bluuebunny/crossref_metadata_2025_split huggingface dataset:
PROMET: the effect of age on patient outcomes in non-laser transvenous lead extraction
Authors: Akhtar Z , Elbatran A , Starck CT , Leung L WM , Sohal M , Gallagher MM 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: None.

OnBehalf
PROMET group

Background
Cardiac implantable electronic devices (CIEDs) improve morbidity and mortality. This has fuelled an upsurge in implantation of these devices across all patient cohorts, simultaneously increasing the need for transvenous lead extractions (TLE). As the global population expands and life-expectancy extends, TLE will play a significant role in CIED management. Advancing patient age is a recognised risk factor for poor outcomes however the association between patient age and TLE outcomes remains unclear.
We sought to evaluate the relationship between patient age and non-laser TLE outcomes.

Method
Data of 2205 patients (3849 leads) was collected retrospectively from six high-volume TLE institutes across Europe (PROMET) between January 2005-December 2018. Propensity 1:1 score matching was performed to limit the effects of confounding variables, pairing 353 patients in the &gt;80 years of age category with 353 patients in &lt;80 years of age group. Procedural outcomes were compared between the two age groups and multivariate regression analysis was used for predictors of 30-day mortality.

Results
In the &lt;80 and &gt;80 years-of-age cohorts, there was a similar proportion of male patients (65.3% vs 67.9%, p = 0.47) treated under general anaesthesia (96.5% vs 93.4%, p = 0.078) for a pre-dominant infectious indication (56.7% vs 60.3%, p = 0.52) but with a higher requirement of the EvolutionTM sheath in the octogenarians (39.4% vs 48.4%, p = 0.015). A similar clinical success per lead was achieved between the two age groups (96.6% vs 98%, &lt;80 vs &gt;80 years, p = 0.245) as was complete lead extraction (95.5% vs 96.6%, &lt;80 vs &gt;80 years, p = 0.44) with a comparable minor complication rate (2.3% vs 3.1%, &lt;80 vs &gt;80 years, p = 0.29) and major complications (1.1% vs 1.4%, &lt;80 vs &gt;80 years, p = 0.74). Thirty-day mortality was higher in the octogenarian cohort than the &lt;80-year-olds without reaching statistical significance (5.4% vs 2.6%, p = 0.08); peri-procedural mortality was similar in both age groups (0.3% vs 0.6%, respectively, p = 0.56). Multivariate regression analysis revealed age (p = 0.013, OR 1.06 [1.01-1.12]), systemic infection (p = 0.026, OR 3.4 [1.16-10.35]) and lead dwell time (p = 0.007, OR 1.01 [1.003-1.017]) increased the odds of 30-day mortality.

Conclusion
Transvenous lead extraction is similar in efficacy and safety across all age groups. Thirty-day mortality is higher in the advanced age group, signifying the importance of post-procedural management in this cohort.
Brugada syndrome in Hong Kong: long term outcome prediction through machine learning
Authors: Lee S , Zhou J , Li KHC , Leung KSK , Lakhani I , Liu T , Wong ICK , Mok NS , Jeevaratnam K , Zhang Q , Tse G 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: None.

Introduction
Brugada syndrome (BrS) is an ion channelopathy that predisposes affected patients to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death (SCD). Despite its greater prevalence in Asia and epidemiological heterogeneity in disease manifestation, the majority of the conducted cohort studies available in current literature are based in Western countries.

Purpose
The aim of this study is to examine the clinical and electrocardiographic predictive factors of spontaneous VT/VF for Asian BrS patients.

Methods
This was a territory-wide retrospective cohort study of patients diagnosed with BrS between 1997 and 2019. The primary outcome was spontaneous VT/VF detected either during hospital admission or by implantable-cardioverter defibrillator (ICD) data. Cox regression was used to identify significant clinical and electrocardiographic risk predictors. Non-linear interactions between variables (latent patterns) were extracted using non-negative matrix factorization (NMF) and used as inputs into the random survival forest (RSF) model.

