bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of aortic stenosis using built-in microphones of commercially available smartphones
Authors: Altstidl J M , Altstidl T R , Anneken L , Gaede L , Eskofier B M , Achenbach S 
Published: November 2023

Abstract

Background
According to recent data, asymptomatic patients with severe aortic stenosis benefit from early treatment. Hence, widely applicable methods to identify patients with severe aortic stenosis in an asymptomatic state may be desirable in the future. While the diagnosis of aortic stenosis is currently based on echocardiography, population-wide use is not feasible due to resource constraints. With recent technological advances in built-in microphones of commercially available smartphones, digital auscultation may provide a viable alternative.

Methods
As part of this study, a smartphone application to perform digital auscultation was developed. To ensure high-quality recordings real-time signal analysis is implemented. The 8 kHz sampling rate of the signal encompasses all typical frequencies of cardiac auscultation while keeping resource usage minimal. To prevent frequency leakage the signal is attenuated using a Blackman-Harris window. After applying Fast Fourier Transformation to the windowed signal, frequencies around 100 Hz and 250 Hz are amplified to match the frequency response of traditional stethoscopes. The resulting signal amplitudes are then visualized. The application provides functionality for storing metadata about each patient in addition to the raw audio recordings.
To verify the feasibility of using the described application to detect severe aortic stenosis, this study aims to enroll 50 persons with no valvular heart disease and 50 persons with severe aortic stenosis. Initial audio recordings by the smartphone application and of a digital stethoscope are compared by visualizing the frequency spectrum using the Mel Frequency Cepstral Coefficients.

Results
The peaks of the first and second heart sound at around 50 Hz are both clearly identifiable, as is the crescendo-decrescendo murmur typically associated with aortic stenosis in the frequency range of 100-600 Hz (Figure 1). This is true for both smartphone and stethoscope recordings, even though the built-in microphone of smartphones results in a lower signal-to-noise ratio. Nevertheless, the quality of the signal is robust enough to clearly indicate the presence of aortic stenoses.

Conclusions
The initial findings of this study indicate that a smartphone application using built-in microphones is a promising approach for identifying severe aortic stenoses. This approach has the potential to be a reliable and cost-effective screening tool for early detection. Further development of automated algorithms may enhance the tool's effectiveness and accessibility.Figure 1
Recognition of heart failure with micro electro-mechanical sensors using commercially available smartphone, the REFLECS study
Authors: Haddad F , Saraste A , Santalahti K , Pankaala M , Kaisti M , Kandolin R , Simonen P , El Nammas W , Jafarian Dehkordi K , Koivisto T , Mahaffey K W , Blomster J I 
Published: November 2023

Abstract

Background
Heart failure (HF) is the leading cause of hospitalization in people over the age of 65 years. More recently, cardiac motion sensor technology has emerged as a promising technology to detect HF.

Methods
In this multicenter study, we examined whether accelerometer and gyroscope signals from motion sensors collected using commercially available smartphones can classify HF status. Participants were enrolled from Finland and the United States. Participants hospitalized with acute decompensated HF were assessed in the acute state and re-assessed in the stabilized state prior to discharge. Outpatient participants were assessed in the stable state. In a pre-specified pooled data analysis, state specific algorithms to detect HF were first derived using logistic regression and validated using boostrap aggregation method (10 repeats) followed by sensitivity analysis in participants with HF with reduced or preserved ejection fractions, participants in atrial fibrillation and according to age, sex or body mass index.

Results
A total of 217 HF participants had a total of 474 assesment: acute state = 174; stabilized state = 128; stable state = 172. 786 controls were included. The mean age of all participants with HF was 67 years, mean ejection fraction (EF) 39% and mean NT-proBNP 5778 ng/l. 74% had HF with reduced EF (HFrEF) and 38% had atrial fibrillation. Across all three HF states, the algorithm had an AUC of 0.95, sensitivity of 85%, specificity 90%, and an accuracy of 89%. The performance of the algorithm was not affected by HF state, ejection fraction, age, sex, body mass index (BMI) or presence of atrial fibrillation.

Conclusion
A simple commercially available smartphone based assessment of cardiac function with motion sensors shows distinct features in participants in decompensated, stabilized and stable HF states in comparison to controls.Heart failure detection ROC curves
Real-time interactive 3d simulation of Atrial Fibrillation for education
Authors: Finlay M , Hurmusiadis V 
Published: November 2023

Abstract

Background
The mechanistic understanding of atrial fibrillation (AF) has burgeoned over the past decades. However, the interplay between triggers, reentry, drivers and therapies is rarely fully appreciated despite being ultimately fundamental to patient care. Readily accessible, high-fidelity interactive simulation has the potential to address this knowledge gap across healthcare providers.

