bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between digoxin use and sudden cardiac death in individuals with the rs10494366 variant of the NOS1AP gene
Authors: Soroush N , Aarnoudse A , Shokri F , Van Den Berg M , Ahmadizar F , Stricker B 
Published: November 2020

Abstract

Background
Digoxin is one of the oldest cardiovascular medications still used to treat heart failure and atrial fibrillation. Due to its narrow therapeutic window, it is associated with life threatening intoxication and arrhythmias, and with QTc-shortening. Common genetic variation in the nitric oxide synthase-1 adaptor protein (NOS1AP) has been associated with QTc interval prolongation.

Purpose
We investigated whether the rs10494366 variant of the NOS1AP gene modified the risk of SCD in patients using digoxin.

Methods
In a prospective population-based cohort study, we included data of the three cohorts, started as of January 1st, 1991 until January 1st 2014. Digoxin current use on the date of cardiac death in cases and the same day of follow-up in the remainder of the cohort was a time-dependent exposure. The main outcome was SCD defined as sudden and unexpected death as a result of cardiac causes, according to international criteria. Identification and adjudication of SCD was performed independently, before the start of this study. We used Cox proportional hazard regression analysis to investigate the associations between NOS1AP rs10494366 variant and incident SCD among digoxin users compared to non-users. Associations were adjusted for age, sex (model 1) in addition to BMI, prevalent diabetes, myocardial infarction, baseline hypertension and smoking status (past, current, never) (model 2).

Results
We included 14,594 individuals, with a mean age of 65.3 (SD 10.3) years. Almost 59% were female. The cumulative incidence of SCD was 9.5% (609 cases) by the end of follow up. Among them, 98 (16%) individuals were exposed to digoxin at the time of death.
In model 1, NOS1AP rs10494366 variant was not associated with SCD in the total study population. However, an interaction term of the gene with the daily dose of digoxin was significantly associated with increased risk of SCD (p-value 0.0001). In model 2, the risk of SCD in current users of digoxin was 4.2 [95% CI 1.3–13.8] for the GG genotype; 2.1 [95% CI 1.1–4.2] for the GT genotype, and 1.5 [95% CI 0.7–3.2] for the TT genotype.

Conclusion
NOS1AP rs10494366 variant modified the risk of sudden cardiac death in users of digoxin. Our study suggests that individuals with the homozygous minor GG allele have a fourfold increased risk of sudden cardiac death.

Funding Acknowledgement
Type of funding source: None
Work environment and nurse outcomes: a secondary analysis from the RN4CAST@IT study
Authors: Zanini M , Bagnasco A , Dasso N , Catania G , Sasso L 
Published: November 2020

Abstract

Introduction
Nursing care in cardiology requires advanced skills, continuity of care and global care of the chronic patient. The relationship with the patient requires a psychological and moral commitment which, combined with time-pressure and high workloads, increases the risk of negatively impacting nurses' well-being. Emotional exhaustion in nurses has negative consequences on care, therefore nursing leaders should monitor this outcome and ensure that its incidence is limited.

Purpose
To test the relationship between the nursing working environment and emotional exhaustion among nurses in cardiology wards.

Methods
The data are drawn from the database of the larger study, RN4CAST@IT. This was a multi-center and multi-level study.
A convenience sample of nurses providing bedside care was included. For the analyses presented here, the data relating to nurses working in Cardiology wards were extracted.
The data were collected through a web survey between September and December 2015.
The questionnaire investigated several topics about nursing staff including nurse-patient ratios, skill mix, working environment measured with PES-NWI, and emotional exhaustion with the Maslach Burnout Inventory.
Descriptive statistical analyses were conducted to describe the sample and variables taken into consideration; a binomial logistic regression model was built to test the relationships between the dependent variable “high Emotional Exhaustion;' and the independent variables “PES-NWI composite score”, “workload” and “skill mix”.

Results
Responses by 291 cardiology nurses were analysed. Binary logistic regression, adjusted for skill mix and staffing, showed that, better working environment conditions reduced the risk that nurses working in the cardiology develop high Emotional Exhaustion by 96% (OR 0.038; 95% CI 0.011–0.136).

