Elevated Presepsin Is Associated With Perioperative Major Adverse Cardiovascular and Cerebrovascular Complications in Elevated-Risk Patients Undergoing Noncardiac Surgery: The Leukocytes and Cardiovascular Perioperative Events Study

[Cardiac biomarkers prior to noncardiac surgery]

BACKGROUND

Cardiac biomarkers, such as high-sensitivity cardiac troponin (hs-cTn) and brain natriuretic peptide (BNP) or N‑terminal prohormone of brain natriuretic peptide (NT-proBNP) are measured perioperatively to improve the prognosis and risk prediction. The European Society of Cardiology (ESC), European Society of Anesthesiology and Intensive Care (ESAIC) and the German Society of Anesthesiology and Intensive Care Medicine (DGAI) have recently published guidelines on the use of cardiac biomarkers prior to surgery.

OBJECTIVE/RESEARCH QUESTION

This article provides an overview of the available evidence on perioperative troponin and BNP/NT-proBNP measurements. Current guideline recommendations are presented and discussed.

MATERIAL AND METHODS

MEDLINE, Cochrane and google.scholar were searched for relevant keywords. Titles and abstracts of identified papers were checked for relevance and published results were summarized. Guideline recommendations from the ESC, ESAIC and DGAI are presented, compared and evaluated based on the available literature. In addition, the significance of new perioperative cardiac biomarkers is discussed based on the existing evidence.

RESULTS

The definitions, diagnosis and management of cardiovascular events in the perioperative context differ from those in the nonsurgical setting. The evidence for the measurement of hs-cTn and BNP/NT-proBNP is evaluated differently in the guidelines and the resulting recommendations are partly contradictory. In particular, recommendations for changes in perioperative management based on biomarker measurements diverge. The ESC guidelines propose an algorithm that uses preoperative biomarkers as the basis for additional cardiac investigations. In particular, invasive coronary angiography is recommended for patients with stable chronic coronary syndrome who have no preoperative cardiac symptoms but elevated biomarkers. In contrast, the ESAIC guidelines emphasize that the available evidence is not sufficient to use perioperative biomarker measurements as a basis for a change in perioperative management.

DISCUSSION

Treating physicians should coordinate interdisciplinary (surgery, anesthesiology, cardiology) recommendations for clinical practice based on the aforementioned guidelines. If cardiac biomarkers are routinely determined in high-risk patients, this should be done in accordance with the ESC algorithm.

Pathophysiological mechanisms underlying increased circulating cardiac troponin in noncardiac surgery: a narrative review

Assay-specific increases in circulating cardiac troponin are observed in 20-40% of patients after noncardiac surgery, depending on patient age, type of surgery, and comorbidities. Increased cardiac troponin is consistently associated with excess morbidity and mortality after noncardiac surgery. Despite these findings, the underlying mechanisms are unclear. The majority of interventional trials have been designed on the premise that ischaemic cardiac disease drives elevated perioperative cardiac troponin concentrations. We consider data showing that elevated circulating cardiac troponin after surgery could be a nonspecific marker of cardiomyocyte stress. Elevated concentrations of circulating cardiac troponin could reflect coordinated pathological processes underpinning organ injury that are not necessarily caused by ischaemia. Laboratory studies suggest that matching of coronary artery autoregulation and myocardial perfusion-contraction coupling limit the impact of systemic haemodynamic changes in the myocardium, and that type 2 ischaemia might not be the likeliest explanation for cardiac troponin elevation in noncardiac surgery. The perioperative period triggers multiple pathological mechanisms that might cause cardiac troponin to cross the sarcolemma. A two-hit model involving two or more triggers including systemic inflammation, haemodynamic strain, adrenergic stress, and autonomic dysfunction might exacerbate or initiate acute myocardial injury directly in the absence of cell death. Consideration of these diverse mechanisms is pivotal for the design and interpretation of interventional perioperative trials.

Biomarkers as Prognostic Predictors and Therapeutic Guide in Critically Ill Patients: Clinical Evidence

A biomarker is a molecule that can be measured in a biological sample in an objective, systematic, and precise way, whose levels indicate whether a process is normal or pathological. Knowing the most important biomarkers and their characteristics is the key to precision medicine in intensive and perioperative care. Biomarkers can be used to diagnose, in assessment of disease severity, to stratify risk, to predict and guide clinical decisions, and to guide treatments and response to them. In this review, we will analyze what characteristics a biomarker should have and how to ensure its usefulness, and we will review the biomarkers that in our opinion can make their knowledge more useful to the reader in their clinical practice, with a future perspective. These biomarkers, in our opinion, are lactate, C-Reactive Protein, Troponins T and I, Brain Natriuretic Peptides, Procalcitonin, MR-ProAdrenomedullin and BioAdrenomedullin, Neutrophil/lymphocyte ratio and lymphopenia, Proenkephalin, NefroCheck, Neutrophil gelatinase-associated lipocalin (NGAL), Interleukin 6, Urokinase-type soluble plasminogen activator receptor (suPAR), Presepsin, Pancreatic Stone Protein (PSP), and Dipeptidyl peptidase 3 (DPP3). Finally, we propose an approach to the perioperative evaluation of high-risk patients and critically ill patients in the Intensive Care Unit (ICU) based on biomarkers.

