Impact of tricuspid regurgitation on postoperative outcomes after non-cardiac surgeries

Preoperative Echocardiographic Unknown Valvopathy Evaluation in Elderly Patients Undergoing Neuraxial Anesthesia during Major Orthopedic Surgery: A Mono-Centric Retrospective Study

Background: The assessment of cardiac risk is challenging for elderly patients undergoing major orthopedic surgery with preoperative functional limitations. Currently, no specific cardiac risk scores are available for these critical patients. Echocardiography may be a reliable and safe instrument for assessing cardiac risks in this population. This study aims to evaluate the potential benefits of echocardiography in elderly orthopedic patients, its impact on anesthesiologic management, and postoperative Major Adverse Cardiac Events (MACEs). Methods: This is a retrospective, one-arm, monocentric study conducted at ''Federico II'' Hospital-University of Naples-from January to December 2023, where 59 patients undergoing hip or knee revision surgery under neuraxial anesthesia were selected. The demographic data, the clinical history, and the results of preoperative Echocardiography screening (pEco-s) were collected. After extensive descriptive statistics, the χ2 test was used to compare the valvopathies and impaired Left Ventricular Function (iLVEF) prevalence before and after echocardiography screening and the incidence of postoperative MACE; a p-value < 0.05 was considered statistically significant. Results: The mean age was 72.5 ± 6.9, and the prevalence of cardiac risk factors was about 90%. The cumulative prevalence of iLVEF and valvopathy was higher after the screening (p < 0.001). The pEco-s diagnosed 25 new valvopathies: three of them were moderate-severe. No patients had MACE. Conclusions: pEco-s evaluation could discover unknown heart valve pathology; more studies are needed to understand if pEco-s could affect the anesthetic management of patients with functional limitations, preventing the incidence of MACE, and assessing its cost-effectiveness.

Echocardiography in the Assessment of Valve Regurgitation- Incremental Role of Three Dimensional Echocardiography

Echocardiography (Echo) has a primary role in the evaluation of cardiac valve regurgitation. Echo valve regurgitation assessment includes multiple qualitative and quantitative methods which require adequate image quality, comprehensive echocardiographic images and precise measurements to obtain accurate assessment. For patient management, it is also important to investigate the mechanism of valve regurgitation. Severity and mechanism of valve regurgitation determine whether continued medical follow up is optimal or surgical or percutaneous valve repair, or replacement option should be necessary. Transthoracic Echo (TTE) is the gold standard most commonly used for the assessment of valve leaflet anatomy, valve motion and regurgitation severity to determine primary versus secondary causes of valve regurgitation, however transesophageal echo (TEE) provides high resolution imaging of valve leaflets and supporting apparatus and oftentimes determines mechanism of valve regurgitation particularly for mitral and tricuspid valves when TTE is unable to determine the mechanism. By providing surgical type views in a moving heart under normal hemodynamic conditions, 3D TEE has greatly improved assessment of mechanism and etiology of valve regurgitation. Besides, TEE also allows quantitation of valve regurgitation severity by Doppler methods as well as direct 3D planimetry of valve area and regurgitant orifice area. Doppler methods are pre and afterload dependent whereas direct 3D planimetry allows assessment of location and severity of valve regurgitation irrespective of ventricular loading conditions. Pre or intraoperative 3D TEE assessment can provide valuable information for surgical planning of valve repair or replacement. This review discusses various valvular pathologies causing regurgitation and the role of TTE and TEE in improving this assessment as shown by several case examples.

Functional tricuspid regurgitation, related right heart remodeling, and available treatment options: good news for patients with heart failure?

Significant functional tricuspid regurgitation (FTR) represents a poor prognostic factor independent of right ventricular (RV) function. It is usually the consequence of left-sided cardiac diseases that induce RV dilatation and dysfunction, but it can also resulted from right atrial (RA) enlargement and consequent tricuspid annular dilatation. FTR is very frequent among patients with heart failure, particularly in those with reduced LVEF and concomitant functional mitral regurgitation. The development of three-dimensional echocardiography enabled detailed assessment of tricuspid valve anatomy, subvavlular apparatus, and RA and RV changes, as well as accurate evaluation of FTR etiology. Due to high in-hospital mortality risk in patients who were operatively treated for isolated FTR, it has been treated only medically for a long time. Percutaneous approach considers mainly transcatheter tricuspid valve repair (edge-to-edge and annuloplasty) and represents a very attractive option for the high-risk patients. Studies that investigated the effects of different devices showed excellent feasibility and safety, followed by significant reduction in FTR grade, improvement in functional capacity and NYHA class, quality of life, and reduction in hospitalization due to heart failure. Some investigations also reported a decreased mortality in FTR patients. Nevertheless, the results of these investigations should be interpreted with cautious due to the small number of participants and relatively short follow-up. The aim of this review was to summarize the existing data about the clinical importance of FTR and FTR-induced right heart remodeling and currently existing therapeutic approaches for treatment of FTR.

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS₂, CHA₂DS₂-VASc, R₂CHADS₂, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.