bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of telemedicine network provided by pharmacies to detect electrocardiographic changes deserving cardiological evaluation in young patients
Authors: Fioretti F , Bollani G , Calzoni A , Praderio A , De Angelis M G , Sammartino A , Inciardi R M , Arabia G , Glisenti F , Nodari S 
Published: November 2023

Abstract

Background
cardiac arrhythmias affecting adolescents and young subjects are a wide-ranging problem. Often-such conditions, in addition to being highly varied and sometimes hard to diagnose, are largely silent and onset variably and suddenly, sometimes resulting in sudden cardiac death. Such individuals, being asymptomatics, usually do not undergo cardiological evaluation, as their condition may be misrecognized or not suspected until it manifests clinically. Thus, there is a need to develop cheap and easily accessible screening systems for patients that allow early identification of electrocardiographic (EKG) findings deserving evaluation by the cardiologist. Recently, pharmacies have proposed EKG screening programs for cardiac arrhythmias using a telematics network, enabling referral to the cardiology specialist and identification of patients worthy of further diagnostic investigations.

Aim of the Study
to assess validity, feasibility and usefulness of an electrocardiographic screening program for the prevention of cardiac sudden death in subjects &lt; 18 years old with the support of a telemedicine service available in local pharmacies.

Materials and Method
we analyzed 12-lead electrocardiograms (ECGs) recorded by 4,500 country pharmacies in subjects with &lt; 18 years old, evaluated and stored in one telemedicine platform provided by Health Telematic Network (HTN), in cooperation with a cardiology department hospital and a university.

Results
from April 2010 to January 2023, 266.381 subjects with &lt; 18 years old underwent electrocardiographic examination in territorial pharmacies. Of them, 142.924 (53.65%) were men. In 440 subjects (0.16%) a first or a second-degree atrio-ventricular block were found. Moreover, in 94 (0.03%) of them a delta-wave in QRS complex was found, suggesting the presence of a Wolff-Parkinson-White syndrome. In 59 (0.022%), a ST segment with Brugada-like pattern was identified, whereas in 118 (0.04%) a long QT was detected. All subjects in which an electrocardiographic alteration was found were referred to outpatient cardiological visit for further diagnostic evaluations.

Conclusion
These data suggest the potential validity, feasibility and usefulness of an electrocardiographic screening program for the prevention of cardiac sudden death in subjects &lt; 18 years old performed in territorial pharmacies. This could be a valuable strategy to noninvasively and easily identify those subjects who need further diagnostic investigations and to rule out conditions that, especially in a young and otherwise healthy population, could manifest dramatically and suddenly, often without the possibility of intervention.
Artificial intelligence (AI) enhanced ECG predicts obstructive cardiovascular disease and death using secondary data; a benchmarked comparison of different methods
Authors: Vogel L H , Kampf J , Dykun I , Totzeck M , Rassaf T , Mahabadi A A 
Published: November 2023

Abstract

Introduction
Assessment of pretest-probability of chronic coronary artery disease (CAD) is based on the modified Diamond and Forrester criteria, including age, gender, and symptoms. However these criteria suffer from low accuracy, requiring further diagnostics prior to coronary angiography (CA), e.g. via coronary computed tomography angiography. Artificial intelligence (AI)-enhanced evaluations of broadly available resting ECGs have shown excellent results in the detection of multiple cardiovascular diseases. In this analysis, we aimed to evaluate and compare the ability of neural networks, random forest and support vector machines as different AI-based approaches regarding obstructive CAD and long-term mortality using structured ECG data.

Methods
The cohort of the ECAD registry includes patients undergoing CA at our heart and vascular center between 2004 and 2019. Patients with a digitally available resting ECG within 90 days prior to CA, containing structured data on 648 characteristics for each ECG, were included. The overall cohort was divided in a learning (60%), validation (20%) and a test cohort (20%). Obstructive CAD was defined by percutaneous intervention as by discretion of the interventional cardiologist. The incidence of death due to any cause was evaluated during follow-up. Logistic regression (LR) and linear discriminate analysis (LDA) were used as benchmarks. Calculations were run 100 times and compared by their mean area under the receiver operating curve (AUC) in the validation cohort. An initial neural net (NN1) was improved by sophisticated hyperparameter tuning, dropout layers and weight regularization (NN2). Feature reduction lead to NN3. The initial random forests (RF1) was adapted for the risk factors (RF2).

