AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial.

The early identification of vulnerable patients has the potential to improve outcomes but poses a substantial challenge in clinical practice. This study evaluated the ability of an artificial intelligence (AI)-enabled electrocardiogram (ECG) to identify hospitalized patients with a high risk of mortality in a multisite randomized controlled trial involving 39 physicians and 15,965 patients. The AI-ECG alert intervention included an AI report and warning messages delivered to the physicians, flagging patients predicted to be at high risk of mortality. The trial met its primary outcome, finding that implementation of the AI-ECG alert was associated with a significant reduction in all-cause mortality within 90 days: 3.6% patients in the intervention group died within 90 days, compared to 4.3% in the control group (4.3%) (hazard ratio (HR) = 0.83, 95% confidence interval (CI) = 0.70-0.99). A prespecified analysis showed that reduction in all-cause mortality associated with the AI-ECG alert was observed primarily in patients with high-risk ECGs (HR = 0.69, 95% CI = 0.53-0.90). In analyses of secondary outcomes, patients in the intervention group with high-risk ECGs received increased levels of intensive care compared to the control group; for the high-risk ECG group of patients, implementation of the AI-ECG alert was associated with a significant reduction in the risk of cardiac death (0.2% in the intervention arm versus 2.4% in the control arm, HR = 0.07, 95% CI = 0.01-0.56). While the precise means by which implementation of the AI-ECG alert led to decreased mortality are to be fully elucidated, these results indicate that such implementation assists in the detection of high-risk patients, prompting timely clinical care and reducing mortality. ClinicalTrials.gov registration: NCT05118035 .

The Angiotensin II Receptor Neprilysin Inhibitor LCZ696 Inhibits the NLRP3 Inflammasome By Reducing Mitochondrial Dysfunction in Macrophages and Alleviates Dextran Sulfate Sodium-induced Colitis in a Mouse Model.

The intracellular sensor protein complex known as the NACHT, LRR, and PYD domain-containing protein 3 (NLRP3) inflammasome plays a crucial role in regulating inflammatory diseases by overseeing the production of interleukin (IL)-1ß and IL-18. Targeting its abnormal activation with drugs holds significant promise for inflammation treatment. This study highlights LCZ696, an angiotensin receptor-neprilysin inhibitor, as an effective suppressor of NLRP3 inflammasome activation in macrophages stimulated by ATP, nigericin, and monosodium urate. LCZ696 also reduces caspase-11 and GSDMD activation, lactate dehydrogenase release, propidium iodide uptake, and the extracellular release of NLRP3 and apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) in ATP-activated macrophages, suggesting a potential mitigation of pyroptosis. Mechanistically, LCZ696 lowers mitochondrial reactive oxygen species and preserves mitochondrial integrity. Importantly, it does not significantly impact NLRP3, proIL-1ß, inducible nitric oxide synthase, cyclooxygenase-2 expression, or NF-κB activation in lipopolysaccharide-activated macrophages. LCZ696 partially inhibits the NLRP3 inflammasome through the induction of autophagy. In an in vivo context, LCZ696 alleviates NLRP3-associated colitis in a mouse model by reducing colonic expression of IL-1ß and tumor necrosis factor-α. Collectively, these findings suggest that LCZ696 holds significant promise as a therapeutic agent for ameliorating NLRP3 inflammasome activation in various inflammatory diseases, extending beyond its established use in hypertension and heart failure treatment.

The Best Strategy for the Black Hole Phenomenon between Intravascular Ultrasound and Optical Coherence Tomography.

The black hole (BH) phenomenon is an intraluminal restenotic lesion. It was identified by intravascular ultrasound (IVUS) and optical coherence tomography (OCT) after intracoronary brachytherapy and drug-eluting stent implantation. Despite the similarity in the mode of action of brachytherapy and drug-eluting stent implantation, the BH phenomenon appears to be uncommon after drug-eluting stent implantation. Specifically, the BH phenomenon is better identified by OCT than by IVUS. Herein, we present a case of in-stent restenosis with suspected BH phenomenon on IVUS and confirmed by OCT.

