bluuebunny/crossref_metadata_2025_split huggingface dataset:
The needs of older persons undergoing heart surgery: unveiling a nursing intervention through focus groups
Authors: Nicola Rodrigues S , Rafael Henriques H , Henriques M A 
Published: October 2021

Abstract

Background
The progressive ageing of the population, the prevalence of chronic diseases, such as cardiovascular diseases and heart disease with surgical indication, have a significant expression, either with a curative objective or to increase quality of life. Waiting for surgery is often a period of anxiety and fear. Preoperative waiting time can be used to better prepare these patients for surgery.

Purpose
The purpose of this research was to identify the needs of older people waiting for heart surgery.

Methods
A qualitative exploratory approach was used to gain an understanding of the participants' perception of what are the needs of older persons waiting for heart surgery. Focus Groups were used to collect information. Three interviews were conducted with: a group of nurses with experience in cardiothoracic care, individuals submitted to heart surgery (≥65 years) and individuals waiting for heart surgery (≥65 years); enrolling up to 17 participants from October 2019 to January 2020. Qualitative data analysis was conducted using the interactive model, at first similar data chunks were categorized by codes, allowing further analysis of data. Condensing data enabled us to retrieve the most significant material, further condensing it into smaller number of categories. The MaxQDA software, and EQUATOR COREQ guidelines were followed.

Results
Three categories emerged: (i) Information focused on facilitating health promotion and self-care, information regarding the hospital stay and granting access to information; (ii) Emotional support described patient's feelings towards the upcoming surgery and the need to keep them calm, involving their families and caregivers; (iii) Access to care, patients should be kept involved and engaged in managing self-care.

Conclusion
A preoperative intervention is necessary to support older persons waiting for heart surgery and the education provided should be focused on patients' needs. Caring for individuals awaiting heart surgery may be challenging due to the barriers imposed by physical distance and emotional distress. The identified needs of individuals awaiting heart surgery allow health professionals to deepen their knowledge regarding the patient's care.

Funding Acknowledgement
Type of funding sources: None. Listing of categories and sub-categories
Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography
Authors: Zha S Z , Rogstadkjernet M , Klaeboe L G , Skulstad H , Edvardsen T , Samset E , Brekke P H 
Published: October 2021

Abstract

Background
Left ventricular outflow tract diameter (LVOTd) is routinely measured to calculate stroke volume and estimate aortic valve area by the continuity equation. Despite LVOTd being regularly measured clinically, significant inter- and intraobserver variability is evident. This variability is highly impactful on both aortic stenosis evaluation and cardiac output calculation due to the squaring of the LVOT radius.

Purpose
We aimed to investigate if LVOTd measurements from clinical echocardiographic examinations could be used in a deep learning (DL) model to automatically perform LVOTd measurements with equivalent accuracy and improved consistency compared to current practice.

Methods
Data was collected from clinical echocardiographic examinations performed on 656 consecutive patients admitted to the cardiac catheterization laboratory at a university hospital in January – December 2018. Parasternal views with cardiologist annotated LVOTd coordinates were assessed for 1314 echocardiographic still images. The quality of the still image and annotated LVOT ground truth were individually graded as high, medium and low by experienced cardiologists to establish a rigorous training basis. Spatial geometry data was preserved for each still image in order to distinguish between different degrees of image zoom. Data was randomly split into training, validation and testing sets (68%, 17%, 15%). A fully convolutional network based on the Resnet50 architecture was used with a custom loss function with heatmap regression. Image augmentations were added to extend the dataset.

Results
When including echocardiographic images of any quality (n=1314) in the model training and inference, the median absolute difference between cardiologist LVOTd and DL LVOTd was 0.97 mm (95% Confidence interval (CI) 0.79–1.14). Using only high and medium quality still images and ground truth (n=869) in the training and inference, median absolute difference decreased to 0.81 mm (95% CI 0.60–0.96). Adding image augmentations to this dataset further improved the model, resulting in a median LVOTd absolute difference of 0.66 mm (95% CI 0.51–0.78). The LVOTd error in inference increased with decreasing image quality, as shown in Figure 1, with two predictions (0.9%) failing completely.

