bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel training concept utilizing a real world-like EP simulator
Authors: Kozlik M , Kosiuk J K , Cogiel M , Kost J K , Hemmerling D , Staniszewski M , Szczesna A , Wojakowski W , Jadczyk T
Published: November 2023

Background

Training in the field of invasive cardiac electrophysiology (EP) is challenging and requires intellectual skills related to the interpretation of intracardiac electrograms. Classic approach to education of young electrophysiologists focused solely on theoretical knowledge and overseen procedures in patients as no real life like simulation of complete diagnostic and therapeutic EP studies was available.

Purpose

The purpose of this study was to assess a novel tool for EP training based on fully interactive, online simulator providing real clinical experience to the users.

Methods

EP Simulator replicates EP recording system routinely used in cardiac electrophysiology laboratories and therefore enable easier transition from training to clinical situation. Users are able to access a fully functional system with simulated surface electrocardiogram and intracardiac electrograms mimicking signals recorded by a catheter located at high right atrium, His bundle, coronary sinus, and right ventricular apex. Normal electrophysiology study and the following cardiac arrhythmias are available for simulation: sick sinus syndrome, AV conduction blocks, atrial ectopy, atrial flutter and fibrillation, atrio-ventricular and atrio-ventricular nodal reentrant tachycardia and ventricular tachycardias. Evaluation by 40 electrophysiologists (mean experience 8±6y) from 16 countries was collected via online questionnaire.

Results

Realism of ECG signals was described as excellent or very good by 90% of responders, realism of intracardial signals by 82.5%. Realism of signal interactions and user experience was judged as excellent or very good by 75% and 70%, respectively. 100% of users agree definitely or mostly that EP Simulator helps to translate theoretical into practical knowledge. 97.5% of responders would include it in EP training programs as it is extremely or very useful for training purposes in opinion of 87.5%. 72.5% of responders think that training on EP simulator can potentially reduce rate of complications. In 87.5% the overall experience of EP simulator was completely or mostly satisfying and it would be recommended by 100% of respondents.

Conclusion

EP simulator is a feasible tool for training of young electrophysiologist and it may be potentially included in cardiologist curriculum. We should particularly emphasize the positive respondents’ evaluation of EP simulator overall realism. Further systematic evaluation of the training progress is needed.
CineECG for visualization of changes in ventricular electrical activity during ischemia
Authors: Van Der Schaaf I , Kloosterman M , Loh P , Van Dam P M
Published: November 2023

Background

CineECG is a newly developed method which provides an anatomical visualization of ventricular de- and repolarization in a non-invasive manner. It combines the electrical information from the 12-lead electrocardiogram (ECG) with a 3D heart model. The location and direction of the average ventricular de- and repolarization over time is projected on the model. Until now, it has not been used for visualization of ventricular electrical activity during myocardial ischemia.

Purpose

We aimed to visualize changes in ventricular de- and repolarization during acute ischemia with CineECG and relate these to changes in the ECG.

Methods

CineECG was used to provide the location and direction of the average ventricular de- and repolarization over time, using a standardized 3D heart/torso model. Continuous ECG’s during percutaneous transluminal coronary angioplasty with prolonged balloon inflation from the STAFF III database were used as study sample. ECG’s at baseline and at every 10 seconds during the first 150 seconds of balloon inflation were analyzed with CineECG. For the terminal QRS-complex, ST-segment and terminal T-wave, the direction of the CineECG was determined. The changes in CineECG direction relative to the direction at baseline were quantified by calculating the Δangle between the CineECG direction at baseline and the direction after every 10 seconds of inflation. Additionally, the root mean square amplitude (rmsA) of the ST-segment of all ECG leads was computed for every ECG.

Results

94 patients from the STAFF III database were included, 10 patients were excluded due to too much signal noise of the ECG. Within 10 seconds after start inflation, the median Δangle for the terminal QRS-complex, ST-segment and terminal T-wave were 21.8° [11.4 – 34.2] , 20.6° [8.0 – 43.9] and 23.5° [11.8 – 48.0], respectively. The median Δangle for the terminal QRS-complex reached its maximum after 130 seconds (41.0° [23.0 – 70.8]). The median Δangle of the ST-segment gradually increased to 26.1° [13.1 – 56.0] after 140 seconds of inflation. The Δangle of the terminal T-wave was greatest 30 seconds after start inflation (26.3° [12.6 – 49.2]), and changed only slightly during inflation. The rmsA of the ST-segment was 0.04 mV [0.03 – 0.08] during the first 10 seconds of inflation, and gradually increased until 0.08 mV [0.05 – 0.15] mV at 150 seconds.

Conclusions

CineECG proves to be very sensitive in detection of early changes in the ECG during prolonged balloon inflation, even before great changes occur in ST-amplitude. Therefore, CineECG might support the early detection of acute ischemia, even before clear ECG changes are visible.
Single-lead ECG based detection of arterial hypertension using artificial intelligence algorithms
Authors: Angelaki-Kaxiras E , Kochiadakis G , Barmparis G , Maragkoudakis S , Fragkiadakis K , Plevritaki A , Savva E , Zacharis E , Kampanieris E , Kassotakis S , Kalomoirakis P , Tsironis G , Marketou M
Published: November 2023

Background

Unawareness of arterial hypertension is an important contributing factor to the inadequate control of the disease and the absence of appropriate antihypertension treatments. Population screening programs have shown that more than 50% of hypertensives were unaware they had hypertension. Wearable devices, a ubiquitous accessory in our days, can record single-lead electrocardiograms that are then analyzed by machine learning (ML) algorithms. The recorded signal, most often of lead I, could potentially reveal an undiagnosed hypertension.

