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Aims Deep learning models (DLMs) have shown superiority in electrocardiogram (ECG) analysis and have been applied to diagnose dyskalaemias. However, no study has explored the performance of DLM-enabled ECG in continuous follow-up scenarios. Therefore, we proposed a dynamic revision of DLM-enabled ECG to use personal pre-annotated ECGs to enhance the accuracy in patients with multiple visits. Methods and results We retrospectively collected 168 450 ECGs with corresponding serum potassium (K+) levels from 103 091 patients as development samples. In the internal/external validation sets, the numbers of ECGs with corresponding K+ were 37 246/47 604 from 13 555/20 058 patients. Our dynamic revision method showed better performance than the traditional direct prediction for diagnosing hypokalaemia [area under the receiver operating characteristic curve (AUC) = 0.730/0.720–0.788/0.778] and hyperkalaemia (AUC = 0.884/0.888–0.915/0.908) in patients with multiple visits. Conclusion Our method has shown a distinguishable improvement in DLMs for diagnosing dyskalaemias in patients with multiple visits, and we also proved its application in ejection fraction prediction, which could further improve daily clinical practice.

Abnormal serum calcium concentrations affect the heart and may alter the electrocardiogram (ECG), but the detection of hypocalcemia and hypercalcemia (collectively dyscalcemia) relies on blood laboratory tests requiring turnaround time.The study aimed to develop a bloodless artificial intelligence (AI)-enabled (ECG) method to rapidly detect dyscalcemia and analyze its possible utility for outcome prediction.This study collected 86,731 development, 15,611 tuning, 11,105 internal validation, and 8401 external validation ECGs from electronic medical records with at least 1 ECG associated with an albumin-adjusted calcium (aCa) value within 4 h. The main outcomes were to assess the accuracy of AI-ECG to predict aCa and follow up these patients for all-cause mortality, new-onset acute myocardial infraction (AMI), and new-onset heart failure (HF) to validate the ability of AI-ECG-aCa for previvor identification.ECG-aCa had mean absolute errors (MAE) of 0.78/0.98 mg/dL and achieved an area under receiver operating characteristic curves (AUCs) 0.9219/0.8447 and 0.8948/0.7723 to detect severe hypercalcemia and hypocalcemia in the internal/external validation sets, respectively. Although 20 % variance of ECG-aCa could be explained by traditional ECG features, the ECG-aCa was found to be associated with more complications. Patients with ECG-hypercalcemia but initially normal aCa were found to have a higher risk of subsequent all-cause mortality [hazard ratio (HR): 2.05, 95 % conference interval (CI): 1.55-2.70], new-onset AMI (HR: 2.88, 95 % CI: 1.72-4.83), and new-onset HF (HR: 2.02, 95 % CI: 1.38-2.97) in the internal validation set, which were also seen in external validation.The AI-ECG-aCa may help detecting severe dyscalcemia for early diagnosis and ECG-hypercalcemia also has prognostic value for clinical outcomes (all-cause mortality and new-onset AMI and HF).

SUMMARY A deep learning model was developed to identify osteoporosis from chest X-ray features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This AI-enabled chest X-ray strategy may function as an early detection screening tool for osteoporosis. OBJECTIVE The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via chest X-ray features and investigate the performance and clinical implications. METHOD This study collected 48,353 CXRs with the corresponding T score according to DXA from the academic medical center. Among these, 35,633 CXRs were used to identify CXR-OP. Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. RESULTS The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83–3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61–1.72) in the internal validation set. The external validation set produced similar results. CONCLUSION Our DLM uses chest X-rays for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

To determine if an electrocardiogram-based artificial intelligence system can identify pneumothorax prior to radiological examination.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 OThe 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.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.

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 ECG and 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 9,904 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 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 AD identification and facilitate differential diagnosis in patients with acute chest pain.

BACKGROUND Valvular heart disease (VHD) is becoming increasingly important to manage the risk of future complications. Electrocardiographic (ECG) changes may be related to multiple VHDs, and (AI)-enabled ECG has been able to detect some VHDs. We aimed to develop five deep learning models (DLMs) to identify aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation. METHODS Between 2010 and 2021, 77,047 patients with echocardiography and 12-lead ECG performed within 7 days were identified from an academic medical center to provide DLM development (122,728 ECGs), and internal validation (7,637 ECGs). Additional 11,800 patients from a community hospital were identified to external validation. The ECGs were classified as with or without moderate-to-severe VHDs according to transthoracic echocardiography (TTE) records, and we also collected the other echocardiographic data and follow-up TTE records to identify new-onset valvular heart diseases. RESULTS AI-ECG adjusted for age and sex achieved areas under the curves (AUCs) of >0.84, >0.80, >0.77, >0.83, and >0.81 for detecting aortic stenosis, aortic regurgitation, pulmonary regurgitation, tricuspid regurgitation, and mitral regurgitation, respectively. Since predictions of each DLM shared similar components of ECG rhythms, the positive findings of each DLM were highly correlated with other valvular heart diseases. Of note, a total of 37.5%-51.7% of false-positive predictions had at least one significant echocardiographic finding, which may lead to a significantly higher risk of future moderate-to-severe VHDs in patients with initially minimal-to-mild VHDs. CONCLUSION AI-ECG may be used as a large-scale screening tool for detecting VHDs and a basis to undergo an echocardiography.

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.

BackgroundProximal 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.MethodsA 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.ResultsThe 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 ConclusionWe 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.

Background Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction. Objective The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction. Methods The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction. Results The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set. Conclusion The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.

Patients bitten by Protobothrops mucrosquamatus typically experience significant pain, substantial swelling, and potentially blister formation. The appropriate dosage and efficacy of FHAV for alleviating local tissue injury remain uncertain. Between 2017 and 2022, 29 snakebite patients were identified as being bitten by P. mucrosquamatus. These patients underwent point-of-care ultrasound (POCUS) assessments at hourly intervals to measure the extent of edema and evaluate the rate of proximal progression (RPP, cm/hour). Based on Blaylock’s classification, seven patients (24%) were classified as Group I (minimal), while 22 (76%) were classified as Group II (mild to severe). In comparison to Group I patients, Group II patients received more FHAV (median of 9.5 vials vs. two vials, p-value < 0.0001) and experienced longer median complete remission times (10 days vs. 2 days, p-value < 0.001). We divided the Group II patients into two subgroups based on their clinical management. Clinicians opted not to administer antivenom treatment to patients in Group IIA if their RPP decelerated. In contrast, for patients in Group IIB, clinicians increased the volume of antivenom in the hope of reducing the severity of swelling or blister formation. Patients in Group IIB received a significantly higher median volume of antivenom (12 vials vs. six vials; p-value < 0.001) than those in Group IIA. However, there was no significant difference in outcomes (disposition, wound necrosis, and complete remission times) between subgroups IIA and IIB. Our study found that FHAV does not appear to prevent local tissue injuries, such as swelling progression and blister formation, immediately after administration. When administering FHAV to patients bitten by P. mucrosquamatus, the deceleration of RPP may serve as an objective parameter to help clinicians decide whether to withhold FHAV administration.