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.

Presepsin for pre-operative prediction of major adverse cardiovascular events in coronary heart disease patients undergoing noncardiac surgery: Post hoc analysis of the Leukocytes and Cardiovascular Peri-operative Events-2 (LeukoCAPE-2) Study

BACKGROUND

Accurate pre-operative evaluation of cardiovascular risk is vital to identify patients at risk for major adverse cardiovascular and cerebrovascular events (MACCE) after noncardiac surgery. Elevated presepsin (sCD14-ST) is associated with peri-operative MACCE in coronary artery disease (CAD) patients after noncardiac surgery.

OBJECTIVES

Validating the prognostic utility of presepsin for MACCE after noncardiac surgery.

DESIGN

Prospective patient enrolment and blood sampling, followed by post hoc evaluation of pre-operative presepsin for prediction of MACCE.

SETTING

Single university centre.

PATIENTS

A total of 222 CAD patients undergoing elective, inpatient noncardiac surgery.

INTERVENTION

Pre-operative presepsin measurement.

MAIN OUTCOME MEASURES

MACCE (cardiovascular death, myocardial infarction, myocardial ischaemia and stroke) at 30 days postsurgery.

RESULTS

MACCE was diagnosed in 23 (10%) patients. MACCE patients presented with increased pre-operative presepsin (median [IQR]; 212 [163 to 358] vs. 156 [102 to 273] pgml, P = 0.023). Presepsin exceeding the previously derived threshold of 184 pg ml was associated with increased 30-day MACCE rate. After adjustment for confounders, presepsin more than 184 pg ml [OR = 2.8 (95% confidence interval 1.1 to 7.3), P = 0.03] remained an independent predictor of peri-operative MACCE. Predictive accuracy of presepsin was moderate [area under the curve (AUC) = 0.65 (0.54 to 0.75), P = 0.023]. While the basic risk model of revised cardiac risk index, high-sensitive cardiac troponin T and N-terminal fragment of pro-brain natriuretic peptide resulted in an AUC = 0.62 (0.48 to 0.75), P = 0.072, addition of presepsin to the model led to an AUC = 0.67 (0.56 to 0.78), P = 0.009 and (ΔAUC = 0.05, P = 0.438). Additive risk predictive value of presepsin was demonstrated by integrated discrimination improvement analysis (integrated discrimination improvement = 0.023, P = 0.022). Net reclassification improvement revealed that the additional strength of presepsin was attributed to the reclassification of no-MACCE patients into a lower risk group.

CONCLUSION

Increased pre-operative presepsin independently predicted 30-day MACCE in CAD patients undergoing major noncardiac surgery. Complementing cardiovascular risk prediction by inflammatory biomarkers, such as presepsin, offers potential to improve peri-operative care. However, as prediction accuracy of presepsin was only moderate, further validation studies are needed.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT03105427.

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.

Preoperative neutrophil to lymphocyte ratio and platelet to lymphocyte ratio are associated with major adverse cardiovascular and cerebrovascular events in coronary heart disease patients undergoing non-cardiac surgery

BACKGROUND

Preoperative risk prediction in patients at elevated cardiovascular risk shows limited accuracy. Platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR) indicate systemic inflammation. Both have been investigated for outcome prediction in the field of oncology and cardiovascular medicine, as well as risk prediction of adverse cardiovascular events in non-surgical patients at increased cardiovascular risk.

METHODS

For this post-hoc analysis, we included all 38 coronary heart disease patients from the Leukocytes and Cardiovascular Perioperative Events cohort-1 study scheduled for elective non-cardiac surgery. We evaluated preoperative differential blood counts for association with major adverse cardiovascular and cerebrovascular events (MACCE) defined as the composite endpoint of death, myocardial ischemia, myocardial infarction, myocardial injury after non-cardiac surgery, or embolic or thrombotic stroke within 30 days after surgery. We used Youden's index to calculate cut-off values for PLR and NLR. Additive risk-predictive values were assessed using receiver operating characteristic curve and net reclassification (NRI) improvement analyses.

RESULTS

Patients with the composite endpoint MACCE had higher PLR and NLR (309 [206; 380] vs. 160 [132; 203], p = 0.001; 4.9 [3.5; 8.1] vs. 2.6 [2.2; 3.4]), p = 0.001). Calculated cut-offs for PLR > 204.4 and NLR > 3.1 were associated with increased risk of 30-day MACCE (OR 7, 95% CI [1.2; 44.7], p = 0.034; OR 36, 95% CI [1.8; 686.6], p = 0.001). Furthermore, NLR improved risk prediction in coronary heart disease patients undergoing non-cardiac surgery when combined with hs-cTnT or NT-proBNP (NRI _total = 0.23, p = 0.008, NRI _total = 0.26, p = 0.005).