Conclusion
Deep learning models are capable of measuring LVOTd with comparable accuracy to cardiologists when trained on clinical data. Data quality affects both training and inference. Even with a slightly lower accuracy when used on lower quality echocardiographic images, DL-assisted LVOT measurement has a clear potential to increase repeatability and consistency of LVOTd measurements.

Funding Acknowledgement
Type of funding sources: Public Institution(s). Main funding source(s): Research Council of Norway (Norges forskningsråd) Figure 1
Multicentre validation of point-of-care screening tool for heart failure: single-lead ECG recorded by smart stethoscope predicts low ejection fraction using artificial intelligence
Authors: Bachtiger P , Scott F , Park S , Petri C , Padam P S , Sahemey H , Dumea B , Ribeiro M , Alquero R , Bual N , Cheung W S , Rana B , Keene D , Plymen C M , Peters N S 
Published: October 2021

Abstract

Background/Introduction
Artificial intelligence (AI) applied to 12-lead ECG can identify left ventricular ejection fraction (EF) ≤35% with a sensitivity and specificity of 86.3% and 85.7%, respectively. Whether AI algorithms trained on 12-lead can accurately predict EF from single-lead ECGs (recorded by a smart stethoscope) remains unknown. This could facilitate point-of-care screening for low EF during routine clinical examination.

Purpose
First independent multicentre real-world UK National Health Service (NHS) prospective validation of 12-lead-ECG-trained AI algorithm applied to single-lead ECG recorded by a smart stethoscope, with AI algorithm tuned to detect EF ≤40%.

Methods
Prospective recruitment of unselected patients attending for echocardiography across six urban NHS hospital sites (UK). In addition to transthoracic echocardiogram (routine care), all participants had 15 seconds of supine, single-lead ECG recorded at six different positions (figure), encompassing standard anatomical positions for cardiac auscultation. A convolutional neural network (CNN) previously trained on 35,970 independent pairings of 12-lead-ECG and echocardiograms was retrained to use the single-lead ECG as input. Accuracy of CNN detection of low EF (binary ≤40%) is reported at a threshold of 0.5 against gold-standard; echo-determined percentage EF.

Results
Among 353 patients recruited (mean age 63±17; 58% male, 43.1% non-white), 309 (87.5%) had an EF &gt;40%, and 44 (12.5%) had EF ≤40%. The best single recording position in isolation was position 3 (sensitivity 57.9% [42.2–73.6], specificity 86.3% [82.2–90.3]). Taking any of the six positions performed during the examination as predicting EF ≤40%, this achieved a sensitivity of 81.2% and specificity of 61.5%.

Conclusion(s)
In this first prospective multicentre validation study the retrained AI algorithm reliably detected low EF from single-lead ECGs acquired using a novel ECG-enabled stethoscope in standard auscultation positions. The ability to identify patients with possible low EF during routine physical examination addresses a significant unmet clinical need in point-of-care ruling in/out of heart failure, and has potential to provide broader population-level screening for asymptomatic cardiovascular disease.

Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health Research, Accelerated Access Collaborative & NHSX: Artificial Intelligence in Health & Social Care Award
Utility of device-derived daily activity, a novel digital biomarker, to predict ventricular arrhythmias – data from the CERTITUDE registry
Authors: Kutyifa V , Rosero S , Harrel C , Singh J , Hicks J , Chinitz L , Varma N , Piccini J , Turakhia M 
Published: October 2021

Abstract

Background
While there have been prior studies showing an association between activity and outcomes, there have been no studies examining the temporal relationship between device-derived daily activity, a novel digital biomarker, and ventricular tachyarrhythmias (VT).

