Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting hypertrophic cardiomyopathy (HCM) and dilated HCM (dHCM) remains uncertain in real-world applications. This retrospective study analyzed data from 19,170 patients (including 140 HCM or dHCM) in the Shinken Database (2010-2017). We evaluated the sensitivity, positive predictive rate (PPR), and F1 score of CNN-enhanced ECG in a ''basic diagnosis'' model (total disease label) and a ''comprehensive diagnosis'' model (including disease subtypes). Using all-lead ECG in the "basic diagnosis" model, we observed a sensitivity of 76%, PPR of 2.9%, and F1 score of 0.056. These metrics improved in cases with a diagnostic probability of ≥ 0.9 and left ventricular hypertrophy (LVH) on ECG: 100% sensitivity, 8.6% PPR, and 0.158 F1 score. The ''comprehensive diagnosis'' model further enhanced these figures to 100%, 13.0%, and 0.230, respectively. Performance was broadly consistent across CNN models using different lead configurations, particularly when including leads viewing the lateral walls. While the precision of CNN models in detecting HCM or dHCM in real-world settings is initially low, it improves by targeting specific patient groups and integrating disease subtype models. The use of ECGs with fewer leads, especially those involving the lateral walls, appears comparably effective.

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

Identification of patients with dilated phase of hypertrophic cardiomyopathy using a convolutional neural network applied to multiple, dual, and single lead electrocardiograms

Background

This study sought to develop an artificial intelligence-derived model to detect the dilated phase of hypertrophic cardiomyopathy (dHCM) on digital electrocardiography (ECG) and to evaluate the performance of the model applied to multiple-lead or single-lead ECG.

Methods

This is a retrospective analysis using a single-center prospective cohort study (Shinken Database 2010-2017, n = 19,170). After excluding those without a normal P wave on index ECG (n = 1,831) and adding dHCM patients registered before 2009 (n = 39), 17,378 digital ECGs were used. Totally 54 dHCM patients were identified of which 11 diagnosed at baseline, 4 developed during the time course, and 39 registered before 2009. The performance of the convolutional neural network (CNN) model for detecting dHCM was evaluated using eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs with the five-fold cross validation method.

Results

The area under the curve (AUC) of the CNN model to detect dHCM (n = 54) with eight-lead ECG was 0.929 (standard deviation [SD]: 0.025) and the odds ratio was 38.64 (SD 9.10). Among the single-lead and double-lead ECGs, the AUC was highest with the single lead of V5 (0.953 [SD: 0.038]), with an odds ratio of 58.89 (SD:68.56).

Conclusion

Compared with the performance of eight-lead ECG, the most similar performance was achieved with the model with a single V5 lead, suggesting that this single-lead ECG can be an alternative to eight-lead ECG for the screening of dHCM.

Association between serum alkaline phosphatase and cardiovascular events in patients with atrial fibrillation

High alkaline phosphatase (ALP) levels are reported to be associated with an increased risk of cardiovascular events in patients with chronic kidney disease (CKD). Given the pathological link with CKD, a similar relationship may exist in patients with atrial fibrillation (AF). We retrospectively evaluated 1,719 patients with AF and normal hepatic function who were registered in the Shinken Database between November 2011 and March 2017. Study patients were divided into three groups according to ALP value tertiles with cut-offs of 175 and 227 IU/L (normal range: 95-350 IU/L). Each group's incidence rate was recorded, and the risks of cardiovascular events and each component for patients in the middle and high ALP tertiles were compared with those in the low tertile and evaluated using Cox regression models. The additional predictive value of the high ALP tertile over the existing risk scores for the components of cardiovascular events was evaluated via receiver operating characteristic (ROC) curve analysis. During the median follow-up of 731 days (IQR: 444-1095 days), 137 cardiovascular events occurred, with incidence rates of 2.94%, 3.44%, and 6.19%/person-year for the low, middle, and high ALP tertiles, respectively. Of these cardiovascular events, heart failure had the highest incidence rates (1.34%, 1.89%, and 4.29%/person-year for the low, middle, and high ALP tertiles, respectively) and the incidence rates of the other components of cardiovascular event were similar in each ALP groups. Multivariate Cox regression analysis yielded hazard ratios of 1.22 (95% confidence interval [CI] 0.70-1.96) and 1.62 (95% CI 1.06-2.48) for cardiovascular events and 1.66 (95% CI 0.87-3.15) and 2.50 (95% CI 1.39-4.48) for heart failure admission in the middle and high ALP tertiles, respectively. By ROC curve analysis for heart failure admission showed that the high ALP tertile lacked significant additive predictive value over the existing risk scores. High serum ALP levels, even those in the normal range, were significantly associated with an increased risk of cardiovascular events, especially heart failure admission in patients with AF.

Cardiovascular events and artificial intelligence-predicted age using 12-lead electrocardiograms

Background

There is increasing evidence that 12-lead electrocardiograms (ECG) can be used to predict biological age, which is associated with cardiovascular events. However, the utility of artificial intelligence (AI)-predicted age using ECGs remains unclear.

Methods

Using a single-center database, we developed an AI-enabled ECG using 17 042 sinus rhythm ECGs (SR-ECG) to predict chronological age (CA) with a convolutional neural network that yields AI-predicted age. Using the 5-fold cross validation method, AI-predicted age deriving from the test dataset was yielded for all ECGs. The incidence by AgeDiff and the areas under the curve by receiver operating characteristic curve with AI-predicted age for cardiovascular events were analyzed.

Results

During the mean follow-up period of 460.1 days, there were 543 cardiovascular events. The annualized incidence of cardiovascular events was 2.24 %, 2.44 %, and 3.01 %/year for patients with AgeDiff < -6, -6 to ≤6, and >6 years, respectively. The areas under the curve for cardiovascular events with CA and AI-predicted age, respectively, were 0.673 and 0.679 (Delong's test, P = 0.388) for all patients; 0.642 and 0.700 (P = 0.003) for younger patients (CA < 60 years); and 0.584 and 0.570 (P = 0.268) for older patients (CA ≥ 60 years).

Conclusions

AI-predicted age using 12-lead ECGs showed superiority in predicting cardiovascular events compared with CA in younger patients, but not in older patients.

Prediction of recurrence after catheter ablation for atrial fibrillation using left atrial morphology on preprocedural computed tomography: application of radiomics

Background

Radiomics is a comprehensive analysis methodology of medical image and involves the extraction of numerous features from standard imaging. Its usefulness has been reported mainly in the field of cancer for diagnosis and prediction of prognosis. In the territory of cardiac imaging, several reports have investigated the utility of radiomics for classifying the risk of prognosis in coronary artery disease, and few practical applications have been reported for patients with atrial fibrillation (AF) who underwent pulmonary vein isolation (PVI). Although the left atrial morphology can affect the clinical course after the PVI procedure, it is unclear whether the radiomics feature values of the left atrial morphology on cardiac computed tomography (CT) is useful for predicting the AF recurrence after PVI.