BackgroundHeart failure (HF) is a global disease with increasing prevalence in an aging society. However, the survival rate is poor despite the patient receiving standard treatment. Early identification of patients with a high risk of HF is important but challenging. Left ventricular end-diastolic diameter (LV-D) increase was an independent risk factor of HF and adverse cardiovascular (CV) outcomes. In this study, we aimed to develop an artificial intelligence (AI) enabled electrocardiogram (ECG) system to detect LV-D increase early.ObjectiveWe developed a deep learning model (DLM) to predict left ventricular end-diastolic and end-systolic diameter (LV-D and LV-S) with internal and external validations and investigated the relationship between ECG-LV-D and echocardiographic LV-D and explored the contributions of ECG-LV-D on future CV outcomes.MethodsElectrocardiograms and corresponding echocardiography data within 7 days were collected and paired for DLM training with 99,692 ECGs in the development set and 20,197 ECGs in the tuning set. The other 7,551 and 11,644 ECGs were collected from two different hospitals to validate the DLM performance in internal and external validation sets. We analyzed the association and prediction ability of ECG-LVD for CV outcomes, including left ventricular (LV) dysfunction, CV mortality, acute myocardial infarction (AMI), and coronary artery disease (CAD).ResultsThe mean absolute errors (MAE) of ECG-LV-D were 5.25/5.29, and the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.8297/0.8072 and 0.9295/0.9148 for the detection of mild (56 ≦ LV-D < 65 mm) and severe (LV-D ≧ 65 mm) LV-D dilation in internal/external validation sets, respectively. Patients with normal ejection fraction (EF) who were identified as high ECHO-LV-D had the higher hazard ratios (HRs) of developing new onset LV dysfunction [HR: 2.34, 95% conference interval (CI): 1.78–3.08], CV mortality (HR 2.30, 95% CI 1.05–5.05), new-onset AMI (HR 2.12, 95% CI 1.36–3.29), and CAD (HR 1.59, 95% CI 1.26–2.00) in the internal validation set. In addition, the ECG-LV-D presents a 1.88-fold risk (95% CI 1.47–2.39) on new-onset LV dysfunction in the external validation set.ConclusionThe ECG-LV-D not only identifies high-risk patients with normal EF but also serves as an independent risk factor of long-term CV outcomes.

Aims: The aim of this study is to investigate the association of physical fitness with longterm Blood Pressure Variability (BPV) in young male adults. Methods: 1,112 healthy military males, aged 18-40 years (mean age, 32 years), in Taiwan were included for the current analysis. Resting blood pressures were measured over the right upper arm in a sitting position every two years from 2012 to 2018 (2012-14, 2014-15, 2015-16, 2016-18). Long-term BPV by Standard Deviation (SDSBP and SDDBP) and Average Real Variability (ARVSBP and ARVDBP) were assessed across 4 visits during the study period. Aerobic fitness was evaluated by the time taken for a 3000-meter run test, and anaerobic fitness was evaluated by the number of 2-minute sit-ups and 2-minute push-ups. Results: After adjusting the systolic and diastolic blood pressure, the time for a 3000-meter run was associated with ARVSBP, SDSBP, and SDDBP (β [SE]: 0.007 [0.002], 0.004 [0.002], and 0.005 [0.002], respectively, all p Conclusion: It was found that there was no association of physical fitness with long-term BPV in young male military personnel. Previous studies have shown no association with cardiorespiratory fitness in the elderly. This study further increased the knowledge of a null association between anaerobic fitness and long-term BPV.

Background: In the past decade, Tri-Service General Hospital has trained many actors-background standardized patients (SPs). However, we also have recruited some nonactor SPs since 2010. Reviewing the literature, the reliability of SPs with or without actor background would be the impact factor on high-stakes OSCE has not been well studied. Aim: The purpose of this retrospective observation is to clarify whether a SP with or without a professional actor background will affect the performances of the examination when participating in the Taiwan high-stakes objective structured clinical examination (OSCE). The result will be the policy for selecting which kind of SPs to participate in the high-stack OSCE in our hospital. Methods: In this retrospective observation, we analyzed 74 actor background SPs (A-SPs), and 70 no actor background SPs (NA-SPs) who have participated in each spring test of 2015–2017 Taiwan high-stakes OSCE. The data of SPs performance have come from two parts: one is from the examinee with the global rating of the SPs performance and the other is from the examiner with eight-item checklist for the SPs performance. The scoring of both examinee and examiner is a five-point Likert scale. Results: The results show that there is no significant difference in SPs performance from the examinee scoring in the subsequent 3 years, stable and satisfactory, and let examinees feel like real patients. The scores from examiners also showed that two groups of SPs performed equally well without significant differences. Their performance is reliable and consistent, simulating to be a real patient in both groups. However, only one interest finding, when compared to the different age subdivision of SPs, showed the aged subgroup of A-SPs and NA-SPs with the significant difference in the item of reliability and consistent (4.67 ± 0.53 vs. 4.41 ± 0.50, P < 0.05), the possible reason is related to the less-experienced associate with mild memory decline in aged NA-SPs.Conclusions: Our observation gives the essential information, that the well-trained and experienced SP are necessary for participating in the high-stakes OSCE examination, even without the background of the actor. What we need is a professional SP who not only has excellent professionalism but also has a good personality literacy to engage in his/her work.