Purpose
In a big dataset with over 55,000 pacemaker, ICD, and CRT devices, we aimed to identify whether changes in activity predict VT, or else VT predict changes in activity.

Methods
The CERTITUDE registry comprises a de-identified database of over 55,000 U.S. BIOTRONIK pacemaker, ICD, CRT devices, and loop recorders active on Home Monitoring®. Daily data on leads, arrhythmias, and physiological parameters such as activity are captured. Patient activity is reported daily as percentage active during the day, assessed by a one-axis accelerometer at ∼0.4 Hz frequency. Analysis to ascertain temporal changes in device-derived activity associated with treated VT was performed using the first event per device and 7-day activity windows (baseline, pre- and post-event). Baseline period was defined as 31–38 days prior to VT. VT events were categorized by heart rate (≤200 bpm,&gt;200 bpm) and treatment (shock with or without ATP, ATP alone). Differences in activity between baseline, and pre- and post-VT were analyzed using the binomial proportion test.

Results
A total of 16,475 devices (9732 ICDs, 6743 CRT-Ds) had activity data available for analysis. The cumulative follow-up duration was 18,355 years (5.6 million days with transmission). Of the 2636 VT events analyzed, 1409 had a heart rate &gt;200 bpm, and 593 were treated with shock. Patients with VT events &gt;200 bpm treated with shock had a significant reduction in activity post-VT with a median −8.7% reduction (IQR −24.6%; 7.3%, p&lt;0.001). However, there was no reduction in activity before the VT&gt;200 bpm (p=0.690) (Figure). VT events &gt;200 bpm treated with ATP alone were not associated with reduction in activity before or after the episode. Similarly, VT events ≤200 bpm treated with shock were also associated with a reduction in activity following the event (−5.8%, IQR −29.5, 12.3%, p=0.003), but not prior to the VT event.

Conclusions
In this report from the CERTITUDE registry, we have shown a temporal decline in device-derived activity following ventricular arrhythmias&gt;200 bpm and ≤200 bpm treated with a shock, but not in patients treated with ATP. Monitoring device-derived activity post-VT events with a shock could provide relevant clinical information and potentially warrant intensified treatment.

Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): Biotronik
Evaluation of the feasibility and accuracy of remote mobile app-based self-reported atrial fibrillation risk factor assessment in patients with atrial fibrillation: TeleCheck-AF results
Authors: Hermans A N L , Gawalko M , Van Der Velden R M J , Verhaert D V M , Betz K , Hemels M E W , Steven D , Duncker D , Gupta D , Manninger M , Lodzinski P , Crijns H J G M , Pluymaekers N A H A , Hendriks J M , Linz D ,   TeleCheck-AF investigators
Published: October 2021

Abstract

Background
Previously, we introduced the TeleCheck-AF approach, which is an on-demand mobile health (mHealth) infrastructure incorporating mobile app-based heart rate and rhythm monitoring as well as mobile app-based self-reported atrial fibrillation (AF) risk factor assessment to allow comprehensive remote AF management through teleconsultation. Herein, we evaluated the feasibility and accuracy of remote mobile app-based self-reported AF risk factor assessment in AF patients.

Methods
In our University Medical Center, 545 patients were managed within the TeleCheck-AF project by an on-demand heart rate and rhythm mHealth infrastructure through teleconsultation. Patients were asked to fill in a short mobile app-based 10-item questionnaire related to AF risk factors. A reminder to complete the questionnaire automatically popped-up after the following four heart rate and rhythm recordings. Furthermore, patient's medical history was retrieved from the electronic health records (EHRs).

