Coronary angiography and failure to rescue after postoperative myocardial infarction in patients with coronary stents undergoing noncardiac surgery

Long-term outcomes of perioperative myocardial infarction/injury after non-cardiac surgery

AIMS

Perioperative myocardial infarction/injury (PMI) following non-cardiac surgery is a frequent cardiac complication. Better understanding of the underlying aetiologies and outcomes is urgently needed.

METHODS AND RESULTS

Aetiologies of PMIs detected within an active surveillance and response programme were centrally adjudicated by two independent physicians based on all information obtained during clinically indicated PMI work-up including cardiac imaging among consecutive high-risk patients undergoing major non-cardiac surgery in a prospective multicentre study. PMI aetiologies were hierarchically classified into 'extra-cardiac' if caused by a primarily extra-cardiac disease such as severe sepsis or pulmonary embolism; and 'cardiac', further subtyped into type 1 myocardial infarction (T1MI), tachyarrhythmia, acute heart failure (AHF), or likely type 2 myocardial infarction (lT2MI). Major adverse cardiac events (MACEs) including acute myocardial infarction, AHF (both only from day 3 to avoid inclusion bias), life-threatening arrhythmia, and cardiovascular death as well as all-cause death were assessed during 1-year follow-up. Among 7754 patients (age 45-98 years, 45% women), PMI occurred in 1016 (13.1%). At least one MACE occurred in 684/7754 patients (8.8%) and 818/7754 patients died (10.5%) within 1 year. Outcomes differed starkly according to aetiology: in patients with extra-cardiac PMI, T1MI, tachyarrhythmia, AHF, and lT2MI 51%, 41%, 57%, 64%, and 25% had MACE, and 38%, 27%, 40%, 49%, and 17% patients died within 1 year, respectively, compared to 7% and 9% in patients without PMI. These associations persisted in multivariable analysis.

CONCLUSION

At 1 year, most PMI aetiologies have unacceptably high rates of MACE and all-cause death, highlighting the urgent need for more intensive treatments.

STUDY REGISTRATION

https://clinicaltrials.gov/ct2/show/NCT02573532.

Pro-Con Debate: Are Patients With Coronary Stents Suitable for Free-Standing Ambulatory Surgery Centers?

With increasing implantation of coronary artery stents over the past 2 decades, it is inevitable that anesthesiologists practicing in the outpatient setting will need to determine whether these patients are suitable for procedures at a free-standing ambulatory surgery center (ASC). Appropriate selection of patients with coronary artery stents for a procedure in an ASC requires consideration of factors that affect the balance between the risk of stent thrombosis due to interruption of antiplatelet therapy and the thrombogenic effects of surgery, and the risk of perioperative bleeding complications that may occur if antiplatelet therapy is continued. Thus, periprocedure care of these patients presents unique challenges, particularly for extensive surgical procedures that are increasingly scheduled for free-standing ASCs, where consultation and ancillary services, as well as access to percutaneous cardiac interventions, may not be readily available. Therefore, the suitability of the ambulatory setting for this patient population remains highly controversial. In this Pro-Con commentary, we discuss the arguments for and against scheduling patients with coronary artery stents in free-standing ASCs.

Pro-Con Debate: Cardiac Troponin Measurement as Part of Routine Follow-up of Myocardial Damage Following Noncardiac Surgery

Elevated troponin levels within 3 days of surgery, independent of the presence of symptoms, are strongly linked to increased risk of short- and long-term morbidity and mortality. However, the value of screening with troponin measurements is controversial. The Canadian Cardiovascular Society guidelines on perioperative cardiac risk assessment and management for patients who undergo noncardiac surgery recommends measuring daily troponin for 48 to 72 hours after surgery in high-risk patients. Nevertheless, others doubt this recommendation, in part because postoperative elevated levels of troponin describe very little in terms of disease or event-specific pathogenesis and etiology, and thus, tailoring an intervention remains a challenge. This Pro-Con debate offers evidence-based data to stimulate physician understanding of daily practice and its significance in this matter, and assist in determining whether to use (Pro) or not to use (Con) this surveillance.

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.

Expert consensus on peri-operative myocardial injury screening in noncardiac surgery: A literature review

Peri-operative myocardial injury, detected by dynamic and elevated cardiac troponin (cTn) concentrations, is a common complication of noncardiac surgery that is strongly associated with 30-day mortality. Although active screening for peri-operative myocardial injury has been suggested in recent guidelines, clinical implementation remains tentative due to a lack of examples on how to tackle such an interdisciplinary project at a local level. Moreover, consensus on which assay and cTn cut-off values should be used has not yet been reached, and guidance on whom to screen is lacking. In this article, we aim to summarise local examples of successfully implemented cTn screening practices and review the current literature in order to provide information and suggestions for patient selection, organisation of a screening programme, caveats and a potential management pathway.

Etiology of Acute Coronary Syndrome after Noncardiac Surgery

BACKGROUND

The objective of this investigation was to determine the etiology of perioperative acute coronary syndrome with a particular emphasis on thrombosis versus demand ischemia.

METHODS

In this retrospective cohort study, adult patients were identified who underwent coronary angiography for acute coronary syndrome within 30 days of noncardiac surgery at a major tertiary hospital between January 2008 and July 2015. Angiograms were independently reviewed by two interventional cardiologists who were blinded to clinical data and outcomes. Acute coronary syndrome was classified as ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, or unstable angina; myocardial infarctions were adjudicated as type 1 (plaque rupture), type 2 (demand ischemia), or type 4b (stent thrombosis).

RESULTS

Among 215,077 patients screened, 146 patients were identified who developed acute coronary syndrome: 117 were classified as non-ST-elevation myocardial infarction (80.1%); 21 (14.4%) were classified as ST-elevation myocardial infarction, and 8 (5.5%) were classified as unstable angina. After coronary angiography, most events were adjudicated as demand ischemia (type 2 myocardial infarction, n = 106, 72.6%) compared to acute coronary thrombosis (type 1 myocardial infarction, n = 37, 25.3%) and stent thrombosis (type 4B, n = 3, 2.1%). Absent or only mild, nonobstructive coronary artery disease was found in 39 patients (26.7%). In 14 patients (9.6%), acute coronary syndrome was likely due to stress-induced cardiomyopathy. Aggregate 30-day and 1-yr mortality rates were 7 and 14%, respectively.

CONCLUSIONS

The dominant mechanism of perioperative acute coronary syndrome in our cohort was demand ischemia. A subset of patients had no evidence of obstructive coronary artery disease, but findings were consistent with stress-induced cardiomyopathy.