Conclusions
Considering the relationship that these analyses have shown between a favourable working environment, capable of involving professionals, of providing adequate resources and leadership, and the risk that professionals may develop a high level of emotional exhaustion, we need to reflect how it impacts on patients. These results are the starting point for analysing the organization of clinical contexts, with their peculiarities, to make them more and more suitable for people, both patients and professionals.

Funding Acknowledgement
Type of funding source: None
Pharmacological Restoration of mOod in HEART Failure: outcomes of the PRO-HEART trial
Authors: Hare D.L , Toia D , Stewart A.G , Toukhsati S 
Published: November 2020

Abstract

Background
Depression is common in chronic heart failure (CHF) patients and is associated with increased morbidity and mortality. Anti-depressant medication has shown benefits in coronary artery disease but efficacy has not been demonstrated in CHF patients.

Purpose
To assess whether escitalopram is more effective than placebo in treating major and/or minor depression in patients with CHF.

Methods
This was a randomized, placebo-controlled, double-blind, parallel group study. Adult patients with systolic CHF (NYHA Class ≥2, nadir LVEF &lt;40%) were screened for depression using the Cardiac Depression Scale (CDS) for a score ≥95 (sensitivity 97% at 85% specificity for major Depression (MDD)) followed by a clinical diagnosis of major or minor depression using the SCID interview. Patients were randomised to active escitalopram or matching placebo for 6 months, stratified for major or minor depression. Blinded therapy started with 5mg with automatic uptitration to 10mg after 2 weeks and further uptitration to 20mg at 2 months if CDS score remained ≥95. The primary end-point was change in CDS score over 6 months with secondary end-point change in Hamilton Depression Rating Scale (HAM-D).

Results
Of 28 CHF patients (males = 25; mean age = 63.61±10.32), mean CHF diagnosis 5.8 years, ischaemic aetiology (n=18), entry LVEF 42.2±9.6%; eGFR 61.8±15.4, 14 were diagnosed with major and 14 with minor depression. Two-way Mixed Model ANOVA showed a significant improvement from baseline to 6 months in both CDS (F(1,25)=18.76, p&lt;0.001) and HAM-D (F(1,25)=17.32, p&lt;0.001), but no significant interaction between condition and change over time. Three-way Mixed Model ANOVA showed no interaction between baseline depression diagnosis (Major or Minor) and response to treatment by condition. There were 7 SAEs for hospitalisation, unrelated to study medications and no differences between groups on AE/SAEs.

Conclusions
Patients showed a significant improvement in depression with both placebo and active drug but with no benefit of escitalopram over placebo. This suggests that there is no additional benefit from antidepressant therapy for CHF patients beyond that conferred by placebo. Further research is needed to explore whether greater benefits might be achieved by combination therapies or in particular subgroups of depressed CHF patients.

Funding Acknowledgement
Type of funding source: Foundation. Main funding source(s): National Heart Foundation of Australia, Beyond Blue
Opportunities to reduce the risk of stroke in AF: the REVIEW AF study
Authors: Gallagher C , Wong C.X , Hendriks J.M , Nyfort-Hansen K , Rowett D , Bednarz J , Elliott A.D , Linz D , Middeldorp M.E , Mahajan R , Lau D.H , Sanders P 
Published: November 2020

Abstract

Background
Stroke is one of the most devastating complications of atrial fibrillation (AF) and is associated with poor patient outcomes. Recent registry data has demonstrated improved use of stroke prevention therapy with the advent of direct-acting oral anticoagulants, but little data exists in the Australian health care setting.

Purpose
To examine the use of oral anticoagulant therapy in a cohort of individuals presenting to the emergency department (ED) due to AF.

Methods
A total of 437 consecutive individuals who presented to the ED with a primary diagnosis of AF to three tertiary hospitals in our city in South Australia between March 2013 and March 2014 were included. Data were collected retrospectively from electronic medical record review. CHA2DS2-VASc and HAS-BLED scores were calculated and any documented contraindications to the use of oral anticoagulation (OAC) taken in to consideration.