Purpose

To predict the recurrence of AF after PVI using the radiomics feature values of the left atrial morphology on cardiac computed tomography (CT).

Methods

We analyzed 525 consecutive three-dimensional cardiac CT in patients with atrial fibrillation who underwent PVI from 2018 to 2019 in our institute. After marking the region of interest on left atrium (including the root of pulmonary veins) semiautomatically, 107 radiomics feature values were obtained by Python program. After excluding the parameters having collinearity or with low predictive capability for the recurrence of AF after PVI, 42 parameters were applied to the final prediction model. Two prediction models were constructed by multivariate Cox regression analysis and machine learning model by support vector machine algorithm.

Results

The area under the curve (AUC) for predicting the recurrence of AF was 0.815 for the multivariate Cox regression model and 0.826 for the machine learning model by support vector machine.

Conclusion

The radiomics feature values on preprocedural cardiac CT could be helpful for predicting the recurrence of AF after PVI. Since radiomics feature analysis yields a huge number of numerical values representing the left atrial morphology in a reproducible manner, it would provide a new direction to construct a good prediction model using machine learning including artificial intelligence out of a routine cardiac CT scan.

Funding Acknowledgement

Type of funding sources: None.

Natural history of severe aortic stenosis in elderly heart failure patients who declined transcatheter aortic valve implantation

Background

Transcatheter aortic valve implantation (TAVI) is an established minimally invasive treatment for elderly patients with severe aortic stenosis (AS). Although it is a relatively minimally invasive procedure, many elderly patients cannot undergo TAVI due to advanced age, advanced dementia, significantly impaired ADL, or because the patient or family does not wish to undergo TAVI. In general, severe AS patients died 5 years after an angina attack, 3 years after syncope symptoms, and 2 years after heart failure symptoms. However, few studies have investigated the prognosis of elderly patients with severe AS who declined TAVI.

Purpose

This study aimed to comprehend the natural history of elderly severe AS patients who declined aortic valve intervention and identify risk factors for death in the TAVI era.

Methods

We retrospectively reviewed 2,786 echocardiographic data from our hospital between 2017 and 2021. We identified 504 patients with AS in the echocardiography database. The severe AS was defined by the following echocardiography parameters: peak aortic flow velocity ≥4.0 m/s, aortic valve mean gradient ≥40 mmHg, aortic valve area (AVA)&lt;1.0 cm2, or AVA index&lt;0.6 cm2/m2. Patients with mild and moderate AS and patients who underwent surgical or transcatheter aortic valve intervention were excluded. Eventually, 82 patients fulfilled the study criteria and were analyzed in this study. Clinical events were investigated, and Cox proportional hazards analysis was performed to elucidate risk factors for mortality.

Results

The mean age was 89±6 years, and 63 (77%) were women. AHA/ACC classification was Stage A/B/C/D: 0%/52.4%/39.0%/8.5%. As event occurrence, we detected 34 (41.5%) all-cause deaths, 41 (50.0%) major adverse cardiovascular events, 32 (39.0%) development of heart failure, 13 (15.9%) chest pain attacks, and 3 (3.7%) syncope. The time from diagnosis of severe AS to all-cause death was 227±273 days, and the 1-, 2-, and 3-year survival rates were 61.5%, 46.2%, and 36.9%, respectively. Of the heart failure-onset population, 16 (50.0%) were found to have severe AS on initial heart failure admission. The incidence of heart failure at 1, 2, and 3 years after diagnosis of severe AS was 60.1%, 56.7%, and 45.4%, respectively. The time from heart failure to death was 107±147 days. Cox proportional hazards analysis adjusted with age, sex, comorbidities revealed that dementia (HR 4.5, P=0.014) and Katz index &lt;5 (HR7.8, P=0.009) were independent risk factors for mortality.

Conclusions

Compared to the comprehension of the natural history of AS patients, the prognosis in severe AS patients who declined TAVI was extremely poor. Although the elderly patients did not have a high rate of angina attacks or syncope symptoms, probably due to their reduced ADL, the results suggest that dementia prevention and maintenance of exercise tolerance are essential since dementia and reduced ADL (Katz&lt;5) are independent risk factors for mortality.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): Clinical Research Promotion Foundation Reiwa 2 Michinari Hieda

Prediction of persistent form of atrial fibrillation using left atrial morphology on preprocedural computed tomography: application of radiomics

Background

Radiomics is a comprehensive analysis methodology of medical image and involves the extraction of numerous features from standard imaging. Its usefulness has been reported mainly in the field of cancer for diagnosis and prediction of prognosis. In the territory of cardiac imaging, several reports have investigated the utility of radiomics for classifying the risk of prognosis in coronary artery disease, and few practical applications have been reported for patients with atrial fibrillation (AF) who underwent catheter ablation (CA).

Purpose

The objective of this study was to evaluate the utility of radiomics analysis applying to the preprocedural cardiac computerized tomography (CT) in AF patients.

Methods

We analyzed 525 consecutive three-dimensional CT in patients with AF who underwent CA. After marking the region of interest on left atrium (LA) (including the root of pulmonary veins) semiautomatically, 107 radiomics feature values were obtained by Python program. We calculated the amount of representative statistics for each radiomics feature for prediction of persistent AF (PeAF) (Wald statistic in logistic regression analysis) and LA diameter (LAD) (coefficient correlation), respectively. To compare the distribution of the two statistics, the relative importance (calculated as the ratio of statistic to the maximum statistics among 107 radiomics features [%]) was calculated for each statistic. Further, we compared the area under the curve (AUC) in receiver operation characteristic (ROC) curve analysis for predicting PeAF between radiomics features (multivariate model) and LAD (single parameter).

Results

In 525 study patients (age 63±10 years old and male 80%), 253 (48%) were PeAF and remaining were paroxysmal AF (PAF). LAD was 43±6 mm and 38±6 mm in patients with PeAF and PAF, respectively. The relative importance of the two statistics (Wald statistic for PeAF and coefficient correlation for LAD) of 107 radiomics features are displayed in Figure 1, which shows similar distribution of two statistics. It means the close relationship between LA morphology and the form of PeAF in AF patients and the radiomics features possibly well explain the relationship. In Figure 2, the predictive capability for PeAF was compared between radiomics feature values and LAD, where the AUC was 0.85 (95% confidence interval [CI], 0.82–0.88) and 0.73 (95% CI, 0.69–0.78) for radiomics feature values and LAD, respectively (Delong test, P&lt;0.001).

Conclusion

We applied the radiomics features for the evaluation of LA morphology. The predictive capability for PeAF in the prediction model with the radiomics feature values was much better than that with LAD alone. Since radiomics feature analysis yields a huge number of numerical values representing the LA morphology in a reproducible manner, it would provide a new direction to construct a good prediction model using machine learning including artificial intelligence out of a routine cardiac CT scan.

