the predictive models.

Results

Data from 1,585 patients [982 (62.0%) were male, aged 18 to 88, 212 (13.4%) with LCOS] were employed to train and validate the LCOS models. Among the full models, the RF model (AUC: 0.909, 95% CI: 0.875–0.943; Sensitivity: 0.849, 95% CI: 0.724–0.933; Specificity: 0.835, 95% CI: 0.796–0.869) and the XGB model (AUC: 0.897, 95% CI: 0.859–0.935; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.809, 95% CI: 0.768–0.845) exhibited well predictive power for LCOS. Eleven predictive features including left ventricular ejection fraction (LVEF), first post-operative blood lactate (Lac), left ventricular diastolic diameter (LVDd), cumulative time of mean artery blood pressure (MABP) lower than 65 mmHg (MABP &lt; 65 time), hypertension history, platelets level (PLT), age, blood creatinine (Cr), total area under curve above threshold central venous pressure (CVP) 12 mmHg and 16 mmHg, and blood loss during operation were used to build the reduced models. Among the reduced models, RF model (AUC: 0.895, 95% CI: 0.857–0.933; Sensitivity: 0.830, 95% CI: 0.702–0.919; Specificity: 0.806, 95% CI: 0.765–0.843) revealed the best performance. SHAP and LIME plot showed that LVEF, Lac, LVDd and MABP &lt; 65 time significantly contributed to the prediction model.

Conclusion

In this study, we successfully developed several machine learning models to predict LCOS after surgery, which may avail to risk stratification, early detection and management of LCOS after cardiac surgery.

New developments in the understanding of right ventricular function in acute care

PURPOSE OF REVIEW

Right ventricular dysfunction has an important impact on the perioperative course of cardiac surgery patients. Recent advances in the detection and monitoring of perioperative right ventricular dysfunction will be reviewed here.

RECENT FINDINGS

The incidence of right ventricular dysfunction in cardiac surgery has been associated with unfavorable outcomes. New evidence supports the use of a pulmonary artery catheter in cardiogenic shock. The possibility to directly measure right ventricular pressure by transducing the pacing port has expanded its use to track changes in right ventricular function and to detect right ventricular outflow tract obstruction. The potential role of myocardial deformation imaging has been raised to detect patients at risk of postoperative complications.

SUMMARY

Perioperative right ventricular function monitoring is based on echocardiographic and extra-cardiac flow evaluation. In addition to imaging modalities, hemodynamic evaluation using various types of pulmonary artery catheters can be achieved to track changes rapidly and quantitatively in right ventricular function perioperatively. These monitoring techniques can be applied during and after surgery to increase the detection rate of right ventricular dysfunction. All this to improve the treatment of patients presenting early signs of right ventricular dysfunction before systemic organ dysfunction ensue.

The Predictive Value of Right Ventricular Longitudinal Strain in Pulmonary Hypertension, Heart Failure, and Valvular Diseases

Right ventricular (RV) systolic function has an important role in the prediction of adverse outcomes, including mortality, in a wide range of cardiovascular (CV) conditions. Because of complex RV geometry and load dependency of the RV functional parameters, conventional echocardiographic parameters such as RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE), have limited prognostic power in a large number of patients. RV longitudinal strain overcame the majority of these limitations, as it is angle-independent, less load-dependent, highly reproducible, and measure regional myocardial deformation. It has a high predictive value in patients with pulmonary hypertension, heart failure, congenital heart disease, ischemic heart disease, pulmonary embolism, cardiomyopathies, and valvular disease. It enables detection of subclinical RV damage even when conventional parameters of RV systolic function are in the normal range. Even though cardiac magnetic resonance-derived RV longitudinal strain showed excellent predictive value, echocardiography-derived RV strain remains the method of choice for evaluation of RV mechanics primarily due to high availability. Despite a constantly growing body of evidence that support RV longitudinal strain evaluation in the majority of CV patients, its assessment has not become the part of the routine echocardiographic examination in the majority of echocardiographic laboratories. The aim of this clinical review was to summarize the current data about the predictive value of RV longitudinal strain in patients with pulmonary hypertension, heart failure and valvular heart diseases.