Purpose

The purpose of this study is to create and train a ML algorithm that detects the existence of arterial hypertension using features derived from a single-lead ECG (lead I),

Methods

We enrolled 1220 consecutive individuals without cardiovascular disease. The participants were classified into hypertensive and normotensive group. Hypertensive patients were recruited from the outpatient clinics of the respective centers. Normotensive healthy individuals were referred either for the investigation of atypical chest pain or for the modification of risk factors for cardiovascular disease such as hyperlipidemia. We trained a Random Forest (RF) model to classify subjects into 2 groups: hypertensive or normotensive. A feature-based machine learning algorithm, such as a RF, can also provide an interpretation of its results in the form of feature importance and interactions between features.

Results

The prevalence of hypertensive people in our study was 66.5%. Our RF model was able to distinguish hypertensive from normotensive patients on a hold-out test set, with an AUC 0.85, accuracy 81%, sensitivity 80% and specificity 83% (both using a threshold of 0.5), and an F1 score 0.86. When predicting on the subset of the test set containing people younger than 59 years old, it achieved AUC of 0.75, sensitivity of 50% and specificity of 87%.

Age, BMI, the area under the T wave divided by the QRS complex area, the area under QRS segment adjusted for BMI, the QT corrected interval, and the PQ interval, were the most important anthropometric and ECG-derived features in terms of the success of our model (Figure 1).

Conclusions

Our findings indicate that one-lead ECG based detection of arterial hypertension is possible by using ML algorithms. This study demonstrates the huge potential of artificial intelligence to transform smartwatches ECG signals into diagnostic tools.Figure 1
The V6R/V5R index, a novel ECG criterion, can identify idiopathic ventricular arrhythmias arising from the aortic cusp
Authors: Naruse Y , Hasegawa K , Nakamura K , Tsuji T , Kaeriyama T , Horiguchi S , Miyoshi M , Okazaki Y , Sasaki K , Minami K , Naito S , Tada H
Published: November 2023

Background

Idiopathic ventricular arrhythmias (VAs) with a left ventricular outflow tract (LVOT) origin often have dominant R waves (R wave amplitude>S wave amplitude) in the precordial leads (V1 through V6). Radiofrequency catheter ablation (RFCA) within the aortic cusp can eliminate LVOT-VAs. However, some require RFCA at the aorto-mitral continuity (AMC), the endocardium just below the LV summit, and/or mitral annulus for a cure. No ECG indices have been fully established to help differentiate between VAs that can be ablated from the aortic cusp and those that require ablation from other sites.

Purpose

The purpose of this study was to identify ECG indices useful in differentiating VAs with an aortic cusp origin from those from outside the cusp among the LVOT-VAs.

Methods

Among 200 patients in whom successful RFCA of idiopathic VAs was obtained at the OT, this study included 36 (totaling 37 premature ventricular complexes [PVCs]) who had dominant R waves in the precordial leads and an LVOT origin. According to the PVC origins, the PVCs were classified into 2 groups: 1) Cusp group (n=22); PVCs were successfully eliminated by RFCA within or just under the aortic cusp and 2) non-cusp group (n=15); PVCs were successfully ablated from the other LVOT sites described above or by a sequential unipolar ablation (combination of the cusp, AMC, and mitral annulus). The detailed PVC morphology was compared between the 2 groups.

Results

There was no significant difference in the ratio of the Q wave amplitude in aVL to aVR, V2 R/S amplitude ratio, V2 R/S duration index, V2 transition ratio, transition zone index, or V2S/V3R index between the 2 groups. However, the ratio of the R wave amplitude in V6 to V4 (V6R/V4R index) was significantly higher in the cusp group than in the non-cusp group (0.68±0.14 mV vs. 0.52±0.15 mV, p=0.002). The receiver operating characteristic curve demonstrated that a V6R/V4R index of >0.56 predicted the PVCs originated from the cusp with a sensitivity of 0.73 and specificity of 0.91 (area under the curve of 0.81).

Conclusion

The V6R/V4R index is a simple and useful ECG parameter and could distinguish PVCs with an aortic cusp origin from those originating from outside the cusp among the LVOT-VAs with a high accuracy.
Deep learning based prediction of atrial fibrillation incidence from 1-lead ECGs: a model development and validation study
Authors: Bremer J , Neyazi M , Knorr M S , Vollmer M , Gross S , Brederecke J , Ojeda F M , Doerr M , Blankenberg S , Schnabel R B
Published: November 2023

Introduction

Wearables capable of deriving biomarkers such as 1-lead ECGs become largely available, therefore offering the potential to screen for prevalent and incident cardiovascular disease within the population. Screening for prevalent atrial fibrillation (AF) from such devices is already widely applied. It has remained unclear whether predicting incident AF using 1-lead ECGs is possible. The assessment of AF risk before clinical manifestation may permit early detection and thus slow or limit the onset and progression of the disease by taking appropriate measures.