BackgroundAlbumin, an important component of fluid balance, is associated with kidney, liver, nutritional, and cardiovascular diseases (CVD) and is measured by blood tests. Since fluid balance is associated with electrocardiography (ECG) changes, we established a deep learning model (DLM) to estimate albumin via ECG.ObjectiveThis study aimed to develop a DLM to estimate albumin via ECG and explored its contribution to future complications.Materials and MethodsA DLM was trained for estimating ECG-based albumin (ECG-Alb) using 155,078 ECGs corresponding to albumin from 79,111 patients, and another independent 13,335 patients from an academic medical center and 11,370 patients from a community hospital were used for internal and external validation. The primary analysis focused on distinguishing patients with mild to severe hypoalbuminemia, and the secondary analysis aimed to provide additional prognostic value from ECG-Alb for future complications, which included mortality, new-onset hypoalbuminemia, chronic kidney disease (CKD), new onset hepatitis, CVD mortality, new-onset acute myocardial infarction (AMI), new-onset stroke (STK), new-onset coronary artery disease (CAD), new-onset heart failure (HF), and new-onset atrial fibrillation (Afib).ResultsThe AUC to identify hypoalbuminemia was 0.8771 with a sensitivity of 56.0% and a specificity of 90.7% in the internal validation set, and the Pearson correlation coefficient was 0.69 in the continuous analysis. The most important ECG features contributing to ECG-Alb were ordered in terms of heart rate, corrected QT interval, T wave axis, sinus rhythm, P wave axis, etc. The group with severely low ECG-Alb had a higher risk of all-cause mortality [hazard ratio (HR): 2.45, 95% CI: 1.81–3.33] and the other hepatorenal and cardiovascular events in the internal validation set. The external validation set yielded similar results.ConclusionHypoalbuminemia and its complications can be predicted using ECG-Alb as a novel biomarker, which may be a non-invasive tool to warn asymptomatic patients.

Background: Left atrium enlargement (LAE) can be used as a predictor of future cardiovascular diseases, including hypertension (HTN) and atrial fibrillation (Afib). Typical electrocardiogram (ECG) changes have been reported in patients with LAE. This study developed a deep learning model (DLM)-enabled ECG system to identify patients with LAE. Method: Patients who had ECG records with corresponding echocardiography (ECHO) were included. There were 101,077 ECGs, 20,510 ECGs, 7611 ECGs, and 11,753 ECGs in the development, tuning, internal validation, and external validation sets, respectively. We evaluated the performance of a DLM-enabled ECG for diagnosing LAE and explored the prognostic value of ECG-LAE for new-onset HTN, new-onset stroke (STK), new-onset mitral regurgitation (MR), and new-onset Afib. Results: The DLM-enabled ECG achieved AUCs of 0.8127/0.8176 for diagnosing mild LAE, 0.8587/0.8688 for diagnosing moderate LAE, and 0.8899/0.8990 for diagnosing severe LAE in the internal/external validation sets. Notably, ECG-LAE had higher prognostic value compared to ECHO-LAE, which had C-indices of 0.711/0.714 compared to 0.695/0.692 for new-onset HTN, 0.676/0.688 compared to 0.663/0.677 for new-onset STK, 0.696/0.695 compared to 0.676/0.673 for new-onset MR, and 0.800/0.806 compared to 0.786/0.760 for new-onset Afib in internal/external validation sets, respectively. Conclusions: A DLM-enabled ECG could be considered as a LAE screening tool and provide better prognostic information for related cardiovascular diseases.

ObjectiveThe biological age progression of the heart varies from person to person. We developed a deep learning model (DLM) to predict the biological age via ECG to explore its contribution to future cardiovascular diseases (CVDs).MethodsThere were 71,741 cases ranging from 20 to 80 years old recruited from the health examination center. The development set used 32,707 cases to train the DLM for estimating the ECG-age, and 8,295 cases were used as the tuning set. The validation set included 30,469 ECGs to follow the outcomes, including all-cause mortality, cardiovascular-cause mortality, heart failure (HF), diabetes mellitus (DM), chronic kidney disease (CKD), acute myocardial infarction (AMI), stroke (STK), coronary artery disease (CAD), atrial fibrillation (AF), and hypertension (HTN). Two independent external validation sets (SaMi-Trop and CODE15) were also used to validate our DLM.ResultsThe mean absolute errors of chronologic age and ECG-age was 6.899 years (r = 0.822). The higher difference between ECG-age and chronological age was related to more comorbidities and abnormal ECG rhythm. The cases with the difference of more than 7 years had higher risk on the all-cause mortality [hazard ratio (HR): 1.61, 95% CI: 1.23–2.12], CV-cause mortality (HR: 3.49, 95% CI: 1.74–7.01), HF (HR: 2.79, 95% CI: 2.25–3.45), DM (HR: 1.70, 95% CI: 1.53–1.89), CKD (HR: 1.67, 95% CI: 1.41–1.97), AMI (HR: 1.76, 95% CI: 1.20–2.57), STK (HR: 1.65, 95% CI: 1.42–1.92), CAD (HR: 1.24, 95% CI: 1.12–1.37), AF (HR: 2.38, 95% CI: 1.86–3.04), and HTN (HR: 1.67, 95% CI: 1.51–1.85). The external validation sets also validated that an ECG-age >7 years compare to chronologic age had 3.16-fold risk (95% CI: 1.72–5.78) and 1.59-fold risk (95% CI: 1.45–1.74) on all-cause mortality in SaMi-Trop and CODE15 cohorts. The ECG-age significantly contributed additional information on heart failure, stroke, coronary artery disease, and atrial fibrillation predictions after considering all the known risk factors.ConclusionsThe ECG-age estimated via DLM provides additional information for CVD incidence. Older ECG-age is correlated with not only on mortality but also on other CVDs compared with chronological age.

Dyskalemias are common electrolyte disorders associated with high cardiovascular risk. Artificial intelligence (AI)-assisted electrocardiography (ECG) has been evaluated as an early-detection approach for dyskalemia. The aims of this study were to determine the clinical accuracy of AI-assisted ECG for dyskalemia and prognostic ability on clinical outcomes such as all-cause mortality, hospitalizations, and ED revisits. This retrospective cohort study was done at two hospitals within a health system from May 2019 to December 2020. In total, 26,499 patients with 34,803 emergency department (ED) visits to an academic medical center and 6492 ED visits from 4747 patients to a community hospital who had a 12-lead ECG to estimate ECG-K+ and serum laboratory potassium measurement (Lab-K+) within 1 h were included. ECG-K+ had mean absolute errors (MAEs) of ≤0.365 mmol/L. Area under receiver operating characteristic curves for ECG-K+ to predict moderate-to-severe hypokalemia (Lab-K+ ≤3 mmol/L) and moderate-to-severe hyperkalemia (Lab-K+ ≥ 6 mmol/L) were >0.85 and >0.95, respectively. The U-shaped relationships between K+ concentration and adverse outcomes were more prominent for ECG-K+ than for Lab-K+. ECG-K+ and Lab-K+ hyperkalemia were associated with high HRs for 30-day all-cause mortality. Compared to hypokalemic Lab-K+, patients with hypokalemic ECG-K+ had significantly higher risk for adverse outcomes after full confounder adjustment. In addition, patients with normal Lab-K+ but dyskalemic ECG-K+ (pseudo-positive) also exhibited more co-morbidities and had worse outcomes. Point-of-care bloodless AI ECG-K+ not only rapidly identified potentially severe hypo- and hyperkalemia, but also may serve as a biomarker for medical complexity and an independent predictor for adverse outcomes.

