bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep learning-based electrocardiogram analysis detecting paroxysmal atrial fibrillation during sinus rhythm in patients with cryptogenic stroke: validation study using implantable cardiac monitoring
Authors: Jeon K H , Kwon J M , Lee M S , Cho Y J , Oh I Y , Lee J H 
Published: October 2022

Abstract

Background
Atrial fibrillation (AF) is the most cause of cardioembolic source causing cryptogenic stroke. In these, anticoagulation therapy could reduce recurrence of stroke. However, paroxysmal AF would not be detected even by 24 hours Holter monitoring. Deep learning-based electrocardiogram (ECG) analysis models were recently developed to detect AF during sinus rhythm.

Purpose
We aimed to develop a deep learning algorithm (DLA) to detect AF during sinus rhythm and validate the model in patients with cryptogenic stroke who underwent implantable cardiac monitoring (ICM) to diagnose paroxysmal AF.

Methods
This cohort study involved three hospitals (A, B, and C). We developed a DLA to detect AF using sinus rhythm 10 s 12-lead ECG. We included adult patients aged ≥18 years from hospital A and B. We used development data from AF adult patients who had at least one atrial fibrillation rhythm in the study period (Jan 2016 to Dec 2021) and non-AF patients who had no reference to AF in the ECG and electronic medical record. DLA was based on convolutional neural network (CNN) using 10 s 12-lead. For external validation, the ECGs from 217 patients (hospital C) with cryptogenic stroke who underwent ICM were analyzed by using the DLA for validating the accuracy in the real-world clinical situations.

Results
We included 10,605 AF adult patients and 50,522 non-AF patients as development data. During the internal validation, the area under the curve (AUC) of the final DLA based on CNN was 0.793 (95% Confidence interval 0.778–0.807). In external validation data from cryptogenic stroke patients, the mean ICM duration was 15.1 months, and AF &gt;5 mins was detected in 32 patients (14.5%). The diagnostic accuracy of DLA was 0.793 to detect AF during sinus rhythm, and AUC was 0.824. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 0.844, 0.784, 0.403, and 0.967, respectively, which outperformed other conventional predictive methods based on clinical factors, such as CHARGE-AF, C2hest, and HATCH.

Conclusions
In this study, DLA accurately detected paroxysmal AF using 12-leads normal sinus rhythm ECG in patients with cryptogenic stroke and outperformed the conventional models. The DLA could be used as a screening tool to identify the cause of stroke in the future.

Funding Acknowledgement
Type of funding sources: None.
PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease
Authors: Holm P , Haue A D , Westergaard D , Banasik K , Koeber L , Brunak S , Bundgaard H 
Published: October 2022

Abstract

Background
Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago.
We developed and externally validated PMHnet-alpha, a neural-network based survival model for risk-stratification in ischemic heart disease that leverages the multitude of clinical features available in modern electronical health records.

Methods
We included 39,746 IHD patients from the regional Heart Registry that had been subjected to a coronary angiography between 2006 and 2017 with confirmed coronary artery disease.
Clinical data was extracted from the Danish National Patient Registry, and electronic health records.
595 different features, consisting of diagnosis codes, procedure codes, biochemical test results, and clinical measurements were used as model inputs.
Prior to model development, patients were randomly divided into a training set (n=34,746) and a tesing set (n=5,000).
The testing set was not used for model development.
Model performance was evaluated at six months, one years, three-, and five years of follow-up using time-dependent ROC curve analysis and Harrels' C-index.
Lastly, we also assessed the calibration of the model.
We benchmarked the performance of PMHnet-alpha against the GRACE Risk Score 2.0, which is widely considered to the best-performing model in current clinical use.
We explored the importance of individual features using SHAP values on the trained models.