Purpose

Utilizing data from the UK-Biobank (UKB), we examined the possibility to predict three-year AF incidence from single-lead ECGs using deep learning-based methods.

Methods

For this study 100,551 1-lead ECGs from 91,898 unique participants were utilized, including 57,897 4-lead resting ECGs during ergometry from 2006-2010 and 9,826 at the first follow-up (FU) from 2010-2013 and 32,828 12-lead resting ECGs during the FU imaging study from 2014-2020. The 12-lead and 4-lead ECGs share information from lead I, which was used for all further analyses. We employ a 1D-Convolutional Neural Network (CNN) to predict the 3-year risk of incident AF. The CNN is compared with the clinically used CHARGE-AF Score. External validation was performed in the SHIP-START cohort including its three FU examinations (n=2,579; nFU1=3,101; nFU2=2,613; nFU3=1,482) and a median FU time of 5-years to FU and SHIP-TREND (n=2,931) with one FU after 7-years.

Results

The CNN provided a good ability to predict of 3-year incident AF at all three UKB FUs (AUC: 0.729, 0.727, 0.705), while the CHARGE-AF Score provides better results at Exam1 (0.773), worse at Exam2 (0.692) and equal at Exam3 (0.717). A combination of the CHARGE-AF Score and the CNN improved the individual predictions at all three FUs (AUC: 0.811, 0.741, 0.753). The CNN provides an equally good ability to predict incident AF at all four FUs in SHIP-START (AUC: 0.659, 0.785 0.689, and 0.772) and in SHIP-TREND (0.780). Combined with the CHARGE-AF-Score score, the performance in SHIP-START increases to 0.757, 0.861, 0.765, and 0.812 and in SHIP-Trend to 0.826 respectively.

Conclusion

Using a CNN to predict incident AF from 1-lead ECGs, we provide further evidence for the added value of clinically applied deep learning-based methods. By combining the CNN predictions on ECG data with the well-established CHARGE-AF score, 3-year AF incidence can be predicted with high accuracy.
Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard
Authors: Ronner E , Van Vliet M , Monnink S , Kuiper M , Hoftijzer D , Constandse J
Published: November 2023

Background

Blood pressure is a key paramater in acute and chronic cardiovascular diseases (CVD) [1]. However, obtaining reliable and reproducible cuff blood pressure (BP) measurements is challenging. We demonstrate that a wristband-based PPG measurement in combination with a custom developed BP algorithm represents a possible alternative to cuff BP measurements.

Purpose

This study aimed to validate a novel cuffless photopletysmography (PPG)-based non-invasive wristband for continuous BP monitoring in accordance with ISO 81060-2:2019.

Methods

The study compared PPG-guided BP algorithm predictions with subclavian arterial reference measurements taken during cardiac catheterization. Eligible patients were consecutively included, and eligibility was screened following ISO 81060-2:2019 requirements. Reference measurements were performed using a validated invasive BP monitoring device with a sampling rate of 100Hz. PPG signals were collected using six light emission diodes and two photodiodes at a sampling rate of 128Hz. Three sequential initialization measurements were taken using a validated blood pressure cuff before the cardiac catheterization exam. These measurements, along with approximately 100 additional features (PPG-derived and based on patient demographics), were used as input for the machine learning-based BP algorithm. Correlation, mean error, and standard deviation (SD) were determined for systolic and diastolic BP measurements between the BP algorithm predictions and invasive reference measurements

Results

The study included 97 patients from whom 420 individual 30-second samples were obtained. The mean age, weight, and height of the analysed subjects were 67.1 (SD 11.1), 83.4 (SD 16.1), and 174.1 (SD 10.0), respectively. In 48 samples (11%), systolic BP was ≤100mmHg, while in 106 samples (25%), systolic BP was ≥160mmHg. Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The BP algorithm predictions showed a high correlation with invasive reference measurements for systolic (r = 0.985) and diastolic (r = 0.961) BP measurements. The mean error of the BP algorithm predictions compared to the invasive reference measurements was ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Results were similar within each gender and skin colour category (Fitzpatrick I-VI).

Conclusion

This study demonstrates that a wristband-based PPG measurement in combination with the developed BP algorithm can provide accurate continuous BP measurements across a wide range of BP distributions. Therefore, wristband BP monitoring may serve as a valid and less burdensome alternative to cuff BP measurements for both in-hospital and at-home BP monitoring. However, further research is necessary to evaluate the precision of the BP algorithm during movement and the stability of the predictions over time.figure of BP
Risk stratification of coronary artery disease using the artificial intelligence-enabled electrocardiogram
Authors: Awasthi S , Sachadeva N , Abbou R , Gupta Y , Anto A , Asfahan S , Hegstrom L , Alger H , Medina-Inojosa J , Mccully R , Lerman A , Friedman P , Attia Z , Soundararajan V , Lopez-Jiminez F
Published: November 2023

Background

Atherosclerotic cardiovascular disease (ASCVD), of which coronary artery disease (CAD) is the primary manifestation, is the leading cause of death worldwide. The AHA/ACC Pooled Cohort Equation (PCE) facilitates risk stratification and initiation of primary prevention of ASCVD, however their performance is suboptimal.