Results
Out of 545 patients, 542 (99.4%) patients (217 female, age 67 (59–72) years) completed the mobile app-based 10-item questionnaire and were included in this analysis. The number of patients with diabetes mellitus was similar in the EHRs and mobile app-based questionnaire (both 11.3%, p=1.000). There was no significant difference in the number of patients who had a medical history of transient ischemic attack (TIA)/cerebrovascular accident (CVA) and artery disease (coronary artery disease and peripheral artery disease) in the EHRs and mobile app-based questionnaire (11.4% vs 12.2%, p=0.608 and 14.8% vs 13.3%, p=0.366, respectively). Heart failure was more frequently reported in the mobile app-based questionnaire compared to the EHRs (33.4% vs 14.0%, p&lt;0.001). A total of 260 (48.0%) patients had a diagnosis of hypertension verified in EHRs and only 239 (44.1%) patients reported hypertension in the mobile app-based questionnaire (p=0.044). There was no significant difference in number of patients with CHA2DS2-VASc-score ≥2 between the EHRs and mobile app-based questionnaire (64.2% vs 66.1%, p=0.275). The accuracy of mobile app-based assessment of diabetes mellitus was 85.4%, of TIA/CVA 78.9%, of artery disease 60.9%, of heart failure 78.8%, and of hypertension 89.3%.

Conclusion
Patient self-reported AF risk factors by a remote mobile app-based assessment is feasible and may be useful for future digital trials and comprehensive remote AF management through teleconsultation.

Funding Acknowledgement
Type of funding sources: None.
Digital therapautic effective in managing metabolic syndrome parameters
Authors: Colwell N , Sheppard A , Egan J 
Published: October 2021

Abstract

Introduction
Lifestyle change is a class one therapeutic indication for persons with overweight, obesity, metabolic syndrome and cardiovascular risk factors including type II diabetes, hypertension and dyslipidaemia. There is synergism in reducing multiple cardiovacular risk factors simultaneously. Currently there is a need for a formalised prescribed digital therapeutic to enact lifestyle change optimally. We have developed a real-time telemedicine lifestyle change digital therapeutic for persons with these conditions with substantive positive impact on weight, Body Mass Index (BMI), blood pressure (BP), and metabolic syndrome parameters.

Purpose
To evaluate a digital lifestyle change therapeutic in subjects with metabolic syndrome parameters in primary care, corporate and pharmacy settings.

Methods
Cohorts of patients were enrolled through primary care physician referrals and in corporate or pharmacy settings; in a GDPR compliant format between 2015–2020 (n=323). The programme offers patient education, guideline dietary recommendations and lifestyle advice specific to the patients requirements via smartphone, tablet or computer. Nudge psychology educational videos, compliment exercise monitored by a smart device and weight loss recorded by a wireless scales. Point of care bloods were tested pre and post-enrollment. Asynchronous dietary advice from a dietician, peer to peer support and an artificial intelligence chat-bot are available at all times via a smartphone application. Statistics used was student two-tailed t-test comparing pretest parameters to those post lifestyle intervention.

Results
Cohort results are delineated below. As the programme underwent refinement more parameters were added. Nevertheless similar trends are seen in each cohort with statistically meaningful reductions in weight, BMI, systolic and diastolic blood pressure, HbA1c and lipids obtained.
Reductions in HbA1c and systolic blood pressure approach or equate with those obtained using drug monotherapy. Moreover the patients Quality of Life is improved.

Conclusion
A real-time internet-based prescribed lifestyle digital therapeutic results in clinically meaningful reductions in weight, BMI, blood pressure, HbA1c and lipids. Results in various cohorts are similar, indicating a reproducible effect of this cost effective scalable digital therapeutic across a spectrum of metabolic risk factors in a variety of settings.

Funding Acknowledgement
Type of funding sources: Private company. Main funding source(s): RedicareDiabetes Ireland Table 1
New era of education: mobile learning of coronary guidewires in cardiovascular medicine
Authors: Kyaw H , Vengrenyuk A , Johal G , Goel S , Sharma S , Kini A 
Published: October 2021

Abstract

Background
Online education has transformed the way we teach and learn, especially in the midst of a global pandemic. Multiple devices, including coronary guidewires, are required to perform a successful coronary intervention, and understanding the engineering aspect of coronary guidewire technology is paramount.

