Increased aortic stiffness can predict perioperative cardiovascular outcomes in patients undergoing noncardiac, nonvascular surgery

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.

Serum resistin is associated with impaired endothelial function and a higher rate of adverse cardiac events in patients with peripheral artery disease

OBJECTIVE

Resistin is a hormone that has been associated with metabolic syndrome and cardiovascular disease. The role of resistin in patients with peripheral artery disease (PAD) has not been fully explored. This study seeks to understand the relationship between serum resistin, vascular function, and cardiovascular outcomes in patients with PAD.

METHODS

There were 106 patients with PAD who were recruited between 2011 and 2016. Patients attended a baseline visit during which a comprehensive vascular physiology assessment including medical and surgical history, radial artery tonometry, and flow mediated-vasodilation (FMD) was completed. A blood sample was drawn, and serum resistin was assayed using enzyme-linked immunosorbent assay kits. Using the time of study enrollment as the time of origin, incident major adverse cardiac events (MACEs) were identified by subsequent chart review and defined as a composite end point of myocardial infarction, coronary revascularization, transient ischemic attack, stroke, or death from a cardiac cause.

RESULTS

Patients had a mean age of 68 ± 8 years, were largely white (75%), and had comorbidities commonly associated with PAD including hypertension (92%), hyperlipidemia (87%), coronary artery disease (37%), and diabetes mellitus (38%). After stratification by resistin quartile, higher resistin quartiles were significantly associated with an older age, a greater number of pack-years smoked, and a lower estimated glomerular filtration rate. Despite similar comorbidities and medication use, endothelial function, as measured by FMD, was significantly lower with increasing resistin quartile (I, 9.1% ± 3.3%; II, 7.1% ± 3.5%; III, 5.8% ± 4.0%; IV, 5.6% ± 3.5%; P = .002). In multivariable linear regression, higher resistin quartiles (III and IV) were associated with lower FMD relative to quartile I after adjusting for several patient characteristics, medications, and comorbidities (III, -2.26 [95% confidence interval (CI), -4.51 to -0.01; P = .05]; IV, -2.53 [95% CI, -4.87 to -0.20; P = .03]). During a median follow-up period of 36 months (interquartile range, 29-45 months), 21 patients experienced the primary end point. In a Cox proportional hazards model adjusted for smoking status, coronary artery disease, and age, each 1 ng/mL increase in resistin was associated with a 10% increased risk of MACEs (hazard ratio, 1.10; 95% CI, 1.00-1.20; P = .04).

CONCLUSIONS

In patients with PAD, higher levels of resistin were associated with impaired endothelial function and an increased rate of MACEs. These results suggest that resistin may be a marker or effector of impaired vascular physiology and adverse cardiac outcomes in patients with PAD. Further research is needed to determine the potential mechanisms by which resistin may impair endothelial function and increase MACEs in this population.