Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance in the Cohort of Two Large-Scale ABC and PPAR Clinical Trials

2020 ◽  
Author(s):  
Kazuhiro Shindo ◽  
Hiroki Fukuda ◽  
Tatsuro Hitsumoto ◽  
Yohei Miyashita ◽  
Jiyoong Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Clinical Trials ◽  
Glucose Intolerance ◽  
Cardiovascular Events ◽  
Large Scale ◽  
Clinical Factors

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance

2020 ◽  
Vol 34 (4) ◽  
pp. 535-545
Author(s):  
Kazuhiro Shindo ◽  
Hiroki Fukuda ◽  
Tatsuro Hitsumoto ◽  
Yohei Miyashita ◽  
Jiyoong Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Glucose Intolerance ◽  
Cardiovascular Events ◽  
Clinical Factors

scholarly journals Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence

2021 ◽  
Vol 22 (19) ◽  
pp. 10291
Author(s):  
Annie M. Westerlund ◽  
Johann S. Hawe ◽  
Matthias Heinig ◽  
Heribert Schunkert
Keyword(s):  
Artificial Intelligence ◽  
Risk Prediction ◽  
Cardiovascular Events ◽  
Regulatory Networks ◽  
Recurrent Events ◽  
Large Scale ◽  
Treatment Strategies ◽  
Molecular Data ◽  
Holistic View ◽  
Prediction And Prevention

Cardiovascular diseases (CVD) annually take almost 18 million lives worldwide. Most lethal events occur months or years after the initial presentation. Indeed, many patients experience repeated complications or require multiple interventions (recurrent events). Apart from affecting the individual, this leads to high medical costs for society. Personalized treatment strategies aiming at prediction and prevention of recurrent events rely on early diagnosis and precise prognosis. Complementing the traditional environmental and clinical risk factors, multi-omics data provide a holistic view of the patient and disease progression, enabling studies to probe novel angles in risk stratification. Specifically, predictive molecular markers allow insights into regulatory networks, pathways, and mechanisms underlying disease. Moreover, artificial intelligence (AI) represents a powerful, yet adaptive, framework able to recognize complex patterns in large-scale clinical and molecular data with the potential to improve risk prediction. Here, we review the most recent advances in risk prediction of recurrent cardiovascular events, and discuss the value of molecular data and biomarkers for understanding patient risk in a systems biology context. Finally, we introduce explainable AI which may improve clinical decision systems by making predictions transparent to the medical practitioner.

scholarly journals An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction

2021 ◽  
Vol 11 (11) ◽  
pp. 1149
Author(s):  
Wen-Cheng Liu ◽  
Chin Lin ◽  
Chin-Sheng Lin ◽  
Min-Chien Tsai ◽  
Sy-Jou Chen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Real World ◽  
Large Scale ◽  
Troponin I ◽  
Validation Cohort ◽  
Cardiac Catheterization Laboratory ◽  
The Impact ◽  
F Measure

(1) Background: While an artificial intelligence (AI)-based, cardiologist-level, deep-learning model for detecting acute myocardial infarction (AMI), based on a 12-lead electrocardiogram (ECG), has been established to have extraordinary capabilities, its real-world performance and clinical applications are currently unknown. (2) Methods and Results: To set up an artificial intelligence-based alarm strategy (AI-S) for detecting AMI, we assembled a strategy development cohort including 25,002 visits from August 2019 to April 2020 and a prospective validation cohort including 14,296 visits from May to August 2020 at an emergency department. The components of AI-S consisted of chest pain symptoms, a 12-lead ECG, and high-sensitivity troponin I. The primary endpoint was to assess the performance of AI-S in the prospective validation cohort by evaluating F-measure, precision, and recall. The secondary endpoint was to evaluate the impact on door-to-balloon (DtoB) time before and after AI-S implementation in STEMI patients treated with primary percutaneous coronary intervention (PPCI). Patients with STEMI were alerted precisely by AI-S (F-measure = 0.932, precision of 93.2%, recall of 93.2%). Strikingly, in comparison with pre-AI-S (N = 57) and post-AI-S (N = 32) implantation in STEMI protocol, the median ECG-to-cardiac catheterization laboratory activation (EtoCCLA) time was significantly reduced from 6.0 (IQR, 5.0–8.0 min) to 4.0 min (IQR, 3.0–5.0 min) (p < 0.01). The median DtoB time was shortened from 69 (IQR, 61.0–82.0 min) to 61 min (IQR, 56.8–73.2 min) (p = 0.037). (3) Conclusions: AI-S offers front-line physicians a timely and reliable diagnostic decision-support system, thereby significantly reducing EtoCCLA and DtoB time, and facilitating the PPCI process. Nevertheless, large-scale, multi-institute, prospective, or randomized control studies are necessary to further confirm its real-world performance.