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.

Objectives The choice of optimal antithrombotic therapy in atrial fibrillation (AF) patients with acute coronary syndrome (ACS) or percutaneous coronary intervention (PCI) remains controversial. The aim of this longitudinal cohort study is to investigate the prescribing pattern of antithrombotic regimen in different cohorts and its subsequent impact. Setting and design Longitudinal data from the Tri-Service General Hospital-Coronary Heart Disease (TSGH-CHD) registry, between January 2016 and August 2018 was screened. Participants and method Patients with prior history of nonvalvular AF, who had ACS presentation or underwent PCI were selected, and these patients were divided into cohort 1 and cohort 2, according to the index date of antithrombotic prescription before and after the PIONEER AF-PCI study. Primary and secondary outcomes The primary safety endpoints were composites of major bleeding and/or clinically relevant non-major bleeding. The secondary efficacy endpoints included the occurrence of all-cause mortality, stroke/systemic embolization, nonfatal myocardial infarction (MI), and >30-days coronary revascularization. Results A total of 121 patients were included into analysis (cohort 1=35; cohort 2=86). Comparing with cohort 1, the prescription rate of triple antithrombotic therapy (TAT) increased from 17.1 to 38.4%, especially the regimen with dual antiplatelet therapy (DAPT) plus low-dose non-vitamin-K dependent oral anticoagulation (NOAC). However, the prescription rate of dual antithrombotic therapy (DAT) decreased (14.3–10.5%), as well as the prescription rate of DAPT (68.6–51.2%). These changes of antithrombotic prescription across different cohorts were not associated with risk of adverse safety (HR= 0.87; 95% CI, 0.42-1.80, p=0.710) and efficacy outcomes (HR=0.96; 95% CI, 0.40-2.32, p=0.930). Conclusions Entering the NOAC era, the prescription of TAT increased alongside the decrease in DAT. As the prescription rate of DAPT without anticoagulation remained high, future efforts are mandatory to improve the implementation of guidelines and clinical practice.

(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 &lt, 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.

Assessment of left ventricular systolic dysfunction provides essential information related to the prognosis and management of cardiovascular diseases. The aim of this study was to develop a deep-learning model to identify left ventricular ejection fraction (LVEF) ≤ 35% via chest X-ray (CXR [CXR-EF≤35%]) features and investigate the performance and clinical implications.This study collected 90,547 CXRs with the corresponding LVEF according to transthoracic echocardiography from the outpatient department in an academic medical center. Among these, 77,227 CXRs were used to develop the identification of CXR-EF≤35%. Another 13,320 CXRs were used to validate the performance, which was evaluated by area under the receiver operating characteristic curve (AUC). Furthermore, CXR-EF≤35% was tested to assess the long-term risks of developing LVEF ≤ 35% and cardiovascular outcomes, which were evaluated by Kaplan-Meier survival analysis and the Cox proportional hazards model.The AUCs of CXR-EF≤35% for the detection of LVEF ≤ 35% were 0.888 and 0.867 in the internal and external validation cohorts, respectively. Patients with baseline LVEF50% but detected as CXR-EF≤35% were at higher risk of long-term development of LVEF ≤ 35% (hazard ratio, internal validation cohort [HRCXR-EF≤35% may serve as a screening tool for early detection of LVEF ≤ 35% and could independently contribute to predictions of long-term development of LVEF ≤ 35% and cardiovascular outcomes. Further prospective studies are needed to confirm the model performance.

Field mobile monitoring of PM

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.

Patients bitten by Naja atra who are treated with bivalent freeze-dried neurotoxic antivenom in Taiwan have an improved survival rate but develop necrotic wound changes. The World Health Organization (WHO) has suggested using the minimum necrotizing dose (MND) of venom as a method of evaluating the neutralization effect of antivenom. The aim of this study was to evaluate the effectiveness of antivenom for the prevention of necrosis based on the MND and clarify which component of the venom of N. atra induces necrosis. The neurotoxins (NTXs) were removed from the crude venom (deNTXs), and different concentrations of deNTXs were injected intradermally into the dorsal skin of mice. After three days, the necrotic lesion diameter was found to be approximately 5 mm, and the MND was calculated. A reduction in the necrotic diameter of 50% was used to identify the MND50. Furthermore, both phospholipase A2 (PLA2) and cytotoxins (CTXs) were separately removed from the deNTXs to identify the major necrosis-inducing factor, and the necrotic lesions were scored. All mice injected with deNTXs survived for three days and developed necrotic wounds. The MND of the deNTXs for mice was 0.494 ± 0.029 µg/g, that of the deNTXs-dePLA2 (major component retained: CTXs) was 0.294 ± 0.05 µg/g, and that of the deNTX-deCTX (major component retained: PLA2) venom was greater than 1.25 µg/g. These values show that CTX is the major factor inducing necrosis. These results suggest that the use of the deNTXs is necessary to enable the mice to survive long enough to develop venom-induced cytolytic effects. CTXs play a major role in N. atra-related necrosis. However, the MND50 could not be identified in this study, which meant that the antivenom did not neutralize venom-induced necrosis.