Purpose
We aim to develop and evaluate a novel teaching tool/mobile learning app to understand a complex guidewire architecture and appropriate wire selection based on a lesion characteristics.

Methods
A guidewire is incredibly complex, consisting of a multitude of technologies allowing a range of tip softness, trackability around curves, and precise torque control. Despite operator preference, the process of choosing an appropriate coronary guidewire has gone mostly unchanged. We envisioned developing the GuidewireAID app with three main parts: 1) wire basics, 2) lesion-based guidewire selection, and 3) cased-based approach. Appropriate teaching cases were selected from a large-volume catheterization laboratory and divided into non-chronic total occlusion (non-CTO) and CTO. Non-CTO cases include simple, calcified, angulated, and bifurcation lesions, as well as thrombotic occlusion and tortuous vessels (Figure 1). Each case is described and analysis is offered on how to select an appropriate wire, followed by teaching points pertinent to the topic.

Results
Twenty-three detailed clinical cases and more than a hundred wires are illustrated in the GuidewireAID app. Case presentation, angiographic analysis, and a thorough understanding of wire characteristics allows the operator to know which wire to use and when it is time to switch, especially when dealing with complex coronary cases.

Conclusion
The GuidewireAID app will help fellows in training (FIT), early career interventionists, and practicing interventional cardiologists understand the complex aspects of a coronary guidewire and appreciate how their nuances could overcome real-world clinical challenging scenarios.

Funding Acknowledgement
Type of funding sources: None. Figure 1
Lack of knowledge on acute stroke in children, adolescents and adults from public schools
Authors: Salles I C , Gouvea G B , Calderaro M , Monteiro V S , Correa R F , Silva S N M F , Shinohara H N I , Umeda I I K , Aikawa P , Carvalho H B , Mansur A P , Carmona M J C , Semeraro F , Bottiger B W , Nakagawa N K 
Published: October 2021

Abstract

Introduction
Stroke is an important cause of disability and death in adults worldwide. However, it is preventable in most cases. This community-based study investigated students' stroke knowledge, emergency medical services (EMS) activation, associated risk factors, warning signs and symptoms, and prior experience from different educational levels.

Methods
We conducted the survey with a structured questionnaire between March 2019 and December 2020.

Results
Students from the elementary school (n=1,187, ∼13 y.o., 14% with prior experience, 51% women), high school (n=806, ∼17 y.o., 13% with prior experience, 47% women) and University (n=1,961, ∼22 y.o., 9% with prior experience, 66% women) completed the survey. Among the students, the awareness of stroke general knowledge, associated risk factors, and warning signs and symptoms varied between 42–66%. When stimulated, less than 52% of the students associated stroke with hypercholesterolemia, smoking, diabetes, and hypertension. When stimulated, 62–65% of students recognised arm weakness, facial drooping, and speech difficulty; only fewer identified acute headache (43%). Interestingly, 67% knew the EMS phone number; 81% were willing to have stroke education included at school, and 75.2% found it essential and mandatory. Female, higher education and prior experience were associated with higher scores of stroke risk factors (OR: 1.28, 95% CI: 1.10–1.48; OR: 2.12, 95% CI:1.87–2.40; and OR: 1.46, 95% CI: 1.16–1.83; respectively), and signs and symptoms (OR: 2.22, 95% CI: 1.89–2.60; OR: 3.30, 95% CI: 2.81–3.87; and OR: 2.04, 95% CI: 1.58–2.63; respectively).

Conclusion
Being female, having prior experience, and a higher educational level increase the identification of associated risk factors and warning signs and symptoms. Stroke awareness should be emphasized among schoolchildren and adolescents.

Funding Acknowledgement
Type of funding sources: None.