2024 TSOC/TSPS Joint Consensus: Strategies for Advanced Vascular Wound Management in Arterial and Venous Diseases.

The Taiwan Society of Cardiology (TSOC) and Taiwan Society of Plastic Surgery (TSPS) have collaborated to develop a joint consensus for the management of patients with advanced vascular wounds. The taskforce comprises experts including preventive cardiologists, interventionists, and cardiovascular and plastic surgeons. The consensus focuses on addressing the challenges in diagnosing, treating, and managing complex wounds; incorporates the perfusion evaluation and the advanced vascular wound care team; and highlights the importance of cross-disciplinary teamwork. The aim of this joint consensus is to manage patients with advanced vascular wounds and encourage the adoption of these guidelines by healthcare professionals to improve patient care and outcomes. The guidelines encompass a range of topics, including the definition of advanced vascular wounds, increased awareness, team structure, epidemiology, clinical presentation, medical treatment, endovascular intervention, vascular surgery, infection control, advanced wound management, and evaluation of treatment results. It also outlines a detailed protocol for assessing patients with lower leg wounds, provides guidance on consultation and referral processes, and offers recommendations for various wound care devices, dressings, and products. The 2024 TSOC/TSPS consensus for the management of patients with advanced vascular wounds serves as a catalyst for international collaboration, promoting knowledge exchange and facilitating advancements in the field of advanced vascular wound management. By providing a comprehensive and evidence-based approach, this consensus aims to contribute to improved patient care and outcomes globally.

The safety and efficacy of the Mo.Ma system device for carotid artery stenting: A single-center experience from Taiwan.

Background: Proximal protection devices, such as the Mo.Ma system provides better neurological outcomes than the distal filter system in the carotid artery stenting (CAS) procedure. This study first evaluated the safety and efficacy of the Mo.Ma system during CAS in a single tertiary referral hospital from Taiwan. The outcomes of distal vs. proximal embolic protection devices were also studied. Methods: A total of 294 patients with carotid artery stenosis who underwent the CAS procedure were retrospectively included and divided into two groups: 152 patients in the distal filter system group and 142 patients in the Mo.Ma system. The outcomes of interest were compared between the two groups. The factors contributing to occlusion intolerance (OI) in the Mo.Ma system were evaluated. Results: The procedure success rates were more than 98% in both groups. No major stroke occurred in this study. The minor stroke rates were 2.8% (4/142) and 4.6% (7/152) in the Mo.Ma system and filter system, respectively (p = 0.419). Patients with hypoalbuminemia significantly predicted the risk of stroke with an odds ratio of 0.08 [95% confidence interval (CI), 0.01-0.68, p = 0.020] per 1 g/day of serum albumin in the filter group. A total of 12 patients developed OI in the Mo.Ma system (12/142, 8%). Low occlusion pressure predicted the occurrence of OI in the Mo.Ma group with the hazard ratios of 0.88 (95% CI: 0.82-0.96) and 0.90 (95% CI: 0.84-0.98) per 1 mmHg of occlusion systolic pressure (OSP) and diastolic pressure (ODP), respectively. We further indicated that patients with an OSP of ≥60 mmHg or an ODP of ≥44 mmHg could tolerate the procedure of occlusion time up to 400 s, while patients with an OSP of <49 mmHg or an ODP of <34 mmHg should undergo the procedure of occlusion time less than 300 s to prevent the occurrence of OI. Conclusion: We have demonstrated the safety and effectiveness of the Mo.Ma system during CAS in an Asia population. By reducing the occlusion time, our study indicated a lower risk of OI in the Mo.Ma system and proposed the optimal occlusion time according to occlusion pressure to prevent OI during the CAS procedure. Further large-scale and prospective studies are needed to verify our results.

A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram.