Funding Acknowledgement

Type of funding sources: None.

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm

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High performance of AI algorithm to detect AF using SR-ECG was confirmed in patients without structural heart disease.

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The performance of AI-enabled ECG to detect AF was high especially when the algorithm included SR-ECG taken after the index AF-ECG.

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A similar tendency was observed when the performance was tested in patients with structural heart diseases.

Background

This study aimed to increase the knowledge on how to enhance the performance of artificial intelligence (AI)-enabled electrocardiography (ECG) to detect atrial fibrillation (AF) on sinus rhythm ECG (SR-ECG).

Methods

It is a retrospective analysis of a single-center, prospective cohort study (Shinken Database). We developed AI-enabled ECG using SR-ECG to predict AF with a convolutional neural network (CNN). Among new patients in our hospital (n = 19,170), 276 AF label (having ECG on AF [AF-ECG] in the ECG database) and 1896 SR label with following three conditions were identified in the derivation dataset: (1) without structural heart disease, (2) in AF label, SR-ECG was taken within 31 days from AF-ECG, and (3) in SR label, follow-up ≥ 1,095 days. Three patterns of AF label were analyzed by timing of SR-ECG to AF-ECG (before/after/before-or-after, CNN algorithm 1 to 3). The outcome measurement was area under the curve (AUC), sensitivity, specificity, accuracy, and F1 score. As an extra-testing dataset, the performance of AI-enabled ECG was tested in patients with structural heart disease.

Results

The AUC of AI-enabled ECG with CNN algorithm 1, 2, and 3 in the derivation dataset was 0.83, 0.88, and 0.86, respectively; when tested in patients with structural heart disease, 0.75, 0.81, and 0.78, respectively.

Conclusion

We confirmed high performance of AI-enabled ECG to detect AF on SR-ECG in patients without structural heart disease. The performance enhanced especially when SR-ECG after index AF-ECG was included in the algorithm, which was consistent in patients with structural heart disease.

Identifying patients with atrial fibrillation during sinus rhythm on ECG: confirming the utility of artificial intelligence algorithm in a small-scale cohort without structural heart diseases

Background

Detection of atrial fibrillation (AF) out of electrocardiograph (ECG) on sinus rhythm (SR) using artificial intelligence (AI) algorithm has been widely studied within recent couple of years. Generally, it is believed that a huge number of ECGs are necessary for developing an AI-enabled ECG to be adequate to correspond to a lot of minor variations of ECGs. For example, structural heart diseases have typical ECG characteristics, but they could be a noise for the purpose of detecting the small signs of electrocardiographic signature of AF. We hypothesized that when patients with structural heart diseases are excluded, AI-enabled ECG for identifying patients with AF can be developed with a small number of ECGs.

Methods

We developed an AI-enabled ECG using a convolutional neural network to detect the electrocardiographic signature of AF present during normal sinus rhythm (NSR) using a digital, standard 10-second, 12-lead ECGs. We included all patients who newly visited the Cardiovascular Institute with at least one NSR ECG between Feb 1, 2010, and March 31, 2018. We classified patients with at least one ECG with a rhythm of AF as positive for AF (AF label) and others as negative for AF (SR label). We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operating characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the sensitivity, specificity, F1 score, and accuracy with two-sided 95% confidence intervals (CIs).

Results

We totally included 19170 patients with 12-lead ECG. After excluding patients with structural heart diseases, 12825 patients with NSR ECGs at the initial visit were identified (1262 were clinically diagnosed as AF anytime during the time course and 11563 were never diagnosed as AF). Of 11563 non-AF patients, 1818 patients who were followed over 1095 days were selected for the analysis with the SR label, to secure the robustness for maintaining SR. Of 1262 AF patients, 251 patients were selected for the analysis with the AF label, of whom a NSR ECG within 31 days before or after the index AF ECG (the first AF ECG during the time course) could be obtained. In the patients with AF label, the NSR ECG of which the date was the nearest to the index AF ECG was selected for the analysis. The AI-enabled ECG showed an AUC of 0.88 (0.84–0.92) with sensitivity 81% (72–88), specificity 80% (77–83), F1 score 50% (43–57), and overall accuracy 80% (78–83).

Conclusion

An AI-enabled ECG acquired during NSR allowed identification of patients with AF in a small population without structural heart diseases.

Funding Acknowledgement

Type of funding sources: None.

Identifying risk patterns in older adults with atrial fibrillation by hierarchical cluster analysis: A retrospective approach based on the risk probability for clinical events

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In older AF patients, representative AF-related outcomes compete, causing difficulty in decision making.

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We proposed cluster analysis using risk probability for four AF-related outcomes.

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Older adults with AF were classified into 3 clusters.

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The clusters could possibly identify older adults with AF with good/poor responses to AF-related treatment.

Background

Older adults with atrial fibrillation (AF) have highly diverse risk levels for mortality, heart failure (HF), thromboembolism (TE), and major bleeding (MB), thus an integrated risk-pattern algorithm is warranted.

Methods

We analyzed 573 AF patients aged ≥ 75 years from our single-center cohort (Shinken Database 2010–2018). The 3-year risk scores (risk probability) for mortality (M-score), HF (HF-score), TE (TE-score), and MB (MB-score) were estimated for each patient by logistic regression analysis. Using the four risk scores, cluster analysis was performed with Ward’s linkage hierarchical algorithm.

Results

Three clusters were identified: Clusters 1 (n = 429, 74%), 2 (n = 24, 5%), and 3 (n = 120, 21%). The clusters were characterized as standard risk (Cluster 1), high TE- and MB-risk (Cluster 2), and high M- and HF-risk (Cluster 3). Oral anticoagulants were prescribed for over 80% of the patients in each cluster. Catheter ablation for AF was performed only in Cluster 1 (8.9%). Compared with Cluster 1, Cluster 2 was more closely associated with males, asymptomatic AF, history of cerebral infarction or transient ischemic attack, history of intracranial hemorrhage, high HAS-BLED score (≥3), and low body mass index (<18.0 kg/m²). Cluster 3 was more closely associated with old age, heart failure, and low estimated creatinine clearance (<30 mL/min).

Conclusion

The cluster analysis identified those at a high risk for all-cause death and HF or a high risk for TE and MB and could support decision making in older adults with AF.

