No abstract

Introduction and objectives Readmission rates at 30 days after heart failure (HF) hospitalizations are high. Disease management programs have shown to reduce them; however, the results of clinical trials are difficult to extrapolate to the entire population with HF. Our hospital has a multidisciplinary program for HF management (MHFP) led by the Cardiology Department, based on early post-discharge follow-up in a day hospital, for clinical-analytical assessment, optimization and titration of pharmacological treatment, structured education, promotion of self-care and intravenous treatment if appropriate. The objective of this study is to assess the impact of the MHFP on our patients all cause 30-day readmission rate (MHFP group), compared with the rest of our region (control group). Methods All discharges with HF as main diagnosis in our region were analysed through anonymized consultation of the Minimum Basic Data Set (an administrative public data base) in the period 2009–2015. The first admission of each patient in the period was identified as index admission. Patients who died in the index admission were excluded. Epidemiological characteristics, comorbidities and hospital stay were compared in the two groups. The outcome variable was the time to first readmission in the first 30 days after discharge. Quantitative variables were compared using Student's t and categorical variables with χ2. Cox method was used for multivariate analysis. Results We included 62,162 patients, 1,447 in the MHFP group and 60,715 in the control group. Epidemiological characteristics and main comorbidities were compared, and the results are shown in the table. Readmission rate was significantly lower in the MHFP group (13.5 vs 16%, HR 0.85, 95% CI 0.74–0.98 in multivariate analysis). The variables independently associated with an increase in readmissions at 30 days were age adjusted Charlson index, hospital length of stay in index admission and several comorbidities (obstructive pulmonary disease, myocardial infarction, and renal failure) Conclusions Over a period of 6 years, a MHFP significantly reduced 30-day readmissions after admission for HF, in its reference area. The generalization of these programs could have a relevant impact on costs reduction. Funding Acknowledgement Type of funding sources: None.

Funding Acknowledgements Type of funding sources: None. Background Left bundle branch (LBB) area pacing (LBBAP) has been widely adopted for an increasing number of antibradycardia and CRT procedures worldwide. Two different types of capture have been described during LBBAP: left ventricular septal myocardial capture (LVSP) or direct LBB capture via non-selective (ns)-LBB or selective (s)-LBB pacing (LBBP). Nevertheless, electrocardiographic diagnosis of direct LBB capture remains one of the challenges of modern conduction system pacing. Purpose We hypothesized that the combination of several ECG-based criteria might discriminate better LBBP from LVSP, than each criterion separately. Methods Single-center study involving all consecutive patients who received LBBAP. LBBP was defined according to QRS morphology transition criteria during decremental pacing. Multivariate logistic regression analysis was performed to develop a predictive score for LBBP (The LBBP score), by entering three ECG-based criteria: the previously reported and widely adopted R wave peak time in lead V6 (V6-RWPT) and V6-V1 interpeak interval, and the novel aVL-RWPT, that had been tested before in our sample by the performance of ROC curve analysis. The predictive performance of the regression model was evaluated through the ROC curve and compared to those of the isolated criteria with the De Long test. The regression equation derived from the multivariate model was obtained. To simplify its performance in clinical practice, a heuristic method was used to develop the score: each criterion was weighted according to the result of the Wald test. The construction of the score is depicted in Figure 2. Results A total of 188 patients with intended LBBAP were screened. Successful LBBAP was achieved in 174 (92.5%). 71 patients with confirmed LBB capture by the QRS morphology transition criteria were analysed to develop the predictive score. The optimal cut-off values of V6-RWPT, V6-V1 interpeak interval and aVL-RWPT for the discrimination of LBBP were <83 ms, ≥33 ms and <79 ms, respectively. The multivariate logistic regression model showed that the three analysed ECG-based criteria were independent and not redundant predictors of LBBP. The ROC analysis for the multivariate regression model showed an AUC of 0.980 to differentiate LBB and LVS captures. The probability of LBBP can be calculated by using the calculation formula shown in Figure 1. The ROC curve for the differential diagnosis of LBBP and LVSP with the score showed an AUC of 0.976, with an optimal cut-off value of 3 points (Sensitivity 89.2%, Specificity 100%) for the differentiation of LBB capture. Conclusions The combination of V6-RWPT, V6-V1 interpeak interval and aVL-RWPT into a new score exhibited a good power to discriminate LBBP from LVSP. None of the reported cut-off values of current ECG-based criteria have shown such high relationship between both sensitivity and specificity, so the LBBP score might be an efficient and precise tool to implement in clinical practice.

