bluuebunny/crossref_metadata_2025_split huggingface dataset:
Referrals to palliative care for cardiovascular patients: a 10-year longitudinal retrospective study
Authors: Ecarnot F , Giffard M , Seydou Sall F , Van Bulck L , Becoulet N , Seronde M F 
Published: October 2022

Abstract

Background
Patients with cardiovascular diseases have a significant symptom burden and often have a progressive course towards end-stage disease and ultimately, death. For this reason, consensus guidelines advocate early integration of palliative care into the care for cardiovascular patients, especially those with heart failure. However, palliative care involvement in the care of cardiovascular patients remains suboptimal, whereby only a small proportion of patients are referred to palliative care specialists, and often too late in the disease course. We aimed to examine the current situation and describe the profile, circumstances of death, and time of referral to palliative care of cardiovascular patients over a 10 year period in our unit.

Methods
This retrospective study included all patients with cardiovascular disease who were referred to the mobile palliative care team by the cardiology unit, in a single, large University Hospital in France between 2010 and 2020. All data were extracted from the medical hospital files. We recorded vital status, day of birth, time of referral to palliative care, day of death, and medical diagnoses. We noted whether patients had advance directives (AD).

Results
A total of 148 patients were identified; full data were available for 144 (97.3%). Of these, 136 (94.4%) died, while 8 (5.6%) were still alive in March 2022. The mean age at the time of death was 75±14 years. The median time between referral to palliative care and death was 9 days (interquartiles, 2, 41.5). We noted the following underlying diseases (patients could have more than one diagnosis): 111 (77%) had heart failure, 58 (40.2%) had ischemic heart disease, 55 (38.2%) had valvular heart disease, 39 (27%) had diabetes, 37 (25.7%) had chronic kidney disease, 30 (20.8%) had pulmonary hypertension, 17 (11.8%) had chronic obstructive pulmonary disease. In addition, 78 (54%) had at least one documented episode of atrial fibrillation, 27 (18.7%) had had a stroke. Place of death was available for 119 patients (82.6%) (Figure): The majority (73, 61.4%) died in the Cardiology department of our hospital; only 17 (14.3%) died at home (see Figure 1). Only 4 patients (2.7%) had documented AD.

Conclusion
This study suggests that palliative care is still widely under-used in cardiology patients, and is often initiated very close to the time of death, and mainly for heart failure patients. A large proportion of patients still die in the hospital setting. Further, prospective studies are warranted to investigate whether these dispositions corresponds to patients' wishes, and whether earlier initiation of palliative care could improve quality of life for cardiology patients with end-stage disease.

Funding Acknowledgement
Type of funding sources: None.
Estimation of Cardiorespiratory fitness using a using a chest mounted accelerometer
Authors: Schmidt S E , Hansen M T , Roemer T , Soegaard P , Helge J W 
Published: October 2022

Abstract

Cardiorespiratory fitness expressed as maximal oxygen consumption (V̇O2 max) is a strong predictor of cardiovascular health. However, quantification of cardiorespiratory fitness by cardiopulmonary exercise (CPX) assessment is complex and costly and therefore not suitable in most clinical settings. In the current study we develop and validate an algorithm for estimation of V̇O2 max using seismocardiography (SCG-V̇O2 max). SCG is measurement of precordial vibrations using an accelerometer.

Methods
SCG recordings and results from ergometer CPX testing of V̇O2 max in 300 subjects from six clinical studies were combined in a database. 83 subjects underwent repeated measurements sessions across several days. SCG was obtained using a sensitive accelerometer, located on the lower sternum at the Xiphoid protrusion and subjects were placed in a supine position. 5 subjects were excluded due to cardiovascular disease, or missing data. A machine learning algorithm was devolved for estimation of V̇O2 max in a training set including 221 subjects. The remaining 74 subjects were included in a test set for validation. Correlation and accuracy between SCG-V̇O2 max and ergometer CPX V̇O2 max assessed and day to day variation was assessed in subjects who underwent multiple ergometer CPX sessions.