Results
This study included 516 consecutive BrS patients (mean age of initial presentation= 50 ± 16 years, male= 92%) with a median follow-up of 86 (interquartile range: 45-118) months. The cohort was divided into subgroups based on initial disease manifestation: asymptomatic (n = 314), syncope (n = 159) or VT/VF (n = 41). Annualized event rates per person-year were 1.70%, 0.05% and 0.01% for the VT/VF, syncope and asymptomatic subgroups, respectively. Multivariate Cox regression analysis revealed initial presentation of VT/VF (HR = 24.0, 95% CI = [1.21, 479] , P= 0.037) and standard deviation of P-wave duration (HR = 1.07, 95% CI = [1.00, 1.13], P = 0.044) were significant predictors. The NMF-RSF showed the best predictive performance compared to RSF and Cox regression models (precision: 0.87 v.s. 0.83 v.s. 0.76, recall: 0.89 v.s. 0.85 v.s. 0.73, F1-score: 0.88 v.s. 0.84 v.s. 0.74).

Conclusions
This is one of the largest territory-wide cohort studies on BrS and the largest study in Asia published to date, with an extensive median follow-up duration of 7 years. Clinical history, electrocardiographic markers and investigation results provide important information for risk stratification. Machine learning techniques using NMF and RSF significantly improves overall risk stratification performance.
The use of vector electrocardiography for pulmonary hypertension screening in patients with atrial septal defects
Authors: El-Bokl A , Cortez D 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
Patients with unrepaired atrial septal defects accumulate yearly risk of developing pulmonary arterial hypertension due to vascular remodeling in response to chronically increased pulmonary circulation. Electrocardiography, though readily available, has so far been of limited screening utility in this population. We hypothesize that vectorcardiographic parameters will aid in identifying increased ventricular afterload and thus provide an additional screening tool for pulmonary hypertension in this patient population.

Purpose
To establish whether vector electrocardiography can be used as a non-invasive screening tool for pulmonary hypertension screening in patients with atrial septal defects.

Methods
Retrospective chart review of patients with the diagnosis of secundum atrial septal defect who underwent cardiac catheterization at the University of Minnesota from January 2012 to September 2020. We excluded patients with other congenital heart diagnoses, insufficient hemodynamic data, or no sinus rhythm electrocardiogram. An electrocardiogram prior to or on the day of the intervention was assessed. Parameters analyzed include: Sokolow-Lion right ventricular hypertrophy criteria, PR duration, QRS duration, corrected QT intervals, QRS and Twave frontal plane axes, rSR’, rSR’ with the R’ of 5mm or more, as well as vectorcardiographic parameters (Kohr’s regression-related method) the QRS vector magnitude, T wave vector magnitude, and spatial QRS-T angle. These results were then used to compare those with and without pulmonary hypertension (diagnosed by mean pulmonary artery pressure of 25mmHg or more along with body surface area-indexed pulmonary vascular resistance of 3 Woods units/meter squared with normal left atrial/pulmonary vein wedge pressure). Mann-Whitney U-testing was used for non-parametric testing for significance within the dataset.

Results
Three hundred and five patients were identified with secundum atrial septal defects, but after exclusion, there were a total of 122 patients meeting criteria for assessment. Eight ASD patients had pulmonary hypertension (37% male, median age 7.5 years, interquartile range 0.4 to 23 years) while 114 patients did not (31% male, median age 5 years, interquartile range 3 to 13 years, p-value 0.68). Only the spatial QRS-T angle significantly differentiated pulmonary hypertension (median 104 degrees, interquartile range 55 to 137 degrees) from those ASD patients without pulmonary hypertension (median 37 degrees, interquartile range 21 to 63 degrees) with a p-value of 0.002. At a cut-off of 124 degrees, the positive and negative predictive values for identification of pulmonary hypertension was 36.4% and 96.4%, respectively, with an odds ratio of 13.4 (95% confidence interval of 2.9 to 63.7).

