High-sensitivity Troponin (hs-Tn) for Cardiovascular Risk Prognostication: A Systematic Review and Meta-analysis

BACKGROUND

Chronic low-grade inflammation is involved in coronary atherosclerosis progression whereas recent research efforts suggest that preventative methods should be tailored to the "residual inflammatory risk". As such, modalities for the early identification of the risk have to be investigated.

METHODS

We performed a systematic review and meta-analysis according to the PRISMA guidelines. Any study that presented the prognostic value of high sensitivity troponin (hs-cTn) of vascular inflammation in stable patients without known cardiac heart disease was considered to be potentially eligible. The Medline (PubMed) database was searched up to April 22, 2021. The main endpoint was the difference in c-index (Δ[c-index]) with the use of hs-cTn for major adverse cardiovascular events (MACEs), cardiovascular and all-cause mortality. We calculated I2 to test heterogeneity.

RESULTS

In total, 44 studies and 112,288 stable patients without known coronary heart disease were included in this meta-analysis. The mean follow-up duration of the whole cohort was 6.8 ± 1.1 years. 77,004 (68.5%) of the patients presented at low cardiovascular risk while 35,284 (31.5%) in high. The overall pooled estimate of Δ[c-index] for MACE was 1.4% (95%CI: 0.7-2.1, I2=0%) and for cardiovascular death 1.3% (95%CI: 0.3-2.3, I2=0%). Finally, the overall pooled estimate of Δ[c-index] for all-cause mortality was 3% (95%CI: 1.9-3.9, I2=86%), while high heterogeneity was observed between the studies.

CONCLUSION

The predictive usefulness of changes in hs-cTn measures in stable individuals with either high or low cardiovascular risk, demonstrates that assessing vascular inflammation in addition to clinical risk factors enhances risk prediction for cardiovascular events and allcause mortality. Further prospective studies are necessary to confirm these findings and assist clinical decision-making regarding the most optimal prevention strategy.

Validation of randomized controlled trial-derived models for the prediction of postintervention outcomes in chronic limb-threatening ischemia

BACKGROUND

Chronic limb-threatening ischemia (CLTI) represents the most severe form of peripheral artery disease and has a large impact on quality of life, morbidity, and mortality. Interventions are aimed at improving tissue perfusion and averting amputation and secondary cardiovascular complications with an optimal risk-benefit ratio. Several prediction models regarding postprocedural outcomes in CLTI patients have been developed on the basis of randomized controlled trials to improve clinical decision-making. We aimed to determine model performance in predicting clinical outcomes in selected CLTI cohorts.

METHODS

This study validated the Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL), Finland National Vascular registry (FINNVASC), and Prevention of Infrainguinal Vein Graft Failure (PREVENT III) models in data sets from a peripheral artery disease registry study (Athero-Express) and two randomized controlled trials of CLTI in The Netherlands, Rejuvenating Endothelial Progenitor Cells via Transcutaneous Intra-arterial Supplementation (JUVENTAS) and Percutaneous Transluminal Angioplasty and Drug-eluting Stents for Infrapopliteal Lesions in Critical Limb Ischemia (PADI). Receiver operating characteristic (ROC) curve analysis was used to calculate their predictive capacity. The primary outcome was amputation-free survival (AFS); secondary outcomes were all-cause mortality and amputation at 12 months after intervention.

RESULTS

The BASIL and PREVENT III models showed predictive values regarding postintervention mortality in the JUVENTAS cohort with an area under the ROC curve (AUC) of 81% and 70%, respectively. Prediction of AFS was poor to fair (AUC, 0.60-0.71) for all models in each population, with the highest predictive value of 71% for the BASIL model in the JUVENTAS population. The FINNVASC model showed the highest predictive value regarding amputation risk in the PADI population with AUC of 78% at 12 months.

CONCLUSIONS

In general, all models performed poor to fair in predicting mortality and amputation. Because the BASIL model performed best in predicting AFS, we propose use of the BASIL model to aid in the clinical decision-making process in CLTI. However, improvements in performance have to be made for any of these models to be of real additional value in clinical practice.

A single preoperative blood test predicts postoperative sepsis and pneumonia after coronary bypass or open aneurysm surgery

BACKGROUND

Major surgery comes with a high risk for postoperative inflammatory complications. Preoperative risk scores predict mortality risk but fail to identify patients at risk for complications following cardiovascular surgery. We therefore assessed the value of preoperative red cell distribution width (RDW) as a predictor for pneumonia and sepsis after cardiovascular surgery and studied the relation of RDW with hematopoietic tissue activity.

METHODS

RDW is an easily accessible, yet seldomly used parameter from routine haematology measurements. RDW was extracted from the Utrecht Patient Orientated Database (UPOD) for preoperative measurements in patients undergoing open abdominal aortic anuerysm repair (AAA)(N = 136) or coronary artery bypass grafting (CABG)(N = 2193). The cohorts were stratified in tertiles to assess effects over the different groups. Generalized Linear Models were used to determine associations between RDW and postoperative inflammatory complications. Hematopoietic tissue activity was scored using fluor-18-(18F)-deoxyglucose positron emission tomography and associated with RDW using linear regression models.

RESULTS

In total, 43(31.6%) and 73 patients (3.3%) suffered from inflammatory complications after AAA-repair or CABG, respectively; the majority being pneumonia in both cohorts. Postoperative inflammatory outcome incidence increased from 19.6% in the lowest to 48.9% in the highest RDW tertile with a corresponding risk ratio (RR) of 2.35 ([95%CI:1.08-5.14] P = 0.032) in AAA patients. In the CABG cohort, the incidence of postoperative inflammatory outcomes increased from 1.8% to 5.3% with an adjusted RR of 1.95 ([95%CI:1.02-3.75] P = 0.044) for the highest RDW tertile compared with the lowest RDW tertile. FDG-PET scans showed associations of RDW with tissue activity in the spleen (B = 0.517 [P = 0.001]) and the lumbar bone marrow (B = 0.480 [P = 0.004]).

CONCLUSION

Elevated RDW associates with increased risk for postoperative inflammatory complications and hematopoietic tissue activity. RDW likely reflects chronic low-grade inflammation and should be considered to identify patients at risk for postoperative inflammatory complications following cardiovascular surgery.

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features-such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.