Wen-Jone Chen

Background: Seizures are common after out‐of‐hospital cardiac arrest and may exacerbate secondary brain injury, yet early prediction remains challenging. Although heart rate variability has been used to predict seizures in epilepsy, its role and optimal prediction window after cardiac arrest remain unclear. We aimed to develop a heart rate variability–based model for predicting post–cardiac arrest seizures and identify the optimal prediction window. Methods: This study prospectively collected and retrospectively analyzed data from adults resuscitated from nontraumatic out‐of‐hospital cardiac arrest and admitted to an academic post–cardiac arrest care center. Electrocardiography and electroencephalography were continuously recorded, and seizures were independently confirmed by a neurologist. Five‐minute time‐, frequency‐, and nonlinear‐domain heart rate variability features from 0 to 30 minutes before seizure onset were normalized to baseline and entered into a support vector machine classifier using training, validation, and testing sets. Results: Among 36 patients, 20 (56%) experienced 93 seizures. Model performance was highest 15 to 20 minutes before seizure onset. At 18 minutes, the model achieved an area under the receiver operating characteristic curve of 0.812 (95% CI, 0.792–0.832), 76% accuracy, 77% sensitivity, and 68% specificity. Compared with baseline, low frequency increased 3.28‐fold, sample entropy 2.58‐fold, high frequency 2.43‐fold, and the low/high frequency ratio 1.47‐fold in seizure segments; corresponding ratios in nonseizure segments remained near baseline. These findings suggest that autonomic function fluctuates substantially before post–cardiac arrest seizures and may provide an early warning window for clinically important neurologic events during intensive care. Conclusions: Heart rate variability changed before post–cardiac arrest seizures, with 18 minutes before onset appearing optimal for prediction.

Background: Guideline‐directed medical therapy (GDMT) improves outcomes in heart failure with reduced ejection fraction, yet its adoption across Asia remains suboptimal. The American Heart Association launched the GWTG‐HF A.S.I.A. (Get With The Guidelines‐Heart Failure Adherence to Science, Implementation in Asia) program in 2021. We examined the association between program participation and GDMT implementation in Taiwan. Methods: We analyzed 1991 patients with heart failure with reduced ejection fraction enrolled in the Taiwan Society of Cardiology HF Registry (2021–2024). Four hospitals participated in the GWTG‐HF A.S.I.A. program (n=462) and 23 nonparticipating hospitals served as controls (n=1529). After propensity‐score matching, prescription trends of 4‐pillar GDMTs were compared using generalized estimating equations. Primary outcomes were patient‐level and hospital‐level GDMT prescription rates; secondary outcomes included echocardiographic remodeling, loss to follow‐up, and 2‐year mortality. Results: Between 2021 and 2024, GDMT use increased in both groups, with greater gains in participating hospitals. Adjusted odds ratios (aOR) for program participation were 2.73 (95% CI, 2.23–3.34) for 4‐pillar GDMTs, 1.59 (95% CI, 1.31–1.92) for angiotensin receptor‐neprilysin inhibitors, 2.06 (95% CI, 1.72–2.47) for SGLT2 (sodium‐glucose co‐transporter 2) inhibitors, and 1.63 (95% CI, 1.34–2.00) for mineralocorticoid receptor antagonists (all P P <0.001), and 2‐year mortality was similar. Conclusions: Participation in the GWTG‐HF A.S.I.A. program was associated with higher GDMT prescription rates in Taiwanese hospitals. Structured quality improvement initiatives may help bridge the evidence‐practice gap in Asia.

Background: Accurately predicting outcomes for cardiac arrest (CA) survivors is critical, and several severity scores have been developed to predict outcomes at hospital discharge. However, the ability of these scores to predict long-term outcomes at 6 months and 1 year in CA survivors remains unexplored. Methods: This retrospective observational study enrolled 1773 adult patients with nontraumatic CA in the emergency department of National Taiwan University Hospital between January 2011 and June 2023 without interhospital transfer. Data on clinical variables, resuscitation events, post-arrest care, and outcomes were collected, and the disease severity was classified using the sCAHP, rCAST, and TIMECARD scores. The outcomes included 6-month and 1-year survival. The predictive ability of these scores was evaluated using receiver operating characteristic curves. Results: Among the 1773 enrolled patients, 501 (28.3%) survived to discharge, 361 (20.4%) survived for 6 months, and 346 (19.5%) survived for 1 year. The area under the receiver operating characteristic curves (AUCs) for 6-month survival were 0.787, 0.733, and 0.810 for the sCAHP, rCAST, and TIMECARD scores, respectively. For 1-year survival, the AUC values were 0.788, 0.734, and 0.816, respectively. The sCAHP and TIMECARD scores demonstrated better discriminatory performance than did the rCAST score for both 6-month and 1-year survival. Compared with the sCAHP and rCAST scores, the TIMECARD score exhibited superior discriminatory ability for 1-year survival, particularly in younger patients (age ≤65 years), those with in-hospital CA (IHCA), and those with good pre-arrest neurological status. Conclusion: The sCAHP, rCAST, and TIMECARD scores demonstrated good discrimination and calibration in predicting long-term outcomes in CA survivors. The TIMECARD score exhibited better discriminatory performance for 1-year survival, particularly in the subgroup of younger age, IHCA, and good pre-arrest neurological function.