(1) Background: Acute pericarditis is often confused with ST-segment elevation myocardial infarction (STEMI) among patients presenting with acute chest pain in the emergency department (ED). Since a deep learning model (DLM) has been validated to accurately identify STEMI cases via 12-lead electrocardiogram (ECG), this study aimed to develop another DLM for the detection of acute pericarditis in the ED. (2) Methods: This study included 128 ECGs from patients with acute pericarditis and 66,633 ECGs from patients visiting the ED between 1 January 2010 and 31 December 2020. The ECGs were randomly allocated based on patients to the training, tuning, and validation sets, at a 3:1:1 ratio. We used raw ECG signals to train a pericarditis-DLM and used traditional ECG features to train a machine learning model. A human-machine competition was conducted using a subset of the validation set, and the performance of the Philips automatic algorithm was also compared. STEMI cases in the validation set were extracted to analyze the DLM ability of differential diagnosis between acute pericarditis and STEMI using ECG. We also followed the hospitalization events in non-pericarditis cases to explore the meaning of false-positive predictions. (3) Results: The pericarditis-DLM exceeded the performance of all participating human experts and algorithms based on traditional ECG features in the human-machine competition. In the validation set, the pericarditis-DLM could detect acute pericarditis with an area under the receiver operating characteristic curve (AUC) of 0.954, a sensitivity of 78.9%, and a specificity of 97.7%. However, our pericarditis-DLM also misinterpreted 10.2% of STEMI ECGs as pericarditis cases. Therefore, we generated an integrating strategy combining pericarditis-DLM and a previously developed STEMI-DLM, which provided a sensitivity of 73.7% and specificity of 99.4%, to identify acute pericarditis in patients with chest pains. Compared to the true-negative cases, patients with false-positive results using this strategy were associated with higher risk of hospitalization within 3 days due to cardiac disorders (hazard ratio (HR): 8.09; 95% confidence interval (CI): 3.99 to 16.39). (4) Conclusions: The AI-enhanced algorithm may be a powerful tool to assist clinicians in the early detection of acute pericarditis and differentiate it from STEMI using 12-lead ECGs.

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis.

BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD.

A deep learning-based system capable of detecting pneumothorax via electrocardiogram.

PURPOSE: To determine if an electrocardiogram-based artificial intelligence system can identify pneumothorax prior to radiological examination. METHODS: This is a single-center, retrospective, electrocardiogram-based artificial intelligence (AI) system study that included 107 ECGs from 98 pneumothorax patients. Seven patients received needle decompression due to tension pneumothorax, and the others received thoracostomy due to instability (respiratory rate ≥ 24 breaths/min; heart rate, < 60 beats/min or > 120 beats/min; hypotension; room air O2 saturation, < 90%; and patient could not speak in whole sentences between breaths). Traumatic pneumothorax and bilateral pneumothorax were excluded. The ECGs of 132,127 patients presenting to the emergency department without pneumothorax were used as the control group. The development cohort included approximately 80% of the ECGs for training the deep learning model (DLM), and the other 20% of ECGs were used to validate the performance. A human-machine competition involving three physicians was conducted to assess the model performance. RESULTS: The areas under the receiver operating characteristic (ROC) curves (AUCs) of the DLM in the validation cohort and competition set were 0.947 and 0.957, respectively. The sensitivity and specificity of our DLM were 94.7% and 88.1% in the validation cohort, respectively, which were significantly higher than those of all physicians. Our DLM could also recognize the location of pneumothorax with 100% accuracy. Lead-specific analysis showed that lead I ECG made a major contribution, achieving an AUC of 0.930 (94.7% sensitivity, 86.0% specificity). The inclusion of the patient characteristics allowed our AI system to achieve an AUC of 0.994. CONCLUSION: The present AI system may assist the medical system in the early identification of pneumothorax through 12-lead ECG, and it performs as well with lead I ECG alone as with 12-lead ECG.

A Deep-Learning Algorithm-Enhanced System Integrating Electrocardiograms and Chest X-rays for Diagnosing Aortic Dissection.

BACKGROUND: Chest pain is the most common symptom of aortic dissection (AD), but it is often confused with other prevalent cardiopulmonary diseases. We aimed to develop deep-learning models (DLMs) with electrocardiography (ECG) and chest x-ray (CXR) features to detect AD and evaluate their performance. METHODS: This study included 43,473 patients in the emergency department (ED) between July 2012 and December 2019 for retrospective DLM development. A development cohort including 49,071 ED records (120 AD type A and 64 AD type B) was used to train DLMs for ECG and CXR, and 9904 independent ED records (40 AD type A and 34 AD type B) were used to validate DLM performance. Human-machine competitions of ECG and CXR were conducted. Patient characteristics and laboratory results were used to enhance the diagnostic accuracy. The DLM-enabled AD diagnostic process was prospectively evaluated in 25,885 ED visits. RESULTS: The area under the curves (AUCs) of the ECG and CXR models were 0.918 and 0.857 for detecting AD in a human-machine competition, respectively, which were better than those of the participating physicians. In the validation cohort, the AUCs of the integrated model were 0.882, 0.960, and 0.813 in all AD, AD type A, and AD type B patients, respectively, with a sensitivity of 100.0% and a specificity of 81.7% for AD type A. In patients with chest pain and D-dimer tests, the DLM could predict more precisely, achieving a positive predictive value of 62.5% in the prospective evaluation. CONCLUSIONS: DLMs may serve as decision-supporting tools for identification of AD and facilitate differential diagnosis in patients with acute chest pain.

