Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke - a single-center study based on 998 patients

Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF. Beyond introducing new indices to characterize the fibrillatory waves (f-waves) recorded through the preoperative electrocardiogram (ECG), the aim of this study is to combine frequency- and time-domain features to improve CA outcome prediction and optimize patient selection for the procedure, given the absence of any study that jointly analyzes information from both domains. Precisely, the f-waves of 151 persistent AF patients undergoing their first CA procedure were extracted from standard V1 lead. Novel spectral and amplitude features were derived from these waves and combined through a machine learning algorithm to anticipate the intervention mid-term outcome. The power rate index (φ), which estimates the power of the harmonic content regarding the dominant frequency (DF), yielded the maximum individual discriminant ability of 64% to discern between individuals who experienced a recurrence of AF and those who sustained SR after a 9-month follow-up period. The predictive accuracy was improved up to 78.5% when this parameter φ was merged with the amplitude spectrum area in the DF bandwidth (A M S A L F ) and the normalized amplitude of the f-waves into a prediction model based on an ensemble classifier, built by random undersampling boosting of decision trees. This outcome suggests that the synthesis of both spectral and temporal features of the f-waves before CA might enrich the prognostic knowledge of this therapy for persistent AF patients.

Preoperative Prediction of Catheter Ablation Outcome in Persistent Atrial Fibrillation Patients through Spectral Organization Analysis of the Surface Fibrillatory Waves

Catheter ablation (CA) is a commonly used treatment for persistent atrial fibrillation (AF). Since its medium/long-term success rate remains limited, preoperative prediction of its outcome is gaining clinical interest to optimally select candidates for the procedure. Among predictors based on the surface electrocardiogram, the dominant frequency (DF) and harmonic exponential decay (γ) of the fibrillatory waves (f-waves) have reported promising but clinically insufficient results. Hence, the main goal of this work was to conduct a broader analysis of the f-wave harmonic spectral structure to improve CA outcome prediction through several entropy-based measures computed on different frequency bands. On a database of 151 persistent AF patients under radio-frequency CA and a follow-up of 9 months, the newly introduced parameters discriminated between patients who relapsed to AF and those who maintained SR at about 70%, which was statistically superior to the DF and approximately similar to γ. They also provided complementary information to γ through different combinations in multivariate models based on lineal discriminant analysis and report classification performance improvement of about 5%. These results suggest that the presence of larger harmonics and a proportionally smaller DF peak is associated with a decreased probability of AF recurrence after CA.

Cardiovascular and renal protective effects of non-vitamin K antagonist oral anticoagulants and warfarin in patients with atrial fibrillation

Aim

Data on the use of non-vitamin K antagonist oral anticoagulants (NOACs) in relation to the risk of cardiovascular (CV) disease and renal protection among patients with atrial fibrillation (AF), are relatively sparse. We aimed to compare the effectiveness and safety of NOACs with those of warfarin for vascular protection in a large-scale, nationwide Asian population with AF.

Methods and results

Patients with AF who were prescribed oral anticoagulants according to the Korean Health Insurance Review and Assessment database between 2014 and 2017 were analyzed. The warfarin and NOAC groups were balanced using propensity score weighting. Clinical outcomes included ischemic stroke, myocardial infarction, angina pectoris, peripheral artery disease, chronic kidney disease (CKD), end-stage renal disease (ESRD), CV death, and all-cause death. NOAC use was associated with a lower risk of angina pectoris (HR, 0.79 [95% CI, 0.69–0.89] p<0.001), CKD stage 4 (HR, 0.5 [95% CI, 0.28–0.89], p = 0.02), and ESRD (HR, 0.15[95% CI, 0.08–0.32], p<0.001) than warfarin use. NOACs and warfarin did not significantly differ with respect to stroke reduction (HR, 1.05 [95% CI, 0.88–1.25], p = 0.19). NOAC use was associated with a lower risk of intracranial hemorrhage (HR, 0.6 [95% CI, 0.44–0.83], p = 0.0019), CV death (HR, 0.55 [95% CI, 0.43–0.70], p<0.001), and all-cause death (HR, 0.6 [95% CI, 0.52–0.69], p<0.001) than warfarin use.

Conclusion

NOACs were associated with a significantly lower risk of adverse CV and renovascular outcomes than warfarin in patients with AF.

The Application of the GP Model to Manage Controllable Risk Factors in Stroke Patients with Diabetes Can Effectively Improve the Prognosis and Reduce the Recurrence Rate

Objective

The aim of this study is to examine the impacts of general practice model (GP) on prognosis and recurrence of stroke patients with diabetes.

Methods

Ninety patients with stroke combined with diabetes mellitus admitted to our hospital from June 2019 to June 2020 were selected for the study and were randomly and equally divided into 45 cases each in the control and experimental groups for the prospective trial. The patients in the control group received routine treatment while those in the experimental group were treated with GP model. Comparison in treatment effects, patients satisfaction, psychological status, quality of life, glycosylated hemoglobin level, and stroke recurrence was carried out between the two groups.

Results

The experimental group showed markedly better treatment effects (P < 0.05), higher satisfaction degree (P < 0.05), higher HAD (P < 0.05), GQOLI-74 score (P < 0.05), and BI index (P < 0.05), lower level of glycosylated hemoglobin (P < 0.05), and much lower recurrence rate (P < 0.05), as compared to the control group.

Conclusion

The application of the GP model to manage controllable risk factors in stroke patients with diabetes can effectively improve the prognosis and reduce the recurrence rate, which is worthy of clinical application and promotion.

Integrated Medical Care and the Continuous 4C Nursing Model to Improve Nursing Quality and Clinical Treatment of Patients with Acute Stroke: Based on a Retrospective Case-Control Study

Objective

This research paper is based on a retrospective case-control study for exploring the effects of medical nursing integration and the continuous 4C nursing model to improve the clinical treatment and nursing quality of patients with acute stroke.

Method

For this purpose, a total of 313 patients with acute stroke, treated in our hospital from February 2020 to April 2021, were enrolled. They were divided into control and study groups with an even number of patients. The control group received integrated medical care number (N = 156), while the study group received integrated medical care and a continuous 4C nursing model (N = 157). In integrated medical care, the general data, self-nursing ability, degree of neurological impairment, Fugl–Meyer Assessment (FMA) score, Barthel index score, and quality of life score were compared between the two groups.

Result

The self-nursing concept, self-nursing responsibility, self-nursing skills, health knowledge, and total score of the patients in the study group were higher than those in the control group (P < 0.05). The neurological function scores of the study group were lower than those of the control group at 1, 3, and 6 months after discharge (P < 0.05). The scores of the study group were higher than those of the control group at 1, 3, and 6 months after discharge (P < 0.05). The Barthel index score of the study group was higher than that of the control group at 1, 3, and 6 months after discharge. The scores of physical function, psychological function, social function, and health self-cognition in the study group were lower than those in the control group (P < 0.05).

Conclusion

The application of integrated medical care and the continuous 4C nursing model for patients with acute stroke is beneficial to enhance the degree of neurological impairment of stroke patients, improve activities of daily life and motor function, and facilitate patients' quality of life. It is helpful to strengthen the attitude and feeling of cooperation between doctors and nurses, promote cooperation between doctors and nurses, reduce the defects of nursing work, heighten the quality of nursing, and achieve the requirement and goal of effectively promoting high-quality nursing.

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.