The Usefulness of Prioritization of Ivabradine Before Beta-Blockers in a Heart Failure Patient Suffering From Intra-hemodialysis Hypotension.

Depressed cardiac systolic function in hemodialysis patients occurs for a variety of reasons and is a clinical problem. Beta-blockers are a key drug in the treatment of heart failure; however, hypotension may occur, particularly in dialysis patients, thereby complicating dialysis. Ivabradine has the unique property of a negative chronotropic effect only, without the negative inotropic effect. A 55-year-old woman who underwent dialysis presented with dyspnea and fatigue even at rest due to low cardiac systolic function. The left ventricular ejection fraction (LVEF) was 30%. Medications for heart failure, such as carvedilol and enalapril, were initiated; however, they were discontinued owing to intradialytic hypotension. Subsequently, her heart rate increased to over 100 beats per minute (bpm); therefore, we administered 2.5 mg of ivabradine before beta-blockers, which reduced her heart rate by approximately 30 bpm without a significant blood pressure decrease. Moreover, her blood pressure stabilized during dialysis. After two weeks, we added 1.25 mg of bisoprolol and adjusted the dose to 0.625 mg. After seven months of treatment with 2.5 mg ivabradine and 0.625 mg bisoprolol, systolic cardiac function significantly improved to 70% of LVEF. Prioritizing ivabradine over beta-blockers may not cause intradialytic hypotension; even low doses of ivabradine and bisoprolol were considered effective heart failure therapies.

Effects of a brief interpersonal mindfulness program embedded in a psychiatric nursing practicum: A controlled pilot study.

BACKGROUND: Nursing school is a stressful environment that demands high performance both professionally and academically. Interpersonal mindfulness training has shown promise for its stress-reducing capacity in other contexts; however, few descriptions or tests of this method in nursing training settings exist in the literature. PURPOSE: This pilot study examined effects of a brief interpersonal mindfulness program embedded in a 4-week psychiatric nursing practicum in Thailand. METHODS: Mixed methods were used with 31 fourth-year nursing students to measure changes in mindfulness and assess their experiences of the program's impact. The control and experimental groups received the same clinical training, but the experimental group was also trained to practice interpersonal mindfulness throughout the course. FINDINGS: The experimental group reported statistically significantly greater increases in Observing, Describing, and Non-reacting subscale scores, and in scores for the overall Five-Facet Mindfulness questionnaire, Thai version, than the control group (p < .05, Cohen's d = 0.83-0.95, large effect sizes). Group interviews revealed themes: initial challenges to mindfulness practice, experiences of becoming more mindful, intrapersonal benefits, and consequences of mindfulness on interpersonal skills. CONCLUSION: Overall, an interpersonal mindfulness program embedded in a psychiatric nursing practicum was effective. Further studies are required to address limitations of the present study.

Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients With Pulmonary Hypertension.

Background: Pulmonary hypertension is a disabling and life-threatening cardiovascular disease. Early detection of elevated pulmonary artery pressure (ePAP) is needed for prompt diagnosis and treatment to avoid detrimental consequences of pulmonary hypertension. Objectives: This study sought to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify patients with ePAP and related prognostic implications. Methods: From a hospital-based ECG database, the authors extracted the first pairs of ECG and transthoracic echocardiography taken within 2 weeks of each other from 41,097 patients to develop an AI model for detecting ePAP (PAP > 50 mm Hg by transthoracic echocardiography). The model was evaluated on independent data sets, including an external cohort of patients from Japan. Results: Tests of 10-fold cross-validation neural-network deep learning showed that the area under the receiver-operating characteristic curve of the AI model was 0.88 (sensitivity 81.0%; specificity 79.6%) for detecting ePAP. The diagnostic performance was consistent across age, sex, and various comorbidities (diagnostic odds ratio >8 for most factors examined). At 6-year follow-up, the patients predicted by the AI model to have ePAP were independently associated with higher cardiovascular mortality (HR: 3.69). Similar diagnostic performance and prediction for cardiovascular mortality could be replicated in the external cohort. Conclusions: The ECG-based AI model identified patients with ePAP and predicted their future risk for cardiovascular mortality. This model could serve as a useful clinical test to identify patients with pulmonary hypertension so that treatment can be initiated early to improve their survival prognosis.

Artificial Intelligence-Enabled Model for Early Detection of Left Ventricular Hypertrophy and Mortality Prediction in Young to Middle-Aged Adults.