CONCLUSIONS

Both PLR and NLR were associated with perioperative cardiovascular adverse events in coronary heart disease patients. NLR proved to be of additional value for preoperative risk stratification. Both PLR and NLR could be used as inexpensive and broadly available tools for perioperative risk assessment.

TRIAL REGISTRATION

NCT02874508, August 22, 2016.

Cardiovascular Risk Assessment for Noncardiac Surgery: Are We Ready for Biomarkers?

Biomarkers aided perioperative cardiac assessment is a relatively new concept. Cardiac biomarkers with historical significance (aspartate transaminase, dehydrogenase, creatinine kinase and myoglobin) have paved the way for traditional biomarkers (cardiac troponin, C-reactive protein, lipoprotein). Contemporary biomarkers like natriuretic peptides (BNP and ProBNP) are validated risk markers in both acute and chronic cardiac diseases and are showing remarkable promise in predicting serious cardiovascular complications after non-cardiac surgery. This review is intended to provide a critical overview of traditional and contemporary biomarkers for perioperative cardiovascular assessment and management. This review also discusses the potential utility of newer biomarkers like galectin-3, sST-2, GDF-15, TNF-alpha, MiRNAs and many others that can predict inflammation, cardiac remodeling, injury and endogenous stress and need further investigations to establish their clinical utility. Though promising, biomarker led perioperative care is still in infancy and it has not been determined that it can improve clinical outcomes.

Frontline Science: Low regulatory T cells predict perioperative major adverse cardiovascular and cerebrovascular events after noncardiac surgery

Immune cells drive atherosclerotic lesion progression and plaque destabilization. Coronary heart disease patients undergoing noncardiac surgery are at risk for perioperative major adverse cardiac and cerebrovascular events (MACCE). It is unclear whether differential leukocyte subpopulations contribute to perioperative MACCE and thereby could aid identification of patients prone to perioperative cardiovascular events. First, we performed a hypothesis-generating post hoc analysis of the LeukoCAPE-1 study (n = 38). We analyzed preoperative counts of 6 leukocyte subpopulations in coronary heart disease patients for association with MACCE (composite of cardiac death, myocardial infarction, myocardial ischemia, myocardial injury after noncardiac surgery, thromboembolic stroke) within 30 d after surgery. Regulatory T cells (Tregs) were the only leukocyte subgroup associated with MACCE. We found reduced Tregs in patients experiencing MACCE versus no-MACCE (0.02 [0.01; 0.03] vs. 0.04 [0.03; 0.05] Tregs nl^-1 , P = 0.002). Using Youden index, we derived the optimal threshold value for association with MACCE to be 0.027 Tregs nl^-1 . Subsequently, we recruited 233 coronary heart disease patients for the prospective, observational LeukoCAPE-2 study and independently validated this Treg cutoff for prediction of MACCE within 30 d after noncardiac surgery. After multivariate logistic regression, Tregs < 0.027 cells nl^-1 remained an independent predictor for MACCE (OR = 2.54 [1.22; 5.23], P = 0.012). Tregs improved risk discrimination of the revised cardiac risk index based on ΔAUC (area under the curve; ΔAUC = 0.09, P = 0.02), NRI (0.26), and IDI (0.06). Preoperative Treg levels below 0.027 cells nl^-1 predicted perioperative MACCE and can be measured to increase accuracy of established preoperative cardiac risk stratification in coronary heart disease patients undergoing noncardiac surgery.

Parameters of Oxidative and Inflammatory Status in a Three-Month Observation of Patients with Acute Myocardial Infarction Undergoing Coronary Angioplasty-A Preliminary Study

Background and Objectives: Patients with acute myocardial infarction (MI) are usually treated with percutaneous transluminal coronary angioplasty (PTCA), which is burdened with a risk of postoperative complications, often accompanied by biochemical disturbances. The aim of our study was to evaluate a set of selected parameters of oxidative and inflammatory status, which could be useful in the management of post-procedural care in MI patients after PTCA. Materials and Methods: In this preliminary study, ischemia modified albumin (IMA), advanced oxidation protein products (AOPP), thiol groups (SH), total antioxidant status (TAS), insulin growth factor-1 (IGF-1), presepsin (PSP), and trimethylamine N-oxide (TMAO) were chosen as candidate biomarkers, and were determined in patients with MI who underwent PTCA at two time points: During cardiac episodes (at admission to the hospital, T0) and 3 months later (T3). Results: Most of the examined parameters were significantly different between patients and control subjects (except for IMA and TAS), but only hsCRP changed significantly during the time of observation (T0 vs. T3). Discriminant analysis created a model composed of AOPP, hsCRP, PSP, and TMAO, which differentiated male subjects into a group with MI and a control (without cardiovascular diseases). Conclusion: This set of parameters seems useful in evaluating inflammatory and oxidative status in MI patients after PTCA.