Current Status of Catheter Ablation for Atrial Fibrillation in Japan

Catheter ablation for atrial fibrillation (AF) has been an established and frequently utilized approach in a variety of clinical settings. Nevertheless, real-world data about the clinical course of AF patients after initial catheter ablation remain limited, and these are mainly derived from particular registries or selected high-volume centers.In this study, we used health check-ups and insurance claims database from a Japanese insurance organization. The study population was comprised of 1777 patients who underwent catheter ablation for AF before June 2016. During the 3-year follow-up period, 396 (22.3%) patients underwent at least one repeated AF ablation, while 74 (4.2%) underwent two or more repeated ablations. In multivariate Cox regression analysis, longer time after AF diagnosis (7-11 months and ≥12 months versus 1-6 months) (HR, 1.05; 95% CI, 1.01-1.08 and HR, 1.04; 95% CI 1.02-1.07) was independently associated with repeated ablation. The discontinuation rates of OACs and AADs after the first ablation were 26.7% and 63.0% at 3 months and 75.2% and 89.1% at 1 year after the initial ablation, respectively. The former was independently associated with shorter time after AF diagnosis and lower diastolic blood pressure, whereas the latter was independently associated with older age, smaller CHADS₂ score, and shorter time after AF diagnosis.We presented real-world data regarding the clinical course of young Japanese AF patients after initial catheter ablation based on a claims database in Japan.

Association between number of medications and mortality among older adult patients in a specialized cardiology hospital

AIM

Although polypharmacy has been associated with poor clinical outcomes, whether taking an increased number of medications is harmful or beneficial for older adult patients treated for cardiovascular diseases might require further discussion.

METHODS

We analyzed data of 2089 patients aged ≥75 years in a single hospital-based cohort. The study population was divided into three groups according to the tertiles of the number of medications at baseline: <3 (n = 647), 3-7 (n = 707) and ≥8 (n = 735).

RESULTS

The cumulative incidences of all-cause death at 3 years among patients taking less than three, three to seven and eight or more medications were 3.7%, 4.1% and 7.8%, respectively (log-rank test P = 0.015). In a Cox regression analysis, taking eight or more total medications (vs 0-2) was independently associated with all-cause death (hazard ratio 1.67, 95% CI 1.01-2.78). For predicting mortality using the number of medications, the maximum Youden Index was 7. In subgroups with certain heart diseases, no regular tendency of an increase in the risk of all-cause death was observed with an increase in the number of medications.

CONCLUSIONS

The number of medications taken was independently associated with mortality among older adult patients, with a relatively high cut-off point. This association was not observed in patients with certain heart diseases, possibly indicating the merit - rather than the harm - of medical treatment in the cardiovascular field. Geriatr Gerontol Int 2021; 21: 985-995.

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

Background

There is a well-established relationship between 12-lead electrocardiogram (ECG) and age and mortality. Furthermore, there is increasing evidence that ECG can be used to predict biological age. However, the utility of biological age from ECG for predicting mortality remains unclear.

Methods

This was a single-center cohort study from a cardiology specialized hospital. A total of 19,170 patients registered in this study from February 2010 to March 2018. ECG was analyzed in a final 12,837 patients after excluding those with structural heart disease or with pacing beats, atrial or ventricular tachyarrhythmia, or an indeterminate axis (R axis > 180°) on index ECG. The models for biological age were developed by principal component analysis (BA) and the Klemera and Doubal’s method (not adjusted for age [BA_E] and adjusted for age [BA_EC]) using 438 ECG parameters. The predictive capability for all-cause death and cardiovascular death by chronological age (CA) and biological age using the three algorithms were evaluated by receiver operating characteristic analysis.

Results

During the mean follow-up period of 320.4 days, there were 55 all-cause deaths and 23 cardiovascular deaths. The predictive capabilities for all-cause death by BA, BA_E, and BA_EC using area under the curves were 0.731, 0.657, and 0.685, respectively, which were comparable to 0.725 for CA (p = 0.760, 0.141, and 0.308, respectively). The predictive capabilities for cardiovascular death by BA, BA_E, and BA_EC were 0.682, 0.685, and 0.692, respectively, which were also comparable to 0.674 for CA (p = 0.775, 0.839, and 0.706, respectively). In patients aged 60–74 years old, the area under the curves for all-cause death by BA, BA_E, and BA_EC were 0.619, 0.702, and 0.697, respectively, which tended to be or were significantly higher than 0.482 for CA (p = 0.064, 0.006, and 0.005, respectively).

Conclusion

Biological age by 12-lead ECG showed a similar predictive capability for mortality compared to CA among total patients, but partially showed a significant increase in predictive capability among patients aged 60–74 years old.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02391-8.

Relationship between number of medications and incidence of falls or bone fracture in elderly patients with non-valvular atrial fibrillation: Shinken database analysis

AIM

Polypharmacy is known to be a risk factor for falls or bone fracture (F/F) in elderly patients. However, this relationship is not fully described in patients with non-valvular atrial fibrillation (NVAF), for which F/F may lead to serious clinical outcomes, including major bleeding.

METHODS

We analyzed 509 elderly (aged ≥75 years) patients with NVAF who had recently visited a hospital specializing in cardiology, of which 272 patients had paroxysmal atrial fibrillation (PAF) and 237 had persistent/permanent atrial fibrillation (PeAF). Patients were divided into four groups according to the number of medications: ≤3, 4-6, 7-9, and ≥10. The relationship between the number of medications and incidence rate of F/F in AF patients was analyzed. In addition, this relationship was analyzed in patients with each AF type.

RESULTS

Cumulative incidence of F/F at 3 years in the respective categories was 3.7%, 5.4%, 4.3% and 5.7% for PAF, and 5.2%, 7.5%, 7.8% and 25.0% for PeAF (log-rank test, P = 0.930 and 0.003, respectively). In a multivariable model, patients with ≥10 medications showed a significantly higher risk for F/F compared with those with ≤3 medications as reference only in PeAF (adjusted hazard ratio 4.82, 95%CI 1.42-16.33), without significant interaction (P = 0.081).

CONCLUSIONS

Elderly NVAF patients using ≥10 medications showed a higher risk for F/F. In subgroup analysis, this association was observed only in patients with PeAF, although there was no significant interaction between number of medications and AF type. Geriatr Gerontol Int 2021; 21: 802-809.

Current status of oral anticoagulant adherence in Japanese patients with atrial fibrillation: A claims database analysis

BACKGROUND

Direct oral anticoagulants (DOACs) are widely used for stroke prevention in patients with non-valvular atrial fibrillation (NVAF). We investigated the adherence of DOACs for years known to be associated with the effectiveness in Japanese NVAF patients, using a claim database.

METHOD

We performed a retrospective evaluation of NVAF patients in a claims database in Japan, who initiated dabigatran, rivaroxaban, and apixaban between April 2011 and June 2016. Drug persistence was assessed by Kaplan-Meier method for the initially-prescribed DOAC and all DOACs including switched ones. Proportion of days covered (PDC) was also evaluated in patients with persistent prescription and compared among DOACs.