BACKGROUND: Concealed left ventricular hypertrophy (LVH) is a prevalent condition that is correlated with a substantial risk of cardiovascular events and mortality, especially in young to middle-aged adults. Early identification of LVH is warranted. In this work, we aimed to develop an artificial intelligence (AI)-enabled model for early detection and risk stratification of LVH using 12-lead ECGs. METHODS: By deep learning techniques on the ECG recordings from 28 745 patients (20-60 years old), the AI model was developed to detect verified LVH from transthoracic echocardiography and evaluated on an independent cohort. Two hundred twenty-five patients from Japan were externally validated. Cardiologists' diagnosis of LVH was based on conventional ECG criteria. The area under the curve (AUC), sensitivity, and specificity were applied to evaluate the model performance. A Cox regression model estimated the independent effects of AI-predicted LVH on cardiovascular or all-cause death. RESULTS: The AUC of the AI model in diagnosing LVH was 0.89 (sensitivity: 90.3%, specificity: 69.3%), which was significantly better than that of the cardiologists' diagnosis (AUC, 0.64). In the second independent cohort, the diagnostic performance of the AI model was consistent (AUC, 0.86; sensitivity: 85.4%, specificity: 67.0%). After a follow-up of 6 years, AI-predicted LVH was independently associated with higher cardiovascular or all-cause mortality (hazard ratio, 1.91 [1.04-3.49] and 1.54 [1.20-1.97], respectively). The predictive power of the AI model for mortality was consistently valid among patients of different ages, sexes, and comorbidities, including hypertension, diabetes, stroke, heart failure, and myocardial infarction. Last, we also validated the model in the international independent cohort from Japan (AUC, 0.83). CONCLUSIONS: The AI model improved the detection of LVH and mortality prediction in the young to middle-aged population and represented an attractive tool for risk stratification. Early identification by the AI model gives every chance for timely treatment to reverse adverse outcomes.

A Deep Learning-Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome.

BACKGROUND: Brugada syndrome is a major cause of sudden cardiac death in young people and has distinctive electrocardiographic (ECG) features. We aimed to develop a deep learning-enabled ECG model for automatic screening for Brugada syndrome to identify these patients at an early point in time, thus allowing for life-saving therapy. METHODS: A total of 276 ECGs with a type 1 Brugada ECG pattern (276 type 1 Brugada ECGs and another randomly retrieved 276 non-Brugada type ECGs for 1:1 allocation) were extracted from the hospital-based ECG database for a 2-stage analysis with a deep learning model. After trained network for identifying right bundle branch block pattern, we transferred the first-stage learning to the second task to diagnose the type 1 Brugada ECG pattern. The diagnostic performance of the deep learning model was compared with that of board-certified practicing cardiologists. The model was further validated in an independent ECG data set collected from hospitals in Taiwan and Japan. RESULTS: The diagnoses by the deep learning model (area under the receiver operating characteristic curve [AUC] 0.96, sensitivity 88.4%, specificity 89.1%) were highly consistent with the standard diagnoses (kappa coefficient 0.78). However, the diagnoses by the cardiologists were significantly different from the standard diagnoses, with only moderate consistency (kappa coefficient 0.63). In the independent ECG cohort, the deep learning model still reached a satisfactory diagnostic performance (AUC 0.89, sensitivity 86.0%, specificity 90.0%). CONCLUSIONS: We present the first deep learning-enabled ECG model for diagnosing Brugada syndrome, which appears to be a robust screening tool with a diagnostic potential rivalling trained physicians.

The Different Substrate Characteristics of Arrhythmogenic Triggers in Idiopathic Right Ventricular Outflow Tract Tachycardia and Arrhythmogenic Right Ventricular Dysplasia: New Insight from Noncontact Mapping.

BACKGROUND: The aim of this study was to investigate the different substrate characteristics of repetitive premature ventricular complexed (PVC) trigger sites by the non-contact mapping (NCM). METHODS: Thirty-five consecutive patients, including 14 with arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC) and 21 with idiopathic right ventricular outflow tract tachycardia (RVOT VT), were enrolled for electrophysiological study and catheter ablation guided by the NCM. Substrate and electrogram (Eg) characteristics of the earliest activation (EA) and breakout (BO) sites of PVCs were investigated, and these were confirmed by successful PVC elimination. RESULTS: Overall 35 dominant focal PVCs were identified. PVCs arose from the focal origins with preferential conduction, breakout, and spread to the whole right ventricle. The conduction time and distance from EA to BO site were both longer in the ARVC than the RVOT group. The conduction velocity was similar between the 2 groups. The negative deflection of local unipolar Eg at the EA site (EA slope3,5,10ms values) was steeper in the RVOT, compared to ARVC patients. The PVCs of ARVC occurred in the diseased substrate in the ARVC patients. More radiofrequency applications were required to eliminate the triggers in ARVC patients. CONCLUSIONS/INTERPRETATION: The substrate characteristics of PVC trigger may help to differentiate between idiopathic RVOT VT and ARVC. The slowing and slurred QS unipolar electrograms and longer distance from EA to BO in RVOT endocardium suggest that the triggers of ARVC may originate from mid- or sub-epicardial myocardium. More extensive ablation to the trigger site was required in order to create deeper lesions for a successful outcome.