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.

Peri-operative approach to esophagectomy: a narrative review from the anesthesiological standpoint

Objective

This review summarizes the peri-operative anesthesiological approaches to esophagectomy considering the best up-to-date, evidence-based medicine, discussed from the anesthesiologist’s standpoint.

Background

Esophagectomy is the only curative therapy for esophageal cancer. Despite the many advancements made in the surgical treatment of this tumour, esophagectomy still carries a morbidity rate reaching 60%. Patients undergoing esophagectomy should be referred to high volume centres where they can receive a multidisciplinary approach to treatment, associated with better outcomes. The anesthesiologist is the key figure who should guide the peri-operative phase, from diagnosis through to post-surgery rehabilitation. We performed an updated narrative review devoted to the study of anesthesia management for esophagectomy in cancer patients.

Methods

We searched MEDLINE, Scopus and Google Scholar databases from inception to May 2021. We used the following terms: “esophagectomy”, “esophagectomy AND pre-operative evaluation”, “esophagectomy AND protective lung ventilation”, “esophagectomy AND hemodynamic monitoring” and “esophagectomy AND analgesia”. We considered only articles with abstract written in English and available to the reader. We excluded single case-reports.

Conclusions

Pre-operative anesthesiological evaluation is mandatory in order to stratify and optimize any medical condition. During surgery, protective ventilation and judicious fluid management are the cornerstones of intraoperative “protective anesthesia”. Post-operative care should be provided by an intensive care unit or high-dependency unit depending on the patient’s condition, the type of surgery endured and the availability of local resources. The provision of adequate post-operative analgesia favours early mobilization and rapid recovery. Anesthesiologist has an important role during the peri-operative care for esophagectomy. However, there are still some topics that need to be further studied to improve the outcome of these patients.

Outcome of Organ Dysfunction in the Perioperative Period

While intraoperative mortality has diminished greatly over the last several decades, the risk of death within 30 days of surgery remains stubbornly high and is ultimately related to perioperative organ failure. Perioperative strokes, while rare (<2% in noncardiac surgery), are associated with a more than 10-fold increase in mortality. Rapid identification and treatment are key to maximizing long-term outcomes. Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are separate but related perioperative neurological disorders, both of which are associated with poor long-term outcomes. To date, there are few known interventions that can ameliorate the risk of perioperative central nervous system dysfunction. Major adverse cardiac events (MACE) are a major contributor to adverse clinical outcomes following surgical procedures. Recently, advances in diagnostic strategies (eg, high-sensitivity cardiac troponin [hs-cTn] assays) have improved our understanding of MACE. Recently, the dabigatran in patients with myocardial injury after noncardiac surgery (MINS; Management of myocardial injury After NoncArdiac surGEry) trial demonstrated that a direct thrombin inhibitor could improve outcomes following MINS. While the risk of acute respiratory distress syndrome (ARDS) after surgery is approximately 0.2%, other less severe complications (eg, pneumonia, reintubation) are closer to 2%. While intensive care unit (ICU) concepts related to ARDS have migrated into the operating room, whether or not adverse pulmonary outcomes impact long-term outcomes in surgical patients remains a matter of debate. The standardization of acute kidney injury (AKI) definition has improved the ability of clinicians to measure and study the incidence of this important source of perioperative morbidity. AKI is associated with increased mortality as well as nonrenal morbidity (eg, myocardial infarction) after major surgery. Gastrointestinal complications after surgery range from ileus (common in abdominal procedures and associated with an increased length of stay) to less common complications such as mesenteric ischemia and gastrointestinal bleeding, both of which are associated with very high mortality. Outside of cardiothoracic surgery, the incidence of perioperative hepatic injury is not well described but, in this population, is associated with worsened long-term outcomes. Hyperglycemia is a common perioperative complication and occurs in patients undergoing both cardiac and noncardiac surgery. Both hyper- and hypoglycemia are associated with worsened long-term outcomes in cardiac and noncardiac surgery. Better diagnosis and increased understanding of perioperative organ injury has led to an increased appreciation for the specific role that particular organ systems play in poor long-term outcomes and has set the stage for targeted therapeutic interventions.