RESULTS

In the total of 671 patients, rivaroxaban (47%) was more prescribed than dabigatran (28%) and apixaban (25%). Drug persistence at 3 years was higher in rivaroxaban (69%) than dabigatran (57%) and apixaban (67%). Including switching to other DOACs, persistence of DOACs was 72% at 3 years without significant differences between index-DOACs. In multivariate Cox regression analysis, absence of hypertension, and prior history of cancer were significantly associated with the drug discontinuation of all DOACs. The mean PDC was ~ 95% and the frequency of high-adherent patients (PDC ≥0.80) was more than 90%, which similarly persisted at 3 years and showed no significant differences between index DOACs.

CONCLUSION

In a real-world Japanese claim data analysis, about 70% of patients under DOAC showed persistence with anticoagulation therapy at 3 years. High adherence to DOACs assessed by PDC (over 90%) persisted to 3 years regardless of the types of DOACs.

Relationship between resting 12-lead electrocardiogram and all-cause death in patients without structural heart disease: Shinken Database analysis

Background

Resting 12-lead electrocardiography is widely used for the detection of cardiac diseases. Electrocardiogram readings have been reported to be affected by aging and, therefore, can predict patient mortality.

Methods

A total of 12,837 patients without structural heart disease who underwent electrocardiography at baseline were identified in the Shinken Database among those registered between 2010 and 2017 (n = 19,170). Using 438 electrocardiography parameters, predictive models for all-cause death and cardiovascular (CV) death were developed by a support vector machine (SVM) algorithm.

Results

During the observation period of 320.4 days, 55 all-cause deaths and 23 CV deaths were observed. In the SVM prediction model, the mean c-statistics of 10 cross-validation models with training and testing datasets were 0.881 ± 0.027 and 0.927 ± 0.101, respectively, for all-cause death and 0.862 ± 0.029 and 0.897 ± 0.069, respectively for CV death. For both all-cause and CV death, high values of permutation importance in the ECG parameters were concentrated in the QRS complex and ST-T segment.

Conclusions

Parameters acquired from 12-lead resting electrocardiography could be applied to predict the all-cause and CV deaths of patients without structural heart disease. The ECG parameters that greatly contributed to the prediction were concentrated in the QRS complex and ST-T segment.

Decline in eGFR over time and incidence of cardiovascular events: Shinken database analysis

BACKGROUND

It has been reported that a large decline in estimated glomerular filtration rate (eGFR) over time is associated with increased incidence of cardiovascular disease. We investigated whether this association differs according to the baseline eGFR.

METHODS

A total of 4666 patients (male 71%) with measurements of eGFR at both baseline and 1 year and that had no cardiovascular events at 1-year follow-up were retrieved from the Shinken Database between June 2004 and March 2015. The study population was divided into three groups by baseline eGFR (mL/min/1.73 m²): high (≥60, n = 1650), intermediate (45-59, n = 1947), and low (<45, n = 1069) eGFR groups. Each eGFR group was further divided into two groups by eGFR slope (change at 1 year, <-10 and ≥-10 mL/min/1.73 m²). The patient characteristics and the incidences of cardiovascular events within 3 years (after 1-year follow-up) were compared between the negatively large eGFR slope (<-10) and others (≥-10) in each eGFR group.

RESULTS

A total of 187 cardiovascular events occurred during the mean follow-up of 2.8 ± 0.6 years. The adjusted hazard ratios of eGFR slope (<-10 with reference to ≥-10) were 2.37 (95% CI, 1.28-4.40), 3.10 (95% CI, 1.78-5.40), and 2.66 (95% CI, 1.15-6.13) in the high, middle, and low eGFR groups, respectively. Similar results were found in patients with structural heart disease, but not in those without.

CONCLUSIONS

Decline in eGFR was associated with an increase in cardiovascular events, and this effect was consistent regardless of the baseline eGFR.

A novel and simple scoring system for assessing the indication for catheter ablation in patients with atrial fibrillation: The HEAL-AF Score

INTRODUCTION

A scoring system to determine indications for catheter ablation (CA) in atrial fibrillation (AF) is desired.

METHODS AND RESULTS

Among 2898 consecutive patients with AF, CA was performed in 938 (32.4%). A new HEAL-AF score has been developed by six variables, all of which were independently associated with CA by multivariate analysis and for each 1 point was assigned: heart failure ≥ NYHA II, elderly patients (age ≥75 years), asymptomatic AF, long-standing persistent AF, atrial dilation (left atrial diameter ≥ 50 mm), and female sex. Low HEAL-AF score was associated with high incidence of CA performance (52.0% for 0, 36.5% for 1, 15.1% for 2, and 5.6% for ≥ 3) and the predictive capability of this score by AUC of ROC curve was 0.720 (95% CI 0.701-0.739, P < .001). The rates of freedom from AF/AT recurrence were 73.2% in HEAL-AF score 0, 71.0% in 1, 60.0% in 2, and 50.0% in ≥ 3 (log-rank test, P = .004). HEAL-AF score 2 and ≥ 3 were significantly associated with recurrence of atrial tachyarrhythmia as compared with HEAL-AF 0 (HR 1.755, P = .002, and HR 2.211, P = .007, respectively).

CONCLUSIONS

A new HEAL-AF score was associated with patient indication for and the recurrence of atrial tachyarrhythmia after CA in patients with AF. Prescription of CA should be considered carefully in AF patients with HEAL-AF score of 2 and ≥ 3.

Prediction of atrial fibrillation by 12-lead electrocardiogram parameters in patients without structural heart disease

Background

Recently, the analysis of electrocardiogram (ECG) waveform by artificial intelligence has been reported to pick out those who have atrial fibrillation (AF) or have a high potential of developing AF, which, however, cannot explain the mechanisms or algorisms for the prediction from its nature.

Purpose

The purpose of this study is to conduct a comprehensive analysis to investigate the difference of weighting in predicting capability for AF among hundreds of automatically-measured ECG parameters using a single ECG at sinus rhythm.

Methods and results

Out of Shinken Database 2010–2017 (n=19170), 12825 patients were extracted, where those with ECG showing AF rhythm at the initial visit (including all persistent/permanent AF and a part of paroxysmal AF) and those with structural heart diseases were excluded. Out of 639 automatically-measured ECG parameters in MUSE data management system (GE Healthcare, USA), 438 were used. [Analysis 1] A predicting model for paroxysmal AF were determined by logistic regression analysis (Total, n=12825; paroxysmal AF, n=1138), showing a high predictive capability (AUC = 0.780, p&lt;0.001). In this model, the relative contribution of ECG parameters (by coefficient of determination) according to the time phase were P:72.4%, QRS:32.7%, and ST-T:13.7%, respectively (Figure A). [Analysis 2] Excluding AF at baseline, a predicting model for new-developed AF were determined by Cox regression analysis (Total, n=11687; new-developed AF, n=87), showing a high predictive capability (AUC = 0.887, p&lt;0.001). In this model, the relative contribution of parameters (by log likelihood) according to the time phase were P:40.8%, QRS:42.5%, and ST-T:24.9%, respectively (Figure B).

