Added value of subjective assessed functional capacity before non-cardiac surgery in predicting postoperative myocardial injury

Cardiovascular risk management in the peri-operative setting

Peri-operative cardiovascular events occur in up to 3% of patients undergoing non-cardiac surgery. Accurate cardiovascular risk assessment is important in the peri-operative setting, as it allows informed and shared decisions regarding the appropriateness of proceeding with surgery, guides surgical and anaesthetic approaches, and may influence the use of preventive medications and post-operative cardiac monitoring. Quantitative risk assessment may also inform a reconsideration of choosing a more limited lower risk type of surgery, or conservative management. Pre-operative cardiovascular risk assessment starts with clinical assessment and should include an estimate of functional capacity. Specialised cardiac investigations are rarely indicated specifically to assess pre-operative cardiovascular risk. The decision regarding cardiac investigations is influenced by the nature, extent and urgency of surgery. The strategy of performing pre-operative revascularisation to improve post-operative outcomes is not evidence-based and recent international guidelines recommend against this.

Risk assessment for major adverse cardiovascular events after noncardiac surgery using self-reported functional capacity: international prospective cohort study

BACKGROUND

Guidelines endorse self-reported functional capacity for preoperative cardiovascular assessment, although evidence for its predictive value is inconsistent. We hypothesised that self-reported effort tolerance improves prediction of major adverse cardiovascular events (MACEs) after noncardiac surgery.

METHODS

This is an international prospective cohort study (June 2017 to April 2020) in patients undergoing elective noncardiac surgery at elevated cardiovascular risk. Exposures were (i) questionnaire-estimated effort tolerance in metabolic equivalents (METs), (ii) number of floors climbed without resting, (iii) self-perceived cardiopulmonary fitness compared with peers, and (iv) level of regularly performed physical activity. The primary endpoint was in-hospital MACE consisting of cardiovascular mortality, non-fatal cardiac arrest, acute myocardial infarction, stroke, and congestive heart failure requiring transfer to a higher unit of care or resulting in a prolongation of stay on ICU/intermediate care (≥24 h). Mixed-effects logistic regression models were calculated.

RESULTS

In this study, 274 (1.8%) of 15 406 patients experienced MACE. Loss of follow-up was 2%. All self-reported functional capacity measures were independently associated with MACE but did not improve discrimination (area under the curve of receiver operating characteristic [ROC AUC]) over an internal clinical risk model (ROC AUC_baseline 0.74 [0.71-0.77], ROC AUC_{baseline+4METs} 0.74 [0.71-0.77], ROC AUC_{baseline+floors climbed} 0.75 [0.71-0.78], AUC_{baseline+fitnessvspeers} 0.74 [0.71-0.77], and AUC_{baseline+physical activity} 0.75 [0.72-0.78]).

CONCLUSIONS

Assessment of self-reported functional capacity expressed in METs or using the other measures assessed here did not improve prognostic accuracy compared with clinical risk factors. Caution is needed in the use of self-reported functional capacity to guide clinical decisions resulting from risk assessment in patients undergoing noncardiac surgery.

CLINICAL TRIAL REGISTRATION

NCT03016936.

Assessment and Correction of the Cardiac Complications Risk in Non-cardiac Operations – What's New?

Cardiovascular complications after non-cardiac surgery are the leading cause of 30-day mortality. The need for surgical interventions is approximately 5,000 procedures per 100,000 population, according to experts, the risks of non-cardiac surgical interventions are markedly higher in the elderly. It should be borne in mind that the aging of the population and the increased possibilities of medicine inevitably lead to an increase in surgical interventions in older people. Recent years have been characterized by the appearance of national and international guidelines with various algorithms for assessing and correcting cardiac risk, as well as publications on the validation of these algorithms. The purpose of this review was to provide new information about the assessment and correction of the risk of cardiac complications in non-cardiac operations. Despite the proposed new risk assessment scales, the RCRI scale remains the most commonly used, although for certain categories of patients (with oncopathology, in older age groups) the possibility of using specific questionnaires has been shown. In assessing the functional state, it is proposed to use not only a subjective assessment, but also the DASI questionnaire, 6-minute walking test and cardiopulmonary exercise test). At the next stage, it is proposed to evaluate biomarkers, primarily BNP or NT-proBNP, with a normal level – surgery, with an increased level – either an additional examination by a cardiologist or perioperative troponin screening. Currently, the prevailing opinion is that there is no need to examine patients to detect hidden lesions of the coronary arteries (non-invasive tests, coronary angiography), since this leads to excessive examination of patients, delaying the implementation of non-cardiac surgery. The extent to which this approach has an advantage over the previously used one remains to be studied.

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.

Diagnosis and Management of Patients With Myocardial Injury After Noncardiac Surgery: A Scientific Statement From the American Heart Association

Myocardial injury after noncardiac surgery is defined by elevated postoperative cardiac troponin concentrations that exceed the 99th percentile of the upper reference limit of the assay and are attributable to a presumed ischemic mechanism, with or without concomitant symptoms or signs. Myocardial injury after noncardiac surgery occurs in ≈20% of patients who have major inpatient surgery, and most are asymptomatic. Myocardial injury after noncardiac surgery is independently and strongly associated with both short-term and long-term mortality, even in the absence of clinical symptoms, electrocardiographic changes, or imaging evidence of myocardial ischemia consistent with myocardial infarction. Consequently, surveillance of myocardial injury after noncardiac surgery is warranted in patients at high risk for perioperative cardiovascular complications. This scientific statement provides diagnostic criteria and reviews the epidemiology, pathophysiology, and prognosis of myocardial injury after noncardiac surgery. This scientific statement also presents surveillance strategies and treatment approaches.