Objective
To develop a, interactive simulation system to allow deep understanding of AF mechanisms and therapies.

Methods
An application for e-Training was developed to be accessible on consumer smartphones and tablets, based on a validated electromechanical simulation model of the heart, with real-time interactive 3D image output. A virtual heart is represented in normal and arrhythmic states, enabled with simulated models for administering antiarrhythmic drugs, performing Pulmonary Vein Isolation (PVI), AV Node ablation, ventricular pacing, DC Cardioversion, plus the formation of atrial thrombus leading to a stroke event. Electrogram and ECG signals are generated from the underlying simulation and respond realistically to drugs, pacing and ablation. A self-study tutorial introduces the arrhythmia and treatment procedures. Learning outcomes are assessed with MCQ tests and receive Continuous Professional Development CPD credits.

Results
The AFib Sim app has received CPD accreditation in the UK and has so far been used at the Universities of Oxford, Cambridge and Leeds medical schools. All trainees and medical students followed the self-study tutorials and took the interactive MCQ test, which are embedded within the AFib Sim app. Development was based on Unity 3D and was made available on iOS, Android, macOS, MS Windows and WebGL platforms.

Conclusion
This project meets an urgent training need whilst expanding expertise and market scope into clinical training. The impact on clinical training was high and the societal benefit came from better and quicker trained staff.

Learning Objective
Upon completion of this session, the attendee will be able understand how a simulation of Atrial Fibrillation can be used for training and deepening the clinical understanding of atrial fibrillation.
Clinician-created video education for patients with atrial fibrillation: a randomised clinical trial
Authors: Mcintyre D , Kovoor J , Chow C K , Quintans D , Marschner S , Thomas S , Kovoor P , Thiagalingam A 
Published: November 2023

Abstract

Background
Improved health literacy is associated with better health outcomes.(1) Atrial fibrillation (AF) is a complex condition and patient understanding of AF is poor.(2) There is little time during routine care to provide education on AF. Clinic waiting time provides an opportunity to deliver education that improves AF knowledge and engagement with clinical care.

Purpose
Assess the efficacy and feasibility of remotely-delivered clinician-created education for patients with AF.

Methods
Single-centre randomised clinical trial (ANZCTRN 12620000729921, registered 13/07/2020) conducted in adults over 18 years of age awaiting outpatient cardiac clinic appointments with electrocardiogram-confirmed AF, CHA²DS²VASc &gt; 0 and an active electronic mailing address or mobile phone. Those with insufficient English proficiency to comprehend video content were excluded. Eligible participants were recruited remotely and randomised 1:1 to an internet-based intervention of 4 animated videos narrated by treating cardiologists totalling 15 minutes with the option to review videos weekly, or usual care. Recruiting staff, outcome assessors and those responsible for statistical analysis were blinded to treatment allocation. The primary outcome was Jessa AF Knowledge Questionnaire (JAKQ) score (a 16-item validated questionnaire consisting of general AF and anticoagulation-specific questions(2)) at 90 days post recruitment adjusted for baseline performance, AF diagnosis, educational level, age and sex. A sample size of 200, allowing for 15% attrition and a standard deviation of 18 described previously(2) was prospectively calculated to have 90% power (2-sided, type 1 error of 5%) to detect an 8.95% difference in 90-day AF knowledge.

Results
Between November 2020 and July 2022, 208 patients were randomised (104 intervention, 104 control). Mean (standard deviation) age was 65.0 (12) years, and 65.2% were male. Most (56.2%) had paroxysmal AF and 6.4% had first episode AF. Intervention participants were more likely to correctly answer JAKQ questions at 90 days (odds ratio [OR] 1.23 [95% confidence interval (CI) 1.01, 1.49]). Highly engaged participants were more likely to have higher JAKQ scores (OR 1.46 [95% CI 1.14, 1.88] among those who watched videos on 3 or more weeks compared to control). Video satisfaction was high (figure 2). There was no significant difference in satisfaction with care or education provided in clinic OR 1.32 [95% CI 0.71, 2.44]).

Conclusions
A clinician-created, low-cost, remotely-delivered educational intervention among patients with AF awaiting cardiac clinic appointments was highly acceptable and improved AF knowledge at 90 days. Increased intervention exposure correlated with higher knowledge scores. Future delivery of this intervention could investigate impact on medication adherence, AF burden, and patient-reported quality of life outcomes.Figure 1Figure 2
Assessing the potential for precision medicine of glucose-lowering drugs in type 2 diabetes mellitus: a meta-regression analysis considering body weight variability from 120 randomized trials
Authors: Vargas K G , Ruetten T , Siemens B , Brockmeyer M , Parco C , Hoss A , Wolff G , Roden M , Kelm M , Kuss O 
Published: November 2023

Abstract

Background
Thus far, randomized controlled trials (RCTs) of glucose-lowering drugs reporting on treatment effects on body weight (BW) as cardiovascular risk factor in type 2 diabetes mellitus have not included precision medicine approaches. However, taking into account treatment response heterogeneity, their potential may be analyzed from published data of RCTs using meta-regression analyses. Both treatment response variation and predictors are required conditions for precision medicine to work.

