bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicted survival and self-reported physical activity in ambulatory patients with stage c/d heart failure
Authors: Boonbumrung N.B , Krailak L.K , Nakviroj P.N , Puwanant S 
Published: November 2020

Abstract

Background
There is a paucity of evidence with regard to the correlation of physical activity (PA) and survival estimated by the Seattle Heart Failure Model.

Objectives
To evaluate the association between 1- and 5-year survival estimated by the Seattle Heart Failure Model and self-reported PA in ambulatory heart failure (HF) patients in a HF clinic.

Methods
We prospectively enrolled 115 consecutive outpatients with HF in multidisciplinary HF clinic. All patients had complete baseline self-reported physical activity data, using a modified version of the international Physical Activity Questionnaire-Short Form questionnaire. PA was categorized as poor, intermediate, or ideal activity according to American Heart Association (AHA) criteria. Predicted survival was computed by the Seattle Heart Failure Model (SHFM). The relationships between these measures were examined.

Results
Among the 115 patients enrolled, the mean age was 57 years, 72% were male, 64% had reduced ejection fraction, and 21% had stage-D HF with AHA criteria. Few patients met the criteria for ideal physical activity (5% ideal, 56% intermediate, 39% poor). Predicted 1- and 5-year survival rates of the cohort were 94±8% and 75±20%, respectively. A daily physical activity was significantly associated with 1-year and 5-year survival estimated by SHFM.

Conclusions
In ambulatory patients with HF, physical inactivity was significantly associated with predicted short-term and long-term mortality in not only stage C HF patients but also stage D patients. The survival benefit of physical activity was sustainably more pronounced at 5 years. Poor physical activity was associated with the largest risk of predicted mortality.

Funding Acknowledgement
Type of funding source: None
Accurate prediction of left ventricular diastolic dysfunction in 2D echocardiography using ensemble of deep convolutional neural networks
Authors: Karuzas A , Sablauskas K , Verikas D , Skrodenis L , Teleisyte E , Matuliauskas D , Balciunas J , Kiziela A , Rumbinaite E , Lapinskas T , Ereminiene E , Mizariene V , Vaskelyte J.J , Jurkevicius R , Plisiene J 
Published: November 2020

Abstract

Introduction
Deep convolutional neural networks (CNNs) have been shown to be reliable in evaluating geometrically assessed systolic heart function, however, studies evaluating diastolic heart function, which reflects hemodynamic status, are scarce. This study presents a novel CNNs approach for recognition and evaluation of different diastolic left ventricular (LV) function measurements.

Purpose
To create a new CNNs approach in the assessment of LV diastolic function and compare the results with clinicians' measurements.

Methods
A total of 4586 2D echocardiographic images were extracted from the studies of 330 patients referred with varying indications. The ensemble of four CNNs was trained (80/20% training/validation patient split) for the assessment of transmitral inflow (E and A wave peak velocities), mitral septal and lateral annular velocities (e'), tricuspid regurgitation systolic velocity (Vmax) and detection the left atrial endocardium in apical four-chamber views. Additionally, E/A ratio, average E/e' ratio and left atrial volume index (LAVi) values were calculated for the evaluation of diastolic dysfunction according to the 2016 ASE/EACVI recommendations. CNNs performance in detecting diastolic dysfunction was compared to expert cardiologists on a set of 20 separate cases.

Results
Study results on the validation data showed that CNNs accurately predicted peak E/A ratio, average E/e' ratio (R2=0.88 and R2=0.89, respectively) and tricuspid regurgitation Vmax (R2=0.82). Figure 1 illustrates different functional LV measurements among the obtained echocardiographic images. Regarding the geometrical assessment of diastolic function, the segmentation model traced the left atrial endocardial border (intersection over union = 0.94) and subsequently was used in predicting the LAVi (R2=0.92). The ensemble of four CNNs had the area under the ROC curve of 0.93 for the detection of diastolic dysfunction when compared to expert cardiologists.

Conclusion
Deep CNNs demonstrate the capacity to detect peak velocities across different Doppler imaging modes and delineate left atrial endocardium border while using a relatively small dataset. Combining multiple CNNs has the potential of performing an accurate assessment of the diastolic LV function.

