The role of brain natriuretic peptide in prognostication and reclassification of risk in patients undergoing vascular surgery*

Brain natriuretic peptide is a long-term cardiovascular predictor in carotid endarterectomy

BACKGROUND

In noncardiac surgery, several biomarkers are known to play a role in predicting long-term complications, such as major adverse cardiovascular events (MACE), myocardial infarction, or death. Carotid endarterectomy (CEA) is considered a low to medium-risk surgery for carotid stenosis aimed at preventing stroke events. Brain natriuretic peptide (BNP) is a biomarker with potential prognostic value regarding MACE. Since its role in patients undergoing CEA is unknown, this study aims to assess the potential role of BNP as a short and long-term predictor of all-cause mortality and MACE in patients undergoing CEA.

METHODS

From a prospective database, patients who underwent CEA under regional anesthesia (RA) at a tertiary hospital center were enrolled, and a post hoc analysis was conducted. Patients on which BNP levels were measured up to fifteen days before surgery, and two groups based on the BNP threshold (200 pg/mL) were defined and compared. Kaplan Meier survival curves and adjusted hazard ratios (aHR) were assessed by multivariable Cox regression. The primary outcome was the incidence of long-term MACE and all-cause mortality. Secondary outcomes included the incidence of AMI and AHF.

RESULTS

A total of 89 patients were evaluated. The mean age of the cohort was 71.2 ± 8.7 years, with 71 (79.8%) males, and presented a median follow-up of 30 [13.5-46.4] months. BNP > 200 pg/mL has demonstrated positive predictive value for MACE (aHR: 5.569, confidence interval (CI): 2.441-12.7, p < 0.001) and all-cause mortality (aHR: 3.469, CI: 1.315-9.150, p = 0.018).

CONCLUSION

BNP has been demonstrated to independently predict long-term all-cause mortality, MACE and AMI following CEA. It serves as a low-cost, ready-to-use biomarker, although further studies are necessary.

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.

Biomarker-Based Preoperative Risk Stratification for Patients Undergoing Non-Cardiac Surgery

Perioperative morbidity and mortality remains a substantial problem and is strongly associated with patients’ cardiac comorbidities. Guidelines for the cardiovascular assessment and management of patients at risk of cardiac issues while undergoing non-cardiac surgery are traditionally based on the exclusion of active or unstable cardiac conditions, determination of the risk of surgery, the functional capacity of the patient, and the presence of cardiac risk factors. In the last two decades, strong evidence showed an association between cardiac biomarkers and adverse cardiac events, with newer guidelines incorporating this knowledge. This review describes a biomarker-based risk-stratification pathway and discusses potential treatment strategies for patients suffering from postoperative myocardial injury or infarction.

A Risk Stratification Model for Cardiovascular Complications during the 3-Month Period after Major Elective Vascular Surgery

Introduction

The Revised Cardiac Risk Index (RCRI) is an extensively used simple risk stratification tool advocated by the European Society of Cardiology and European Society of Anesthesiology (ESC/ESA).

Purpose

The aim of this study was to find the best model for predicting 3-month cardiovascular complications in elective major vascular surgical patients using preoperative clinical assessment, calculation of the RCRI and Vascular Physiological and Operative Severity Score for the enumeration of mortality and morbidity (V-POSSUM) scores, and the preoperative levels of N-terminal brain natriuretic peptide (NT pro-BNP), high-sensitivity troponin I (hs TnI), and high-sensitivity C-reactive protein (hs CRP).

Materials and Methods

We included 122 participants in a prospective, single-center, observational study. The levels of NT pro-BNP, hs CRP, and hs TnI were measured 48 hours prior to surgery. During the perioperative period and 90 days after surgery the following adverse cardiac events were recorded: myocardial infarction, arrhythmias, pulmonary edema, acute decompensated heart failure, and cardiac arrest.

Results

During the first 3 months after surgery 29 participants (23.8%) had 50 cardiac complications. There was a statistically significant difference in the RCRI score between participants with and without cardiac complications. ROC analysis showed that a combination of RCRI with hs TnI has good discriminatory power (AUC 0.909, p<0,001). By adding NT pro-BNP concentrations to the RCRI+hs TnI+V-POSSSUM combination we obtained the model with the best predictive power for 3-month cardiac complications (AUC 0.963, p<0,001).

Conclusion

We need to improve preoperative risk assessment in participants scheduled for major vascular surgery by combining their clinical scores with biomarkers. Therefore, it is possible to identify patients at risk of cardiovascular complications who need adequate preoperative diagnosis and treatment.

Sleep apnea in total joint arthroplasty patients and the role for cardiac biomarkers for risk stratification: an exploration of feasibility

Obstructive sleep apnea (OSA) is highly prevalent in patients undergoing total joint arthroplasty (TJA) and is a major risk factor for postoperative cardiovascular complications and death. Recognizing this, the American Society of Anesthesiologists urges clinicians to implement special considerations in the perioperative care of OSA patients. However, as the volume of patients presenting for TJA increases, resources to implement these recommendations are limited. This necessitates mechanisms to efficiently risk stratify patients having OSA who may be susceptible to post-TJA cardiovascular complications. We explore the role of perioperative measurement of cardiac troponins (cTns) and brain natriuretic peptides (BNPs) in helping determine which OSA patients are at increased risk for post-TJA cardiovascular-related morbidity.

