Patients with critical limb ischemia (CLI) are a heterogeneous population with respect to risk for mortality and limb loss, complicating clinical decision-making. Endovascular options, as compared to bypass, offer a tradeoff between reduced procedural risk and inferior durability. Risk stratified data predictive of amputation-free survival (AFS) may improve clinical decision making and allow for better assessment of new technology in the CLI population. METHODS This was a retrospective analysis of prospectively collected data from patients who underwent infrainguinal vein bypass surgery for CLI. Two datasets were used: the PREVENT III randomized trial (n=1404) and a multicenter registry (n=716) from 3 distinct vascular centers (2 academic, 1 community-based). The PREVENT III cohort was randomly assigned to a derivation set (n=953) and to a validation set (n=451). The primary endpoint was AFS. Predictors of AFS identified on univariate screen (inclusion threshold, p<0.20) were included in a stepwise selection Cox model. The resulting 5 significant predictors were assigned an integer score to stratify patients into 3 risk groups. The prediction rule was internally validated in the PREVENT III validation set and externally validated in the multicenter cohort. RESULTS The estimated 1 year AFS in the derivation, internal validation, and external validation sets were 76.3%, 72.5%, and 77.0%, respectively. In the derivation set, dialysis (HR 2.81, p<.0001), tissue loss (HR 2.22, p=.0004), age ≥75 (HR 1.64, p=.001), hematocrit ≤30 (HR 1.61, p=.012), and advanced CAD (HR 1.41, p=.021) were significant predictors for AFS in the multivariable model. An integer score, derived from the β coefficients, was used to generate 3 risk categories (low ≤ 3 [44.4% of cohort], medium 4–7 [46.7% of cohort], high ≥8 [8.8% of cohort]). Stratification of the patients, in each dataset, according to risk category yielded 3 significantly different Kaplan-Meier estimates for one year AFS (86%, 73%, and 45% for low, medium, and high risk groups respectively). For a given risk category, the AFS estimate was consistent between the derivation and validation sets. CONCLUSION Among patients selected to undergo surgical bypass for infrainguinal disease, this parsimonious risk stratification model reliably identified a category of CLI patients with a >50% chance of death or major amputation at 1 year. Calculation of a “PIII risk score” may be useful for surgical decision making and for clinical trial designs in the CLI population.

Objective The calcium composition of atherosclerotic plaque is thought to be associated with increased risk for cardiovascular events, but whether plaque calcium itself is predictive of worsening clinical outcomes remains highly controversial. Inflammation is likely a key mediator of vascular calcification, but immune signaling mechanisms that promote this process are minimally understood. Approach and Results Here we identify Rac2 as a major inflammatory regulator of signaling that directs plaque osteogenesis. In experimental atherogenesis, Rac2 prevented progressive calcification through its suppression of Rac1-dependent macrophage IL-1β expression, which in turn is a key driver of vascular smooth muscle cell calcium deposition by its ability to promote osteogenic transcriptional programs. Calcified coronary arteries from patients revealed decreased Rac2 expression but increased IL-1β expression, and high coronary calcium burden in patients with coronary artery disease was associated with significantly increased serum IL-1β levels. Moreover, we found that elevated IL-1β was an independent predictor of cardiovascular death in those subjects with high coronary calcium burden. Conclusions Overall, these studies identify a novel Rac2-mediated regulation of macrophage IL-1β expression, which has the potential to serve as a powerful biomarker as well as therapeutic target for atherosclerosis.

Individuals with established cardiovascular disease or a high burden of cardiovascular risk factors may be particularly vulnerable to develop complications from coronavirus disease 2019 (COVID-19). We conducted a prospective cohort study at a tertiary care center to identify risk factors for in-hospital mortality and major adverse cardiovascular events (MACE; a composite of myocardial infarction, stroke, new acute decompensated heart failure, venous thromboembolism, ventricular or atrial arrhythmia, pericardial effusion, or aborted cardiac arrest) among consecutively hospitalized adults with COVID-19, using multivariable binary logistic regression analysis. The study population comprised 586 COVID-19 positive patients. Median age was 67 (IQR: 55-80) years, 47.4% were female, and 36.7% had cardiovascular disease. Considering risk factors, 60.2% had hypertension, 39.8% diabetes, and 38.6% hyperlipidemia. Eighty-two individuals (14.0%) died in-hospital, and 135 (23.0%) experienced MACE. In a model adjusted for demographic characteristics, clinical presentation, and laboratory findings, age (odds ratio [OR], 1.28 per 5 years; 95% confidence interval [CI], 1.13-1.45), prior ventricular arrhythmia (OR, 18.97; 95% CI, 3.68-97.88), use of P2Y 12 -inhibitors (OR, 7.91; 95% CI, 1.64-38.17), higher C-reactive protein (OR, 1.81: 95% CI, 1.18-2.78), lower albumin (OR, 0.64: 95% CI, 0.47-0.86), and higher troponin T (OR, 1.84; 95% CI, 1.39-2.46) were associated with mortality (p<0.05). After adjustment for demographics, presentation, and laboratory findings, predictors of MACE were higher respiratory rates, altered mental status, and laboratory abnormalities, including higher troponin T (p<0.05). In conclusion, poor prognostic markers among hospitalized patients with COVID-19 included older age, pre-existing cardiovascular disease, respiratory failure, altered mental status, and higher troponin T concentrations.

Background A recent large-scale clinical trial found that an initial invasive strategy does not improve cardiac outcomes beyond optimized medical therapy in patients with stable coronary artery disease (CAD). Novel methods to stratify at-risk patients may refine therapeutic decisions to improve outcomes. Methods and Results In a cohort of 815 consecutive patients referred for evaluation of myocardial ischemia, we determined the net reclassification improvement of the risk of cardiac death or nonfatal MI (MACE) incremental to clinical risk models, using guideline–based low (<1%), moderate (1–3%), and high (>3%) annual risk categories. In the whole cohort, inducible ischemia demonstrated strong association with MACE (hazard ratio 14.66, P<0.0001) with low negative event rates of MACE and cardiac death (0.6% and 0.4%). This prognostic robustness maintained in patients with prior CAD (hazard ratio 8.17, P<0.0001, and 1.3% and 0.6%, respectively). Adding inducible ischemia to the multivariable clinical risk model (age and prior CAD adjusted) improved discrimination of MACE (C-statistic 0.81 to 0.86, P=0.04; Adjusted hazard ratio 7.37, P<0.0001) and reclassified 91.5% of patients at moderate pre-test risk (65.7% to low risk; 25.8% to high risk) with corresponding changes in the observed event rates (0.3%/year and 4.9%/year, for low and high risk post-test, respectively). Categorical net reclassification index was 0.229 (95% CI 0.063–0.391). Continuous NRI was 1.11 (95% CI 0.81–1.39). Conclusions Stress CMR effectively reclassifies patient risk beyond standard clinical variables, specifically in patients at moderate to high pre-test clinical risk and in patients with prior CAD. Clinical Trial Registration Information http://clinicaltrials.gov/. Identifier: NCT01821924.