Results
In 144 recordings from 74 test set subjects SCG-V̇O2 max was 45.0±9.3 ml/min/kg which was comparable to the ergometer CPX V̇O2 max at 44.3±10.1 ml/min/kg (p=0.09) and correlation between ergometer CPX V̇O2 max and SCG-V̇O2 max was r=0.875. Mean average percentage error was 8.7%. Day to day variation measured as the within-subjects-standard-deviation was 1.2 ml/min/kg for SCG-V̇O2 max and 1.7 ml/min/kg for ergometer CPX.

Conclusions
SCG based estimation of VV̇O2 max is a novel, low cost, easy to use and accurate estimation method of CRF with a high level of reproducibility. SCG-V̇O2 max can potentially facilitate that cardiorespiratory fitness becomes an integrated part of modern health assessment.

Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): VentriJect A/S DK
Effects of lockdown on physical activity and arrhythmia burden in patients with an implantable cardioverter-defibrillator: the COVID-19 Lockdown ICD-Carelink (CLIC) study
Authors: Ghossein M A , Linz D , Van Kraaij D J W , Van Stipdonk A M W , De Melis M , Vernooy K , Heijman J 
Published: October 2022

Abstract

Background
Both COVID-19 and the measures taken to control the pandemic may significantly affect cardiovascular health. The effects of a lockdown on physical activity and its potential consequences for arrhythmia burden remain largely unknown.

Purpose
In this study, we investigated the effect of the lockdown during the first COVID-19 wave on patients' physical activity and arrhythmia burden.

Methods
All patients with an ICD connected to a Carelink home-monitoring system from two Dutch hospitals were included. Anonymized data on physical activity, heart rate, and occurrence of ventricular tachycardia/fibrillation (VT/VF), and atrial fibrillation/tachycardia (AF/AT) were obtained and were compared between March-April 2020 (lockdown) and March-April 2019 (reference) within each patient. The study was approved by the local ethics committee.

Results
The ICDs of 531 patients registered significantly less activity during de lockdown period compared to the reference period (210±104 min vs 182±103 min, p&lt;0.0001, Figure 1, panels A and B), while weather conditions improved (1A). Daytime and nighttime heart rates were significantly lower during lockdown compared to the reference period (71.3±9 bpm vs 72.6±9 bpm, p&lt;0.0001 and 63.4±9 vs 63.8±9, p=0.02, respectively). AF/AT burden increased (Figure 2A) while number of VT/VF episodes decreased (2B). There was no significant difference in number of NSVT episodes.

Conclusion
During the lockdown in the first COVID-19 wave, the Carelink system revealed significantly less activity, increase in AF/AT burden and decrease in VT/VF episodes. Further investigation is needed to understand the relationship between physical activity and the occurrence of arrhythmias in ICD patients.

Funding Acknowledgement
Type of funding sources: None.
Multi-category classification of left ventricle ejection fraction using a convolutional neural network
Authors: Carter R , Hardway H , Johnson P , Douglass E , Adedinsewo D 
Published: October 2022

Abstract

Background
Screening for left ventricular (LV) systolic dysfunction (defined as ejection fraction ≤35%) based on data from a standard 12-lead electrocardiogram (ECG) has become well established when standard digital ECGs are available–8 independent leads sampled at least 250 hertz for 10 seconds. As the algorithm has been incorporated into various clinical scenarios and ancillary research projects, a limitation of the binary classification at 35% has become apparent.

Purpose
The objective of this study was to develop and validate a deep learning-based algorithm that would classify LVEF into three categories based on only the digital ECG input.

Methods
After IRB approval, native digital resting ECGs acquired between 1/1/2010 and 12/31/2021 on patients seen in Mayo Clinic in Jacksonville were extracted from the institutional electronic ECG database management system (MUSE, GE Healthcare). These ECGs were matched with transthoracic echocardiograms obtained up to four days prior or 30 days after the ECGs acquisition. A convolutional neural network consisting of 8 layers of convolutions, batch normalization and pooling was trained using Keras and Tensorflow with hyper-parameter optimization for L1 and L2 regularization, learning rate adjustments, and class weights to predict three classes of LVEF: ≤35%, 36–51%, and ≥52% based on clinical relevance. The primary measure of overall performance was the detection of LVEF ≤35%; however, the triad of model predictions was also considered in translating the model output to human interpretable findings.