Results
Data from 7076 CAs were included. An obstructive CAD requiring revascularization was present in 2075 cases (29.3 %). Neural networks with feature selection based on ECG characteristics outperformed traditional risk factors and provided highest area under the ROC-curve (table 1). During a median follow-up of 2.4 years (Q1: 0.8; Q3: 6.3), 1137 patients (16.1 %) died. For prediction of all-cause mortality, neural networks based on structured ECG data provided better prediction as compared to risk factors. All NNTs and the RF models exceeded the LR and LDA as benchmark, while SVM did not hold up to conventional multivariate analyses (table 1).

Conclusion
Based on structured data from resting ECGs, NNTs and RF analyses improve the prediction of obstructive CAD and long-term survival in patients undergoing CA.
Reduction of patient trust in physicians when supported by artificial intelligence: results of a vignette study among patients
Authors: Zondag A G M , Rozestraten R , Grimmelikhuijsen S G , Jongsma K R , Van Solinge W W , Bots M L , Vernooij R W M , Haitjema S 
Published: November 2023

Abstract

Background
Clinical Decision Support Systems (CDSSs) based on routine care data using artificial intelligence (AI) are increasingly deployed. CDSSs, however, may affect patient-physician relationships (Figure 1) and potentially result in a decrease in treatment adherence. Communication about these systems has mostly focused on technical aspects and less on their acceptance and trust. We aimed to investigate whether patient trust in physicians is affected when supported by a CDSS.

Methods
We conducted a vignette study among the patient panel (N=860) of a University Medical Center. Patients were presented either a high-risk or a low-risk vignette. For both vignettes, a physician made a treatment decision with (intervention group) or without (control group) the use of a CDSS. Using a questionnaire with a 1-7 point Likert scale (1 = strongly disagree, 7 = strongly agree), we collected data on patient trust in physicians in three dimensions: competence, integrity and benevolence, and trust in technology in general. We assessed differences between the control and intervention groups using Mann-Whitney U tests and potential effect modification by sex, age and education level using multivariate analyses.

Results
In total, 398 individuals participated. Trust in general was high (5.8 in the high-risk case and 6.0 in the low-risk case). Figure 2 illustrates the distribution of the dimensions of trust, by case and group. In the high-risk case, perceived competence and integrity was lower in the intervention group compared to the control group (5.8 vs. 5.6 and 6.3 vs. 6.0, respectively). In the intervention group, perception of the physician’s competence and integrity was significantly lower in women than in men. No differences between the groups were found in the trust dimensions within the low-risk case. Participants with higher trust in technology in general also showed a higher confidence in physicians using a CDSS in terms of benevolence and integrity (P=.009 and P=.044, respectively).

Conclusion
We found that, in general, trust in physicians was high. However, our findings indicate a potentially negative effect of AI applications on the patient-physician relationship, especially in women and high-risk situations. Trust in technology in general might increase the likelihood of embracing the use of CDSSs by the treating professional.
Performance of modified ResNet model for calcium score from non-contrast computed tomography
Authors: Tan E W P , Cheng N , Leng S , Baskaran L , Teo L , Yew M S , Singh M , Go M C R , Huang W M , Raffiee N A S , Chan M Y Y , Ngiam K Y , Tan S Y , Lee H K , Zhong L 
Published: November 2023

Abstract

Aims
Calcium score measured from non-contrast CT is considered as the reference method for risk assessment of coronary artery disease. We aimed to develop a modified ResNet layers model for calcium score assessment in a cohort of multi-ethnic Asian patients with suspected coronary artery disease.

