Genomics: risk and outcomes in cardiac surgery

Cardioprotective Effect of Anesthetics: Translating Science to Practice

Cardiovascular diseases are the number one cause of mortality in the world, particularly among the aging population. Major adverse cardiac events are also a major contributor to perioperative complications, affecting 2.6% of noncardiac surgeries and up to 18% of cardiac surgeries. Cardioprotective effects of volatile anesthetics and certain intravenous anesthetics have been well-documented in preclinical studies; however, their clinical application has yielded conflicting results in terms of their efficacy. Therefore, better understanding of the underlying mechanisms and developing effective ways to translate these insights into clinical practice remain significant challenges and unmet needs in the area. Several recent reviews have focused on mechanistic dissection of anesthetic-mediated cardioprotection. The present review focuses on recent clinical trials investigating the cardioprotective effects of anesthetics in the past five years. In addition to highlighting the main outcomes of these trials, the authors provide their perspectives about the current gap in the field and potential directions for future investigations.

Pharmacological management of perioperative bleeding in cardiac surgery

[first paragraph of article]In spite of the growing discoveries in cardiac surgery, perioperative bleeding remains one of the most common causes of morbidity and mortality. Around 50-60% of cardiac surgery patients receive blood transfusions, which are harmful and strongly associated with increased morbidity, mortality, hospital stay, and hospital cost. Patients taken back to the OR for re-exploration and control of bleeding after cardiac surgery, have a four-fold increase in the incidence of sternal infection and mortality.

Pharmacogenomics in Anesthesia

A significant number of commonly administered medications in anesthesia show wide clinical interpatient variability. Some of these include neuromuscular blockers, opioids, local anesthetics, and inhalation anesthetics. Individual genetic makeup may account for and predict cardiovascular outcomes after cardiac surgery. These interactions can manifest at any point in the perioperative period and may also only affect a specific system. A better understanding of pharmacogenomics will allow for more individually tailored anesthetics and may ultimately lead to better outcomes, decreased hospital stays, and improved patient satisfaction.

[Role of antithrombin iii in cardiac surgery]

Coagulation of blood is of multidisciplinary interest. Cardiac surgery produces major changes in the delicate balance between pro-and anti-coagulant serum factors. The role of antithrombin iii has been analysed after finding evidence that associated decreased levels of protein activity to postoperative morbidity and mortality. Supplementing exogenous antithrombin is considered with the aim of optimising outcomes. Its intrinsic anticoagulant and anti-inflammatory properties have stimulated a growing interest, and suggests new lines of research.

Single nucleotide polymorphism network: a combinatorial paradigm for risk prediction

Risk prediction for a particular disease in a population through SNP genotyping exploits tests whose primary goal is to rank the SNPs on the basis of their disease association. This manuscript reveals a different approach of predicting the risk through network representation by using combined genotypic data (instead of a single allele/haplotype). The aim of this study is to classify diseased group and prediction of disease risk by identifying the responsible genotype. Genotypic combination is chosen from five independent loci present on platelet receptor genes P2RY1 and P2RY12. Genotype-sets constructed from combinations of genotypes served as a network input, the network architecture constituting super-nodes (e.g., case and control) and nodes representing individuals, each individual is described by a set of genotypes containing M markers (M = number of SNP). The analysis becomes further enriched when we consider a set of networks derived from the parent network. By maintaining the super-nodes identical, each network is carrying an independent combination of M-1 markers taken from M markers. For each of the network, the ratio of case specific and control specific connections vary and the ratio of super-node specific connection shows variability. This method of network has also been applied in another case-control study which includes oral cancer, precancer and control individuals to check whether it improves presentation and interpretation of data. The analyses reveal a perfect segregation between super-nodes, only a fraction of mixed state being connected to both the super-nodes (i.e. common genotype set). This kind of approach is favorable for a population to classify whether an individual with a particular genotypic combination can be in a risk group to develop disease. In addition with that we can identify the most important polymorphism whose presence or absence in a population can make a large difference in the number of case and control individuals.

Postoperative pain trajectories in cardiac surgery patients

Poorly controlled postoperative pain is a longstanding and costly problem in medicine. The purposes of this study were to characterize the acute pain trajectories over the first four postoperative days in 83 cardiac surgery patients with a mixed effects model of linear growth to determine whether statistically significant individual differences exist in these pain trajectories, and to compare the quality of measurement by trajectory with conventional pain measurement practices. The data conformed to a linear model that provided slope (rate of change) as a basis for comparing patients. Slopes varied significantly across patients, indicating that the direction and rate of change in pain during the first four days of recovery from surgery differed systematically across individuals. Of the 83 patients, 24 had decreasing pain after surgery, 24 had increasing pain, and the remaining 35 had approximately constant levels of pain over the four postoperative days.

Association of the 98T ELAM-1 polymorphism with increased bleeding after cardiac surgery

OBJECTIVE

Hemorrhage continues to be a major problem after cardiac surgery despite the routine use of antifibrinolytic drugs, with striking inter-patient variability poorly explained by already known risk factors. The authors tested the hypothesis that genetic polymorphisms of inflammatory mediators and cellular adhesion molecules are associated with bleeding after cardiac surgery.

DESIGN

Prospective, observational study.

SETTING

Single, tertiary referral university heart center.

PARTICIPANTS

Adult patients undergoing aortocoronary surgery with cardiopulmonary bypass.

INTERVENTIONS

Patients (n = 759) had 10 mL of blood drawn preoperatively and genomic DNA isolated then genotyped for 17 polymorphisms in 7 candidate genes: tumor necrosis factor, interleukins 1beta and 6, interleukin 1 receptor antagonist, intercellular adhesion molecule-1 (ICAM-1), P-selectin and endothelial leucocyte adhesion molecule-1 (E-selectin). Multivariate analyses were used to relate clinical and genetic factors to bleeding and transfusion.

MEASUREMENTS AND MAIN RESULTS

The 98G/T polymorphism of the E-selectin gene was independently associated with bleeding after cardiac surgery (p = 0.002), after adjusting for significant clinical predictors (patient size and baseline hemoglobin concentration). There was a gene dose effect according to the number of minor alleles in the genotype; carriers of the minor allele bled 17% (GT) and 54% (TT) more than wild type (GG) genotypes, respectively (p = 0.01). Carriers of the minor allele also had longer activated partial thromboplastin times (p = 0.0023) and increased fresh frozen plasma transfusion (p = 0.03) compared with wild type.

CONCLUSIONS

The authors found a dose-related association between the 98T E-selectin polymorphism and bleeding after cardiac surgery, independent of and additive to standard clinical risk factors.