Findings
PMHnet-alpha had very high model discrimination on the testing data with time-dependent AUCs of 0.88 (95% CI 0.86–0.90) at six months, 0.88 (95% CI 0.86–0.90) at one year, 0.84 (95% CI 0.82–0.86) at three years, and 0.82 (95% CI 0.80–0.84) at five years.
The discrimination of the benchmark model GRACE2.0 on the same data was considerably lower, 0.77 (95% CI 0.73–0.80) at six months, 0.77 (95% CI 0.74–0.80) at one year, and 0.73 (95% CI 0.70–0.75) at three years.
PMHnet-alpha is undergoing external validation in other nordic countries.
We identified that on-average, age, coronary pathology and smoking status were the most impactful features.

Interpretation
Here we present a significant improvement of the state of the art in cardiac risk prediction.
PMHnet-alpha supports better and optimized use of available healthcare data, signified by the vast improvement compared to GRACE2.0.
This also signifies an important paradigm shift in which data-driven strategies are necessary to transform the increasing amount of data generated in the modern healthcare system into evidence-based clinical decision making.

Funding Acknowledgement
Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation, NordForsk
Performance metrics of AI-enhanced single lead EKG maintained after entry of organised clustered data
Authors: Mehta S , Vieira D , Zerpa D , Guillen V , Gonzalez A , Brena-Pastor L , Siyam T , Stoica S , Ozair S , Pinos D , Martinez F , Fleming M , Carrera K , Rossitto F , Whuking C 
Published: October 2022

Abstract

Background
Our experience in creating innovative Artificial Intelligence-guided single lead EKG methodologies for ST-Elevation Myocardial Infarction (STEMI) detection within complex EKG records has been previously validated.

Purpose
By expanding the intricate variables of our previously tested algorithm input, we seek to further improve our STEMI detecting tool.

Methods
11,567 12-lead EKG records (10-s length, 500 Hz sample frequency) derived from the Latin America Telemedicine Infarct Network database from April 2014 to December 2019. From these records, we included the following balanced classes: angiographically confirmed and unconfirmed STEMI (divided by wall affected), branch blocks, non-specific ST-T changes, normal, and abnormal (Remaining 200+ CPT codes). Cardiologist annotations ensured precision (Ground truth). Determined classes were “STEMI” and “Not-STEMI”. A 1-D Convolutional Neural Network model was trained and tested for each lead with dataset proportions of 90/10, respectively. The last dense layer outputs a probability for each record being STEMI/Not-STEMI. The analysis also included performance metrics and false-negative reports.

Results
Overall, the most promising Single lead for STEMI detection was V2 (91.2% Accuracy, 89.6% Sensitivity, and 92.9% Specificity). 55% of false negatives were inferior wall STEMI (Table 1).

Conclusion
Appreciable progress of our new methodology compared to our previous experiences in AI-guided Single Lead for STEMI detection, especially for lead V2. By performing a thorough analysis of false-negative reports, we aspire to identify potential areas of STEMI detection weakness which will become the focus of future ventures.

Funding Acknowledgement
Type of funding sources: None.
Development of an app-based bedside clinical decision-making tool for mechanical cardiocirculatory support in patients with cardiogenic shock: the MCS-Aid app
Authors: Giustino G , Serrao G W , Melarcode-Krishnamoorty P , Vengrenyuk A , Kyaw H , Gidwani U , Sharma S K , Kini A 
Published: October 2022

Abstract

Background
Patients presenting with cardiogenic shock remain at high risk of morbidity and mortality. Several mechanical cardiocirculatory support (MCS) devices have been developed and their use is rapidly increase in clinical practice. However, there is significant heterogeneity in patient selection, timing of implantation, and post-implantation management across centers and operators.

Purpose
We sought to develop and smartphone app-based clinical decision-making tool to help bedside selection and post-implantation management of MCS devices in patients presenting with cardiogenic shock or cardiac arrest.

Methods
The MCS-Aid app will consistent of 3 major sections: (i) initial device selection based on clinical presentation (patients with cardiogenic shock or cardiac arrest post-ROSC); (ii) guide for escalation or addition of MCS based on the individual hemodynamic scenario; (iii) guide for weaning after implantation of MCS device. The app will have an interactive interface that will allow the user to select the most appropriate next step in management based on the clinical information being entered. A calculator to derive key hemodynamic parameters (e.g. cardiac power output or pulmonary artery pulsatility index) will be incorporated in the App to inform clinical decision-making when appropriate. An example of an algorithm that will be part of the MCS-Aid app is illustrated in the figure.

