Preoperative Myocardial Ischemia Detected With Electrocardiography Is Associated With Reduced 1-Year Survival Rate in Patients Undergoing Liver Transplant

Evaluation of recipients with significant comorbidity - Patients with cardiovascular disease

Liver transplant(ation) (LT) is the most effective treatment for patients with decompensated liver disease. The increasing prevalence of obesity and type 2 diabetes and the growing number of patients with non-alcoholic fatty liver disease being evaluated for LT, have resulted in a greater proportion of LT candidates presenting with a higher risk of cardiovascular disease. As cardiovascular disease is a major cause of morbidity and mortality after LT, a thorough cardiovascular evaluation pre-LT is crucial. In this review, we discuss the latest evidence on the cardiovascular evaluation of LT candidates and we focus on the most prevalent conditions, namely ischaemic heart disease, atrial fibrillation and other arrhythmias, valvular heart disease, and cardiomyopathies. LT candidates undergo an electrocardiogram, a resting transthoracic echocardiography and an assessment of their cardiopulmonary functional ability as part of their standardised pre-LT work-up. Further diagnostic work-up is undertaken based on the results of the baseline evaluation and may include a coronary computed tomography angiography in patients with cardiovascular risk factors. The evaluation of potential LT candidates for cardiovascular disease requires a multidisciplinary approach, with input from anaesthetists, cardiologists, hepatologists and transplant surgeons.

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.

Preoperative cardiac assessment in liver transplant candidates

New and extended indications, older age, higher cardiovascular risk, and the long-standing cirrhosis-associated complications mandate specific skills for an appropriate preoperative assessment of the liver transplant (LT) candidate. The incidence of cardiac diseases (dysrhythmias, cardiomyopathies, coronary artery disease, valvular heart disease) are increasing among LT recipients: however, no consensus exists among clinical practice guidelines for cardiovascular screening and risk stratification. In spite of different "transplant center-centered protocols", basic "pillars" are common (electrocardiography, baseline echocardiography, functional assessment). Owing to intrinsic limitations, yields and relevance of noninvasive stress tests, under constant scrutiny even if used, are discussed, focusing the definition of the "high risk" candidate and exploring noninvasive imaging and new forms of stress imaging. The aim is to find an appropriate and rational stepwise algorithm. The final commitment is to select the right candidate for a finite resource, the graft, able to save (and change) lives.

MELD-XI Score and Coronary Artery Disease Prevalence and Extent Among In-Hospital Patients With End-Stage Liver Failure Awaiting Transplant

OBJECTIVES

Liver transplant is a life-saving procedure for a variety of end-stage liver diseases. Cardiovascular disorders are among the leading cause of death among patients with end-stage liver disease and those undergoing liver transplant procedures. MELD-XI score is a newly developed score for mortality prediction in patients with end-stage liver failure. In this study, we investigated the relationships among MELD-XI score, total in-hospital mortality, and coronary artery disease severity and extent among patients with end-stage liver failure who were awaiting transplant.

MATERIALS AND METHODS

We retrospectively reviewed medical records of 121 patients with end-stage liver failure on transplant wait list. Study patients had undergone coronary angiography as part of pretransplant cardiac evaluation. We determined prevalence of coronary artery disease and Gensini score (which indicates extent of coronary artery disease) using coronary angiography and reviewed MELD-XI score and in-hospital mortality rates. We compared MELD-XI score and Gensini score in deceased and surviving patients and correlated both scores with mortality and with each other.

RESULTS

Of 121 patients, 79 (65.3%) were men; mean age of the study population was 59.6 ± 10.2 years. Twenty-eight patients (23.1%) had coronary artery disease, and 13 (10.7%) had severe coronary artery disease on coronary angiography. Twenty-three patients (19%) died while on the transplant wait list. Gensini score and MELD-XI scores were significantly higher in those who died (P < .05). MELD-XI score, but not Gensini score, was a significant independent predictor of death among patients awaiting liver transplant (hazard ratio = 1.35; 95% confidence interval, 1.04-1.78; P < .05).

CONCLUSIONS

MELD-XI score independently predicted in-hospital death among patients scheduled to undergo liver transplant. These patients also had increased prevalence and extent of coronary artery disease.