Funding Acknowledgement
Type of funding source: None
The artificial intelligence system in prediction of the prevalence of acute coronary syndromes based on weather conditions
Authors: Molek P , Wlodarczyk A , Bochenek B , Wypych A , Nessler J , Zalewski J 
Published: November 2020

Abstract

Background
It was shown that different individual weather conditions are associated with the incidence of acute coronary syndromes (ACS). Despite this, the prediction of the number of ACS depending on the weather conditions in the given place and time is not effective to date.

Purpose
We sought to investigate whether the artificial intelligence system might be useful in prediction of the prevalence of ACS based on weather conditions.

Methods
In this study, data of 159307 consecutive patients obtained from National Health Service registry, hospitalized due to ACS in Lesser Poland Province (province area of 15008 km2, population of 3.4 M in 2014) between 2008 and 2018 have been compared with meteorological conditions collected in the Institute of Meteorology and Water Management from five weather stations scattered across Lesser Poland Province. Because of small sample size in three of them, only data from two stations (Krakow-Balice, n=75565, Tarnow, n=30079) were used for further analysis. In four separate seasons, the number of ACS events in each day was compared with meteorological conditions on a given day and six days before. We analysed weather conditions such as: wind 10 metres above ground (W_10), temperature (T), dew point temperature (T_dp), relative humidity 2 metres above the ground (Hum_2), atmospheric pressure reduced to mean sea level (Pres), atmospheric precipitation (Prec), and 3 hours atmospheric pressure changes (Pres_3h). For all parameters extreme (maximum – max, minimum – min) values and ranges of these parameters in each day were analysed. All data were used in a system based on machine learning (Random Forest), which allowed to create a model that predicted the incidence of ACS and to determine importance of each inputted weather parameter in this prediction.

Results
All weather parameters were divided into machine learning data (70%) and test data (30%) to verify functioning of the model. The correlation between real number of ACS and predicted number of ACS for two meteorological stations for spring ranges from 0.69 to 0.71 with confidence intervals (CI) of 0.63–0.77, for summer the correlation was 0.66–0.75 with CI of 0.59–79, for autumn 0.69–0.74 with CI of 0.63–0.79 and for winter 0.69–0.72 with CI 0.63–0.77 (P&lt;0.0001 for each prediction, example of prediction in the Figure 1A). Among all analysed meteorological parameters the most important in the machine learning were range of relative humidity, range of dew point temperature and maximal relative humidity (Figure 1B).

Conclusions
Artificial intelligence system seems to be useful in predicting the prevalence of ACS with model based on weather conditions.
Figure 1. ACS prediction for summer in Krakow

Funding Acknowledgement
Type of funding source: None
Ventricular transmural decremental conduction explains short t-peak-t-end interval in “in silico” ECG reconstruction model
Authors: Benchimol Barbosa P.R , Barbosa E.C , Barbosa E.C 
Published: November 2020

Abstract

Introduction
In chronic hepatic cirrhosis (CHC), T-wave peak to T-wave end interval (TPTE) has shown prognostic value for survival and liver transplantation. Altered cellular ionic regulatory systems may produce decremental conduction in ventricular endocardial to epicardial activation wavefront propagation and ECG waveforms, particularly the T-wave. It was investigated whether ventricular transmural activation wavefront conduction may affect T-wave shape and impact TPTE duration, using “in silico” ECG reconstruction.

Methods
Mammalian-derived ventricular endocardial to epicardial AP waveforms (APW) were simulated and deployed in 10 discrete layers in homogeneous impedance ventricular wedge-like model. A uniform conduction model was employed to mimic normal heart, in which the speed of propagation of the activation wavefront was nonzero and constant in all layers. In CHC heart, a decremental conduction model was employed, in which the speed of propagation of the activation wavefront was maximal at the endocardial layer and exponentially decayed to greater than zero speed at the epicardial layer. ECG was computed as the sum of dipoles weighted by the inverse of the squared distance to an observation electrode arbitrarily located outside the wedge. One dipole was one layer thick, and its charge was assumed as the time-integral of the current generated by the difference of potential between adjacent APW, from endocardial to epicardial layers. ECG was reconstructed in one axis.