Patients bitten by Naja atra who are treated with bivalent freeze-dried neurotoxic antivenom in Taiwan have an improved survival rate but develop necrotic wound changes. The World Health Organization (WHO) has suggested using the minimum necrotizing dose (MND) of venom as a method of evaluating the neutralization effect of antivenom. The aim of this study was to evaluate the effectiveness of antivenom for the prevention of necrosis based on the MND and clarify which component of the venom of N. atra induces necrosis. The neurotoxins (NTXs) were removed from the crude venom (deNTXs), and different concentrations of deNTXs were injected intradermally into the dorsal skin of mice. After three days, the necrotic lesion diameter was found to be approximately 5 mm, and the MND was calculated. A reduction in the necrotic diameter of 50% was used to identify the MND50. Furthermore, both phospholipase A2 (PLA2) and cytotoxins (CTXs) were separately removed from the deNTXs to identify the major necrosis-inducing factor, and the necrotic lesions were scored. All mice injected with deNTXs survived for three days and developed necrotic wounds. The MND of the deNTXs for mice was 0.494 ± 0.029 µg/g, that of the deNTXs-dePLA2 (major component retained: CTXs) was 0.294 ± 0.05 µg/g, and that of the deNTX-deCTX (major component retained: PLA2) venom was greater than 1.25 µg/g. These values show that CTX is the major factor inducing necrosis. These results suggest that the use of the deNTXs is necessary to enable the mice to survive long enough to develop venom-induced cytolytic effects. CTXs play a major role in N. atra-related necrosis. However, the MND50 could not be identified in this study, which meant that the antivenom did not neutralize venom-induced necrosis.

Diabetes mellitus is a metabolic syndrome that affects millions of people worldwide. Recent studies have demonstrated that protein kinase C (PKC) activation plays an important role in hyperglycemia-induced atherosclerosis. PKC activation is involved in several cellular responses such as the expression of various growth factors, activation of signaling pathways, and enhancement of oxidative stress in hyperglycemia. However, the role of PKC activation in pro-atherogenic and anti-atherogenic mechanisms remains controversial, especially under hyperglycemic condition. In this review, we discuss the role of different PKC isoforms in lipid regulation, oxidative stress, inflammatory response, and apoptosis. These intracellular events are linked to the pathogenesis of atherosclerosis in diabetes. PKC deletion or treatment with PKC inhibitors has been studied in the regulation of atherosclerotic plaque formation and evolution. Furthermore, some preclinical and clinical studies have indicated that PKCβ and PKCδ are potential targets for the treatment of diabetic vascular complications. The current review summarizes these multiple signaling pathways and cellular responses regulated by PKC activation and the potential therapeutic targets of PKC in diabetic complications.

Background: glycated hemoglobin (HbA1c) provides information on diabetes mellitus (DM) management. Electrocardiography (ECG) is a noninvasive test of cardiac activity that has been determined to be related to DM and its complications. This study developed a deep learning model (DLM) to estimate HbA1c via ECG. Methods: there were 104,823 ECGs with corresponding HbA1c or fasting glucose which were utilized to train a DLM for calculating ECG-HbA1c. Next, 1539 cases from outpatient departments and health examination centers provided 2190 ECGs for initial validation, and another 3293 cases with their first ECGs were employed to analyze its contributions to DM management. The primary analysis was used to distinguish patients with and without mild to severe DM, and the secondary analysis was to explore the predictive value of ECG-HbA1c for future complications, which included all-cause mortality, new-onset chronic kidney disease (CKD), and new-onset heart failure (HF). Results: we used a gender/age-matching strategy to train a DLM to achieve the best AUCs of 0.8255 with a sensitivity of 71.9% and specificity of 77.7% in a follow-up cohort with correlation of 0.496 and mean absolute errors of 1.230. The stratified analysis shows that DM presented in patients with fewer comorbidities was significantly more likely to be detected by ECG-HbA1c. Patients with higher ECG-HbA1c under the same Lab-HbA1c exhibited worse physical conditions. Of interest, ECG-HbA1c may contribute to the mortality (gender/age adjusted hazard ratio (HR): 1.53, 95% conference interval (CI): 1.08–2.17), new-onset CKD (HR: 1.56, 95% CI: 1.30–1.87), and new-onset HF (HR: 1.51, 95% CI: 1.13–2.01) independently of Lab-HbA1c. An additional impact of ECG-HbA1c on the risk of all-cause mortality (C-index: 0.831 to 0.835, p &lt, 0.05), new-onset CKD (C-index: 0.735 to 0.745, p &lt, 0.01), and new-onset HF (C-index: 0.793 to 0.796, p &lt, 0.05) were observed in full adjustment models. Conclusion: the ECG-HbA1c could be considered as a novel biomarker for screening DM and predicting the progression of DM and its complications.

Background: Atherosclerosis is driven by synergistic interactions between pathological, biomechanical, inflammatory, and lipid metabolic factors. Our previous studies demonstrated that absence of caveolin-1 (Cav1)/caveolae in hyperlipidemic mice strongly inhibits atherosclerosis, which was attributed to activation of endothelial nitric oxide (NO) synthase (eNOS) and increased production of NO and reduced inflammation and low-density lipoprotein trafficking. However, the contribution of eNOS activation and NO production in the athero-protection of Cav1 and the exact mechanisms by which Cav1/caveolae control the pathogenesis of diet-induced atherosclerosis are still not clear. Methods: Triple-knockout mouse lacking expression of eNOS , Cav1 , and Ldlr were generated to explore the role of NO production in Cav1-dependent athero-protective function. The effects of Cav1 on lipid trafficking, extracellular matrix remodeling, and vascular inflammation were studied both in vitro and in vivo with a mouse model of diet-induced atherosclerosis. The expression of Cav1 and distribution of caveolae regulated by flow were analyzed by immunofluorescence staining and transmission electron microscopy. Results: We found that absence of Cav1 significantly suppressed atherogenesis in Ldlr −/− eNOS −/− mice, demonstrating that athero-suppression is independent of increased NO production. Instead, we find that the absence of Cav1/caveolae inhibited low-density lipoprotein transport across the endothelium and proatherogenic fibronectin deposition and disturbed flow-mediated endothelial cell inflammation. Consistent with the idea that Cav1/caveolae may play a role in early flow-dependent inflammatory priming, distinct patterns of Cav1 expression and caveolae distribution were observed in athero-prone and athero-resistant areas of the aortic arch even in wild-type mice. Conclusions: These findings support a role for Cav1/caveolae as a central regulator of atherosclerosis that links biomechanical, metabolic, and inflammatory pathways independently of endothelial eNOS activation and NO production.