Methods
This is a multicenter study which performs CT scans in 5000 Asian Admixture patients in Singapore from 3 different hospitals (APOLLO study). In this current study, 369 subjects (mean age 56 ± 11 years; 147 female) were analyzed. Of these patients, 36% were shown to have very low risk (CAC = 0), 49% had mildly increased risk (CAC = 1–99), 11% had moderately increased risk (CAC = 100–299), and 5% had moderate to severe risk (CAC&gt;300). The number of labelled 2D axial calcium lesions were as follows: LAD – 929, LM – 101, LCX – 545 and RCA – 940. Data augmentation was carried out to increase the overall sample size and balance number of labelled calcium lesions in the dataset. The dataset was split at the patient level prior to data augmentation to prevent any data leakage issues (60% Training: 20% Validation and 20% Test). The calcium score model was developed by consisting of three modified ResNet layers (MRN), and a series of fully connected layers. Axial, coronal and sagittal CT images were fed into the modified ResNet individually and the output of the model was the probability of the calcium lesion being in any of the four arteries. Cross-entropy loss was used as the loss function during the training process, while F1 score was used to evaluate the model performance at per-voxel level.

Results
The correlation coefficient between our MRN model and clinical ground truth for the total calcium score was r = 0.90 (P &lt; 0.0001). The MRN model performed well in detecting the location of calcium lesions belonging to any of the three arteries, with accuracies of 0.95 in LAD, 0.90 in LCX, and 0.80 in RCA. Additionally, the MRN model demonstrated good diagnostic accuracy, F1 score, and intra-class correlation coefficient (ICC) of 0.80, 0.79, and 0.92, respectively, in predicting the CAC risk categories (low, mildly increased risk, moderately increased risk, and moderately to severely increased risk). A confusion matrix for the CAC risk category between the expert reader and MRN is shown in Figure 1.

Conclusion
Our modified ResNet-based machine learning model attained excellent prediction of calcium score using non-contrast CT. Figure 1.Confusion matrix for CAC risk.
Deep learning in digital health with chatgpt: a study on efficient code generation
Authors: Loh B C S , Fong A Y Y , Ong T K , Then P H H 
Published: November 2023

Abstract

Background/Introduction
Artificial Intelligence (AI) plays a significant role in the healthcare industry by providing innovative solutions to many health-related problems. Deep learning (DL) is a critical component in the development of AI-powered solutions; however, extensive knowledge is required to implement DL processes such as data preparation, model design, model training, and model evaluation. This has limited the widespread adoption of AI in the medical field, particularly in scenarios where non-technical professionals possess limited coding proficiency.

Purpose
This study evaluates the feasibility of using ChatGPT, a large language model developed by OpenAI, as an assistive tool for non-technical professionals to develop DL applications. The objective is to reduce the time and effort required in source code creation, while simplifying the overall process of application development.

Methods
ChatGPT was utilised to generate relevant source code for the creation of a DL application for echocardiogram view classification. Natural language input prompts were fed through the ChatGPT interface to produce modular scripts that fulfilled the purposes of video frame extraction, image pre-processing, dataset splitting, model training, and model evaluation. Input prompts were specified as a series of logical steps which explained the desired functionality, along with the necessary software libraries to be integrated.

Results
Experiments on code generation involved iteratively modifying input prompts through the rewording of sentences, reordering of steps, and usage of different libraries, to obtain working source code from ChatGPT responses. Results demonstrated that ChatGPT can generate fully usable programming scripts from natural language inputs, without the need for any modifications, to accomplish the various tasks involved in the DL process flow. Experiments were also conducted on locally sourced dobutamine stress echocardiogram videos, to verify script functionality and output reliability. The scripts successfully extracted, pre-processed, and split the video frames into training, validation, and testing sets. These datasets were then employed in the training and evaluation of a convolutional neural network which achieved an accuracy of above 80% when classifying seven different echocardiogram views.