Results
Two-dimensional transmural APW distribution and respective reconstructed ECGs are presented in Figures 1 and 2. In uniform endocardial to epicardial transmural conduction model, QRS complex was taller and shorter as well as TPTE was larger (Fig. 1) than respective counterparts in decremental conduction model (Fig. 2). Additionally, peak-amplitude of the T-wave as well as the maximal slope of the TPTE were lower in decremental as compared to uniform conduction model.

Conclusion
Using “in silico” ECG reconstruction, decrementally conducted model of epicardial to endocardial ventricular transmural activation wavefront propagation yields wider and lower amplitude QRS complex as well as shorter TPTE, as compared to respective counterparts in uniformly conducted activation wavefront model. Ventricular transmural decremental conduction model offers an insight into electrophysiological background of ECG findings in CHC. (NCT01433848)
Transmural APW distribution vs ECG

Funding Acknowledgement
Type of funding source: Public Institution(s). Main funding source(s): Universidade do Estado do Rio de Janeiro; Rio de Janeiro, RJ - Brazil
Cost-effectiveness analysis of a telemonitoring program on patients with heart failure in Normandy: an 8-year retrospective analysis (2009–2017)
Authors: Sabatier R , Legallois D , Jodar M , Donio V , Courouve L , Hauchard K , Boudevin F , De Chalus T , Belin A , Milliez P 
Published: November 2020

Abstract

Background
SCAD is a 3-month telemonitoring program for HF patients, associated with therapeutic education, proposed after an acute HF episode. SCAD is based on an interactive algorithm allowing to generate educative messages and alarms based on patients' responses registered on a digital tablet. It is funded by the French Health Insurance through a pilot program: ETAPES (470€/patient).

Purpose
To describe the profile of patients using SCAD & assess the medico-economic impact of the SCAD system.

Methods
Multicenter retrospective cohort study using SCAD data matched with French Health Insurance data. All patients telemonitored by SCAD in 7 centers have been included, since 01/01/2010 to 12/31/2016. Only direct costs were considered, estimated from a societal perspective limited to reimbursements. Analyses were performed to assess the difference in healthcare consumptions and costs between the year before and the year after inclusion in the SCAD program. Patients who died in the 12 months after SCAD initiation were excluded.

Results
627 patients benefited from SCAD program between 01/01/2010 and 12/31/2016 and were retrieved in French Health Insurance data through probabilistic matching. Out of the 627 patients, 99 died in the 12 months after SCAD initiation. Analyses were performed on 528 patients.72.2% were male, mean age was 66.0 years old and mean BMI 28.2. HFrEF represented 51.9% of patients, HFmrEF 25.9% and HFpEF 22.2%. 58.0% were in NYHA class 2 at baseline, 29.2% in class 3, 8.5% in class 1 and 4.3% in class 4. Mean Charlson Comorbidity Index score was 2.6 at baseline. Patients reported their level of fatigue (10 representing significant fatigue) and morale (10 = good morale) at baseline: mean fatigue=4.0/10 & mean morale=7.4/10. Medico economic results are presented in table 1 and show an important & significant decrease of hospitalizations costs and some transfer of cost toward ambulatory care.

Conclusion
On the year following remote monitoring, total health expenditure has been reduced by 18% on average (mean=3 210€/patient) and 42% in median (5 500€/patient) vs 12 months before.

Funding Acknowledgement
Type of funding source: Private company. Main funding source(s): This analysis has been funded through an institutional grant from Amgen
The risk of sudden cardiac death or ventricular arrhythmias on Immune checkpoint Inhibitors
Authors: Mirabel M , Karapetiantz P , Marijon E , Le Beller C , Reda Al-Sayed Z , Hulot J.S , Lillo-Lelouet A 
Published: November 2020

Abstract

Background
Immune checkpoint inhibitors (ICI) have significantly improved the prognosis of many advanced cancers, and may be given in non-metastatic cancer in the near future. ICI have recently been reported to induce fulminant cardiotoxic effects such as myocarditis, responsible for ∼50% mortality rates.