Funding Acknowledgements Type of funding sources: None. Background Left bundle branch (LBB) area pacing (LBBAP) has been widely adopted for an increasing number of antibradycardia and CRT procedures worldwide. Two different types of capture have been described during LBBAP: left ventricular septal myocardial capture (LVSP) or direct LBB capture (LBBP). Nevertheless, electrocardiographic diagnosis of LBBAP remains one of the challenges of modern conduction system pacing because there is a big overlap between both electrocardiographic morphologies. Among the new ECG-based criteria, the R wave peak time in lead V6 (V6-RWPT) and V6-V1 interpeak interval seem to be the most practical ones and have been widely adopted. However, their performance have never been validated in an external population. Purpose We aim to validate the reported criteria V6-RWTP and the V6-V1 interpeak interval in an our population. Methods Single-center study involving all consecutive patients who received LBBAP. LBBP was defined according to the presence of QRS morphology transition criteria during decremental pacing. The performance of binary decision rules was described using sensitivity (SN) and specificity (SP). The performance of V6-RWPT and V6-V1 interpeak interval in discriminating between LBBP and LVSP was assessed using the receiver operating characteristic (ROC) curve. Results A total of 188 patients with intended LBBAP were screened. Successful LBBAP was achieved in 174 (92.5%). Only 71 patients with confirmed LBBP by the QRS morphology transition criteria were included to analyse the performance of V6-RWPT and V6-V1 interpeak interval. The V6-RWPT was 75.7±9.6 ms for LBBP tracings and 94.4±10.1 ms for LVS tracings (p<0.001), and V6-V1 interpeak interval was 41.9±12.1 ms for LBB capture and 25.6±8.7 ms for LVS capture (p<0.001). The average difference in V6-RWPT for the transition from LBBP to LVSP was 16.5±5.2 ms. The ROC curves for the differential diagnosis of LBBP and LVS capture (Figure 1, which compares them with those from the original articles) showed a diagnostically optimal V6-RWPT value for the differentiation of LBBP and LVS capture of 83 ms (SN of 80.3% and SP of 91.1%). A SP of 100% for the diagnosis of LBBP was achieved with a cut-off value of 79.5 ms (SN 69.0%). For the V6-V1 interpeak interval, the diagnostically optimal value for the differentiation of LBBP was 33.5 ms (SN of 77.1% and SP of 84.6%). A SP of 100% for the diagnosis of LBBP was obtained with a cut-off value >44 ms (SN 37.1%) (Figure 2). Conclusions Our study comprises an external validation of the proposed ECG-based criteria for LBB capture. We found a nearly absolute concordance with the originally reported V6-RWPT and V6-V1 interpeak interval cut-off values. Only the V6-RWPT cut-off value with 100% specificity differed from the initially reported in the general cohort. Thus, the V6-RWPT and V6-V1 interpeak interval criteria seem to be consistent and emerge as practical and easily reproducible surrogates of the QRS transition criteria.

Funding Acknowledgements Type of funding sources: None. Background: Several clinical parameters and echocardiographic markers of atrial function were associated with A4 signal amplitude and high atrioventricular synchrony (AVS) in the MARVEL 2 study. Purpose To study the correlation between the amplitude of the A4 signal determined by the MICRA AV and, the active filling diastolic waves (A and A´ waves velocity) obtained by a transthoracic echocardiogram (TTE). We also aimed to assess echocardiographic and clinical predictors for optimal AVS. Methods The OptiVALL study is a single-centre and prospective study of consecutive patients implanted with MICRA AV. Baseline and procedure characteristics were obtained at implant and a TTE was performed within 48 h after the implant. Follow-up was routinely performed at 24 hours and at 1, 3, 6 and 12 months. Reprogramming of the atrial sensing parameters was guided by device counters, rate histograms and manual atrial mechanical tests. The pacing mode was programmed to VDD in all patients and AV conduction mode switch (VVI +) was deactivated. AVS was studied by an ambulatory 24-h Holter monitoring placed at 3-months follow-up. ECG recorded tracings were automatically and blindly analyzed with an ECG delineation system. Cardiac cycles were defined as synchronous if a ventricular event followed the P-wave by ≤300 ms. AV synchrony was calculated by dividing the number of synchronous cycles by the total number of cardiac cycles. Optimal AVS was defined as AVS≥ 85% of total cardiac cycles during the 24-h Holter-ECG monitor. Results Twenty-six patients who remained in VDD mode at all follow-up visits were included for analysis. Baseline, echocardiographic and procedure-related data is displayed in table 1. Total ECG recorded time was 550.8 hours and 2,291,953 cardiac cycles were analyzed. Average ambulatory AVS during the 24 h Holter monitoring was 87.3±6.3% and 20 out of 26 patients exhibited optimal AVS (≥85% of cardiac cycles). There was no correlation between the active filling diastolic waves and the A4 signal amplitude (r Pearson=-0.251, p=0.237 for A4 signal Vs A´wave and r Pearson=0.018, p=0.932 for A4 signal vs A wave). We did not find any relationship between several diastolic filling parameters (E, A, E/A ratio, E´, A´) and optimal AVS (table 2). There was also a trend towards better optimal AVS with higher E´/A´ ratios. Optimal AVS was related to patients with smaller right atrium size and with lower body mass index. A trend towards optimal AVS was also found in devices deployed in mid interventricular septum locations. On the other hand, patients with diabetes showed lower rates of optimal AVS. Conclusions The correlation between the echocardiographic diastolic filling parameters and diastolic signals detected by the accelerometer of the MICRA AV is poor, so it seems to be as poor guidance to select proper candidates for MICRA AV.