Precision Monitoring of Antithrombotic Therapy in Cardiovascular Disease

Thrombosis, the process of blood clot formation in blood vessels, is an important protective mechanism for avoiding excessive blood spillage when an individual is exposed to trauma. The body has both a thrombosis inhibition and a thrombus removal system, which interact in a balanced manner. If these mechanisms become unbalanced, and too many clots form and block the lumen, thrombosis occurs. Thrombosis is currently the leading cause of death from disease in humans and is one of the most common events leading to many cardiovascular diseases. Antithrombotic drugs are an integral part of the pharmacological treatment regimens, and interventional strategies are currently recommended for thrombotic complications in patients with thrombosis. Despite major advances in these therapies, the high risk associated with thrombosis and bleeding remains, because of the complex interplay among patient comorbidities, drug combinations, multifaceted dose adjustments, and care settings. Detailed assessment of the effects of bleeding and thrombosis is necessary to establish optimal treatment plans for patients with thrombosis. This study retrospectively evaluated methods for assessing the risk of bleeding/ischemia in thrombosis and the individualized use of these methods

Prediction models for major adverse cardiovascular events following ST-segment elevation myocardial infarction and subgroup-specific performance

BackgroundST-segment elevation myocardial infarction (STEMI) patients are at a high residual risk of major adverse cardiovascular events (MACEs) after revascularization. Risk factors modify prognostic risk in distinct ways in different STEMI subpopulations. We developed a MACEs prediction model in patients with STEMI and examined its performance across subgroups.MethodsMachine-learning models based on 63 clinical features were trained in patients with STEMI who underwent PCI. The best-performing model (the iPROMPT score) was further validated in an external cohort. Its predictive value and variable contribution were studied in the entire population and subgroups.ResultsOver 2.56 and 2.84 years, 5.0% and 8.33% of patients experienced MACEs in the derivation and external validation cohorts, respectively. The iPROMPT score predictors were ST-segment deviation, brain natriuretic peptide (BNP), low-density lipoprotein cholesterol (LDL-C), estimated glomerular filtration rate (eGFR), age, hemoglobin, and white blood cell (WBC) count. The iPROMPT score improved the predictive value of the existing risk score, with an increase in the area under the curve to 0.837 [95% confidence interval (CI): 0.784–0.889] in the derivation cohort and 0.730 (95% CI: 0.293–1.162) in the external validation cohort. Comparable performance was observed between subgroups. The ST-segment deviation was the most important predictor, followed by LDL-C in hypertensive patients, BNP in males, WBC count in females with diabetes mellitus, and eGFR in patients without diabetes mellitus. Hemoglobin was the top predictor in non-hypertensive patients.ConclusionThe iPROMPT score predicts long-term MACEs following STEMI and provides insights into the pathophysiological mechanisms for subgroup differences

Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IV. Hβ\beta Time Lags and Implications for Super-Eddington Accretion

We have completed two years of photometric and spectroscopic monitoring of a large number of active galactic nuclei (AGNs) with very high accretion rates. In this paper, we report on the result of the second phase of the campaign, during 2013--2014, and the measurements of five new H$\beta$ time lags out of eight monitored AGNs. All five objects were identified as super-Eddington accreting massive black holes (SEAMBHs). The highest measured accretion rates for the objects in this campaign are $\dot{\mathscr{M}}\gtrsim 200$, where $\dot{\mathscr{M}}= \dot{M}_{\bullet}/L_{\rm Edd}c^{-2}$, $\dot{M}_{\bullet}$ is the mass accretion rates, $L_{\rm Edd}$ is the Eddington luminosity and $c$ is the speed of light. We find that the H$\beta$ time lags in SEAMBHs are significantly shorter than those measured in sub-Eddington AGNs, and the deviations increase with increasing accretion rates. Thus, the relationship between broad-line region size ($R_{_{\rm H\beta}}$) and optical luminosity at 5100\AA, $R_{_{\rm H\beta}}-L_{5100}$, requires accretion rate as an additional parameter. We propose that much of the effect may be due to the strong anisotropy of the emitted slim-disk radiation. Scaling $R_{_{\rm H\beta}}$ by the gravitational radius of the black hole, we define a new radius-mass parameter ($Y$) and show that it saturates at a critical accretion rate of $\dot{\mathscr{M}}_c=6\sim 30$, indicating a transition from thin to slim accretion disk and a saturated luminosity of the slim disks. The parameter $Y$ is a very useful probe for understanding the various types of accretion onto massive black holes. We briefly comment on implications to the general population of super-Eddington AGNs in the universe and applications to cosmology.Comment: 53 pages, 12 figures, 7 tables, accepted for publication in The Astrophysical Journa