Purpose

We develop an artificial intelligence-based electrocardiogram (ECG) analysis (AI-ECG) algorithm to detect different stages of CAD and assessed the value of AI-ECG over PCE to predict incident myocardial infarction (MI) and survival over 3-years.

Methods

Using electronic health record data from approximately 7 million patients seen at over 70 hospitals and clinics across 5 states in the USA, we created independent AI-ECG models to identify elevated coronary artery calcium (CAC) by computer tomography, the presence of obstructive CAD by stress test or angiography, and left ventricular regional akinesis by echocardiogram, presumed to represent prior MI. Deep structured data, AI-enabled natural language processing of unstructured data were used to ensure cohort, and physician review of labeling were used to ensure cohort accuracy. These models were ensembled into a single model that was used to assess the utility of AI-ECG for predicting new occurrence MI in PCE- and age-stratified cohorts of patient with no prior history of ASCVD, over a 3-year timeframe using Cox’s proportional hazards analysis.

Results

A total of 6,080 patients (15,192 ECGs) were labeled positive for CAC ≥ 300, 12,561 patients (18,915 ECGs) positive for obstructive CAD, and 10,767 patients (23,870 ECGs) positive for regional akinesis. AI-ECG for identifying elevated CACS, obstructive CAD, and regional akinesis achieved AUROCs of 0.86, 0.80, and 0.93, respectively. All AI-ECG models identified increased 3-year risk for MI and death in AI-ECG positive versus AI-ECG negative patients and subgroups with 2 or 3 positive results had stepwise increased risk (Figure A). In a 3-year survival analysis of all-comer patients, the hazard ratio for those that tested positive on 1, 2, or 3 versus 0 positive disease-specific models were 1.61 (1.53-1.69). 2.40 (2.27-2.55), 3.42 (3.21-3.65), respectively. Risk stratification was also observed in cohorts without prior ASCVD stratified by low and high PCE-derived 10-year ASCVD risk (Figure B) and in cohorts stratified by age, including under the age of 40.

Conclusions

AI-ECG has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimate ASCVD risk, and in whom risk assessment over periods of time shorter than 10 years is desired.
Biomarker associated trends in mortality in myocardial infarction as an example of clinical data warehouse analyses - new opportunities of data-driven cardiovascular research
Authors: Geng J , Gessler N , Reimers J , Bohnen S , Dreher A , Wohlmuth P , Hakmi S , Willems S , Tigges E P , Kaiser L
Published: November 2023

Background

Healthcare data collected during clinical routine bears an immense potential for various clinical research questions. With the rapid use and development of structured electronic health records, this abundant source of data can be utilized to efficiently comprise large datasets based on actual, unselected patients. In this study, we examined 4,205 patients who presented with myocardial infarction (MI) since the implementation of a new hospital information system at a tertiary hospital. We sought to evaluate mortality trends using routinely collected biomarkers.

Methods

4,205 patients admitted with MI at a high-volume tertiary hospital between September 2016 and September 2022, were retrospectively analyzed. Statistical analyses comprised data from a data warehouse sourced from the hospital information system including ICD and OPS codes, biometric data, and biomarker values.

Results

Of the 4,205 patients with MI, 28% (1,176) were female, the mean age was 67 years. 28% (1,171) were diagnosed with ST-segment elevation myocardial infarction (STEMI). 15% of all MI patients (645) showed coronary single-vessel disease, whereas 65% of patients (2.746) suffered from multi-vessel disease. Percutaneous coronary intervention was performed in 47% (1,961) of all included NSTEMI and STEMI patients. Concomitant arterial hypertension was present in 51% of STEMI patients compared to 65% of the NSTEMI population. Regarding STEMI, diabetes mellitus (DM) was prevalent in 22% of cases, whereas 31% in NSTEMI were diagnosed with DM. We observed a significant linear association between in-hospital mortality and peak biomarker levels of high-sensitive Troponin I (hsTnI), serum lactate, and creatine kinase myocardial band (CKMB) (figure 1). MI patients with a peak lactate measurement of > 17.01 mmol/l had a mortality rate of > 80%. Two-thirds of patients (66%) with CKMB values of > 1474.50 U/I died during the index hospitalization.

Conclusion

In our study, we observed significant linear associations between biomarker levels and in-hospital mortality in patients with myocardial infarction. Furthermore, we demonstrated that hospital information systems can efficiently be used to generate large, comprehensive datasets not only to characterize real-world patient populations but also to evaluate data of prognostic relevance. With careful evaluation of data quality and within the frameworks of patient privacy, future research in this field holds promising potential.
Does a digitally enabled telerehabilitation program reduce hospital admissions and length of stay for private health insurance patients in Australia?
Authors: Braver J , Afoakwah C , Yiallourou S , Salim A , Keating C , Marwick T , Oldenburg B , Carrington M
Published: November 2023

Introduction

Traditional cardiac rehabilitation (CR) programs are associated with reduced cardiovascular morbidity, hospital admissions and mortality. However, participation continues to be poor. Novel digital health models of care have emerged, providing a new frontier in healthcare delivery, but evidence for their impact on healthcare utilization is limited. We implemented and investigated a telehealth program complemented by a CR mobile app for patients following a hospitalization for coronary heart disease (CHD).