Conclusions
The spatial QRS-T angle may be a useful, non-invasive screening tool for pulmonary hypertension in patients with secundum atrial septal defects.
Associations of exercise-based cardiac rehabilitation with all-cause mortality among patients with atrial fibrillation
Authors: Buckley B , Harrison S , Fazio-Eynullayeva E , Underhill P , Lane D , Thijssen D , Lip G 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
There is limited evidence of long-term impact of exercise-based CR on clinical endpoints for patients with AF. We therefore compared 18-month all-cause mortality, hospitalisation, stroke, and heart failure in patients with AF and an electronic medical record (EMR) of exercise-based CR to matched controls.
Methods and Results
This retrospective cohort study included patient data obtained on 11 December 2020, from a global federated health research network. AF patients undergoing exercise-based CR were propensity score matched to AF patients without exercise-based CR by age, sex, race, medication, and co-morbidities. We ascertained 18-month incidence of all-cause mortality, hospitalisation, stroke, and heart failure.
Of 1,350,886 patients with AF, 10,625 patients had an EMR of exercise-based CR within 6-months of incident AF. The propensity score matched cohort of 21,250 patients with AF demonstrated that exercise-based CR was associated with 64% lower odds of all-cause mortality (odds ratio 0.36, 95% confidence interval (CI) 0.33-0.40), 41% lower odds of hospitalisation (0.59, 95% CI 0.56-0.63), and 17% lower odds of incident stroke (0.83, 95% CI 0.71-0.98) compared to propensity score matched controls. No significant associations were shown for heart failure at 18-months (0.92, 95% CI 0.81-1.02). The beneficial association of exercise-based CR on all-cause mortality was independent of sex, older age, comorbidities, and AF subtype.

CONCLUSIONS
Exercise-based CR among patients with incident AF was associated with lower odds of all-cause mortality, hospitalisation, and stroke at 18-months follow-up. The longitudinal nature of this retrospective follow-up study strongly supports the provision of exercise programmes for patients with incident AF.
Explanation-visualised deep learning model for accessory pathway localisation using 12-lead electrocardiography
Authors: Chan CP , Arnold AD , Howard JP , Shun-Shin MJ , Maclean E , Cullen B , Chow J , Lim PB , Ng FS , Linton NWF , Peters NS , Schilling RJ , Kanagaratnam P , Francis DP , Whinnett ZI 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: Public Institution(s). Main funding source(s): British Heart Foundation Imperial Centre of Research Excellence
Background/Introduction
ECG algorithms for identifying accessory pathway (AP) locations are inaccurate and difficult to use. Human expert interpretation is poorly reproducible. Artificial intelligence (AI) techniques such as machine learning can improve accuracy in classification tasks by eschewing theory-driven predictions. More reproducible and accurate AP localisation could shorten procedure time and personalise ablation consent.
Purpose
We developed a neural network to perform AP localisation using 12-lead ECGs. Its decision-making process was analysed to enable explainability of the neural network.
Methods
A convolutional neural network was trained on raw, digital, intra-procedural 12-lead ECGs of patients with manifest APs who underwent successful ablation. ECGs were labelled with AP locations as left-sided, septal or right-sided using procedure reports, fluoroscopy and electro-anatomical maps. Accuracy of the neural network was assessed via 4-fold cross-validation and was compared to the Arruda algorithm. Five cardiologists were also assessed for their accuracy in determining locations in sub-groups of cases. The neural network was retrospectively analysed to identify areas of ECGs most influential to its predictions using importance mapping.
Results
In 156 cases, accuracy of the neural network (92.9%) was significantly higher than the Arruda algorithm (76.9%; p &lt; 0.0001) and all five cardiologists (37.5% to 65.9%; p = 0.0001 to 0.0290). Importance mapping demonstrated that the QRS complexes of leads aVL and V1 were perceived as most influential, indicating interrogation of the lateral and anterior-posterior axes respectively.
The figure shows (A) architecture of the neural network, (B) accuracy of the neural network, Arruda algorithm and five cardiologists, (*, p = 0.05 – 0.01; **, p = 0.01 – 0.001; ***, p = 0.001 - 0.0001; ****, p &lt; 0.0001; as compared to the neural network) and (C) example importance maps for 12-lead ECGs of left-sided, septal and right-sided APs (in order from left to right), with darker regions corresponding to greater relative importance.
Conclusion
AI ECG interpretation allows accurate, reproducible prediction of AP locations, superior to conventional algorithms and human interpretation. Although AI decision-making is thought of as a ‘black box’, explanation visualisation techniques such as importance mapping allow humans to understand aspects of how a neural network make decisions. A prospectively validated neural network could be integrated into clinical practice to improve pre-procedural AP localisation. Abstract Figure. Summary of results
Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning
Authors: Bleijendaal H , Van Der Leur RR , Taha K , Mast T , Gho JMIH , Winter MM , Zwinderman AH , Doevendans PA , Pinto YM , Asselbergs FW , Van Es R , Tjong FVY 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: Public hospital(s). Main funding source(s): The Netherlands Organisation for Health Research and Development (ZonMw) University of Amsterdam Research Priority Area Medical Integromics