Conclusions
The MCS-Aid app is an user-friendly bedside clinical decision tool that could help fellows-in-training, early-career interventionalist and interventional cardiologist to select the appropriate MCS device according to the individual clinical and hemodynamic scenario.

Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): Abiomed
Development of knowledge-based clinical decision support system for the management of cardiovascular diseases
Authors: L Canoa J , Pena Gil C , Monserrat L 
Published: October 2022

Abstract

Background
Knowledge-based clinical decision support systems (CDSS) are technological tools that analyze patient information and present justified diagnostic and therapeutic suggestions to the user. They improve adherence to evidence-based medicine and reduce medical errors.

Material and methods
We have developed an advanced CDSS with the following components: 1. Knowledge Management System, which allows the introduction of interoperable clinical variables organized in taxonomies, and the generation, validation and direct maintenance of decision rules by experts doctors. 2. Smart Assistant for Medical Reports Generation, which facilitates user interaction for the introduction of clinical data (natural language and/or data entry forms) and decission making supported by real-time suggestions that are automatically generated by 3. Inference Engine. Rules and variables derived from the most relevant cardiovascular Clinical Practice Guidelines (CPGs) are introduced by cardiologists into the system. The rules undergo a process of peer review and internal and external validation with anonymized clinical cases.

Results
More than 2,000 clinical variables and more than 5,000 rules corresponding to more than 80 CPGs and consensuns papers have been created and validated. An internal and external pilot validation of the system has been successfully carried out for the main cardiovascular problems, such as heart failure, atrial fibrillation, valvulopathies, coronary syndromes, cardiomyopathies, hypertension, diabetes and dyslipidaemias, using more than 400 cases.

Conclusion
Our CDSS is capable of providing real-time diagnostic and therapeutic recommendations for complex scenarios in cardiovascular disease, combining thousands of variables and decision rules in a single system. Prospective evaluation of the system in real clinical environments, certification and integration in Electronic Medical Records for clinical use is required.

Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): DILEMMA solutions, SL
Perspectives on the use of digital health technologies in cardiology among specialists from an ESC member country: results from a survey
Authors: Kocyigit D , Keser N , Simsek E , Bozyel S , Dalgic Y , Seker M , Korkmaz Y , Guler A , Cinar T , Erturk M 
Published: October 2022

Abstract

Background
Digital health technologies (DHTs) have the potential of facilitating both physicians' and patients' lives with regards to the diagnosis, treatment and follow-up of cardiovascular disease. A huge acceleration in relevant research has been noted particularly after the COVID-19 pandemic.

Purpose
The goal of this study was to determine the reasons for using DHTs in cardiology, as well as the perceived barriers to its use.

Methods
An electronic survey with 43 question multiple-choice questionnaire was conducted between January 10-March 3, 2022. National cardiac society member cardiology fellows in training and specialists were contacted via e-mail (n=2789).

Results
308/2789 (11.04%) subjects responded to the survey (72.40% males, 62.01% aged 30–44 years). 42.53% and 44.81% were affiliated with university hospitals and state hospitals, respectively. 88/297 (29.63%) stated having at least good understanding of DHTs in cardiology. 44.16% utilized smart devices to track their own health status. 117/290 (40.34%) and 193/299 (64.55%) used social media platforms to share medical information with their patients and other physicians, respectively. WhatsApp and Instagram were the most popular platforms for sharing with patients (92/117, 78.63% and 48/117, 41.03%), while WhatsApp and Twitter were the most popular platforms for sharing with other physicians (151/193, 78.24% and 91/193, 47.15%). Considerations and recommendation/utilization patterns of DHTs by physicians are summarized in Table 1. Perceived barriers to the use of DHTs in cardiology is shown on Figure 1 (A: physician-related, B: patient-related, C: technical).