Purpose

To assess whether a digitally enabled CR program is equivalent to traditionally delivered CR (F2F-CR) and whether it reduces rehospitalizations and total hospital days compared to patients who did not undertake CR (no-CR).

Methods

Privately insured patients, following a hospitalization for CHD, were recruited nationally, across Australia, between October 2019 - 2020. Participants received weekly telehealth consultations for 8 weeks, supplemented by a CR mobile app. Using administrative private health insurance claims data, we assessed differences in 90-day and 12-month rehospitalizations and total hospital days for the digital-CR group (n=138) compared with a) F2F-CR (n=115) and b) no-CR (n=266).

Results

Among the cohort, 27% lived in regional/rural areas, 71% were male and the majority were aged over 60 years (83%). The digital-CR group had lower total hospital days relative to the no-CR group in the 12 months post index event (4.5 vs 7.2 days; mean difference 2.8 days; p=0.019; 95% CI: 5.1-0.5). Readmission rates were lower at 90 days and similar at 12 months post discharge in the digital-CR group compared with the no-CR group, although not statistically significant. There were 0.17 and 0.21 (p=0.43) readmissions per patient at 90 days post discharge and 1.08 and 1.07 (p=0.94) readmissions per patient within 12 months in the digital-CR group and no-CR group respectively. There were no significant differences for digital-CR relative to F2F-CR on any measures.

Conclusion

Rehospitalization rates were similar between the groups, whilst the digital-CR group had significantly fewer days in hospital than the no-CR group. Digital-CR may reduce the severity of rehospitalizations highlighted by fewer days in hospital. The digital-CR and F2F-CR outcomes were similar, confirming that digitally enabled CR programs provide similar benefits regarding reduced days in hospital, whilst providing additional benefits of improved access and choice. Digital-CR should be made widely available for cardiac patients post hospital discharge.Mean hospital days Digital-CR vs No-CRMean hospital days Digital-CR vs F2F-CR
Long-term impact of remote monitoring after hospitalisations for acute decompensated heart failure: a systematic review and meta-analysis of randomised controlled trials
Authors: Rodrigues Coutinho Clemente M , Felix N S , Navalha D D P , Ferreira R O M , Pasqualotto E P , Nogueira A K S , Marinho A D , Alexandre F K B , Amanda A D F
Published: November 2023

Background

Remote monitoring (RM) has demonstrated to improve outcomes of patients with heart failure (HF) in different settings. However, the long-term efficacy of RM after discharge of patients hospitalised for acute decompensated heart failure (ADHF) remains uncertain.

Purpose

We aimed to perform a systematic review and meta-analysis comparing RM with usual care (UC) in patients recently hospitalised for ADHF.

Methods

We systematically searched PubMed, EMBASE, and Cochrane Library in February 2023 in accordance with PRISMA guidelines for randomised controlled trials (RCTs) comparing RM with UC initiated within 6 weeks of discharge after an admission for ADHF, with a minimum follow-up of 6 months. Statistical analyses were performed using R Software 4.1.0., and heterogeneity was assessed using I² statistics. We extracted the data following the intention-to-treat principle.

Results

We included six RCTs comprising 2,817 patients, of whom 1,436 (51.0%) were randomised to RM. Follow-up ranged from 6 to 15 months. In the pooled analysis, RM was associated with a significant reduction in cardiovascular mortality (OR 0.59; 95% CI 0.36–0.97; p=0.036; I²=0%; Fig. 1A) and cardiovascular hospitalisations (OR 0.71; 95% CI 0.57–0.88; p=0.002; I²=0%; Fig. 1B), with a low heterogeneity between studies. Nonetheless, there was no significant difference between RM and UC with regards to hospitalisations for HF (OR 0.88; 95% CI 0.67–1.15; p=0.340; I²=66%; Fig. 2A) or all-cause hospitalisations (OR 0.89; 95% CI 0.73–1.08; p=0.241; I²=67%; Fig. 2B), albeit with a high heterogeneity between studies.

Conclusion

In this meta-analysis, post-discharge RM was associated with a reduction in cardiovascular mortality and cardiovascular hospitalisations rates as compared with UC in patients admitted for ADHF.Figure 1Figure 2
Mathematical software tool that helps clinicians to classify clinicand and spurious alerts in patients with heart failure and cardiac implantable devices
Authors: Sanchez Vallejo C , Flores Soler J , Garcia-Torrent M , Canadas-Godoy V , Salgado Aranda R , Garcia Gonzalez F , Casas De La Cal L , Herreros Gil M , Perez Serrano M , Perez-Villacastin Dominguez J , Perez Castellano N
Published: November 2023

Introduction

Heart failure (HF) is the leading cause of cardiovascular hospitalisation in developed countries. More and more HF patients are receiving cardiac implantable electronic devices (CIED) that collect physiological data that may be used to predict HF decompensations and avoid hospitalisation through early medical interventions. Some CIEDs have a Multiparameter heart failure index (HF-I), which is an algorithm based on the information provided by five sensors (heart sounds, respiration, chest impedance, heart rate and activity level) capable of predicting 34 days before HF decompensation episodes when the reported index is ≥16 (HF-I alert) with a sensitivity of 70%, although with a significant rate of false positive HF diagnosis.