Objective
To estimate the risk of sudden death (SD) and ventricular arrhythmias in patients receiving ICI using the World Health Organization individual case safety report (ICSR) database, Vigibase (WHO international pharmacovigilance database).

Methods
The system organ class MEDRA was used to identify cases as ICSR with the terms sudden death, sudden cardiac death, cardiac arrest, ventricular fibrillation, ventricular tachycardia, ventricular arrhythmia and torsades de pointes (named as SD events) from Nov 1967 to Nov 2019. We used the ATC code L01 which regroups 219 antineoplastic agents including ICI avelumab (anti-PDL1), ipilimumab (anti CTLA4), nivolumab (anti-PD1) and pembrolizumab (anti-PD1). A disproportionality analysis was performed to estimate of relative Odds Ratio (ROR). Signals were considered significant when the lower boundary of the 99.97% confidence interval (ROR0.25) was ≥1.

Results
We found that avelumab was significantly associated with SD events (ROR0.25=1.7). This overreporting was not observed for other ICIs. Avelumab was associated with 12 cases of cardiac arrest (n=11) or sudden death (n=1), which were reported since 2017 as the drug became available. There were however no signals regarding other terms including ventricular arrhythmias.

Conclusions
In spite of the potential severity of ICI-myocarditis, ICI do not appear as associated with the occurrence of sudden death or life-threatening arrhythmias, with the exception of avelumab (anti-PDL1), one of the latest developed ICI, indicated in metastatic Merkel cell carcinoma and advanced renal cell carcinoma. Further attention is warranted to confirm this signal that may vary among ICI therapies.

Funding Acknowledgement
Type of funding source: None
Reconfiguring traditional EKG interpretation with artificial intelligence – a reliable, time-saving alternative?
Authors: Mehta S , Gibson M , Avila J , Villagran C , Fernandez F , Niklitschek S , Vera F , Rocuant R , Cardenas G , Frauenfelder A , Vieira D , Merchant S , Vijayan Y , Tamayo C.J , Pinos D 
Published: November 2020

Abstract

Background
Time and accuracy are key factors that may make or break an efficient triage and management in most medical premises, particularly so when expedited diagnosis saves lives - a not so uncommon scenario in the field of cardiology. By studying the different variables involved in cardiologist-EKG interactions that lead to the identification and management of different cardiovascular entities, we delved into the applications of Artificial Intelligence (AI) algorithms in order to improve upon the classic, but dated, EKG methodology. With this study, we pit our algorithm against cardiologists to perform a thorough analysis of the time invested to diagnose an EKG as Normal, as well as an assessment of the accuracy of said label.

Purpose
To present a faster and reliable AI-guided EKG interpretation methodology that outperforms cardiologists' capabilities in identifying Normal EKG records.

Methods
The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real time. During the month of April 2019; 35 ITMS cardiologists reported a grand total of 61,441 EKG records, later subjecting them to the AI algorithm, implemented through the “One Click Report” process. Through this simple 2-step approach, the algorithm provides a suggestion of “Normal” or “Abnormal” to the cardiologist based on the patterns of the fiducial points included in said EKG reports. A comparison of the time of normal EKG diagnosis is made and the correlation between AI and cardiologists is assessed.

Results
On average, our AI algorithm discerned a normal EKG within 30.63s (95% CI 26.51s to 34.75s), in solid contrast with cardiologists' interpretations alone, which amounted to 83.54s (95% CI from 69.43s to 97.65s). This accounts for an overall saving of 52.91s (95% CI 42.45s to 63.83s) by implementing this innovative methodology in a cardiologist practice. In addition, this method correctly reported 23,213 Normal EKG records out of the total 25,013 AI output, reaching a 92.8% correlation between man and machine. The total average time saved in normal EKG readings with AI (23,213) would accrue an approximate of 20,470 minutes (ie, 42 8-hours work shifts worth of time dedicated to diagnosing a normal EKG).

Conclusions
The implementation of automated AI-driven technologies into daily EKG interpretation tasks poses an attractive time-saving alternative for faster and accurate results in a modern cardiology practice. By further expanding on the concept of an intelligent EKG characterization device, a more efficient and patient-centered clinical exercise will ensue.