OnBehalf
CAPACITY-COVID19 Registry
Background
The electrocardiogram (ECG) is an easy to assess, widely available and inexpensive tool that is frequently used during the work-up of hospitalized COVID-19 patients. So far, no study has been conducted to evaluate if ECG-based machine learning models are able to predict all-cause in-hospital mortality in COVID-19 patients.
Purpose
With this study, we aim to evaluate the value of using the ECG to predict in-hospital all-cause mortality of COVID-19 patients by analyzing the ECG at hospital admission, comparing a logistic regression based approach and a DNN based approach. Secondly, we aim to identify specific ECG features associated with mortality in patients diagnosed with COVID-19.
Methods and results
We studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw-format 12-lead ECGs recorded after admission (&lt;72 hours) were collected, manually assessed, and annotated using pre-defined ECG features. Using data from five out of seven centers (n = 634), two mortality prediction models were developed: (a) a logistic regression model using manually annotated ECG features, and (b) a pre-trained deep neural network (DNN) using the raw ECG waveforms. Data from two other centers (n = 248) were used for external validation. Performance of both prediction models was similar, with a mean area under the receiver operating curve of 0.69 [95%CI 0.55–0.82] for the logistic regression model and 0.71 [95%CI 0.59–0.81] for the DNN in the external validation cohort. After adjustment for age and sex, ventricular rate (OR 1.13 [95% CI 1.01–1.27] per 10 ms increase), right bundle branch block (3.26 [95% CI 1.15–9.50]), ST-depression (2.78 [95% CI 1.03–7.70]) and low QRS voltages (3.09 [95% CI 1.02-9.38]) remained as significant predictors for mortality.

Conclusion
This study shows that ECG-based prediction models at admission may be a valuable addition to the initial risk stratification in admitted COVID-19 patients. The DNN model showed similar performance to the logistic regression that needs time-consuming manual annotation. Several ECG features associated with mortality were identified.
Figure 1:
Overview of methods, using and example case: (left) logistic regression and (right) deep learning. This specific case had a high probability of in-hospital mortality (above the threshold of 30%). Follow-up of this case showed that the patient had died during admission. Abstract Figure. Overview of ML methods used
Artificial intelligence: an attempt to automate remote device follow-up
Authors: Ferreira HDS , Ferrer F , Cabanelas N , Nelumba T , Ferreira AR , Faustino M , Antunes S , Vasconcelos H , Beringuilho M , Polena C , Borralho C , Madeira F , Morais C 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: None.