Results
A total of 30,153 patients (60,169 ECG pairings; mean age 63 years; 48% male) were randomly split at the patient level into training (24,172 patients), validation (2,973 patients) and testing (3,008 patients). The trained model provided robust discrimination in the withheld testing data – AUROC of 0.941 (95% CI: 0.931 to 0.950). Using the optimal model threshold based on Youden's index from the validation data (0.186), sensitivity and specificity were estimated to be 87.9% (95% CI: 83.8% to 91.2%) and 86.3% (95% CI: 85.4% to 87.2%) in the testing data. In instances where discordant predictions were observed, the posterior distribution of model probabilities provide additional insights into the possible underlying value of LVEF (Figure 1).

Conclusions
The utilization of a multi-category deep learning classification model for the detection of reduced ejection fraction adds new dimensions to the use of AI technologies on digital ECGs. This work shows high discrimination can still be obtained when using three classes of LVEF.

Funding Acknowledgement
Type of funding sources: None.
Prognostic yield of stress CMR to evaluate chronic coronary syndromes: a systematic review and meta-analysis
Authors: Bisaccia G , Khanji M Y , Gallina S , Petersen S E , Bucciarelli-Ducci C , Ricci F 
Published: October 2022

Abstract

Background
Assessment of ischemia with stress cardiovascular magnetic resonance (CMR) imaging is recommended in patients with stable chest pain and intermediate or high pre-test probability of coronary artery disease (CAD).

Purpose
To provide an updated synthesis on prognostic significance of stress CMR imaging in patients with stable chest pain and suspected or known CAD.

Methods
After prospective registration and approval of the study protocol, we performed a systematic review and meta-analysis of studies published between 2000 through 2021, enrolling ≥100 patients, and reporting outcome data of CAD patients undergoing stress CMR. Odds ratios (ORs) and 95% confidence intervals (CIs) for all-cause death, cardiovascular (CV) death and major adverse cardiac events (MACE: CV death and myocardial infarction), were pooled through inverse variance random-effects meta-analysis to compute summary effect size. Annualized event rates (AERs) were extracted from each study and compared by χ2-statistic. A warranty period was defined as the time interval with an AER &lt;1%.

Results
We identified a total of 33 studies pooling an overall tested population of 68920 patients (mean age 62 years; 56% males; known CAD 32%; 386117 person-years). Ischemia was found in 13617 (20%). Mean follow-up was 3.5±2.1 years. Presence of ischemia was associated with increased risk of all-cause death (OR 2.0 95% CI: 1.7–2.3), CV death (OR 6.4 95% CI: 4.5–9.1), and MACE (OR 5.0 95% CI: 3.6–6.8). Cumulative AERs for all-cause death, CV death and MACE were 2.97%, 2.51%, and 3.99% in patients with ischemia, and 1.40%, 0.59%, and 0.98% in patients without ischemia, respectively (p&lt;0.0001 for all comparisons).

Conclusion
Stress CMR imaging yields robust prognostic information in patients with suspected or known CAD. Presence of ischemia is associated with increased risk of all-cause death, CV death and MACE. Patients with negative stress CMR have a very low risk (&lt;1%) of CV death and MACE with a warranty period of at least 3.5 years.

Funding Acknowledgement
Type of funding sources: None.
Predicting cardiovascular risk factors from facial & full body photography using deep learning
Authors: Knorr M S , Neyazi M , Bremer J P , Brederecke J , Ojeda F M , Ohm F , Augustin M , Blankenberg S , Kirsten N , Schnabel R B 
Published: October 2022

Abstract

Introduction
The early and easy detection of pathological cardiovascular phenotypes can lead to an early medical intervention and thus slow or limit the development of cardiovascular diseases. As full body photographs are easily obtainable without the need of medical expertise, this modality holds the potential to be viable for screening of populations.

Purpose
Utilizing data from a population-based study, we examined the possibility to detect cardiovascular risk factors from total body photographs using deep learning.

Methods
A population-based cohort study was utilized. The first data release provides facial and full body photographs in dermatologic standard poses of 6500 participants (median age 62.0 years, 49.6% men) and corresponding cardiovascular risk factors. Here, we focus on the most prevalent ones: smoking status (prevalence: 19.0%), hypertension (35.3%) and diabetes (8.2%). Here we employ a 2D-Convolutional Resnet-18 Neural Network for predicting the risk factors. It receives the full body image, the facial image and age and sex as input. We compare this to a logistic regression model only including sex and age. Logistic Regression and Neural Network are employed in a 5-fold validation scheme and t-tested for significance.