Results
Mean age was 69±15 years and 49.9% were male. A total of 244 (55.8%) individuals had a prior diagnosis of AF at index presentation. 179 (73.4%) of these individuals recorded a CHA2DS2-VASc score of ≥2. A total of 124 of these individuals were appropriately anticoagulated (69.3%) whilst 37 individuals were not treated with OAC and did not have a documented contraindication (20.7%). Anticoagulation status was unknown in 18 individuals due to a lack of documentation (10.0%). A CHA2DS2-VASc score of 0 was recorded in 25 individuals with a prior diagnosis of AF at index presentation with 3 individuals (12%) inappropriately overtreated with OAC. The remaining 193 individuals (44.2%) presented with a first episode of AF. In 129 (66.8%) of these individuals a CHA2DS2-VASc score of ≥2 was recorded and only 10 individuals had a documented contraindication to the use of OAC. In 119 (61.7%) individuals there were no documented contraindications to the use of OAC. In 74 (62.2%) of these presentations OAC was not commenced, whilst in 45 (37.8%) individuals OAC was prescribed.

Conclusions
Significant opportunity exists to improve the use of stroke prevention therapy in AF. Despite repeated opportunities to commence this therapy within the hospital setting, it remains frequently underutilised in individuals at high risk of stroke.

Funding Acknowledgement
Type of funding source: None
Impact of radial compression protocols on the compression time and radial artery occlusion
Authors: Takamatsu S , Kagiyama N , Shiomi T , Mizobuchi M , Sone N , Tougi K , Yamauchi S , Yuri T , Fukuyama S , Shibata M , Nakazawa R , Ii N , Masutani M , Hirohata A 
Published: November 2020

Abstract

Background
Trans-radial access (TRA) has been established as a safe and established approach for invasive coronary catheter procedures. However, protocols for post-procedural hemostasis varies depending on institutes and an evidence-based protocol is lacking.

Purpose
The objective of this study was to investigate the clinical implications of procedural hemostasis.

Methods
Consecutive patients who were referred to outpatient catheter examination before and after April 2018 were treated with an old and a new protocol, respectively. In both protocols, we used the same commercially available hemostasis band with injecting an air of 16 ml for hemostasis. In the old protocol, the amount and timing of deflation were fixed, whereas the air was removed as much as possible for every 30 minutes in the new protocol. Time to complete hemostasis, the rate of major bleeding, and the rate of radial artery occlusion (RAO) at 6 months after the catheter examination were compared between the protocols.

Results
Total of 1,843 (71±10 years old, 77% male) patients was included in the study. Between patients in the old and the new protocol group (n=1,000 and 843, respectively), there was no significant difference in age, gender, body size, or systolic blood pressure. The new group had significantly higher prevalence of diabetes (47% vs 39%, p=0.002), slightly larger sheath size (4.1±0.3 vs 4.0±0.2 Fr, p&lt;0.001), and lower rate of dual antiplatelet therapy (DAPT: 24% vs. 35%, p&lt;0.001). Time for complete hemostasis was about one-third with the new protocol compared with the old protocol (65±32 vs. 190±16 min, p&lt;0.001) and there was no major bleeding in either group. The rate of radial artery occlusion was 0.7% and 9.8% in the old and the new group (p&lt;0.001). Multivariate analysis showed that the significant predictor of prolonged hemostasis time were the old protocol (odds ratio: OR 80.5, p&lt;0.001) and the prescription of DAPT (OR 2.9, p&lt;0.001), while the factors associated with higher risk of radial occlusion were the old protocol (OR 13.9, p&lt;0.001), the number of previous TRA (OR 1.1, p&lt;0.001), and smaller body size (OR 0.127 per 1 m2 increase p=0.005).

Conclusions
Our new protocol for hemostasis after TRA was strongly associated with shorter hemostasis time and a lower rate of radial artery occlusion. This approach will decrease the post-procedural hospital time with even fewer complication rates.
Study outline

Funding Acknowledgement
Type of funding source: None
Psychosocial factors predict type 2 diabetes mastery
Authors: Ski C , McGuigan K , Hill A , Coates V , Thompson D.R , O'Kane M , McCay D 
Published: November 2020

Abstract

Background
Psychosocial aspects of chronic diseases such as Type 2 diabetes (T2D) and cardiovascular disease (CVD) are increasingly recognised as impacting effective self-management. Until now, little was known regarding the moderating effects of empowerment and depression on the relationship between diabetes-specific distress and mastery.

