Advances in High-performance computing (HPC) technology have reached the capacity to inform cardiovascular (CV) science in the realm of both inductive and constructive approaches. Clinical trials ...allow for the comparison of the effect of an intervention without the need to understand the mechanism. This is a typical example of an inductive approach. In the HPC field, training an artificial intelligence (AI) model, constructed by neural networks, to predict future CV events with the use of large scale multi-dimensional datasets is the counterpart that may rely on as well as inform understanding of mechanistic underpinnings for optimization. However, in contrast to clinical trials, AI can calculate event risk at the individual level and has the potential to inform and refine the application of personalized medicine.Despite this clear strength, results from AI analyses may identify otherwise unidentified/unexpected (i.e. non-intuitive) relationships between multi-dimensional data and clinical outcomes that may further unravel potential mechanistic pathways and identify potential therapeutic targets, therebycontributing to the parsing of observational associations from causal links. The constructive approach will remain critical to overcome limitations of existing knowledge and anchored biases to actualize a more sophisticated understanding of the complex pathobiology of CV diseases.HPC technology has the potential to underpin this constructive approach in CV basic and clinical science. In general, even complex biological phenomena can be reduced to combinations of simple biological/chemical/physical laws. In the deductive approach, the focus/intent is to explain complex CV diseases by combinations of simple principles.
Antithrombotic agents are widely used on the globe for prevention of thrombotic events such as atherothrombotic events and thromboembolic stroke in atrial fibrillation or for prevention and treatment ...of venous thromboembolism. However, the net clinical benefit of antithrombotic intervention may differ substantially in various sub-population of patients. Here, the authors attempt to address the risk of serious bleeding in East Asian as compared to the other regions of the world. The community-based epidemiological data suggest numerically higher risk of hemorrhage stroke in East Asian as compared to the globe. Importantly, the life-time risk of ischemic stroke in East Asia is higher than that of the globe. Regarding the serious bleeding risk in East Asians with the use of antithrombotic agents, various clinical trials and international registries provided conflicting information. It is hard to draw generalized conclusion, but there are some specific sub-population in East Asian with higher risk of specific serious bleeding events with the use of specific antithrombotic agents such as the risk of intra-cranial bleeding (ICH) with Vitamin K antagonists. Specific characteristics in East Asian such as higher prevalence of lacunar stroke may contribute higher risk of ICH in East Asian, but the detailed mechanism is still to be elucidated. In conclusion, further investigations are necessary to clarify the specific conditions where the risk of serious bleeding events in East Asian patients differ substantially compared to the global. In addition, further understanding of the mechanisms causing the different bleeding response in specific conditions in East Asian is awaited.
Background:Few data on the relative efficacy and safety of new P2Y12inhibitors such as prasugrel and ticagrelor in Japanese, Taiwanese and South Korean patients with acute coronary syndromes (ACS) ...exist.Methods and Results:The multicenter, double-blind, randomized PHILO trial compared the safety and efficacy of ticagrelor vs. clopidogrel in 801 patients with ACS (Japanese, n=721; Taiwanese, n=35; South Korean, n=44; unknown ethnicity, n=1). All were planned to undergo percutaneous coronary intervention and randomized within 24 h of symptom onset. Primary safety and efficacy endpoints were time to first occurrence of any major bleeding event and to any event from the composite of myocardial infarction, stroke or death from vascular causes, respectively.At 12 months, overall major bleeding occurred in 10.3% of ticagrelor-treated patients and in 6.8% of clopidogrel-treated patients (hazard ratio (HR), 1.54; 95% confidence interval (CI): 0.94–2.53); the composite primary efficacy endpoint occurred in 9.0% and in 6.3% of ticagrelor- and clopidogrel-treated patients, respectively (HR, 1.47; 95% CI: 0.88–2.44). For both analyses, the difference between groups was not statistically significant.Conclusions:In ACS patients from Japan, Taiwan and South Korea, event rates of primary safety and efficacy endpoints were higher, albeit not significantly, in ticagrelor-treated patients compared with clopidogrel-treated patients. This observation could be explained by the small sample size, imbalance in clinical characteristics and low number of events in the PHILO population. (Circ J 2015; 79: 2452–2460)
Aims: Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. Methods: ...Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. Results: AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). Conclusion: Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.
Background: The global ROCKET AF study evaluated once-daily rivaroxaban vs. warfarin for stroke and systemic embolism prevention in patients with atrial fibrillation (AF). A separate trial, J-ROCKET ...AF, compared the safety of a Japan-specific rivaroxaban dose with warfarin administered according to Japanese guidelines in Japanese patients with AF. Methods and Results: J-ROCKET AF was a prospective, randomized, double-blind, phase III trial. Patients (n=1,280) with non-valvular AF at increased risk for stroke were randomized to receive 15mg once-daily rivaroxaban or warfarin dose-adjusted according to Japanese guidelines. The primary objective was to determine non-inferiority of rivaroxaban against warfarin for the principal safety outcome of major and non-major clinically relevant bleeding, in the on-treatment safety population. The primary efficacy endpoint was the composite of stroke and systemic embolism. Non-inferiority of rivaroxaban to warfarin was confirmed; the rate of the principal safety outcome was 18.04% per year in rivaroxaban-treated patients and 16.42% per year in warfarin-treated patients (hazard ratio HR 1.11; 95% confidence interval 0.87–1.42; P<0.001 non-inferiority). Intracranial hemorrhage rates were 0.8% with rivaroxaban and 1.6% with warfarin. There was a strong trend for a reduction in the rate of stroke/systemic embolism with rivaroxaban vs. warfarin (HR, 0.49; P=0.050). Conclusions: J-ROCKET AF demonstrated the safety of a Japan-specific rivaroxaban dose and supports bridging the global ROCKET AF results into Japanese clinical practice. (Circ J 2012; 76: 2104–2111)
Aim:The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear. ...Methods: The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model. Results: The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively. Conclusion: An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.
Platelet activation and subsequent accumulation at sites of vascular injury are the first steps in hemostasis. Excessive platelet activation after atherosclerotic plaque rupture or endothelial cell ...erosion may also lead to the formation of occlusive thrombi, which are responsible for acute ischemic events. Multiple pathways are involved in platelet activation, including those activated by adenosine diphosphate (ADP), thromboxane A2 (TXA2), serotonin, collagen, and thrombin. Antiplatelet agents used for prevention of atherothrombosis have focused on blocking the formation of TXA2 (eg, aspirin) and interfering with ADP stimulation mediated by the P2Y12 receptor (eg, clopidogrel). These agents, used alone or in combination, significantly decrease the risk for atherothrombotic events, but a significant residual risk for recurrent ischemic events remains. This has been, in part, attributed to persistence of elevated platelet reactivity despite the use of these agents. Several novel antiplatelet agents are currently under clinical development, with the goal of achieving more efficacious platelet inhibition. These include agents that more efficiently block TXA2-mediated effects, as well as more potent P2Y12 receptor antagonists. In addition, inhibition of the protease-activated receptor-1 platelet activation pathway stimulated by thrombin has emerged as a rational target for clinical development. An overview of the basic principles of platelet biology is given and currently available antiplatelet agents, as well as those under clinical development, are reviewed. (Circ J 2010; 74: 597-607)
Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the ...emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.
To make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.
We developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.
Of the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84-0.93) for detecting patients who required urgent revascularization.
Our artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.
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