Results
Our model provided a good detection of arterial hypertension (AUC 0.711, CI 0.684–0.739), while a logistic regression on age and sex yielded a significantly worse prediction (AUC 0.681, CI 0.679– 0.683, p&lt;0.05). Additionally, it made a good detection of a positive smoking status (AUC 0.733, CI 0.711–0.754), being significantly better than a respective logistic regression on age and sex (AUC 0.598, CI 0.597–0.6, p&lt;0.001). Lastly, it classified diabetes well (AUC 0.744, CI 0.724–0.764, p&lt;0.001) in comparison to the logistic regression (AUC 0.681, CI 0.679–0.683, p&lt;0.001).

Conclusion
The presence of cardiovascular risk factors can be detected from a total body photograph. As total body photographs can be easily obtained with a majority of digital cameras, including smart phones, this model represents a potentially widely applicable diagnostic tool to easily screen large parts of the population for relevant cardiovascular risk factors, making an early medical intervention possible.

Funding Acknowledgement
Type of funding sources: None.
Cardiovascular disease risk prediction via machine learning using mental health data
Authors: Dorraki M , Liao Z , Abbott D , Psaltis P J , Baker E , Bidargaddi N , Van Den Hengel A , Narula J , Verjans J W 
Published: October 2022

Abstract

Background
Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

Purpose
The main purpose of this study was to assess capability of mental health factors along with traditional risk factors to be used in cardiovascular predictive machine learning models, and to develop a combined machine learning approach using both traditional risk and psychological factors in 375,145 participants of the UK Biobank.

Methods
A comprehensive Pearson correlation analysis is carried out on UK Biobank data. Subsequently, an ensemble model containing decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN) classification approaches was built to predict cardiovascular diseases (CVD) in UK Biobank participants. The model was first trained using traditional cardiovascular risk factors, and subsequently trained using a combination of cardiovascular risk and psychological factors.

Results
The correlation analysis revealed that there is a correlation between CVD and mental health factors suggesting the potential of mental health application for machine learning models. Our ensemble machine learning model was able to predict CVD with an accuracy of 73.49% using CVD risk factors alone. However, by combining psychological factors with CVD risk factors in the training data, an improved accuracy of 95.70% was achieved. The accuracy and robustness of ensemble machine learning model outperformed any of five constituent learning algorithms alone.

Conclusions
Our results suggest that mental health assessment data along with traditional risk factors provides a powerful, safe and affordable machine learning model enrichment that can be used for state-of-the-art prediction of CVD.

Funding Acknowledgement
Type of funding sources: None.
Diagnostic accuracy of stress CMR to evaluate chronic coronary syndromes: an updated meta-analysis
Authors: Bisaccia G , Khanji M Y , Gallina S , Petersen S E , Bucciarelli-Ducci C , Ricci F 
Published: October 2022

Abstract

Background
Stress cardiovascular magnetic resonance (CMR) imaging is utilised for the evaluation of patients with stable chest pain and intermediate or high pre-test likelihood of coronary artery disease (CAD).

Purpose
To provide an updated synthesis of diagnostic accuracy of stress CMR imaging for the diagnosis of anatomically and functionally significant CAD in patients with stable chest pain and suspected or known CAD.

Methods
After prospective registration and approval of the study protocol, we performed a systematic review and meta-analysis of studies published between 2000 through 2021, enrolling ≥100 patients, and reporting on the diagnostic accuracy of stress CMR imaging to diagnose anatomically and functionally significant CAD with invasive coronary angiography (ICA) or ICA and fractional flow reserve (FFR &lt;0.80) as the reference standard. The novel split component synthesis method was used through the SCSmeta function in R. The meta-analysis yielded pooled diagnostic indicators including diagnostic odds ratio (DOR), sensitivity, specificity, positive likelihood ratio (pLR), negative likelihood ratio (nLR) and area under the curve (AUC).