Takotsubo Syndrome Occurring after mRNA COVID-19 Vaccination in a Patient with Graves' Disease.

Cardiovascular events such as myocarditis following mRNA COVID-19 vaccination are increasing. We present a 67-year-old postmenopausal woman with Takotsubo Syndrome and Graves' disease after mRNA COVID-19 vaccination. She developed chest pain and shortness of breath one week after vaccination. An electrocardiogram revealed ST elevation in the precordial leads. Coronary angiography revealed the absence of obstructive coronary artery disease, and the left ventriculography showed a typical feature with apical ballooning. Laboratory workup showed the elevation of free T4 and thyrotropin receptor antibodies. It was presumed that Takotsubo Syndrome and Graves' disease were probably related to the COVID-19 mRNA vaccination. The patient was treated with low-dose bisoprolol, diuretics, carbimazole, and steroid and discharged uneventfully. The mRNA COVID-19 vaccination is still safe and effective to defend against COVID-19 pandemic. However, clinicians should be aware of the possible cardiovascular adverse events other than myocarditis following vaccination.

Generation of Functional Cardiomyocytes from Human Gastric Fibroblast-Derived Induced Pluripotent Stem Cells.

Coronary artery diseases are major problems of the world. Coronary artery disease patients frequently suffer from peptic ulcers when they receive aspirin treatment. For diagnostic and therapeutic purposes, the implementation of panendoscopy (PES) with biopsy is necessary. Some biopsy samples are wasted after the assay is completed. In the present study, we established a protocol for human gastric fibroblast isolation and induced pluripotent stem cell (iPSC) generation from gastric fibroblasts via PES with biopsy. We showed that these iPSCs can be differentiated into functional cardiomyocytes in vitro. To our knowledge, this is the first study to generate iPSCs from gastric fibroblasts in vitro.

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

(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.

Prognostic Analysis of Patients with Acute Myocardial Infarction Undergoing Implantation of Different Stents for the First Time.

For patients with acute myocardial infarction scheduled to undergo percutaneous coronary stent implantation, in most cases a drug-eluting stent is recommended as the first choice for treatment. However, there is a lack of research on the effectiveness of bare-metal stents and drug-eluting stents on patients with different types of myocardial infarction. Our objective was to explore the effects of bare-metal stents and drug-eluting stents on patients with different types of myocardial infarction in terms of major cardiovascular incidents. This retrospective cohort study included 934 patients with myocardial infarction undergoing coronary artery stent implantation for the first time at the cardiac catheter room of the Tri-Service General Hospital in the Neihu District between 2014 and 2018. Patients' information, including demographic data, laboratory data, cardiac echocardiography results, and angiocardiography results, was collected by reviewing medical records. Cox proportional hazards regression was used to adjust the potential confounding factors, and the adjusted data were then used to compare the correlation between different types of stents and major cardiovascular incidents in patients with ST-elevation myocardial infarction and non-ST-elevation myocardial infarction. After the confounding factors were adjusted, in patients with ST-elevation myocardial infarction receiving a drug-eluting stent compared with those receiving a bare-metal stent, it was found that the mortality risk was lower in terms of all causes of death (Adj-HR = 0.26, 95% CI = 0.14-0.48, p < 0.001) and cardiogenic death (Adj-HR = 0.20, 95% CI = 0.08-0.55, p = 0.002), the risk of non-fatal myocardial infarction was lower (Adj-HR = 0.17, 95% CI = 0.04-0.73, p = 0.017), and there was no difference in the risk of revascularization at the lesion site (Adj-HR = 0.59, 95% CI = 0.24-1.43, p = 0.243). It terms of the findings in patients with non-ST-elevation myocardial infarction, those receiving a drug-eluting stent had a lower risk of revascularization at the lesion site (Adj-HR = 0.48, 95% CI = 0.24-0.97, p = 0.04); however, there was no difference in the mortality risk in terms of all causes of death (Adj-HR = 0.71, 95% CI = 0.37-1.35, p = 0.296) or cardiogenic death (Adj-HR = 0.59, 95% CI = 0.18-1.90, p = 0.379),or in the risk of non-fatal myocardial infarction (Adj-HR = 0.27, 95% CI = 0.06-1.25, p = 0.093). Compared with bare-metal stents, drug-eluting stents provide better protection against death to receivers with ST-elevation myocardial infarction; however, this protection is decreased in receivers with non-ST-elevation myocardial infarction. It is recommended that for patients with non-ST-elevation myocardial infarction who are indicated to receive a drug-eluting stent, the clinical effectiveness of the treatment must be considered.