Conclusion
ChatGPT has the potential to significantly reduce the time and effort required in developing DL applications. The successful implementation of the programming scripts suggest that ChatGPT can function as an assisting tool for non-technical professionals. Its ability to generate operational source code and streamline the development process could facilitate greater adoption of AI in healthcare and other industries. Future works shall explore the usage of ChatGPT in generating solutions to more heart-related use cases.Code generation and DL process flow
Use of machine learning to predict mortality in patients with type 2 diabetes mellitus, according to socioeconomic status
Authors: Kaur N , Deligianni F , Pellicori P , Cleland J G F 
Published: November 2023

Abstract

Introduction
Patients with Type 2 diabetes mellitus (T2DM) are at increased risk of developing cardiovascular disease (CVD) that adversely affects prognosis. Socioeconomic deprivation may be associated with lifestyle choices that contribute to adverse outcomes. The influence of social deprivation on the prognosis of T2DM is rarely investigated in large cohorts. Therefore, we assessed the associations between socioeconomic status and survival in patients with T2DM by applying conventional statistical methods and state-of-the-art, machine learning (ML) models.

Methods
We obtained routinely collected, linked administrative data for individuals with T2DM aged &gt;50 years from the National Health Service (NHS) Scotland, including demographic data, laboratory measurements, prescriptions, death records and primary and secondary care diagnostic codes. We developed a random survival forest model for predicting all-cause mortality, overall and for each quintile of socioeconomic status using the Scottish Index of Multiple Deprivation (SIMD), which is based on postcodes that reflect a combination of income, employment, local crime rates, education, housing and health. We used Cox proportional hazards models to investigate the risk between the most and least deprived quintiles. Subsequently, we applied Shapely Additive Explanations (SHAP), a state-of-the-art ML interpretability method, to identify key prognostic factors that influence the survival probability prediction score for each sub-group.

Results
30,495 people had a newly recorded diagnosis of T2DM between 2009-2019, of whom 4,300 died within 10 years. Table 1 shows the c-index (global assessment of model discrimination) and time-brier score (measure of calibration for time-dependent probability prediction) for the whole cohort and for each SIMD quintile: use of loop diuretics, older age, lower serum concentrations of albumin and alanine transaminase (ALT) and estimated glomerular function rate (eGFR) were strong predictors of death for all quintiles. Prevalence of chronic obstructive pulmonary disease (COPD) was strongly associated with mortality for the most deprived quintile (Q1, whilst strokes were strongly associated with mortality for the most affluent (Q5). A history of heart failure, in addition to use of loop diuretics, predicted a higher risk, in 3 of the 5 quintiles. Overall Q1 had a 36% higher mortality than Q5 (HR: adjusted for age and sex 1.36 [95% CI 1.24 – 1.50 (&lt;0.005)]).

Conclusion
Greater socioeconomic deprivation is associated with a worse prognosis in patients with T2DM. Results of commonly measured blood tests, allied to readily available patient characteristics (including heart failure and use of loop diuretics) predict mortality risks across all deprivation groups for people with T2DM.Table:Predictors of Mortality in T2DM
Impact of team-based health information technology to improve low-density lipoprotein cholesterol goal attainment after acute coronary syndrome hospital discharge: a single-center experience
Authors: Chen Y S , Lin H H , Wu Y W 
Published: November 2023

Abstract

Objectives
The reduction in circulating low-density lipoprotein cholesterol (LDL-C) is the primary aim of lipid-lowering therapies as a method of atherosclerotic cardiovascular disease risk reduction. Although guidelines recommended strictly control of low-density lipoprotein cholesterol (LDL-C) below 70 mg/dL by high-intensity statin therapy in patients with acute coronary syndrome (ACS), the LDL-C target goal is not often attained. Health information technology (HIT) with information integration, decision making algorithm, is being applied on chronic illness care.

Purpose
We aimed to perform a qualitative review of the impact of HIT interventions on lipid management and LDL-C goal attainment in patients after ACS hospital discharge.