Results
We identified a total of 32 studies pooling an overall population of 7,678 individuals (mean age 62 years, 70% males, CAD prevalence 52%). Compared with ICA (29 studies, 7,360 patients), stress CMR yielded a pooled DOR of 19.2 (95% CI: 12.5–29.4), a sensitivity of 84% (95% CI: 79–88%), a specificity of 79% (95% CI: 73–84%), a pLR of 3.9 (95% CI: 3.0–5.3), a nLR of 0.2 (95% CI: 0.2–0.3), and an AUC of 0.81 (95% CI: 0.78–0.84) for the detection of anatomically obstructive CAD. Compared with ICA and FFR (8 studies, 1,196 patients), stress CMR yielded a pooled DOR of 26.4 (95% CI: 10.6–65.9), a sensitivity of 81% (95% CI: 68–89), a specificity of 86% (95% CI: 75–93%), a pLR of 5.8 (95% CI: 3.0–11.4), a nLR of 0.2 (95% CI: 0.1–0.4), and an AUC of 0.84 (0.77–0.89) for the detection of functionally obstructive CAD. Higher diagnostic accuracy was observed for 3 Tesla myocardial perfusion imaging studies to detect both anatomically and functionally obstructive CAD, with pooled DORs of 24.3 and 33.2, respectively.

Conclusions
In patients with stable chest pain and known or suspected CAD, stress CMR imaging yields high diagnostic accuracy to detect both anatomically and functionally obstructive CAD. Stress CMR perfusion imaging at 3 Tesla is to be associated with overall greater diagnostic accuracy.

Funding Acknowledgement
Type of funding sources: None.
The use of handheld ultrasound device in cardiac examination of patients with history of COVID-19
Authors: Filipiak D , Kasprzak J D , Lipiec P 
Published: October 2022

Abstract

Background
Clinical usefulness of Handheld Ultrasound Device [HUD] was previously confirmed in numerous clinical scenarios. During the previous two years Covid-19 patients become a focal point of healthcare worldwide. The assessment of long term consequences of this infection is bound to overload already burdened healthcare system.

Purpose
To assess clinical usefulness of HUD as an adjunct to physical cardiac examination of patients with history of COVID-19.

Methods
Study population consisted of randomly selected patients with no symptoms of cardiovascular pathology, who had been hospitalized due to COVID-19 one year prior to examination. Physical examination and clinical assessment was augmented with short examination with the use of HUD, which included: visual evaluation of the global and regional LV function, measurement of RV size, screening for the significant valve defects and the presence of pericardial effusion. Subsequently full echocardiographic examination with the use of high-end workstation was performed, which results were treated as reference.

Results
54 patients (35 men, mean age 63±13 years) were enrolled into the study. In clinical examination no significant cardiovascular abnormalities were discovered. In 30 [56%] of patients cardiac abnormalities in HUD examination were detected. In 18 patients [33%] LV function assessment was not performed, due to insufficient quality of registered view. In the remaining group significant impairment of LV ejection fraction (&lt;50%) was detected in HUD examination in 3 [6%] patients (2 confirmed in full examination, positive predictive value [PPV] 57%, negative predictive value [NPV] 97%, AUC 0,82±0,17, P 0,057). WMA were diagnosed in 6 [11%] patients (4 confirmed in full examination, PPV 84% NPV 78%, AUC 0,69±0,17, P 0,02). RV enlargement was identified in 21 [39%] patients (PPV 57%, NPV 97%, AUC 0,85±0,05, P&lt;0,0001), mild pericardial effusion in 3 [6%] patient (1 confirmed in full echocardiographic examination; 2 false positive, no false negative), at least moderate mitral/tricuspid/aortic valve insufficiency in 7 [13%] patients (3 confirmed, 4 false positive cases, no false negative). A total mean time of the heart and lungs HUD examination was 2,1±0,6 minute.

Conclusion
Cardiac abnormalities exposed in brief assessment with the use HUD are a relatively common finding in asymptomatic patients previously hospitalized due to COVID infection in a 1-year follow-up, despite normal physical examination. Normal HUD examination excludes the presence of significant cardiac abnormalities with high probability. However one should keep in mind a relatively high percentage of false positive results, which may lead to an exceeding number of patients referred for a full echocardiographic examination.

Funding Acknowledgement
Type of funding sources: None.