Purpose
To evaluate the potential mediating role of diabetes empowerment and depression on the relationship between diabetes-specific distress and mastery.

Methods
Sample comprised 131 participants diagnosed with T2D, mean [SD] age 62.3 [8.8]; 59.5% male. Mean time since diagnosis was 10.4 years. Assessments included: emotional distress (Problem Areas in Diabetes Scale; PAID); depression and anxiety (Hospital Anxiety and Depression Scale; HADS); mastery (Pearlin Mastery Scale); and empowerment (Diabetes Empowerment Scale – Short Form; DES-SF). Data were examined using SPSS: PROCESS a logistic regression-based path analytical framework for multiple mediator models.

Results
Regression coefficients for the model identified distress (b=−0.249, t(5,112)=−3.71, p&lt;0.001), empowerment (b=0.280, t(5,112)=3.02, p&lt;0.001) and depression (b=−0.980, t(5,112)=−5.73, p&lt;0.001) were all statistically significant predictors of mastery. The PROCESS model assessed ΔR2 as a result of the interaction between distress and depression, which indicated a significant increase (ΔR2=0.08) in variance explained due to the inclusion of both moderators: F(2,112)=16.88, p&lt;0.001, ΔR2=0.14. Also identified were interaction effect sizes: at low levels of empowerment, increasing depression led to increasing levels of distress predicting overall levels of mastery. This held true at moderate and high levels of empowerment; increasing levels of depression led to increasing distress predicting mastery.

Conclusions
These findings elucidate the impact of empowerment and depression on the association between distress and mastery in a diabetes cohort. The evidence suggests that the psychosocial interventions likely to have greatest impact on mastery are those that target key moderators such as empowerment and depression.

Funding Acknowledgement
Type of funding source: None
Reliability of low electrocardiogram sampling frequencies for short-term heart rate variability analysis to estimate transient psychophysiological stress induced by car driving
Authors: Brisinda D , Fenici R 
Published: November 2020

Abstract

Background
Heart rate variability analysis (HRVa) is increasingly used to evaluate stress-induced adaptation of the autonomic nervous system (ANS), basing on electrocardiogram (ECG) recorded with novel wearable wireless devices (WWD) for physiological monitoring. However, in order to transfer ECG data to mobile phones efficiently, WWD use low sampling frequency (SF), usually ∼100 Hz, thus much lower than the 0.5–1 kHz, previously suggested to optimize R–R intervals detection, precise enough for HRVa in the time-domain (TD), frequency-domain (FD) and with nonlinear (NL) methods. Moreover, the latter are sensitive to SF as a function of data length.

Aim
Aim of this study was to quantify the relative error (RE%) of HRVa, when calculated from ECG downsampled at 100 Hz, compared with that digitized at 1 kHz, considering different data lengths.

Methods
ECG of four healthy professional pilots was continuously recorded with a WWD (Vi-grade, Udine), at 1 kHz SF, while driving a professional simulator, inducing different degree of psychophysiological stress by changing suddenly the vehicle's behavior without advising the driver.
TD, FD and NL HRV parameters were calculated (Kubios 3.0.2, Finland), from time intervals of 300, 120, 60 and 30 seconds, of ECG downsampled at 100 Hz SF from original recordings acquired at 1 kHz SF (assumed as goldstandard). The RE% and the intraclass correlation coefficient (ICC) were calculated.

Results
A good correlation (ICC ≥0.79–0.84) was found for the majority of HRV parameters, in both driving conditions, for all selected intervals lengths. The average RE% ranged between zero and 3%, increasing if the length of the interval selected for HRVa was lower than 60 second (Table 1). However higher RE was occasionally found. In Figure 1, an example of HRVa calculated from 2 minutes tachogram's segments, at 100 Hz (left) and at 1 kHz SF (right) is shown.