An update on predictive biomarkers for major adverse cardiovascular events in patients undergoing vascular surgery

Cardiovascular complications signify a major cause of morbidity and mortality in patients undergoing vascular surgery adversely affecting both short- and long-term prognosis. During the last decade, unmet needs for a distinct cardiovascular risk assessment have led to an intensive research for establishment of biomarkers with sufficient predictive value. This literature review aims in examining the value of several biomarkers in predicting the incidence of major adverse cardiac events in vascular surgery patients. We reviewed the English language literature and analyzed the biomarkers as independent predictors or in correlation with other factors. We found several biomarkers showing a significant predictive value for a major adverse cardiovascular event in patients undergoing vascular surgery. These biomarkers can be used in clinical practice as outcome predictors, although sensitivity and specificity varies. Detection of subclinical cardiovascular damage may improve total risk estimation and facilitate clinical assessment of patients at risk for future cardiovascular events. The wide variety of sensitivity and specificity in predicting a MACE of these biomarkers exert the need for future trials in which these markers will be tested as adjunctive tools of cardiovascular risk estimation scoring systems.

Predicting the occurrence of major adverse cardiac events within 30 days of a vascular surgery: an empirical comparison of the minimum p value method and ROC curve approach using individual patient data meta-analysis

We aimed to compare the minimum p value method and the area under the receiver operating characteristics (ROC) curve approach to categorize continuous biomarkers for the prediction of postoperative 30-day major adverse cardiac events in noncardiac vascular surgery patients. Individual-patient data from six cohorts reporting B-type natriuretic peptide (BNP) or N-terminal pro-B-type natriuretic peptide (NTproBNP) were obtained. These biomarkers were dichotomized using the minimum p value method and compared with previously reported ROC curve-derived thresholds using logistic regression analysis. A final prediction model was developed, internally validated, and assessed for its sensitivity to clustering effects. Finally, a preoperative risk score system was proposed. Thresholds identified by the minimum p value method and ROC curve approach were 115.57 pg/ml (p < 0.001) and 116 pg/ml for BNP, and 241.7 pg/ml (p = 0.001) and 277.5 pg/ml for NTproBNP, respectively. The minimum p value thresholds were slightly stronger predictors based on our logistic regression analysis. The final model included a composite predictor of the minimum p value method’s BNP and NTproBNP thresholds [odds ratio (OR) = 8.5, p < 0.001], surgery type (OR = 2.5, p = 0.002), and diabetes (OR = 2.1, p = 0.015). Preoperative risks using the scoring system ranged from 2 to 49 %. The minimum p value method and ROC curve approach identify similar optimal thresholds. We propose to replace the revised cardiac risk index with our risk score system for individual-specific preoperative risk stratification after noncardiac nonvascular surgery.

Prevalent cardiac, renal and cardiorenal damage in patients with advanced abdominal aortic aneurysms

Chronic kidney disease (CKD), cardiac damage (CD) and the combination of the two are associated with increased morbidity and death in patients admitted to vascular surgery units. We assessed the prevalence of cardiac and renal damage and cardiorenal syndrome (CRS) in 563 patients with abdominal aortic aneurysms (AAA) who underwent cardiac screening before either an endovascular procedure (EVAR) or open surgery (OS) for aneurysm repair. CD was defined by ≥stage B as per the ACC/AHA classification of congestive heart failure (CHF), while CKD was defined by estimated GFR <60 mL/min/1.73 m(2) (CKD-EPI). Anemia [World Health Organization (WHO) guidelines] and iron deficiency (ID) (criteria for CHF patients) were also calculated. AAA patients were stratified into the following groups: CD, CKD, CRS or none of these conditions [no risk factors (NoRF)]. The prevalence of isolated cardiac and renal structural damage, of combined cardiorenal damage and of ID was 24.1, 15.0, 20.6 and 23.4 %, respectively. The frequency of anemia (mostly unrecognized) among the groups increased from NoRF (12.8 %)/CKD (19 %)/CD (25 %) up to CRS (38.8 %). This large-scale observational study provides clues for the increased CD/CKD risk profiles of unselected AAA patients, and underlines the need for better identification of ID/anemia and for appropriate treatment of CKD and CD before these patients undergo EVAR/OS.

Cardiac biomarkers in the prediction of risk in the non-cardiac surgery setting

B-Type natriuretic peptides and troponin measurements have potential in predicting risk in patients undergoing non-cardiac surgery. Using the American Heart Association framework for the evaluation of novel biomarkers, we review the current evidence supporting the peri-operative use of these two biomarkers. In patients having major non-cardiac surgery who are risk stratified using clinical risk scores, the measurement of natriuretic peptides and troponin, both before and after surgery, significantly improves risk stratification. However, only pre- and postoperative natriuretic peptide measurement and postoperative troponin measurement have shown clinical utility. It is now important for trials to be conducted to determine whether integrating pre- and postoperative natriuretic peptide and postoperative troponin measurement into clinical practice is able to improve clinical outcomes in patients undergoing non-cardiac surgery.