Purpose
To assess the potential benefits of precision medicine by quantifying the variation of body weight as patient response to treatment, and to identify predictors which could explain this variation.

Methods
We used four recent systematic reviews on RCTs comparing pharmacological treatment of type 2 diabetes (included but not limited to sodium-glucose cotransporter-2 inhibitors, glucagon-like peptide-1 receptor agonists and thiazolidinediones) to placebo. RCTs reporting on BW at baseline and after follow-up to allow for calculation of its logarithmic standard deviation (Log(SD)) in treatment and placebo groups were included. Meta-regression models accounting for treatment means, differing sample sizes, and correlation across treatment from the same trial were used. A separate meta-regression model to evaluate interactions of treatment and individual predictor variables (age, male sex, pre-randomization BW, duration of disease, duration of treatment, year of study and drug class) was applied.

Results
120 randomized trials with a total of 43,663 participants were included. The median BW after treatment was 85.2 kg in the placebo groups and 86.2 kg in the therapy groups. A slightly greater treatment response heterogeneity was shown in the therapy groups with a median Log(SD)=2.83 compared to 2.79 from the placebo. After full adjustment in the meta-regression model the difference in BW Log(SD)s was -0.020 (95% CI: -0.063; 0.022) (Figure 1). Scatter plots adjusted for different sample sizes did not show any interaction (i.e. slope divergence) between predictors and the respective treatment (therapy or placebo). Figure 2 demonstrates this using pre-randomization BW as an example.

Conclusions
We found no major treatment response heterogeneity in RCTs of glucose-lowering drugs reporting on body weight in type 2 diabetes. In addition, none of the considered predictors were able to explain a potential variation in treatment response. The precision medicine approach may thus be of limited value in this setting.
Statin non-adherence findings from the My Experience of Taking Statin Medicines Questionnaire (mySTATINmed)
Authors: Kampouraki E , Wardman D , Mcfarlane L , Khatib R 
Published: November 2023

Abstract

Background
Non-­adherence to statins among patients with hyperlipidaemia is estimated to be around 50%. We designed and tested a modified version of the MYMEDS© tool (1), called The My Experience of Taking Statin Medicines (mySTATINmed) questionnaire. The aim was to identify actual and potential modifiable barriers to adherence for use in clinical practice to support patients on statins.

Methods
The mySTATINmed was integrated within SystmOne, the most commonly used primary care records system in Leeds, and was sent to patients registered with the 32 participating Leeds GP Practices via text message. Patients completed the questionnaire remotely and the response was saved within their medical records. The questionnaire explored patient perspectives on the main modifiable factors that are likely to be associated with statin non­-adherence. Five domains were included within the questionnaire; understanding and satisfaction with statins, concerns, practicalities, fitting the statin into daily routine and self-reported adherence within the last month.

Results
The mySTATINmed questionnaire was responded by 466 patients [59% males; age mean±SD (range): 65±11 (21-86) years] across 29 GP Practices in Leeds (91% of eligible). There were more males (62% v 55%), but no significant difference in age, in the adherent compared to the non-adherent group.
Overall, 57% of patients reported a high level of understanding and satisfaction with statins. Half of the respondents demonstrated some level of concern with their statin treatment. Being on too many medicines concerned 32% of statin users. The news related to statins have affected the perception of 14% of respondents. At least one practical problem was reported by 22% of patients, most commonly because they forget to bring their statin when they travel or leave home. Almost 23% of patients had some level of inconvenience fitting their statin into their daily routine and 21% reported that they sometimes forget to take their statin. The mySTATINmed detected an overall self-reported non-adherence within the last month of treatment of 27%, however 46% of participants displayed some level of actual or potential barriers to adherence based on their responses.
Around 36% felt it was not their decision to start their statin and 20% did not have the opportunity to ask questions or openly express concerns. Patients reported not having enough information about expected side effects (43%) and how statins work (41%). Adherent patients consistently demonstrated a better understanding and experience with statins (Table 1).