Conclusions

We determined ECG parameters that potentially contribute to picking up existing AF or predicting future development of AF, where the measurement of P wave strongly contributed in the former whereas all time phases were similarly important in the latter.

Weighting of parameters to predict AF

Funding Acknowledgement

Type of funding source: Private hospital(s). Main funding source(s): Self funding of the institute

Heart rate recovery after exercise as a prognostic predictor in patients with atrial fibrillation

Background

A delayed heart rate recovery (HRR) after exercise is related to mortality in sinus rhythm. This study aimed to investigate this concept can be applied to patients with atrial fibrillation (AF).

Methods

We analyzed 483 patients with AF (mean 65 years, male 74%). HRR integral was calculated by integrating the difference in HR in every 3 second between the end of exercise and the specified time after the exercise (30, 60, 120 and 180 seconds). After evaluating the prognostic power of each HRR integral, we selected HRR integral of 180 seconds (180HRR-integral).

Results

We divided the patients into two groups using median value of 180HRR-integral. All-cause mortality, the incidence of cardiovascular events and heart failure events were higher in the poor 180HRR-integral. After adjustment for covariates, the impact of the high 180HRR-integral for all-cause mortality was 3.15 (p=0.057), 1.77 for cardiovascular events (p=0.067) and 1.28 for heart failure events (p=0.519).

Conclusion

Poor HRR was associated with worse prognosis in patients with AF.

Funding Acknowledgement

Type of funding source: None

Seasonal Variations in the Incidence of Ischemic Stroke, Extracranial and Intracranial Hemorrhage in Atrial Fibrillation Patients

BACKGROUND

Ischemic stroke (IS) and major bleeding, which are serious adverse events in patients with atrial fibrillation (AF), could have seasonal variations, but there are few reports.Methods and Results:In the Shinken Database 2004-2016 (n=22,018), 3,581 AF patients (average age, 63.5 years; 2,656 men, 74.2%; 1,388 persistent AF, 38.8%) were identified. Median CHADS₂and HAS-BLED scores were both 1 point. Oral anticoagulants were prescribed for 2,082 (58.1%) patients (warfarin, 1,214; direct oral anticoagulants [DOACs], 868). Incidence and observation period (maximum 3 years) of IS, extracranial hemorrhage (ECH), and intracranial hemorrhage (ICH) were counted separately for the northern hemisphere seasons. During the mean follow-up period of 2.4 years, there were totals of 90 IS, 73 ECH, and 33 ICH cases. The respective incidence rates per 1,000 patient-years in spring, summer, autumn, and winter were 8.5, 8.8, 7.5, and 16.8 for IS, 7.2, 9.7, 3.8, and 13.1 for ECH, and 2.7, 1.9, 3.8, and 7.0 for ICH. The number of patients with DOACs relatively increased among those with ECH in summer.

CONCLUSIONS

Significant seasonal variations were observed for IS, ECH, and ICH events in AF patients, and were consistently the highest in winter. A small peak of ECH was observed in summer, which seemed, in part, to be related to increased DOAC use.

Exploratory Analysis of Circulating miRNA Signatures in Atrial Fibrillation Patients Determining Potential Biomarkers to Support Decision-Making in Anticoagulation and Catheter Ablation

Novel biomarkers are desired to improve risk management for patients with atrial fibrillation (AF). We measured 179 plasma miRNAs in 83 AF patients using multiplex qRT-PCR. Plasma levels of eight (i.e., hsa-miR-22-3p, hsa-miR-128-3p, hsa-miR-130a-3p, hsa-miR-140-5p, hsa-miR-143-3p, hsa-miR-148b-3p, hsa-miR-497-5p, hsa-miR-652-3p) and three (i.e., hsa-miR-144-5p, hsa-miR-192-5p, hsa-miR-194-5p) miRNAs showed positive and negative correlations with CHA₂DS₂-VASc scores, respectively, which also showed negative and positive correlations with catheter ablation (CA) procedure, respectively, within the follow-up observation period up to 6-month after enrollment. These 11 miRNAs were functionally associated with TGF-β signaling and androgen signaling based on pathway enrichment analysis. Seven of possible target genes of these miRNAs, namely TGFBR1, PDGFRA, ZEB1, IGFR1, BCL2, MAPK1 and DICER1 were found to be modulated by more than four miRNAs of the eleven. Of them, TGFBR1, PDGFRA, ZEB1 and BCL2 are reported to exert pro-fibrotic functions, suggesting that dysregulations of these eleven miRNAs may reflect pro-fibrotic condition in the high-risk patients. Although highly speculative, these miRNAs may potentially serve as potential biomarkers, providing mechanistic and quantitative information for pathophysiology in daily clinical practice with AF such as possible pro-fibrotic state in left atrium, which would enhance the risk of stroke and reduce the preference for performing CA.

Association between plasma concentration of edoxaban determined by direct and indirect methods in Japanese patients with non-valvular atrial fibrillation (CVI ARO 7)

Direct oral anticoagulants, including edoxaban, primarily do not need routine monitoring of the anticoagulant effect. However, extremely high/low plasma concentrations of edoxaban (PC-Ed) should be properly evaluated, especially when patients under anticoagulation therapy are at an emergency state. For this purpose, PC-Ed determined by an anti-Xa assay (indirect PC-Ed) is more convenient and, therefore, more useful compared with PC-Ed determined by an LC-MS/MS (direct PC-Ed) in daily clinical practice. Consecutive 97 patients with non-valvular atrial fibrillation (NVAF) under edoxaban therapy were evaluated, in whom edoxaban 60/30 mg doses were prescribed for 48/49 patients, 71 (73.2%) were men, and the average age was 69 years. CHADS2 score 0, 1, and ≥ 2 were 26.8%, 44.3%, and 28.9%, while CHA2DS2-VASc score 0, 1, and ≥ 2 were 14.4%, 16.5%, and 69.1%, respectively. Median values of direct and indirect PC-Ed by LC-MS/MS and anti-Xa assay were 187.1 and 176.1 ng/mL at peak (2-4 h post-dose) and 14.4 and 17.5 ng/mL at trough (pre-dose), respectively. The PC-Ed at peak and trough by two methods were significantly correlated, and the correlation coefficients were r = 0.973 and 0.963 (both, p < 0.0001), respectively. By a Bland-Altman plot, mean differences between the direct and indirect PC-Ed [lower to upper percent limit of agreement] were - 4.87 [- 46.71 to 36.98] and 4.66 [- 1.37 to 10.69] ng/mL at peak and trough, respectively. Moreover, mean % error for difference between the direct and indirect PC-Ed [lower to upper percent limit of agreement] was - 1.22 [- 20.59 to 18.14] and 31.75 [- 14.03 to 77.53] % at peak and trough, respectively, where the % error extremely increased around the lower limit of detection (LLOD) in the anti-Xa assay. Strong similarity was observed between the direct and indirect PC-Ed, especially at peak. The indirect PC-Ed was higher than the direct PC-Ed, especially around the LLOD, suggesting the need for caution when we use the anti-Xa assay for measurement of trough PC-Ed (UMIN 000032492).