Conclusion
Findings suggest that nearly half of the physicians use DHTs to collect their own health data and use social media to disseminate health information. The majority of physicians believe that DHT is beneficial to both themselves and their patients, and that DHT use in cardiology has increased as a result of the COVID-19 pandemic. To overcome the challenges to the use of DHTs in cardiology, a multilayered collaborative effort involving patient and professional organizations, as well as technical stakeholders and lawmakers, is required.

Funding Acknowledgement
Type of funding sources: None.
Assessment of heart failure parameters by telemonitoring patients with an implantable cardioverter defibrillator
Authors: Fily M , Feliachi S , Leven F , Inamo J C , Hamonic J B , Mansourati J 
Published: October 2022

Abstract

Introduction
Different heart failure (HF) parameters have been evaluated in patients with an implantable cardioverter defibrillator (ICD). Recently, a multi-parameters HF index available in some ICDs has been proposed for patients monitoring in order to prevent hospitalizations for HF. We sought to analyze this index in our patients, based on the occurrence of hospitalizations for HF and/or all-cause mortality. As a second endpoint, the level of NT-pro-BNP, the treatment of HF and the Minnesota score (quality of life) were as well evaluated.

Method
This was on an open, single-center, case-control study. Inclusions started on March 2017 to end up in November 2020 with the main criteria of HF with left ventricular ejection fraction (LVEF) &lt;35%, DDD or CRT-D implanted in primary prevention. A control group was composed of patients receiving telemonitoring and a control group composed of patients receiving standard monitoring. A 1/1 pairing was carried out, based on 3 criteria: age, sex and the date of ICD implantation. Data collection was carried out in August 2021.

Results
74 patients were included in the study; none was lost to follow-up. The occurrence of hospitalization for HF and/or death from any cause was found at the end of the study in 7 of 37 patients in the remote monitoring group versus 22 events of 37 in patients receiving standard monitoring (p=0.011). The number of deaths from any cause was 4 in the study group vs 8 in the control group (NS, p=0.21). The average length of hospital stay is 6.27 days (± 23.6) for patients receiving remote monitoring compared to 9.14 days (± 22.1) p=0.64. NT-pro-BNP was found with a median level of 553 pg/mL [146; 1541] in the remotely monitored group against 498 pg/mL [211; 1400] in controls (p=0.77). The assessment of quality of life by the Minnesota score shows a similar result (p=0.7). Analysis of the treatment of HF shows a significantly (p&lt;0.01) greater presence of anti-aldosterone in patients receiving remote monitoring.

Conclusion
Our study demonstrates the benefit of the telemonitoring protocol on the occurrence of hospitalizations for heart failure and/or all-cause mortality in heart failure patients with an ICD. We observe a significantly higher percentage of anti-aldosterone treatment in monitored patients, suggesting a more optimized medical treatment.

Funding Acknowledgement
Type of funding sources: None.
Performance of a multisensor implantable defibrillator algorithm for HF monitoring in presence of comorbidities
Authors: Santobuono V E , Tavoletta V , Manzo M , Calo' L , Bertini M , Santini L , Savarese G , Dello Russo A , Viscusi M , Lavalle C , Amellone C , Calvanese R , Valsecchi S , Favale S 
Published: October 2022

Abstract

Background
Cardiovascular and non-cardiovascular comorbidities are common in heart failure (HF) patients and impact disease severity and prognosis. Select modern implantable defibrillators (ICDs) are equipped with multisensor algorithms for HF monitoring. The HeartLogic index combines multiple ICD-based sensor data (heart rate, heart sounds, thoracic impedance, respiration, activity), and the associated alert has proved to be a sensitive and timely predictor of impending HF decompensation in cardiac resynchronization therapy (CRT-D) patients The algorithm was developed using data from CRT-D patients; the performance in non-CRT ICD patients and the impact of selected comorbidities on performance requires further study.

Methods
The HeartLogic feature was activated in 568 ICD patients (410 with CRT) from 26 centers. The median follow-up was 25 months [25th–75th percentile: 15–35].