Diabetes is a common comorbidity in patients hospitalized for acute heart failure (AHF), but the relationship between admission glucose level, glycemic gap, and in-hospital mortality in patients with both conditions has not been investigated thoroughly. Clinical data for admission glucose, glycemic gap and in-hospital death in 425 diabetic patients hospitalized because of AHF were collected retrospectively. Glycemic gap was calculated as the A1c-derived average glucose subtracted from the admission plasma glucose level. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff value for glycemic gap to predict all-cause mortality. Patients with glycemic gap levels >43 mg/dL had higher rates of all-cause death (adjusted hazard ratio, 7.225, 95% confidence interval, 1.355–38.520) than those with glycemic gap levels ≤43 mg/dL. The B-type natriuretic peptide levels incorporated with glycemic gap could increase the predictive capacity for in-hospital mortality and increase the area under the ROC from 0.764 to 0.805 (net reclassification improvement = 9.9%, p

Background: Taiwan is located in a subtropical area where there are approximately 50 kinds of land snakes and six principal kinds of venous snakes. Snakebite envenomation is not an uncommon issue in Taiwan. We design a study to obtain an understanding of the characteristics of snakebites and snakebite patients in a medical center in northern Taiwan. Materials and Methods: This was a retrospective medical records study of an 8-year period. We used the key word “snake” to search the emergency department (ED) diagnosis of all patients and then used antivenom management codes to select patients for inclusion. The species of snake involved, time and site of the bite, geographic data of the patients and patient outcomes were recorded. Results: In total, there were 125 snakebite patients (male 70%, female 30%). The snakes involved were Protobothrops mucrosquamatus (50%), followed by Trimeresurus stejnegeri (7%). The feet and ankles were the most frequently bitten areas (42.4%). The peak months were July and October. The average ED stasis time was 2 hours and 45 minutes, and 65.6% of snakebite patients received antivenom. In patients bitten by P. mucrosquamatus, there was a high surgical rate, 23.8% higher than those reported in other studies. No mortalities or side effects of antivenom were reported in these patients. Conclusion: We propose a clinical flow chart for physicians who are treating patients bitten by P. mucrosquamatus. Snakebites, especially those inflicted by P. mucrosquamatus, induce severe soft tissue swelling, which can mimic compartment syndrome. Antivenom for P. mucrosquamatus is the first and only choice of treatment. The amount of antivenom needs to match the clinical symptoms. Patients bitten by P. mucrosquamatus may need longer observation times than patients bitten by other species of snakes, in addition to toxicologist consultations, sonographic examinations, and measurement of the objective compartment pressure before surgery.

Context Thyrotoxic periodic paralysis (TPP) characterized by acute weakness, hypokalemia, and hyperthyroidism is a medical emergency with a great challenge in early diagnosis since most TPP patients do not have overt symptoms. Objective This work aims to assess artificial intelligence (AI)-assisted electrocardiography (ECG) combined with routine laboratory data in the early diagnosis of TPP. Methods A deep learning model (DLM) based on ECG12Net, an 82-layer convolutional neural network, was constructed to detect hypokalemia and hyperthyroidism. The development cohort consisted of 39 ECGs from patients with TPP and 502 ECGs of hypokalemic controls; the validation cohort consisted of 11 ECGs of TPP patients and 36 ECGs of non-TPP individuals with weakness. The AI-ECG–based TPP diagnostic process was then consecutively evaluated in 22 male patients with TTP-like features. Results In the validation cohort, the DLM-based ECG system detected all cases of hypokalemia in TPP patients with a mean absolute error of 0.26 mEq/L and diagnosed TPP with an area under curve (AUC) of approximately 80%, surpassing the best standard ECG parameter (AUC = 0.7285 for the QR interval). Combining the AI predictions with the estimated glomerular filtration rate and serum chloride boosted the diagnostic accuracy of the algorithm to AUC 0.986. In the prospective study, the integrated AI and routine laboratory diagnostic system had a PPV of 100% and F-measure of 87.5%. Conclusion An AI-ECG system reliably identifies hypokalemia in patients with paralysis, and integration with routine blood chemistries provides valuable decision support for the early diagnosis of TPP.

The impact of the coronavirus disease 2019 (COVID-19) pandemic on health-care quality in the emergency department (ED) in countries with a low risk is unclear. This study aimed to explore the effects of the COVID-19 pandemic on ED loading, quality of care, and patient prognosis. Data were retrospectively collected from 1 January 2018 to 30 September 2020 at the ED of Tri-service general hospital. Analyses included day-based ED loading, quality of care, and patient prognosis. Data on triage assessment, physiological states, disease history, and results of laboratory tests were collected and analyzed. The number of daily visits significantly decreased after the pandemic, leading to a reduction in the time to examination. Admitted patients benefitted from the pandemic with a reduction of 0.80 h in the length of stay in the ED, faster discharge without death, and reduced re-admission. However, non-admitted visits with chest pain increased the risk of mortality after the pandemic. In conclusion, the COVID-19 pandemic led to a significant reduction in low-acuity ED visits and improved prognoses for hospitalized patients. However, clinicians should be alert about patients with chest pain due to their increased risk of mortality in subsequent admission.

The incidence of acute compartment syndrome (ACS) following snakebite envenomation may be seriously overestimated in Taiwan. Snakebite-induced ACS is difficult to determine solely by clinical examination. Snakebite patients previously underwent surgical intervention based on speculation and general clinical examinations suggesting ACS presentations instead of direct intracompartmental pressure (IP) measurement prior to fasciotomy. Point-of-care ultrasound (POCUS) is a relatively widely available noninvasive tool. This study aimed to evaluate snakebite-envenomated patients for the presence of subcutaneous edema and diastolic retrograde arterial flow (DRAF).Snakebite patients were prospectively recruited between 2017 and 2019. All patients were examined with POCUS to locate edema and directly visualize and measure the arterial flow in the compressed artery. The presence of DRAF in the compressed artery is suggestive of ACS development because when compartment space restriction occurs, increased retrograde arterial flow is observed in the artery.Twenty-seven snakebite patients were analyzed. Seventeen patients (63%) were bitten by Crotalinae snakes, seven (26%) by Colubridae, one (4%) by Elapidae, and two (7%) had unidentified snakebites. All Crotalinae bit patients received antivenom, had subcutaneous edema and lacked DRAF in a POCUS examination series.POCUS facilitates clinical decisions for snakebite envenomation. We also highlighted that the anatomic site of the snakebite is an important factor affecting the prognosis of the wounds. There were limitations of this study, including a small number of patients and no comparison with the generally accepted invasive evaluation for ACS.We are unable to state that POCUS is a valid surrogate measurement of ACS from this study but see this as a starting point to develop further research in this area. Further study will be needed to better define the utility of POCUS in patients envenomated by snakes throughout the world.