Funding Acknowledgement
Type of funding source: None
Prognostic value of a normal Artificial Intelligence applicative response in subjects with stable chest pain. From the ARTICA co-operative registry
Authors: Mazzanti M , Shirka E , Marini M , Pottle A , Goda A , Pugliese F 
Published: November 2020

Abstract

Background
An innovative artificial intelligence (AI) Decision Support System (DSS) ESC guidelines based has already been used at point of care with efficacy for evaluating subjects with stable chest pain (SCP) and it has been proved to correctly identify absence of significant coronary artery disease (CAD) versus standard care approach (SD) without known prognostic implication yet.

Purpose
The aim is to determine the prognostic value of “no testing/Follow up” AI DSS response in a referral population evaluated for SCP.

Methods
From 2016 to 2019, an AI DSS ESC guidelines based applicative was used on 1.291 subjects with SCP to determine testing appropriateness compared with human specialist standard evaluation. 590 of them, 332 males, mean age 62±11 years deemed to be completely negative – by “no testing/follow up” response - were evaluated. The negative response was defined and confirmed by a normal Coronary Tomography Angiography scan executed in all these subjects. Mean follow-up was 3.46±1.76 years. Two groups based on pre-test likelihood of having CAD were analyzed – low and intermediate. No subjects with high pre-test likelihood were present. The primary endpoint was cumulative incidence of cardiovascular death, hospitalizations for acute coronary syndrome and coronary revascularizations.

Results
The primary endpoints classification is displayed in the Table. The unadjusted hazard ratio for primary endpoint was 3.84 (95% CI 0.32–8.68, p=0.009) in patients with intermediate compared to those with low pre-test likelihood of CAD. Moreover, the “no testing and Follow up” response showed an incremental prognostic value over conventional risk factors (χ2=7, P=0.022) and over a combination of conventional factors and ST-T changes (χ2=9, P=0.014).

Conclusions
In an outpatient population without known CAD evaluated for chest pain, after the administration of AI DSS, a “No tests/Follow up” confers an excellent prognosis regardless of the ESC SCORE Charts and rest ECG abnormalities. These preliminary data confirms the safety of ARTICA AI DSS use in subjects with stable chest pain.

Funding Acknowledgement
Type of funding source: None
The assessment of the accuracy and credibility of cardiovascular health information on social media platforms
Authors: Ragab A.A , Arinze J.T , Akbulut F , Orji R 
Published: November 2020

Abstract

Background
Cardiovascular diseases (CVD) is a major cause of death worldwide. Effective and accurate dissemination of scientific information is key to addressing this public health problem. Social media platforms play major roles in public health promotion considering that a significant percent of the world population use social media. About 40% of the adults in the US use social media for health information. There is little knowledge about the accuracy and credibility of CVD related information posted on social media platforms.

Purpose
The study aimed to assess the scientific credibility and accuracy of cardiovascular health information of highly shared social media posts.

Methods
Cardiovascular related posts on social media platforms (Facebook and Twitter) were mined using the content analyzer platform (BuzzSumo). All articles published between June 2018 and June 2019, with over 10,000 shares, were selected and ranked by two physicians based on the accuracy and credibility of the titles, reported statistics, interpretation of results, sample size, and external validity. A point was scored for each of the five assessment criteria and articles with scores of less than five were considered to be misleading.

Results
A total of 50 articles, with 3,397,918 shares (median: 44350, IQR:25100–78000), were selected and scored. Misleading articles were 32/50 (64%) with 2,528,800 shares (median: 44350, IQR: 39575–80250). Twenty-six percent of the articles had misleading titles, 16% had misleading reported statistics, 30% had wrong interpretation of results, 36% reported small sample size studies, and 54% of them lacked external validity. Overall, articles on diet (41%) had more misleading titles than articles on other topics (14%), p=0.03.

Conclusion
More than half of the articles on widely used social media platforms contain misleading information on cardiovascular health. A concerted effort is needed to ensure the accuracy and credibility of public health information shared on these platforms to avoid misleading users.
Figure 1

Funding Acknowledgement
Type of funding source: None