Introduction
Artificial intelligence (AI) through machine learning (ML) refers to the simulation of human intelligence with the capacity for achieving goals within computers. In electrophysiology, ML has many applications in electrocardiography, intracardiac mapping and cardiac implantable electronic devices (CIEDs).
Remote monitoring (RM) of patients equipped with CIEDs associates the analysis of event reports and calendar-based remote follow-ups (FU). ML applications have allowed for risk stratification, improved arrhythmia localisation and streamlined remote monitoring which may significantly reduce the workload faced by electrophysiologists.
Aim
To develop a system that automates cardiac implantable electronic devices remote follow-up.
Methods and Results
We created a Java software application, that uses the latest optical character recognition techniques combined with artificial intelligence and natural language processing to extract information from PDF reports of RM of CIEDs from different manufacturers. The current version is HIPAA (Health Insurance Portability and Accountability Act) complaint and runs on local computers only.
Using the current system, we were able to run and extract data from 30 remote follow-up PDF reports of Cardiac Implantable Defibrillators (ICDs) and Cardiac Resynchronization Therapy with Defibrillator (CRT-Ds).
Time taken from data extraction to conversion of all 30 device PDFs was under 5 minutes.
Process and data extracted are presented in the figure below. (Figure 1)

Conclusion
This machine learning algorithm proved that it is possible to facilitate and automate remote follow-up of cardiac implantable electronic devices.
In a near future this will allow to us to efficiently increase productivity, by speeding and facilitating interpretation of remote device follow-ups, leading to improvements in patientcare and precision cardiovascular medicine.
Furthermore, in the current and future pandemics it may help prevent unnecessary in-person medical visits, avoiding additional, unnecessary strain on an already overburdened and overwhelmed healthcare system, and saving costs. Abstract Figure 1
Clinic time required to manage remote monitoring of cardiac implantable electronic devices: impact of outsourcing initial data review and triage
Authors: Nicolle E , Lanctin D , Rosemas S , De Melis M 
Published: May 2021

Abstract

Funding Acknowledgements
Type of funding sources: Private company. Main funding source(s): Medtronic

Background
Remote monitoring is guideline-recommended to manage cardiac implantable electronic device (CIED) patients. With the continuous growth of implanted patients, clinic workload to review transmissions is increasing. Outsourcing initial data review and triage to an external monitoring center could be a valuable option for more efficient allocation of staff time, as high-skilled healthcare professionals can focus on patients in need rather than non-actionable data.

Purpose
The objective was to estimate the potential clinic staff time saved when outsourcing part of remote transmission review.

Methods
A previous time and motion evaluation described workflow tasks and time required for remote transmission review (4 EU sites, 674 observations). From real-world experience with a third-party monitoring service, the steps that can be outsourced were determined considering existing clinic-driven protocols for triage and transmission escalation. Staff time required with and without the monitoring service was thus modeled and compared.

Results
Outsourcing to an external monitoring center can reduce clinic staff time between 77.4% and 84.7% depending on device type. Absolute time savings range from 32.7 to 82.6 hours per year per 100 patients for therapeutic devices, and 301.3 hours for insertable cardiac monitor (ICM) patients, due to the higher frequency of transmissions in diagnostic devices.

Conclusion
Time to review remote transmissions can become overwhelming for clinics as growing CIED population often outpaces available staffing resources. Outsourcing initial review and triage to an external monitoring center (ensuring quality and regulatory compliance) can be an efficient option to save dedicated staff and facility time for other crucial healthcare activities. Clinic Staff Time Savings Per YearPacemakerICDCRTICMTRANSMISSIONS PER PATIENT PER YEARGreen (no further review required when outsourcing)3.04.35.020.8Yellow/Red (medical action/decision required)0.60.80.93.8ANNUAL STAFF TIME PER PATIENT, minutesNo outsourcing25.346.658.5219.7Outsourcing to monitoring center5.77.78.938.9ANNUAL STAFF TIME PER 100 PATIENTS, hoursNo outsourcing42.277.697.4366.2Outsourcing to monitoring center9.612.914.964.8TIME SAVED PER 100 PATIENTS, hours (%)32.7 (77.4%)64.7 (83.4%)82.6 (84.7%)301.3 (82.3%)Abstract Figure. Outsourcing Remote Transmission Review