T cells secrete several inflammatory cytokines that play a critical role in the progression of atherosclerosis. Although green tea epigallocatechin-3-gallate (EGCG) exerts anti-inflammatory and anti-atherosclerotic effects in animals, few studies have identified the mechanism underlying these effects in human primary T cells. This study investigated the pathway involved in EGCG modulation of cytokine secretion in activated human primary T cells. We pre-treated human primary T cells with EGCG (0.1, 1, 5, 10, and 20 μM) for 4 h and incubated them with or without phorbol 12-myristate 13-acetate and ionomycin (P/I) for 20 h. The cytokine production, activator protein (AP)-1 binding activity, and level of mitogen-activated protein kinase (MAPK) were assessed using enzyme-linked immunosorbent assay, electrophoretic mobility shift assay, and Western blotting, respectively. At 10 and 20 μM, EGCG decreased interleukin (IL)-2 levels by 26.0% and 38.8%, IL-4 levels by 41.5% and 55.9%, INF-γ levels by 31.3% and 34.7%, and tumor-necrosis factor (TNF)-α levels by 23.0% and 37.6%, respectively. In addition, the level of phosphorylated c-Jun N-terminal (p-JNK) and extracellular signal-regulated kinase (p-ERK) was decreased, but not the level of p-p38 MAPK. EGCG did not alter any of the total protein amounts, suggesting a selective effect on specific types of MAPKs in stimulated human T cells. EGCG tended to inactivate AP-1 DNA-binding activity. The P/I-induced production of IL-2, IL-4, INF-γ, and TNF-α by human T cells was suppressed by AP-1 inhibitor in a concentration-dependent manner. In conclusion, EGCG suppressed cytokine secretion in activated human primary T cells, and this effect was likely mediated by AP-1 inactivation through the ERK and JNK, but not p38 MAPK, pathways. These results may be related to the mechanisms through which EGCG inhibits immune- or inflammation-related atherogenesis.

The prevalence of mitral valve prolapse (MVP) among middle- and older-aged individuals is estimated to be 2–4% in Western countries. However, few studies have been conducted among Asian individuals and young adults. This study included a sample of 2442 consecutive military adults aged 18–39 years in Hualien, Taiwan. MVP was defined as displacement of the anterior or posterior leaflet of the mitral valve to the mid portion of the annular hinge point > 2 mm in the parasternal long-axis view of echocardiography. Cardiac chamber size and wall thickness were measured based on the latest criteria of the American Society of Echocardiography. The clinical features of participants with MVP and those without MVP were compared using a two-sample t test, and the cardiac structures were compared using analysis of covariance with adjustment for body surface area (BSA). Eighty-two participants were diagnosed with MVP, and the prevalence was 3.36% in the overall population. Compared with those without MVP, participants with MVP had a lower body mass index (kg/m2) (24.89 ± 3.70 vs. 23.91 ± 3.45, p = 0.02) and higher prevalence of somatic symptoms related to exercise (11.0% vs. 4.9%, p = 0.02) and systolic click in auscultation (18.3% vs. 0.6%, p

Atherosclerotic cardiovascular diseases (ASCVDs), associated with vascular inflammation and lipid dysregulation, are responsible for high morbidity and mortality rates globally. For ASCVD treatment, cholesterol efflux plays an atheroprotective role in ameliorating inflammation and lipid dysregulation. To develop a multidisciplinary agent for promoting cholesterol efflux, octimibate derivatives were screened and investigated for the expression of ATP-binding cassette transporter A1 (ABCA1). Western blotting and qPCR analysis were conducted to determine the molecular mechanism associated with ABCA1 expression in THP-1 macrophages; results revealed that Oxa17, an octimibate derivative, enhanced ABCA1 expression through liver X receptors alpha (LXRα) activation but not through the microRNA pathway. We also investigated the role of Oxa17 in high-fat diet (HFD)-fed mice used as an in vivo atherosclerosis-prone model. In ldlr-/- mice, Oxa17 increased plasma high-density lipoprotein (HDL) and reduced plaque formation in the aorta. Plaque stability improved via reduction of macrophage accumulation and via narrowing of the necrotic core size under Oxa17 treatment. Our study demonstrates that Oxa17 is a novel and potential agent for ASCVD treatment with atheroprotective and anti-inflammatory properties.

BACKGROUND: Sex differences in heart failure mortality might be affected by age, race, and treatment response. Many large studies in Western countries have shown conflicting results, however few studies have been conducted in Asian patients. OBJECTIVES: We prospectively investigated the mortality risk in a multicenter cohort of 1,093 male and 416 female heart failure patients with reduced ejection fraction (HFrEF) hospitalized for worsening symptoms in Taiwan between 2013 and 2015. METHODS: Kaplan-Meier curve and Cox proportional regression analyses were used to determine the one-year mortality risk by sex. RESULTS: There were no significant differences in major adverse cardiovascular events, re-admission rate, and mortality between sexes in the overall cohort and the young subgroup during one-year of follow-up. In the elderly subgroup, the overall and cardiac mortality rate of the male patients were higher than those of the female patients (p = 0.035, p = 0.049, respectively). We found that the prognostic effect of old age on overall mortality rate appeared to be stronger in the male patients (p < 0.0001) than in the female patients (p = 0.69) in Cox regression analysis and Kaplan-Meier survival curves. Male sex was a risk factor for all-cause mortality in the elderly (hazard ratio: 1.50, 95% confidence interval 1.02-2.25) independently of systolic blood pressure, diabetes mellitus, hemoglobin concentration, kidney function, and medications. CONCLUSIONS: In the Taiwan HFrEF registry, the highest mortality risk was observed in male patients aged 65 years or more. Clinicians need to pay more attention to these patients.

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&ndash, 2.05, p = 0.025), hypertension (aHR, 1.26, 95% CI, 1.00&ndash, 1.59, p = 0.047), or chronic obstructive pulmonary disease (COPD) (aHR, 1.74, 95% CI, 1.02&ndash, 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.