Conclusions
Compared to 1 kHz SF, downsampling ECG at 100 Hz doesn't affect significantly HRVa for data lengths between 5 and 1 minutes. However, for shorter time intervals the RE increases. This must be taken into account if HRVa is used to track transient short-lasting changes of ASN modulation induced by acute stress.
Figure 1

Funding Acknowledgement
Type of funding source: None
Distinguishing sinus rhythm from atrial fibrillation on single-lead ECGs using a deep neural network
Authors: Oudkerk Pool M , De Vos B.D , Wolterink J.M , Blok S , Schuuring M.J , Bleijendaal H , Dohmen D.A.J , Tulevski I.I , Somsen G.A , Mulder B.J.M , Pinto Y , Bouma B.J , Isgum I , Winter M.M 
Published: November 2020

Abstract

Background
The growing availability of mobile phones increases the popularity of portable telemonitoring devices. An atrial fibrillation diagnosis can be reached with a recording of 30s on such telemonitoring devices. However, current commercially available automatic algorithms still require approval by experts.

Purpose
In this research we aimed to build an artificial intelligence (AI) algorithm to improve automatic distinction of atrial fibrillation (AF) from sinus rhythm (SR), to ultimately save time, costs, and to facilitate telemonitoring programs.

Methods
We developed a deep convolutional neural network (CNN), based on a residual neural network (ResNet), tailored to single-lead ECG analysis. The CNN was trained using publicly available single-lead ECGs from the 2017 PhysioNet/ Computing in Cardiology Challenge. This dataset consists of 60% SR, 9% AF, 30% alternative rhythm, and 1% noise ECGs. The 8528 available ECGs were divided into a training (90%) and validation set (10%) for model development and hyperparameter optimization.

Results
The trained CNN was applied to an independent set containing single-lead ECGs of 600 patients equally divided into two groups: SR and AF. Both groups comprised of 300 unique ECGs (SR; 60% male, 63±11 years, AF; 38% male, 56±14 years). In distinguishing between AF and SR, the method achieved an accuracy of 0.92, an F1-score of 0.91, and area under the ROC-curve of 0.98.

Conclusion
The results demonstrate that distinguishing SR and AF by a fully automatic AI algorithm is feasible. This approach has the potential to reduce cost by minimizing expert supervision, especially when extending the algorithm to other heart rhythms, like premature atrial/ventricular contractions and atrial flutter.
Figure 1. ROC curve

Funding Acknowledgement
Type of funding source: Public grant(s) – National budget only. Main funding source(s): Dekkerbeurs - Hartstichting
Deep-learning-based artificial intelligence algorithm for detecting anemia using electrocardiogram
Authors: Jeon K.H , Kwon J.M , Kim K.H , Kim M.J , Lee S.H , Baek S.D , Jeung S.M , Park J.S , Choi R.K , Oh B.H 
Published: November 2020

Abstract

Background
Anemia changed the morphology of electrocardiography (ECG), and researchers suggested that mismatching oxygen demand and supply in the myocardium affects the ECG

Purpose
A deep-learning-based algorithm (DLA) that enables non-invasive anemia screening from electrocardiograms (ECGs) may improve the detection of anemia.

Methods
A DLA was developed using 57,435 ECGs from 31,898 patients and was internally validated using 7,369 ECGs from 7,369 patients taken at one hospital. External validation was performed using 4,068 ECGs from 4,068 patients admitted at another hospital. Three types of DLA were developed using 12-lead ECGs to detect hemoglobin levels of 10 mg/dL or less. The DLA was built by a convolutional neural network and used 500-Hz raw ECG, age, and sex as input data.

Results
During internal and external validation, the area under the receiver operating characteristics curve (AUROC) of the DLA using a 12-lead ECG for detecting anemia was 0.941 and 0.904, respectively. Using a 90% sensitivity operating point, the specificity, negative predictive value, and positive predictive value of internal validation were 0.889, 0.998, and 0.151, respectively, and those of external validation were 0.785, 0.994, and 0.166, respectively. The predicted Hgb level based on the DLA was correlated with the actual Hgb level (r=0.891, 95% CI 0.890–0.893, P&lt;0.0001). 57 patients of moderate to severe anemia were treated with appropriated blood transfusion and predicted DLA score of most patients who received transfusion decreased after transfusion according to increase in hemoglobin level.

Conclusion
In this study, using raw ECG data, a DLA accurately detected anemia. The application of artificial intelligence to ECGs may enable screening for anemia.
Correlation between DLA and actual Hb

Funding Acknowledgement
Type of funding source: None