Association between bisoprolol plasma concentration and worsening of heart failure: (CVI ARO 6)

BACKGROUND

Although bisoprolol has been established to prevent heart failure (HF), finding the optimal dose remains a challenge. It is crucial to understand the distribution of bisoprolol plasma concentration (Bis-PC) and association with outcomes.

METHODS

This was a single-center observational study in 114 HF patients under once-daily bisoprolol. After obtaining trough Bis-PC, patients were followed-up for 1 year. The primary endpoint was worsening of HF. Patients were divided according to the tertiles of Bis-PC.

RESULTS

In multivariate logistic regression analysis, independent predictors of high Bis-PC (1st tertile: ≥ 5.38 ng/mL) were age, eGFR, and bisoprolol dose. The cumulative incidence rates of the primary endpoint were 10.5%/13.2%/26.3% in low/middle/high Bis-PC categories, respectively (log rank test, p = 0.087). Bis-PC was independently associated with the primary endpoint (hazard ratio [HR], 1.19 [per ng/mL], 95% CI 1.03-1.36). In subgroups, high Bis-PC was independently associated with the primary endpoint in elderly (HR 6.32, 95% CI 1.34-29.83) and HF with preserved ejection fraction (HFpEF) (HR 3.52, 95% CI 1.06-11.70).

CONCLUSIONS

Bis-PC was increased by age and renal dysfunction, and high Bis-PC was associated with worsening of HF in elderly and HFpEF patients. Care should be taken to avoid overdose.

A Simple Formula for Predicting the Maintenance Dose of Warfarin with Reference to the Initial Response to Low Dosing at an Outpatient Clinic

Objective The pharmacodynamic effect of warfarin varies among individuals, and its maintenance dose is widely distributed. Although many formulae for predicting the maintenance dose of warfarin have been developed, most of them are complex and not in practical use. Methods and Materials Among 12,738 new patients visiting the Cardiovascular Institute between 2004 and 2009, we identified 127 patients (66.6±8.8 years, 89 men) with atrial fibrillation for whom warfarin was newly started with an initial dose of 2 mg/day and the international normalized ratio (INR) at 1 year after warfarin was started was within the therapeutic range. The prediction models for the maintenance dose were developed by an exponential equation and a first-order equation. Results The initial response of the INR to the dose of 2 mg/day (initial INR) ranged from 1.00-3.24 (mean 1.43), while the maintenance dose of warfarin ranged from 0.5-14 mg (mean 3.8 mg). The maintenance dose showed an exponential correlation to the initial INR: (predicted maintenance dose) =5.522× (initial INR) ^-1.556 (R²=0.795, p<0.001). Excluding the patients with a poor response to the initial dose (initial INR <1.1, n=32) permitted a simple correlation with a first-order approximation: (predicted maintenance dose) =-2.009× (initial INR) +6.172 (R²=0.706, p<0.001). Conclusion We developed a simple formula for predicting the maintenance dose of warfarin using the initial response of the INR to low-dose warfarin.

Responses of prothrombin time and activated partial thromboplastin time to edoxaban in Japanese patients with non-valvular atrial fibrillation: characteristics of representative reagents in Japan (CVI ARO 7)

The aims of this study were to determine the distribution of plasma concentration of edoxaban (PC-Ed) with their 90% interval (on therapy range) and its correlation with anticoagulation markers in patients with non-valvular atrial fibrillation (NVAF). Consecutive 97 NVAF patients under edoxaban therapy were evaluated (60/30 mg dose, n = 48/49; men/women, n = 71/26; age, 69 years). CHADS2 score 0, 1, and ≥ 2 were 27%, 44%, and 29%, respectively. The mean (90% interval) of PC-Ed by LC-MS/MS was 194.3 (49.4-345.3) and 17.0 (4.8-40.7) ng/mL at peak (2-4 h post-dose) and trough (pre-dose), respectively. Correlation of prothrombin time (PT) with PC-Ed was higher than that of activated partial thromboplastin time (aPTT). Among 6 PT reagents, Coagupia PT-N and Simplastin Excel S (both PT reagents) showed the highest predictive capability for the upper outlier of PC-Ed at peak and trough. Among 4 aPTT reagents, only Thrombocheck APTT measured at peak had a significant predictive capability. When using PT reagents, both peak and trough sampling showed a similar predictive capability for the upper outliers of PC-Ed with a high sensitivity, but a relatively low specificity. We demonstrated the distributions of plasma concentration, PT with 6 reagents, and aPTT with 4 reagents under edoxaban therapy in Japanese patients with NVAF, showing their 90% intervals. For predicting the upper outlier of PC-Ed, PT was more sensitive compared with aPTT, whereas predicting capability for the outliers of PC-Ed was mostly similar between peak and trough samplings among PT reagents (UMIN 000032492).

Myocardial bridging is an independent predictor of positive spasm provocation testing by intracoronary ergonovine injections: a retrospective observational study

The relationship between myocardial bridging (MB) and coronary spasms during spasm provocation testing (SPT) remains unclear. We aimed to investigate whether MB was correlated with the SPT by ergonovine (ER) injections in a retrospective observational study. Of the 3340 patients who underwent a first coronary angiography, 166 underwent SPT using ER injections and were divided into 2 groups: MB(+) (n = 23), and MB(−) (n = 143). MB was defined as an angiographic reduction in the diameter of the coronary artery during systole. The patients who had severe organic stenosis in the left anterior descending coronary artery were excluded. The MB(+) group more frequently had diabetes mellitus and chronic kidney disease, and a thicker interventricular septum thickness. The rate of SPT-positivity was higher in the MB(+) group than MB(−) group (56.5% vs. 22.4%, P = 0.001). A multivariate regression analysis showed that the presence of MB was independently associated with SPT-positivity (odds ratio 5.587, 95% confidence interval 2.061–15.149, P = 0.001). In conclusion, coronary spasms during provocation tests with ER independently correlated with the MB. MB may predict coronary spasms.

Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis

AIMS

Non-linear models by machine learning may identify different risk factors with different weighting in comparison to conventional linear models.

METHODS AND RESULTS

The analyses were performed in 15,933 patients included in the Shinken Database (SD) 2004-2014 (n = 22,022) for whom baseline data of blood sampling and ultrasound cardiogram and follow-up data at 2 years were available. Using non-linear models with machine learning software, 118 risk factors and their weighting of risk for all-cause mortality, heart failure (HF), acute coronary syndrome (ACS), ischemic stroke (IS), and intracranial hemorrhage (ICH) were identified, where the top two risk factors were albumin/hemoglobin, left ventricular ejection fraction/history of HF, history of ACS/anti-platelet use, history of IS/deceleration time, and history of ICH/warfarin use. The areas under the curve of the developed models for each event were 0.900, 0.912, 0.879, 0.758, and 0.753, respectively.