Prostate cancer (PC) is one of the leading causes of cancer-related deaths among men globally, and it frequently coexists with cardiovascular disease (CVD). With increasing cancer survival rates and an aging population, the incidence of CVD among PC patients is also on the rise. The treatments for PC include androgen deprivation therapy, androgen receptor pathway inhibitors, and docetaxel-based chemotherapy, and the cardiovascular complications include hypertension, metabolic syndrome, heart failure and arrhythmias. As it is important to provide personalized treatment planning and close monitoring, cardio-oncology collaboration plays a crucial role in optimizing treatment outcomes, emphasizing cardiovascular risk assessments and tailored treatment plans that balance cancer control and cardiovascular health. In this consensus statement, we review several important issues including the association between CVD and PC, screening for cardiovascular diseases and risk factors, timing and criteria for cardiology referral, cardiovascular risk assessments and management, and multidisciplinary collaboration and patient education. However, challenges remain in screening and preventive measures, and future research should focus on developing robust cardiovascular risk assessment tools, preventive measures, and interdisciplinary approaches to improve patient outcomes and reduce cardiovascular morbidity in patients being treated for PC.

Twelve-lead electrocardiogram (ECG) may provide prognostic information for in-hospital cardiac arrest (IHCA). This study aimed to identify post-arrest ECG features and their temporal changes associated with IHCA outcomes. This single-center retrospective study included patients experiencing IHCA between 2005 and 2022. Post-arrest ECGs were obtained within 48 h after an IHCA, admission ECGs upon hospital admission, and pre-arrest ECGs within 72 h before an IHCA. Multivariable logistic regression analyses were conducted to identify ECG features associated with neurologically intact survival. A total of 708 patients were included, with 131 (18.5%) achieving neurologically intact survival. The median age was 70.4 years (interquartile range: 59.2–82.6), and 362 (62.7%) patients were male. Four post-arrest ECG features were associated with survival: sinus rhythm (odds ratio [OR]: 1.81, 95% confidence interval [CI]: 1.11–2.93), QRS duration between 80 and 120 ms (OR: 1.91, 95% CI 1.19–3.08), low QRS voltage (OR: 0.50, 95% CI 0.25–0.99), and prolonged QTc (OR: 1.89, 95% CI 1.08–3.28). Comparing with admission ECGs, new-onset right bundle branch block (OR: 0.39, 95% CI 0.16–0.95) and increases in the number of leads with ST depression (OR: 0.85, 95% CI 0.77–0.94) on post-arrest ECGs were inversely associated with survival. Compared with pre-arrest ECGs, increases in the number of leads with ST depression (OR: 0.91, 95% CI 0.88–0.96) on post-arrest ECGs were also inversely associated with survival. Post-arrest ECGs may serve as a valuable prognostic tool for IHCA. Further exploration is warranted to determine whether incorporating these ECG features can enhance the performance of prediction models for IHCA outcomes.

In the past, patients with pulmonary hypertension (PH) were advised to avoid exercise due to concerns that it might strain cardiac function and exacerbate symptoms. However, recent evidence indicates that structured exercise programs can enhance outcomes and improve health-related quality of life for these patients. Consequently, exercise rehabilitation is now recommended in international guidelines as a key component of PH management. This consensus statement, developed by experts from the Taiwan Society of Cardiology Pulmonary Hypertension and Circulation Committee, the Taiwan Academy of Cardiovascular and Pulmonary Rehabilitation, and the Taiwan Association of Caring for pulmonary arterial hypertension, emphasizes the significance of rehabilitation and tailored exercise programs for PH patients. This article aims to raise awareness and promote the adoption of these practices among healthcare professionals treating patients with PH.

Background: The aim of this study was to validate and compare the performance of statistical (Utstein‐Based Return of Spontaneous Circulation and Shockable Rhythm–Witness–Age–pH) and machine learning–based (Prehospital Return of Spontaneous Circulation and Swedish Cardiac Arrest Risk Score) models in predicting the outcomes following out‐of‐hospital cardiac arrest and to assess the impact of the COVID‐19 pandemic on the models' performance. Methods and Results: This retrospective analysis included adult patients with out‐of‐hospital cardiac arrest treated at 3 academic hospitals between 2015 and 2023. The primary outcome was neurological outcomes at hospital discharge. Patients were divided into pre‐ (2015–2019) and post‐2020 (2020–2023) subgroups to examine the effect of the COVID‐19 pandemic on out‐of‐hospital cardiac arrest outcome prediction. The models' performance was evaluated using the area under the receiver operating characteristic curve and compared by the DeLong test. The analysis included 2161 patients, 1241 (57.4%) of whom were resuscitated after 2020. The cohort had a median age of 69.2 years, and 1399 patients (64.7%) were men. Overall, 69 patients (3.2%) had neurologically intact survival. The area under the receiver operating characteristic curves for predicting neurological outcomes were 0.85 (95% CI, 0.83–0.87) for the Utstein‐Based Return of Spontaneous Circulation score, 0.82 (95% CI, 0.81–0.84) for the Shockable Rhythm–Witness–Age–pH score, 0.79 (95% CI, 0.78–0.81) for the Prehospital Return of Spontaneous Circulation score, and 0.79 (95% CI, 0.77–0.81) for the Swedish Cardiac Arrest Risk Score model. The Utstein‐Based Return of Spontaneous Circulation score significantly outperformed both the Prehospital Return of Spontaneous Circulation score ( P P =0.007). Subgroup analysis indicated no significant difference in predictive performance for patients resuscitated before versus after 2020. Conclusions: In this external validation, both statistical and machine learning–based models demonstrated excellent and fair performance, respectively, in predicting neurological outcomes despite different model architectures. The predictive performance of all evaluated clinical scoring systems was not significantly influenced by the COVID‐19 pandemic.