BackgroundThe initial detection and diagnosis of ST-segment or non-ST-segment elevation myocardial infarction (STEMI or NSTEMI) definitely rely on a 12-lead electrocardiogram (ECG). Delay or misdiagnosis is not unusual by subjective interpretation. Our aim is to develop a DLM as a diagnostic support tool to detect MI based on a 12-lead ECG and to evaluate the performance of this model.MethodsThis study included 1,051 ECGs from 737 coronary angiography (CAG)-validated STEMI patients, 697 ECGs from 287 CAG-validated NSTEMI patients, and 140,336 not-MI ECGs from 76,775 patients at emergency departments. DLM was trained and validated for the performance using 80% and 20% of the ECGs, respectively. 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 DLM and experts. STEMI versus not-STEMI, and MI versus not-MI were evaluated by DLM.ResultsThe AUCs of DLM for identifying STEMI and MI were 0.976 and 0.944 in the human-machine competition, respectively, which were significantly better than those of our best clinicians. In the real world setting, DLM presented with AUC of 0.995/0.916 with corresponding sensitivities of 96.9%/77.0%, and specificities of 96.2%/92.9% in the identification of STEMI and MI, respectively. Furthermore, DLM demonstrated sufficient diagnostic capacity for STEMI without the aid of troponin I (TnI) (AUC= 0.996) with corresponding sensitivity and specificity of 98.4% and 96.9%. The AUC of combined DLM and the first recorded TnI for the detection of NSTEMI were increased to 0.978 with corresponding sensitivity and specificity of 91.6% and 96.7%, which was better than that of DLM (0.877) or TnI (0.949) alone. ConclusionsDLM may serve as a diagnostic decision tool to assist intensive or emergency medical system-based networks and frontline physicians in identifying STEMI and NSTEMI in a timely and precise manner to prevent delay or misdiagnosis, and thereby to facilitate subsequent reperfusion therapy.

Many studies have estimated particulate matter (PM) removal by urban trees using dry deposition models; however, few studies have quantified the accuracy of their results. Thus, this study investigated the dry deposition of PM and its associated soluble ions in five broadleaved species in three districts of Taichung, central Taiwan, through field experiments. The total suspended particulate (TSP) dry deposition flux on leaf surfaces varied with sampling time, site, and tree species. By contrast, single-factor effects were observed for PM10 and PM2.5. The average dry deposition velocities of TSPs, PM10, and PM2.5 were 0.63, 0.062, and 0.028 cm s−1, respectively. Moreover, the dry deposition velocities of sulfate and nitrate were estimated to be 0.186 and 0.194 cm s−1, respectively. A significant relationship was observed between the ambient concentration and the dry deposition flux for all size fractions of PM. By contrast, weak and negative correlations were found between particle deposition velocity and wind speed. The measured PM2.5 dry deposition velocity was approximately equal to the dry deposition velocity obtained with the i-Tree model (0.03 cm s−1), which indicated the promising application potential of i-Tree in Taiwan. Compound and rough leaves, such as leaves of the Taiwan golden-rain tree, intercepted a high amount of PM2.5, whereas the pongam tree, which has thin leaves and wax surfaces, exhibited the lowest TSP interception. Species difference mostly occurred in the dry deposition flux of nitrate rather than sulfate; however, the interception of sulfate by trees revealed the possibility of the long-range transport of air pollutants. The results of this study elucidate the dry deposition of PM and its associated soluble ions in real-world situations.

Hyperuricemia has been associated with metabolic syndrome, and the association with various cardiometabolic risk factors may be affected by sex. We made a cross-sectional examination in a military cohort of 6738 men and 766 women, aged 18 to 50 years of Taiwan in 2013 to 2014. Hyperuricemia were defined as serum uric acid levels ≥7.0 mg/dL for men and ≥5.7 mg/dL for women, respectively. Multivariable logistic regression analyses were used to determine the associations between hyperuricemia and various metabolic abnormalities. In the overall population, hyperuricemia was associated with high blood pressure (odds ratio [OR]: 1.59, and 95% confidence intervals: 1.42–1.77), low high-density lipoprotein (OR: 1.75, 1.56–1.97), high triglycerides (OR: 2.14, 1.90–2.42), high low-density lipoprotein (OR: 1.71, 1.51–1.93), high fasting plasma glucose (OR: 1.29, 1.13–1.48), and central obesity (OR: 2.85, 2.55–3.18) after adjusting for age and serum creatinine concentrations. However, the associations with atherogenic lipid profiles including high triglycerides and high low-density lipoprotein were merely significant in men but not in women. In addition, there was a tendency for a sex difference in the association of hyperuricemia and raised blood pressure ≥130/85 mm Hg, which was greater in women than that in men (OR: 2.92, 1.37–6.25 and 1.54, 1.37–1.72, respectively; P for interaction = .059). Our findings suggest that the association between hyperuricemia and various cardiometabolic abnormalities in young adults may differ by sex, possibly due to a regulation of sex hormones and uneven effects of uric acid at the same levels between sexes on lipid metabolisms and arterial stiffness.

Purpose: To investigate the association of preexisting hypertension at admission with the mortality in patients with systolic heart failure (HF). Method: We prospectively investigated the association of preexisting hypertension with the mortality among 1351 patients with HF in Taiwan during an average 12 months (range: 8 months–18 months) follow-up period. A multivariate cox regression analysis for the overall cohort and a subgroup analysis by age were performed. Results: After adjustment for all potential risk factors, the associations of preexisting hypertension with cardiovascular mortality were significantly reduced in the overall cohort and those aged less than 65 years (hazard ratios (HR): 0.53, 95% confidence intervals (CI): 0.33–0.84, and 0.28, 95% CI: 0.11–0.67, respectively). However, the associations with all-cause mortality were not significantly different in these two groups (HR: 0.77, 95% CI: 0.54–1.09, and 0.59, 95% CI: 0.32–1.07, respectively). Furthermore, the associations were all nonsignificant in the patients aged older than 65 years. Conclusion: Preexisting hypertension have an inverse association with cardiovascular mortality in the Asian patients with systolic HF, particularly for those with younger ages. Keywords: Reverse epidemiology, Pre-existing hypertension, Heart failure

BACKGROUND Atopic dermatitis (AD) is associated with systemic inflammation and may have a similar pathogenesis as obstructive sleep apnea (OSA). However, to date, studies on the association between AD and OSA are limited. OBJECTIVES This study evaluated the association between OSA and AD in children in a large-scale, population-based cohort. METHODS A total of approximately 120 736 children (