CONCLUSION

Here, we described our experience with the development of models for predicting cardiovascular prognosis by machine learning. Machine learning could identify risk predicting models with good predictive capability and good discrimination of the risk impact.

Impact of electrophysiological and pharmacological noninducibility following pulmonary vein isolation in patients with paroxysmal and persistent atrial fibrillation

BACKGROUND

Two methods for testing inducibility of atrial fibrillation (AF)-atrial pacing and isoproterenol infusion-have been proposed to determine the endpoint of catheter ablation. However, the utility of the combination for testing electrophysiological inducibility (EPI) and pharmacological inducibility (PHI) is unclear.

METHODS

After pulmonary vein isolation (PVI), inducibility of atrial tachyarrhythmia was assessed with the dual methods in 291 consecutive patients with AF (65% paroxysmal) undergoing initial catheter ablation.

RESULTS

The incidence of EPI was significantly higher in patients with persistent AF than paroxysmal AF (32.0% vs 11.7%, respectively, P < .001). The incidence of PHI was not significantly different between the two groups (25.2% vs 26.1%, respectively, P = .87). There was no significant difference in AF recurrence according to inducibility in paroxysmal AF. In persistent AF, however, patients achieving neither EPI nor PHI under PVI-only strategy had significantly lower rates of AF recurrence than those achieving either EPI or PHI and consequently requiring additional ablation for inducible atrial tachyarrhythmia (68.5% vs 49.0%, respectively; log-rank test, P = .022). In persistent AF, multivariate Cox regression analysis showed that achieving neither EPI nor PHI was a negative independent predictor of AF recurrence (HR 0.492, 95% CI 0.254-0.916, P = .026).

CONCLUSIONS

Achieving neither EPI nor PHI following PVI was associated with favorable outcome in patients with persistent AF. The combination of tests may discriminate patients responsive to the PVI-only strategy. Further selective approaches are necessary to improve outcome for inducible atrial tachyarrhythmia in patients with persistent AF.

Prognostic value of the heart rate profile during exercise in patients with atrial fibrillation

Aims

This study aimed to investigate the correlation of heart rate profile during exercise with exercise capacity and heart failure occurrence in patients with atrial fibrillation compared with patients with sinus rhythm.

Methods

We analyzed 2231 patients (atrial fibrillation: n = 321, sinus rhythm: n = 1910) who underwent a symptom-limited maximal cardiopulmonary exercise test at our institute. Their heart rate profile during exercise was assessed using peak heart rate and chronotropic response; (peak heart rate−resting heart rate)/(220−age−resting heart rate). The endpoint was the occurrence of heart failure events, defined as hospitalization for heart failure or heart failure-related death.

Results

There were significant positive correlations of peak heart rate and chronotropic response to peak oxygen consumption, both in atrial fibrillation and sinus rhythm. During a median follow-up period of 1262 (interquartile range 974–2921) days, 117 (5.2%) heart failure events were observed. Multivariate analyses showed that peak heart rate and chronotropic response were statistically significant predictors of heart failure events both in atrial fibrillation (peak heart rate: heart rate 0.975, p = 0.002, chronotropic response: heart rate 0.196, p = 0.003) and in sinus rhythm (peak heart rate: heart rate 0.988, p = 0.036, chronotropic response: heart rate 0.347, p = 0.020). Bivariate models showed that compared with chronotropic response, peak heart rate was a stronger predictor of heart failure in atrial fibrillation, whereas the finding was reversed in sinus rhythm.

Conclusion

The exercise- heart rate profile was significantly related to exercise capacity and future heart failure events, regardless of rhythm. However, the impacts of peak heart rate and chronotropic response on the endpoint varied according to the cardiac rhythm.

Relationship between the prognostic value of ventilatory efficiency and age in patients with heart failure

Background

Ventilatory efficiency decreases with age. This study aimed to investigate the prognostic significance and cut-off value of the minute ventilation/carbon dioxide production (VE/VCO2) slope according to age in patients with heart failure.

Methods and results

We analysed 1501 patients with heart failure from our observational cohort who performed maximal symptom-limited cardiopulmonary exercise testing and separated them into three age groups (≤55 years, 56–70 years and ≥71 years) in total and according to the three ejection fraction categories defined by European Society of Cardiology guidelines. The endpoint was set as heart failure events, hospitalisation for heart failure or death from heart failure. The VE/VCO2 slope increased with age. During the median follow-up period of 4 years, 141 heart failure (9%) events occurred. In total, univariate Cox analyses showed that the VE/VCO2 slope (cont.) was significantly related to heart failure events, while on multivariate analysis, the prognostic significance of the VE/VCO2 slope (cont.) was poor, accompanied by a significant interaction with age ( P < 0.0001). The cut-off value of the VE/VCO2 slope increased with the increase in age in not only the total but also the sub-ejection fraction categories. Multivariate analyses with a stepwise method adjusted for estimated glomerular filtration rate, peak oxygen consumption, atrial fibrillation and brain natriuretic peptide, showed that the predictive value of the binary VE/VCO2 slope separated by the cut-off value varied according to age. There was a tendency for the prognostic significance to increase with age irrespective of ejection fraction.

Conclusion

The prognostic significance and cut-off value of the VE/VCO2 slope may increase with advancing age.

Presentations and outcomes of patients with acute decompensated heart failure admitted in the winter season

BACKGROUND

Seasonal variations in cardiovascular disease is well recognized. However, little is known about the presentations and outcomes of Japanese heart failure (HF) patients in the winter season.

METHODS AND RESULTS

We used a single hospital-based cohort from the Shinken Database 2004-2012, comprising all new patients (n=19,994) who visited the Cardiovascular Institute Hospital. A total of 375 patients who were admitted owing to acute decompensated HF were included in the analysis. Of these patients, 136 (36%) were admitted in winter. Winter was defined as the period between December and February. The HF patients admitted in winter were older, and had a higher prevalence of hypertension and diabetes mellitus than the patients admitted in other seasons. Patients with conditions categorized as clinical scenario 1 tended to be admitted more commonly in winter. HF with preserved left ventricular ejection fraction (LVEF) was more common in HF patients admitted in winter than in those admitted in other seasons. Beta-blocker use at hospital discharge was more common in the patients admitted in other seasons. Kaplan-Meier curves and log-rank test results indicated that the incidences of all-cause death, cardiovascular death, and HF admission were comparable between the patients admitted in winter and those admitted in other seasons.

CONCLUSIONS

HF admission was frequently observed in the winter season and HF patients admitted in the winter season were older, and had higher prevalence of hypertension and diabetes mellitus, and preserved LVEF suggesting that we might need to pay more attention for elderly patients with hypertension, diabetes mellitus, and HF with preserved LVEF to decrease HF admissions in the winter season.