Prognostic Role of High-sensitivity Cardiac Troponin I and Soluble Suppression of Tumorigenicity-2 in Surgical Intensive Care Unit Patients Undergoing Non-cardiac Surgery

ESAIC focused guideline for the use of cardiac biomarkers in perioperative risk evaluation

BACKGROUND

In recent years, there has been increasing focus on the use of cardiac biomarkers in patients undergoing noncardiac surgery.

AIMS

The aim of this focused guideline was to provide updated guidance regarding the pre-, post- and combined pre-and postoperative use of cardiac troponin and B-type natriuretic peptides in adult patients undergoing noncardiac surgery.

METHODS

The guidelines were prepared using Grading of Recommendations Assessment Development and Evaluation (GRADE) methodology. This included the definition of critical outcomes, a systematic literature search, appraisal of certainty of evidence, evaluation of biomarker measurement in terms of the balance of desirable and undesirable effects including clinical outcomes, resource use, health inequality, stakeholder acceptance, and implementation. The panel differentiated between three different scopes of applications: cardiac biomarkers as prognostic factors, as tools for risk prediction, and for biomarker-enhanced management strategies.

RESULTS

In a modified Delphi process, the task force defined 12 critical outcomes. The systematic literature search resulted in over 25,000 hits, of which 115 full-text articles formed the body of evidence for recommendations. The evidence appraisal indicated heterogeneity in the certainty of evidence across critical outcomes. Further, there was relevant gradient in the certainty of evidence across the three scopes of application. Recommendations were issued and if this was not possible due to limited evidence, clinical practice statements were produced.

CONCLUSION

The ESAIC focused guidelines provide guidance on the perioperative use of cardiac troponin and B-type natriuretic peptides in patients undergoing noncardiac surgery, for three different scopes of application.

Incidence and Severity of Acute Myocardial Injury after Thoracic Surgery: Effects of Nicorandil

Aim. To study the perioperative dynamics of myocardial injury biomarkers high-sensitivity cardiac troponin I (hs-cTnI), ischemia-modified albumin (IMA) and soluble ST2 (sST2) when taking nicorandil in lung cancer patients with concomitant coronary heart disease (CHD) undergoing surgical lung resection.

Material and methods. The study included 54 patients (11 women and 43 men) with non-small cell lung cancer and concomitant stable CHD who underwent lung resection in the volume of lobectomy or pneumonectomy. Patients were randomly assigned to the nicorandil group (oral administration 10 mg BID for 7 days before and 3 days after surgery; n=27) and the control group (n=27). In the study groups, the perioperative dynamics of hscTnI, IMA and sST2, determined in the blood before and 24 and 48h after surgery, were compared. We calculated the incidence of acute myocardial injury in the groups, which was diagnosed in cases of postoperative hs-cTnI increase of more than one 99th percentile of the upper reference limit. The associations of nicorandil intake and acute myocardial injury were evaluated.

Results. The groups were comparable in gender, age, basic clinical characteristics, as well as baseline levels of myocardial injury biomarkers. After the intervention, both samples showed an increase in the hs-cTnI and sST2 levels and a decrease in IMA concentration (all p<0.02 for related group differences). In the nicorandil group, in comparison with the control one, 48h after surgery, we found lower mean levels of hs-cTnI [16.7 (11.9;39.7) vs 44.3 (15.0;130.7) ng/l; p<0.05) and sST2 [62.8 (43.6;70.1) vs 76.5 (50.2;87.1) ng/ml; p<0.05), concentration increase rates of hs-cTnI [14.8 (0.7;42.2) vs 32.5 (14.0;125.0) ng/l; p<0.01) and sST2 [24.4 (10.3;42.4) vs 47.4 (17.5;65.3) ng/ml; p<0.05), as well as highest concentrations for the entire postoperative period of hs-cTnI [30.7 (12.0;53.7) vs 79.0 (20.3;203.3) ng/L, p<0.01] and sST2 [99.8 (73.6;162.5) vs 147.8 (87.8;207.7) ng/mL; p<0.05]. The serum IMA decreased when taking nicorandil to a greater extent [-8.0 (-12.6; -2.0) vs -2.7 (-6.0; +5.5) ng/ ml; p<0.01] 24h after surgery. Acute myocardial injury was diagnosed in 7 people in the nicorandil group (25.9%) and in 15 in the control one (55.6%; pχ2=0.027). The adjusted odds ratio of acute myocardial injury when taking nicorandil was 0.35 (95% confidence interval 0.15-0.83, p=0.017).

Conclusion. Taking  nicorandil  in patients with lung cancer and concomitant CHD  who underwent  surgical  lung resection is associated  with a lower postoperative  increase in hs-cTnI  and sST2  and a reduced risk of acute myocardial  injury, which may indicate the cardioprotective effect of nicorandil under acute surgical stress conditions.

High-Sensitivity Cardiac Troponin I Thresholds to Identify Myocardial Injury After Noncardiac Surgery: A Cohort Study

BACKGROUND

Myocardial injury after noncardiac surgery (MINS) is common and associated with short- and long-term major cardiovascular events. Diagnostic criteria for MINS using Abbott high-sensitivity cardiac troponin I (hs-cTnI) are unknown.

METHODS

We performed a prospective cohort study of adults who had in-patient noncardiac surgery and measured hs-cTnI (Abbott Laboratories) on postoperative serum samples collected up to postoperative day 3. The objective was to determine prognostically important hs-cTnI thresholds associated with major cardiac events and death at 30 days after noncardiac surgery. Using Cox proportional iterative analyses, we determined peak postoperative hs-cTnI thresholds associated with the occurrence of the 30-day composite of major cardiac events (ie, nonfatal myocardial infarction after 3 postoperative days, cardiac arrest, and congestive heart failure) and death.

RESULTS

Of 3953 included patients, 66 (1.7%) experienced the primary outcome at 30 days. Peak hs-cTnI values and associated incidence of major cardiac events and death were as follows: < 60 ng/L: 1.0% (95% CI 0.7-1.3); 60 to < 700 ng/L: 8.6% (5.6-13.0); and ≥ 700 ng/L: 27.3% (16.4-41.9). Compared with peak hs-cTnI < 60 ng/L, adjusted hazard ratios were 7.54 (95% CI% 4.27-13.32) for hs-cTnI values of 60 to < 700 ng/L and 26.87 (13.27-54.41) for values ≥ 700 ng/L.

CONCLUSIONS

Hs-cTnI elevation within the first 3 days after noncardiac surgery independently predicts major cardiac events and death at 30 days. A postoperative hs-cTnI ≥ 60 ng/L was associated with a > 7-fold increase in the risk of subsequent major cardiac events and mortality at 30 days.

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.

The concept of peri-operative medicine to prevent major adverse events and improve outcome in surgical patients: A narrative review

: Peri-operative Medicine is the patient-centred and value-based multidisciplinary peri-operative care of surgical patients. Peri-operative stress, that is the collective response to stimuli occurring before, during and after surgery, is, together with pre-existing comorbidities, the pathophysiological basis of major adverse events. The ultimate goal of Peri-operative Medicine is to promote high quality recovery after surgery. Clinical scores and/or biomarkers should be used to identify patients at high risk of developing major adverse events throughout the peri-operative period. Allocation of high-risk patients to specific care pathways with peri-operative organ protection, close surveillance and specific early interventions is likely to improve patient-relevant outcomes, such as disability, health-related quality of life and mortality.

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.