Method
Consecutive ACS patients with dyslipidemia (LDL-C &gt;100 mg/dL) at discharge between January 2018 and December 2021 in a tertiary medical center were retrospectively reviewed. The LDL-C &lt;70 mg/dL is defined as target. The team-based approach quality improvement program that consisted of patient education and high-intensity statin initiation before discharge, outpatient clinics with periodic laboratory follow-up, titration of lipid-lowering agents, and system-level interventions that involve database monitoring and outreach by centralized care teams, was introduced in October 2018. Electronical medical records including the medications, laboratory data at discharge and within 1 year were compared, before and after initiation of HIT system.

Result
A total 2248 ACS patients were included. The LDL-C &lt;70 mg/dL attainment rate (36.52%, 53.57%, 59.22%, 62.18% in 2018, 2019, 2020, and 2021) and medium serum LDL-C level (80.5 mg/dL, 68 mg/dL, 65 mg/dL, 64 mg/dL in 2018, 2019, 2020, and 2021) significantly improved within 6 months after initiation of the team-based integrated intervention with assisted by HIT (2018 as the reference, all p&lt;0.001). The LDL-C goal attainment rate at 12-month kept increasing gradually (53.80%, 61.82%, 64.21%, in 2019, 2020, 2021, p= 0.019). Most of the population initiated with moderate-intensity statin in 2018 (76.24%, 20.92%, 10.95%, 6.41% in 2018, 2019, 2020, and 2021), then switched to a high-intensity statin after initiation of the program (0.57%, 63.67%, 72.41%, 84.44% in 2018, 2019, 2020, and 2021) (2018 as the reference, all p&lt;0.001), while the adverse event rates including hepatotoxicity and rhabdomyolysis remained similarly low.

Conclusion
Our results confirmed that multifaceted intervention with HIT assisted multidisciplinary approach could substantially improve LDL-C goal attainment in post-ACS patients. There was a direct relationship between high-intensity statins and LDL-C goal attainment, without significant increase of adverse events.LDL-C goal attainment rate and levelLower lipid treatment and adverse event
Detection of supraventricular arrhythmias with apple watch
Authors: Soysal A , Golcuk E , Atici A , Tokdil H , Yalman H , Incesu G , Ikitimur B , Yalin K , Karpuz H 
Published: November 2023

Abstract

Introduction
With the recent developments in wearable technology, detecting arrhythmias has become much easier. Although smartwatches are commonly used to detect patients with atrial fibrillation, there is no consensus on their efficiency in detecting supraventricular tachycardias (SVT).

Methods & Results
Electrophysiological procedures were performed on 47 patients that have been previously documented to have SVT by a 12-lead ECG. All patients had sinus rhythm before the procedure. A narrow complex SVT which patients had at least once was induced by electrophysiological methods. Induced SVTs were recorded with a 6th-generation Apple Watch as one rhythm strip. Among the induced tachycardias, 27 typical AVNRT, 11 AVRT and 9 AT/AFLwere diagnosed. These records obtained from the smartwatch were evaluated by 3 cardiology residents and 3 attending cardiologists. Evaluated records were predicted among attending physicians with a minimum sensitivity of 66.0% and maximum sensitivity of 76.6%. Among residents, minimum and maximum sensitivity rates were 68.1% and 74.5%, respectively. The reliability between residents and attendings was assessed separately using Fleiss’s kappa method. The interrater reliability was found to be Kappa=0.465 (p&lt;0.001), 95% CI (0.30-0.63) within the group of residents and Kappa=0.519, (p&lt;0.001), 95% CI (0.35-0.68) within the group of attendings. Overall Kappa value was 0.417 (p&lt;0.001), 95% CI (0.34-0.49), indicating a significantly important moderate level of agreement (p&lt;0.001).

Conclusion
To the best of our knowledge, this is the first study showing how accurately clinicians can interpret the recording of SVTs with wearable devices such as smart watches, whose definitive diagnosis was made invasively by electrophysiological study. As presented in our study, induced, and subsequently recorded SVTs were predicted with high sensitivity and a moderate reliability rate. Adoption of smartwatches could significantly limit the delays in diagnosis and treatment of patients admitted to the hospitals with complaints of palpitations that cannot be documented.Statistics