Purpose

Develop a computational tool to analyse HF-I curve dynamics to find the mathematical variables with the highest predictive accuracy of true clinical events in a tertiary hospital.

Methods

We developed a computational tool to analyse curves of 71 HF-I alerts in 25 patients with HF who were implanted with an implantable cardiac defibrillator (ICD) or cardiac resynchronisation-defibrillator (CRT-D) between 2017 and 2021, using mathematical software (fig 1). It includes peak, time to peak, area under the curve, increase and decrease slopes, variability and monotony. Alerts (HF-I ≥16) were clinically classified as real HF decompensation (clinical alerts) and false positive HF decompensation (spurious/subclinical alerts).

Results

Median age of the study group was 68 years, and 60% had non-ischemic cardiomyopathy. Median left ventricle ejection fraction (LVEF) was 29%; 60% had non-ischaemic cardiomyopathy. Patients with clinical alerts had higher raise slopes 7 days after alert onset (p=0.0055), higher HF-I values 3 days after crossing the alert, higher raise variability and less monotonic curves. Additionally, significant differences were observed in alert duration (p=0.004), HF-I maximum value (p< 0.0001), area under the overall alert curve (p< 0.0001), area under the alert curve at 3 / 7 days (p=0.04/0.002 respectively) and time to maximum peak of the alert (p=0,0032). HF-I value at the 7th day from the alert onset was an early and robust predictor of clinical events (P< 0.0001). A value at day 7 from alert onset ≥ 28 had a 95% sensitivity and 100% specificity for detecting clinical events (fig 2).

Conclusion

a computational mathematical tool can help to classify true positive clinical alerts versus spurious alerts in patients with a multiparameter heart failure index and CIED.Figure 1.Figure 2.
The beneficial effects of adherence to outpatient telecare intervention on clinical outcomes after an episode of acute heart failure: insights from the prospective randomised controlled AMULET trial
Authors: Krzesinski P , Galas A , Piotrowicz K , Chrom P , Jankowska E A , Siebert J , Stanczyk A , Siwolowski P , Gutknecht P , Szalewska D , Banasiak W , Ponikowski P , Gielerak G
Published: November 2023

Background

The prevention of hospitalisations and deaths is a major therapeutic aim in patients with heart failure (HF). In our previously published prospective randomised controlled trial, we demonstrated that an outpatient intervention comprising nurse-led non-invasive assessments, telemedicine support and remote cardiologist decision-making (AMULET telecare) reduced the risk of HF hospitalisation or cardiovascular death by 31% during the 12-month follow-up in HF patients after an episode of acute HF.

Purpose

To present the results of the prespecified per-protocol analysis of the AMULET trial that reveal the impact of AMULET telecare adherence on the prognosis of patients.

Methods

Patients with HF and left ventricular ejection fraction (LVEF) ≤49% and who experienced an episode of acute HF in the past 6 months were randomly assigned to receive either AMULET telecare (n=298) or standard care (n=305). Those who completed the predefined schedule of AMULET telecare (n=190) and all standard care patients were included in the per-protocol population.

Results

In this per-protocol analysis, when compared to standard care, the implementation of AMULET telecare reduced the risk of the primary outcome, a composite of unplanned HF hospitalisation or cardiovascular death (by 51%), and the following secondary outcomes: first unplanned HF hospitalisation, first unplanned cardiovascular hospitalisation, first all-cause unplanned hospitalisation, total unplanned HF hospitalisations and the number of days lost due to HF hospitalisations or death. There was no statistically significant difference in cardiovascular mortality, mortality due to HF and overall mortality between the study groups (Table).

Conclusions

Adherence to AMULET telecare enhances the positive effects of this intervention on the prognosis of HF patients with an LVEF≤49% after an episode of acute HF.
The frequency of arrhythmia in patients with palpitations who had COVID-19 with 24-hour holter compared to the control group
Authors: Korkmaz K , Tulunay Kaya C , Tan Kurklu T S
Published: November 2023

Introduction

COVID-19 affects many systems especially cardiovascular system. Cardiovascular outcomes includes acute coronary syndrome, arrhythmias, myocarditis, cardiogenic shock, sudden cardiac deaths. Overall arrhythmia incidence of %17. Almost all arrhythmias can occur in COVID-19.

Proposition: In this study, we aimed to calculate an arrhythmia incidence, including parameters that have not been studied before, with 24-hour holter results between the COVID-19 survivors and the control group.

Method

The participants of the study were selected among the patients who applied to the Cardiology Department between March 2020 and March 2021 with the complaint of palpitation. From patients with 24-hour rhythm holter (approximately 1500 patients) retrospectively, patients who had COVID-19 at any time before holter insertion (n=190) were identified. The same number of patients (n=221) with similar characteristics were taken as the control group.