Conclusions
The mySTATINmed is a simple tool, integrated within SystmOne, which successfully identified many modifiable actual and potential barriers to statin adherence that can be addressed in clinical practice. It allows generation of longitudinal data, safely stored within individual medical records, to better understand modifiable barriers to statin adherence and non-adherence levels.
Mortality prediction in patient with atrial fibrillation and 0 or 1 nongender-related CHA2DS2-Vasc risk factor using machine learning: a French nationwide study
Authors: Bisson A , Lemrini Y , Romiti G F , Proietti M , Angoulvant D , Bentounes S , El-Bouri W , Lip G Y H , Fauchier L 
Published: November 2023

Abstract

Background
Atrial fibrillation is associated with important mortality included in patients with low or intermediate thromboembolic risk. Usual clinical risk scores are challenged in this specific population and predict only modestly mortality. Machine learning approach helps to identify new risk factors and outperform existing tools for the prediction of outcomes in atrial fibrillation patients.

Purpose
This study aimed to developpe a machine learning model for the prediction death occurrence within the year following atrial fibrillation diagnosis and to assess performances in patients with 0 or 1 nongender-related CHA2DS2-Vasc risk factor compared to previously described clinical scores.

Methods
We used for this purpose a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in all the French hospitals from 2011 to 2019. Among 110 variables, the 18 most predictive variables were identified and selected using a Random Forest algorithm as adding any other variable did not improve prediction (Figure 1). Three machine learning models (logistic regression, random forests, deep neural networks) were trained to predict mortality within the first year on a train set (70% of the cohort). The best model was selected to be evaluate on the validation set (30% of the cohort). We finally compared our best model with previously published scores in patients with 0 or 1 nongender-related CHA2DS2-Vasc risk factor using the C index.

Results
Among atrial fibrillation patients, 373,155 (15.32%) had 0 or 1 nongender-related risk factor. Within the first year following AF diagnosis, 23,857 patients (6.39%) died after a mean time of 86 (SD 26) days. After training, the best machine learning model selected was a deep neural network with a C index of 0.915 (95% CI, 0.905- 0.925) on the validation set. Compared to traditional clinical risk scores, the deep neural network model was significantly superior to the CHA2DS2-VASc and HAS-BLED scores, and dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following AF diagnosis (C indexes: 0.521; 0.633; 0.849; 0.800 respectively. P&lt;0.0001) (Figure 2, Panel A). The ability to predict AF was improved as shown by the net reclassification index and integrated discriminatory improvement increase (P&lt;0.0001, respectively) and decision curve analysis (Figure 2, Panel B).

Conclusion
Patients with atrial fibrillation and 0 or 1 nongender-related CHA2DS2-Vasc risk factor remain at risk of mortality within the first year of the diagnosis. A ML approach may help clinicians to better risk stratify AF patients with low or intermediate thromboembolic risk at high risk of mortality.Figure 1.Feature importanceFigure 2.ROC and decision curves
Influence of socioeconomic status on the presence of obstructive coronary artery disease and cardiovascular outcomes in patients undergoing invasive coronary angiography
Authors: Chung J 
Published: November 2023

Abstract

Background
Although it has been documented that low socioeconomic status (SES) is associated with increased risk of mortality and cardiovascular disease (CVD), this issue has not been addressed in patients at high CVD risk. This study was performed to investigate the association of the patient’s SES with the presence of obstructive coronary artery disease (CAD) and long-term cardiovascular outcomes who undergo invasive coronary angiography (ICA).

Methods
A total of 9,530 patients who underwent ICA for the evaluation and treatment of CAD (66.0 ± 12.3 years and 60.2% male) were retrospectively reviewed. The patients were divided into two groups according to the health insurance type: those with low SES who had the Medical aid program (Medical aid beneficiary [MAB] group; n = 1,436) and those with high SES who had the National Health Insurance program (National Health Insurance beneficiary [NHIB] group; n = 8,094). The primary outcome was a composite of cardiac death, acute myocardial infarction, coronary revascularization, and ischemic stroke.

Results
Of the study patients, 1,436 (15.1%) were in the MAB group. The prevalence of cardiovascular risk factors was higher in the MAB group compared to the NHIB group. However, the prevalence of obstructive CAD was similar between the two groups (62.8% vs. 64.2%; p = 0.306). During a median follow-up period of 3.5 years (interquartile range, 1.0-5.9 years), the incidence of the composite cardiovascular event was significantly higher in the MAB group than in the NHIB group (20.2% vs. 16.2%, p &lt; 0.001). In multivariable Cox regression analysis, compared to the NHIB group, the MAB group was independently associated with worse clinical outcomes even after controlling for potential confounders (adjusted odds ratio, 1.28; 95% confidence interval, 1.07-1.54; p = 0.006).

Conclusions
Although CAD prevalence was similar, MABs showed an increased risk of composite cardiovascular events than NHIBs in Korean adults undergoing ICA. This provides additional evidence for the association between low SES and an increased risk of CVD, even in high-risk subjects.