Results
During follow-up, 97 hospitalizations were reported (53 cardiovascular) and 55 patients died. We recorded 1200 HeartLogic alerts (0.71 alerts/patient-year) in 370 patients. Overall, the time IN the alert state was 13% of the total observation period. The rate of cardiovascular hospitalizations or death was 0.48/patient-year (95% CI: 0.37–0.60) with the HeartLogic IN alert state and 0.04/patient-year (95% CI: 0.03–0.05) OUT of alert state, with an incidence rate ratio of 13.35 (95% CI: 8.83–20.51, p&lt;0.001). Among patient characteristics, atrial fibrillation (AF) at implantation (HR: 1.62, 95% CI: 1.27–2.07, p&lt;0.001) and chronic kidney disease (CKD) (HR: 1.53, 95% CI: 1.21–1.93, p&lt;0.001) independently predicted alerts. HeartLogic alerts were not associated with CRT vs. non-CRT device implantation (HR: 1.03, 95% CI: 0.82–1.30, p=0.775). The comparisons of the clinical event rates in the IN alert state with those in the OUT of alert state yielded incidence rate ratios ranging from 9.72 to 14.54 (all p&lt;0.001) in all groups of patients stratified by: CRT/non-CRT, AF/non-AF, CKD/non-CKD. Indeed, after multivariate correction for CKD and AF at implantation, the time IN the HeartLogic alert state &gt;13% was associated with the occurrence of the combined endpoint of cardiovascular hospitalization or death (HR: 2.54, 95% CI: 1.61–4.01, p&lt;0.001).

Conclusions
The burden of HeartLogic alerts appears similar between CRT and non-CRT patients, while patients with AF and CKD seem more exposed to alerts. Nonetheless, the ability of the HeartLogic algorithm to identify patients during periods of significantly increased risk of clinical events is confirmed regardless of the type of device, the presence of AF, or CKD.

Funding Acknowledgement
Type of funding sources: None.
Insights into diurnal variations of advanced hemodynamic parameters in ambulatory individuals enabled by a photoplethysmography-based wearable monitor
Authors: Nachman D , Kolben Y , Carmon E , Hazan E , Goldstein N , Eisenkraft A , Fons M , Amir O , Asleh R , Gepner Y 
Published: October 2022

Abstract

Background
Recent technological developments enable non-invasive monitoring of advanced hemodynamic parameters, allowing big data-driven insights on diurnal changes for the first time. This study aimed to describe the trends of multiple advanced cardiovascular parameters among ambulatory individuals using a medical grade wearable monitor.

Methods
Common and advanced cardio-respiratory parameters were monitored for 24 hours in ambulatory volunteers using a photoplethysmography-based wearable monitor. The multi vital ambulatory monitoring (MVAM) included stroke volume (SV), cardiac index (CI), systemic vascular resistance (SVR), blood oxygen saturation (SpO2), respiratory rate (RR), systolic (SBP) and diastolic blood pressure (DBP), mean arterial pressure (MAP), pulse pressure (PP), and heart rate (HR). Diurnal variations were evaluated among all participants, and analyses were stratified based on sex, age, and body-mass index (BMI).

Results
A total of 256 participants were included in the analysis. All measured parameters showed significant (p&lt;0.001) diurnal changes. Nighttime values were lower excluding RR and SVR, reaching the nadir at 5 am, and then increasing to a maximum at 10 am, plateauing, and then gradually declining from 7 to 8 pm. CI demonstrated a reduction of 0.6 L/min/m2 (20%), while SVR demonstrated the opposite trend and was higher during the night increasing by up to 212 dynes/sec/cm5 (16%). Females demonstrated higher HR and CI with lower SVR during daytime and nighttime. Obese participants (BMI &gt;30 kg/m2) had a higher SBP and DBP, and lower SV and CI. SVR was higher among the elderly.

Conclusions
Diurnal changes using MVAM corresponding with awake-sleep hours were noticed in the monitored parameters and differed between sex, age, and BMI groups. This suggests that wearable monitoring platforms could shed more light on hemodynamic changes in subgroups, and might help with future efforts to provide tailored personalized medicine and pre-symptomatic diagnosis and prevention.

Funding Acknowledgement
Type of funding sources: None.