Results

The probability of having AF in the COVID-19 group was found to be 3.2 times higher (%95 Cl, 1.002-10.48) than control group. There was no difference in the development of arrhythmia in patients who had COVID-19, when patients with CT involvement and patients without involvement were compared. In addition, there was no difference between having cardiac risk factors in terms of development of arrhythmia in both groups, even when looked at separately. Average rate was lower in serology positive group (p=0.04). Heart rate was lower in those who received favipiravir than those who received plaquenil or favipravir plus plaquenil (p=0.03). Atrial extra systole incidence were higher in those who were taken plaquenil (p=0.48).

Conclusion

According to our knowledge, there is no other study investigating the effect of pharmacological treatment changes used in Covid-19 on arrhythmia frequency. Also CT involvement has no effect on the incidence of arrhythmia.

The main result is when multivariate regression analysis was performed, being COVID-19 was an independent indicator of the presence of atrial fibrillation (p=0.05).
Underprescribing of cardioprotective novel antidiabetic drugs in marginalized patients with type II diabetes mellitus
Authors: Khidhir A , Aladesua T
Published: November 2023

Background

Cardiovascular disease is the most common cause of increased mortality in patients with type 2 diabetes mellitus (T2DM). Novel antidiabetic agents such as sodium-glucose co-transporter 2 (SGLT-2) inhibitors and glucagon-like peptide 1 (GLP-1) agonists were shown to reduce this risk. SGLT-2 inhibitors are considered a secondary treatment option in patients with heart failure with reduced ejection fraction (HFrEF) in patients with or without T2DM. In patients with T2DM, these medications not only reduce hyperglycemia but also reduce the progression of chronic kidney disease (CKD). GLP-1 agonists are indicated for treatment of not only T2DM, but also obesity, and were shown to improve left ventricular ejection fraction, myocardial contractility, and endothelial function. The literature suggests that there may be disparities in prescribing patterns in marginalized populations.

Objective

The purpose of this study was to conduct a retrospective chart review to assess the adherence to the new guidelines regarding the use of SGLT2 inhibitors and GLP-1 agonists in marginalized patients with T2DM.

Methods

The study was conducted at a resident-run clinic that serves a large number of under-resourced individuals. Inclusion criteria included patients between the ages of 18 and 75 with T2DM seen in the clinic within 18 months prior to the start of the study with an A1c greater than 9.0. Exclusion criteria included patients with type 1 diabetes mellitus and patients with T2DM under care of endocrinology. We performed retrospective chart review to determine providers’ prescription practices. We created electronic patient database for this study.

Results

The data showed that 26.87% (n= 381) of the clinic’s population had an A1c greater than 9.0, of those 343 patients met the inclusion criteria. About 7.87% (27/343) of patients were identified with HFrEF. Of those, only 40.74% (11/27) were found to be on an SGLT2 inhibitor. Patients with CKD constituted 17.49% (60/343) of patients, and only 16.67% (10/60) of those were on an SGLT2 inhibitor. We identified 27.70% (95/343) of patients with a BMI between 30-35, and 30.32% (104/343) with a BMI greater than 35, but only 56.84% and 64.42% of those, respectively, were on a GLP-1 agonist. The patients that were not on the cardioprotective agents were 50.19% (135/269), 11.15% (9/269), and 12.64% (34/269) African American, Hispanic, and Asian American, respectively.

Conclusions

This study demonstrates that SGLT2 inhibitors and GLP-1 agonists are underprescribed in marginalized patients diagnosed with T2DM, even in those with the additional indication of heart failure and CKD. Most of those patients are of racial minorities. There are a number of barriers that could be contributing to the poor adherence to the guidelines in this population. Our next step is to analyze those barriers and develop a quality improvement project that would improve guideline-directed therapy for this under-resourced population.
Initial clinical experience with new technology: ePatch extended Holter monitoring
Authors: Blanch P , Ortega A , Escura S , Gomez-Choco M , Freixa-Pamias R
Published: November 2023

Introduction

Holter monitor electrocardiography is used to detect potential cardiac arrhythmias such as atrial fibrillation (AF). However, conventional ECG monitoring, data analysis and reporting methods can be labor-intensive and inefficient, leading to high upfront costs and long turnaround times. At the same time, traditional Holter devices can be cumbersome to wear, while inconvenient processes may reduce patient compliance and satisfaction – which impacts diagnostic yield. This new device (ePatch®) is a simple patch application, enrollment and activation with no battery charging needed and no cable attachments (Figure 1). Up to 14 days of continuous ECG recording. Wearable in shower, during exercise and while sleeping. Single-channel, up to 14 days. Two-channel, up to 5 days

Purpose

Our goal is to evaluate the quality and diagnostic efficacy of ePatch® in different clinical settings.

Methods

Prospective study of patients with ePatch® placed in our center requested for study of undocumented palpitations, syncope or cryptogenic stroke. Clinical variables, diagnostic accuracy, and time to diagnostic event are analyzed.