scholarly journals Abacavir Use and Risk for Myocardial Infarction and Cardiovascular Events: Pooled Analysis of Data From Clinical Trials

2018 ◽  
Vol 5 (5) ◽  
Author(s):  
Cassandra Nan ◽  
Mark Shaefer ◽  
Rimgaile Urbaityte ◽  
James Oyee ◽  
Judy Hopking ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Clinical Trials ◽  
Cardiovascular Events ◽  
Pooled Analysis ◽  
Exposed Group ◽  
Sensitivity Analyses ◽  
Primary Analysis ◽  
Unexposed Group ◽  
Increased Risk

Abstract Background Some observational studies and randomized controlled trials (RCTs) have suggested an association between abacavir (ABC) use and myocardial infarction (MI), whereas others have not. Methods This pooled analysis of 66 phase II–IV RCTs estimates exposure-adjusted incidence rates (IRs) and relative rates (RRs) of MI and cardiovascular events (CVEs) in participants receiving ABC- and non-ABC-containing combination antiretroviral therapy (cART). The primary analysis of MI included ABC-randomized trials with ≥48-week follow-up. Sensitivity analyses of MI and CVEs included non-ABC-randomized and &lt;48-week follow-up trials. Results In 66 clinical trials, 13 119 adults (75% male, aged 18–85 years) were on ABC-containing cART and 7350 were not. Exposure-adjusted IR for MI was 1.5 per 1000 person-years (PY; 95% confidence interval [CI], 0.67–3.34) in the ABC-exposed group and 2.18 per 1000 PY (95% CI, 1.09–4.40) in the unexposed group. The IR for CVEs was 2.9 per 1000 PY (95% CI, 2.09–4.02) in the exposed group and 4.69 per 1000 PY (95% CI, 3.40–6.47) in the unexposed group with studies of ≥48 weeks of follow-up, with an RR of 0.62 (95% CI, 0.39–0.98). The inclusion of nonrandomized and shorter-duration trials did not significantly change the RR for MI or coronary artery disease. Conclusions This pooled analysis found comparable IRs for MI and CVEs among ABC-exposed and -unexposed participants, suggesting no increased risk for MI or CVEs following ABC exposure in a clinical trial population. Modifiable risk factors for MI and CVEs should be addressed when prescribing ART.

Ongoing randomized clinical trials for the treatment of thrombosis in the antiphospholipid syndrome

2001 ◽  
Vol 21 (02) ◽  
pp. 77-81 ◽  
Author(s):  
G. Finazzi
Keyword(s):  
Clinical Trials ◽  
Large Scale ◽  
Low Dose ◽  
Randomized Clinical Trials ◽  
Oral Anticoagulants ◽  
Collaborative Study ◽  
Clinical Problem ◽  
Vascular Events ◽  
Dose Oral ◽  
Adverse Pregnancy

SummaryThrombotic events are a major clinical problem for patients with antiphospholipid antibodies (APA). However, current recommendations for their prevention and treatment are still based on retrospective studies. Data from large scale, prospective clinical trials are required to ultimately identify the optimal management of these patients. To date, at least four randomized studies are underway. The WAPS and PAPRE clinical trials are aimed to establish the correct duration and intensity of oral anticoagulation in APA patients with major arterial or venous thrombosis. The WARSS-APASS is a collaborative study to evaluate the efficacy and safety of aspirin or low-dose oral anticoagulants in preventing the recurrence of ischemic stroke. The recently announced UK Trial compares low-dose aspirin with or without low-intensity anticoagulation for the primary prevention of vascular events in APA-positive patients with SLE or adverse pregnancy history, but still thrombosis-free. It is hoped that the results of these trials will be available soon since clinicians urgently need more powerful data to treat their patients with the APA syndrome.

ElevatedChlamydia pneumoniaeAntibodies, Cardiovascular Events, and Azithromycin in Male Survivors of Myocardial Infarction

Circulation ◽  
1997 ◽  
Vol 96 (2) ◽  
pp. 404-407 ◽  
Author(s):  
Sandeep Gupta ◽  
Edward W. Leatham ◽  
David Carrington ◽  
Michael A. Mendall ◽  
Juan Carlos Kaski ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Cardiovascular Events ◽  
Male Survivors

Immunoglobulin G levels predicts risk of recurrent adverse cardiovascular events in myocardial infarction patients

2019 ◽  
Vol 75 (6) ◽  
pp. 497-502
Author(s):  
Kathryn E. Hally ◽  
Ana S. Holley ◽  
Gisela A. Kristono ◽  
Scott A. Harding ◽  
Peter D. Larsen
Keyword(s):  
Myocardial Infarction ◽  
Immunoglobulin G ◽  
Cardiovascular Events

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

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