Cilostazol Induces eNOS and TM Expression via Activation with Sirtuin 1/Krüppel-like Factor 2 Pathway in Endothelial Cells.

Cilostazol was suggested to be beneficial to retard in-stent atherosclerosis and prevent stent thrombosis. However, the mechanisms responsible for the beneficial effects of cilostazol are not fully understood. In this study, we attempted to verify the mechanism of the antithrombotic effect of cilostazol. Human umbilical vein endothelial cells (HUVECs) were cultured with various concentrations of cilostazol to verify its impact on endothelial cells. KLF2, silent information regulator transcript-1 (SIRT1), endothelial nitric oxide synthase (eNOS), and endothelial thrombomodulin (TM) expression levels were examined. We found cilostazol significantly activated KLF2 expression and KLF2-related endothelial function, including eNOS activation, Nitric oxide (NO) production, and TM secretion. The activation was regulated by SIRT1, which was also stimulated by cilostazol. These findings suggest that cilostazol may be capable of an antithrombotic and vasculoprotective effect in endothelial cells.

Profiles of Hospitalized Patients with Angiographic Coronary Heart Disease in Taiwan during 2014-2016: Report of a Tertiary Hospital.

BACKGROUND: The Taiwan Society of Cardiology (TSOC) has established multicenter registries for coronary artery disease (CAD) to investigate clinical characteristics, management and risks for mortality. However, the impacts of newly-emerged evidence-based therapies, including the use of drug-eluting stents (DESs), on patients with CAD in Taiwan remain unclear. METHODS: The Tri-Service General Hospital-Coronary Heart Disease (TSGH-CHD) registry is a single-center, prospective, longitudinal registry in Taiwan containing data from 2014-2016. Individuals who were admitted for coronary angiography were enrolled. Patient profiles, management and in-hospital outcome data were collected. RESULTS: We included 3352 patients: 2349 with stable angina and 1003 with acute coronary syndrome (ACS). In the stable angina group, both patients receiving stenting and those receiving medical treatment had a 0.7% mortality rate; DESs were used in 70.4% of the patients receiving stenting. In the ACS group, the patients receiving stenting and those receiving medical treatment had a 4.9% and 10.7% mortality rate, respectively; DESs were used in 63.1% of the patients receiving stenting. In the 2008-2010 Taiwan ACS registry, DESs were used in only 28% of all stenting procedures, and the estimated hospital mortality rate was 1.8%. Multivariate analysis indicated that older age, prior stroke, and cardiogenic shock on admission were associated with an increased risk of in-hospital mortality in the ACS group. CONCLUSIONS: Compared with the Taiwan ACS cohort, the TSGH-CHD registry revealed increased DES use and increased disease complexity and severity after 2010. Although unlikely to significantly improve survival, interventionists seemed to perform high-risk procedures for complex CAD more often in the new DES era.

A deep learning algorithm for detecting acute myocardial infarction.