Results

We enrolled the first 57 patients, of whom 27 (50%) were women, with a mean age of 67.8 years (range 27 to 89). The most common indication was undocumented palpitations (35.1%) and cryptogenic stroke (35.1%), followed by syncope (29.6%). All the patients had correct compliance, tolerance and the ECG monitored was of good quality. A conclusive diagnosis: (symptomatic sustained tachyarrhythmia, atrial-ventricular block with bradyarrhythmia, significant pauses or AF) was obtained in 5 patients (8.7% of the total). The average event time since ePatch registration started was 2.4 days (range 1 to 5 days).

Conclusions

The ePatch® is a new extended Holter device offering a simple, complete and efficient ECG monitoring system. This new technology is safe and has numerous advantages: spend less time on logistics and device handling, prepare your patients efficiently, eliminate the need for additional consumables and constant battery replacement. The ECG monitor of our study is of 5 days, but it can be used for up to 14 days. It is designed to address the challenges of traditional Holter monitoring.
Early cardiologist follow-up determines treatment rates and survival among aortic stenosis patients
Authors: Rapp O , Shimoni O , Klempner R , Segev A , Pounds C , Massalha E
Published: November 2023

Background

Aortic valve replacement (AVR) has been constant as it is considered an effective disease-modifying procedure for patients with severe aortic stenosis (sAS). However, the implanter is only one part of it. Patients with sAS generally require ongoing care and support from a multidisciplinary team that includes general cardiologists (GC) and other primary care providers, who guide and manage the patient through the therapeutic process.

Purpose

The current study aims to assess the impact of a cardiologist or other primary care provider managing the therapeutic process in the early period first phase following the sAS diagnosis.

Methods

A large-scale database of Maccabi Health Services, the second largest health maintenance organization (HMO) in Israel, counting Israel with nearly 2.8 million members, was retrospectively analyzed from 2005 to 2021 for all patients over 60 years, with detailed echocardiography compatible with a diagnosis of sAS. The database was extracted using the MDClone healthcare data platform, generating the synthetic cohort database reliably representing the original population. sAS patients managed by GC versus non-GC care providers were assessed for differences in AVR rates, and all-cause mortality was assessed in sAS patients managed by GC versus non-GC care providers.

Results

A total of 1570 sAS patients were included in the final analysis. Of them, GCs managed GC managed 65% in the early phase (3 months) following the sAS diagnosis. sAS with heart failure (21% vs. 17%) and atrial fibrillation (26% vs. 19%) were less likely managed by GC. No statistically significant differences were observed in other baseline characteristics, including age, gender, and comorbidities.

A propensity score matching analysis revealed a significant difference in 12-month and 24-month AVR rates following diagnosis between the GC versus non-GG groups (19% vs. 3%, and 28% vs. 9%, respectively. P<0.01 for both). sAS patients managed by a GC show a significant survival benefit, 31% vs. 36% within five years after diagnosis P < 0.05.

Conclusions

Patients with sAS managed by a GC within three months after diagnosis had significantly higher chances for AVR and, consequently, better prognosis. These findings emphasize the importance of GC management in sAS.
Outcomes in the South African SHARE-TAVI registry: comparison of mortality and risk between treated and untreated cohort, and recent 1-year outcomes in the maturing local TAVR programme
Authors: Schaafsma E , Weich H , Scherman J , Tsabedze N , Ntsekhe M
Published: November 2023

The South African SHARE TAVI Registry collects local TAVI data to inform health policy, promote access to healthcare, and benchmark the local TAVI programme against recognised best practice. Strong Funder resistance still hampers treatment of aortic stenosis patients after >10 years of TAVI in SA.

Method

A web-based voluntary all-comers prospective registry captured 2083 patients' clinical history, demographics, TAVI outcomes, complications (VARC 2 definitions) and follow up at 30d, annually post TAVI (8 years). 462 patients assessed for TAVI but not implanted(49 exited prior to funding application, 413 during or after process) were also captured and followed up to 1-year, 413 of these exited during/after the funding application process, 49 exited prior to process (patient declined/unsuitable anatomy).

Results

Patient populations are similar to other registries and studies using similar generation devices (Schaafsma et al 2022).

Outcomes at 30d, 1, 2, and 5 years are comparable to other early TAVI programmes (GARY, Corevalve, PARTNER 1).

Average days to Mortality of 813/2545 full patient cohort calculated from date of TAVI Assessment & TAVI procedure date if applicable was highest in implanted patients (693d & 612d) , and lowest in those exiting the programme with no changes to treatment (216d). Most common exit reason was declined funding (71 pts) or death in the funding application process (105pts). Mortality in 68 pts waiting for a response averaged at 93d with STS of 10.996%, and 37 pts with approved funding demised prior to the TAVI date at average 78d in funding process with STS of 9.7%.

NON-TAVI patients (n=49) referred for medical or surgical treatment had a longer life expectancy (475 & 366d) than patients who immediately declined implants after the assessment (182d with STS score 7.943). 1year mortality in the 2021 cohort is 14.1%, and in 2022 cohort procedural success is 99.3%, mortality at 30d is 2.53%.

Conclusion

SA's TAVI programme provides clear benefit to Aortic stenosis patients in terms of life expectancy and the maturing programme's high procedural success and extended life expectancy as shown in local data may be used to support access to treatment in the face of funding resistance.Table 1