BACKGROUND: Delayed diagnosis or misdiagnosis of acute myocardial infarction (AMI) is not unusual in daily practice. Since a 12-lead electrocardiogram (ECG) is crucial for the detection of AMI, a systematic algorithm to strengthen ECG interpretation may have important implications for improving diagnosis. AIMS: We aimed to develop a deep learning model (DLM) as a diagnostic support tool based on a 12-lead electrocardiogram. METHODS: This retrospective cohort study included 1,051/697 ECGs from 737/287 coronary angiogram (CAG)-validated STEMI/NSTEMI patients and 140,336 ECGs from 76,775 non-AMI patients at the emergency department. The DLM was trained and validated in 80% and 20% of these ECGs. A human-machine competition was conducted. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the performance of the DLM. RESULTS: The AUC of the DLM for STEMI detection was 0.976 in the human-machine competition, which was significantly better than that of the best physicians. Furthermore, the DLM independently demonstrated sufficient diagnostic capacity for STEMI detection (AUC=0.997; sensitivity, 98.4%; specificity, 96.9%). Regarding NSTEMI detection, the AUC of the combined DLM and conventional cardiac troponin I (cTnI) increased to 0.978, which was better than that of either the DLM (0.877) or cTnI (0.950). CONCLUSIONS: The DLM may serve as a timely, objective and precise diagnostic decision support tool to assist emergency medical system-based networks and frontline physicians in detecting AMI and subsequently initiating reperfusion therapy.

Prognostic Effect of Restoring Sinus Rhythm in Patients with New-Onset Atrial Fibrillation during Acute Coronary Syndrome.

BACKGROUND: New-onset atrial fibrillation (NOAF) in acute coronary syndrome (ACS) may be associated with a poor prognosis. However, whether restoring sinus rhythm (SR) at discharge in patients with ACS improves outcomes remains unknown. METHODS: A total of 552 patients with ACS at an emergency department during 2011-2016 were enrolled. According to documented electrocardiography at admission and medical records, the patients were classified into without atrial fibrillation (WAF), NOAF, and prior atrial fibrillation (PAF) groups. Major adverse events (MAEs) were defined as cardiac death, recurrent myocardial infarction, heart failure requiring hospitalization, target lesion revascularization, and stroke. The mean follow-up period of MAEs was 25 ± 15 months. RESULTS: Compared with the NOAF and PAF groups, the WAF group was younger and had a significantly lower heart rate, prior stroke rate, CHA2DS2-VASc score, and lower Global Registry of Acute Coronary Events (GRACE) score in the emergency department (p < 0.001). The patients in the NOAF group had the highest incidence of MAEs (p < 0.001) during follow-up, and those whose SR was restored at discharge had a lower MAE rate than those with AF at discharge (p = 0.001). In multivariable analysis, prior myocardial infarction, GRACE score, use of beta-blockers, and restoring SR at discharge were independent predictors of MAEs in the NOAF group. CONCLUSIONS: The patients with ACS who presented with NOAF had worse outcomes than those with PAF or WAF. The patients with NOAF whose rhythm was restored to SR at discharge were associated with better outcomes than those with AF at discharge.

Effects of Pitavastatin, Atorvastatin, and Rosuvastatin on the Risk of New-Onset Diabetes Mellitus: A Single-Center Cohort Study.

Statins constitute the mainstay treatment for atherosclerotic cardiovascular disease, which is associated with the risk of new-onset diabetes mellitus (NODM). However, the effects of individual statins on the risk of NODM remain unclear. We recruited 48,941 patients taking one of the three interested statins in a tertiary hospital between 2006 and 2018. Among them, 8337 non-diabetic patients taking moderate-intensity statins (2 mg/day pitavastatin, 10 mg/day atorvastatin, and 10 mg/day rosuvastatin) were included. The pitavastatin group had a higher probability of being NODM-free than the atorvastatin and rosuvastatin groups during the 4-year follow-up (log-rank test: p = 0.038). A subgroup analysis revealed that rosuvastatin had a significantly higher risk of NODM than pitavastatin among patients with coronary artery disease (CAD) (adjusted HR [aHR], 1.47, 95% confidence interval [CI], 1.05-2.05, p = 0.025), hypertension (aHR, 1.26, 95% CI, 1.00-1.59, p = 0.047), or chronic obstructive pulmonary disease (COPD) (aHR, 1.74, 95% CI, 1.02-2.94, p = 0.04). We concluded that compared with rosuvastatin, reduced diabetogenic effects of pitavastatin were observed among patients treated with moderate-intensity statin who had hypertension, COPD, or CAD. Additional studies are required to prove the effects of different statins on the risk of NODM.