bluuebunny/crossref_metadata_2025 huggingface dataset:
Palliative care for cardiovascular patients: description of motives for referral and actual care provided
Authors: Ecarnot F , Giffard M , Van Bulck L , Sall F S , Becoulet N , Seronde M F
Published: November 2023

Background

Palliative care (PC) for cardiovascular patients remains suboptimal, whereby only a small proportion of patients are referred to specialist palliative care, and often too late in the disease course. We investigated the reasons that prompted cardiologists to request an intervention from the PC team, and we describe the actions implemented by the PC in response to referrals from the cardiology department.

Methods

This retrospective study included all patients with cardiovascular disease who were referred to the mobile palliative care team of a large University Hospital in France between 2010 and 2020. All data were extracted from the medical hospital files. For all patients referred to PC during the study period, we recorded the original motive for requesting assistance, as cited in request sent from cardiology to PC. We also recorded the types of services provided by PC, the date of the first and last consultation of the patient with PC, and the number of PC consultations per patient.

Results

From a total of 142 cardiology patients for whom PC assistance was requested, 136 (95.8%) died, while 6 (4.2%) are still alive. In 42 patients (29.6%), there was a generic request for intervention without indicating any particular domain where specific assistance was needed. In other cases, a motive for referral to the PC mobile team was provided by cardiologists, with ethical dilemmas (35 patients; 24.6%), symptom management (23 patients; 16.2%), and discussion about where the patient could live (11 patients; 7.7%), as the most common motives (see Figure 1). In response to these referrals, the PC team provided assistance with ethical dilemmas in 69 patients (48.6%), symptom management in 28 patients (19.7%), discussion about where the patient could live in 15 patients (10.6%), 2 or more of these issues in 16 patients (11.3%), and other issues in 14 patients (9.8%) (e.g. reorientation to oncology or family support). The majority of patients had 1 (n=90, 63.4%) or 2 PC consultations (n=27, 19%), while 10 patients (7%) had 3, 9 patients (6.4%) had 4, and 6 (4.2%) had 5 or more PC consultations. Among those who died, the median number of days between the first and the last PC consultation was 0 (quartile 1=0, quartile 3=3).

Conclusion

This study shows that cardiologists do not necessarily have a specific motive in mind when referring patients to PC. Because many more patients received assistance with ethical dilemmas than was requested, this indicates that cardiologists may not be aware of the ethical issues at stake in the care of cardiology patients, or that they only refer patients to PC when they have no other solution or do not know what else to do for the patient. There is clearly a need to raise awareness among cardiologists about ethical issues and about the services that the PC team can provide.
Device specific quality of life in subcutaneous versus transvenous implantable cardioverter defibrillator implantations: results from the ATLAS multi-centre randomized controlled trial
Authors: Carroll S , Mondesert B , Krahn A D , Bashir J , Fisher K , Nair K , Healey J S
Published: November 2023

Background

Newer innovative subcutaneous implantable cardioverter defibrillators (S-ICDs) offer an alternative to transverse ICDs (TV-ICDs), placing leads outside of the heart thereby potentially reducing lead-related complications. The ATLAS trial, a multi-centre randomized controlled trial, showed a significant reduction in perioperative complications in TV-ICD versus S-ICD patients. The following results report device-related quality-of-life (QoL) measured in the trial. QoL measures are recognized as key patient-reported outcome measures (PROMs).

Purpose

To compare TV-ICD and S-ICD post implantation device-related QoL in the ATLAS trial.

Methods

Sample included all randomized ATLAS trial patients. The Florida Patient Acceptance Survey (FPAS) was completed at 1- and 6-months post implantation. The FPAS includes a total score and 4 subscale scores; 1) return to function, 2) device-related distress, 3) positive appraisal, and 4) body image concerns. Scores are scaled from 0-100, with total scores below 63 suggesting poor device acceptance. The primary analysis was based on complete cases and used analysis of covariance (ANCOVA) with 6-month scores as the dependent variable, group as the independent variable, and 1-month FPAS scores as the covariate. Change in FPAS total score over time within each group was explored using paired t-tests.

Results

There were 252 TV-ICD and 251 S-ICD patients enrolled in the main ATLAS trial, and of these, in the present analysis, 404 patients (197 TV-ICD; 207 S-ICD) had complete FPAS data. Sample mean age (SD) was 49.31 (11.54) years, males (74.3%) and Caucasian (83.7%). There were no substantive differences in patient characteristics between groups. The primary analyses found no differences between TV-ICD and S-ICD groups for FPAS total score (Mean Difference=-0.75; 95% CI, -3.15, 1.66; p=0.510) or FPAS subscale scores. Mean FPAS total scores increased from 74.43 (15.35) to 78.25 (15.88) and 73.73 (16.09) to 77.05 (16.13) between 1-month and 6-months in the TV-ICD and S-ICD groups respectively. The increase from 1-month to 6-months was statistically significant in both groups (p< 0.001). These findings suggest that regardless of ICD type, there is device acceptance in both groups from implantation and over time.

Conclusion

Analysis of the FPAS measure, a device-specific QoL measure, found both TV-ICD and S-ICD patients reported good acceptance of their devices, with no significant differences between groups for up to 6 months post implantation. These findings provide an important piece to the TV-ICD versus S-ICD line of inquiry in ICD patient populations particularly for this newer S-ICD innovation and can be included in decision making between health professionals and patients.
The perceptions of disease and its symptoms compared by gender among patients with AF and their quality of life
Authors: Tsai Y H , Lou M F , Kuo C T , Weng L C
Published: November 2023

Background

Patients' atrial fibrillation (AF) symptoms can vary by gender, and their quality of life (QOL) is affected by their disease perceptions should be considered.

Purpose

This study assessed patients with AF about their perceptions of the disease and its symptoms. Participants' symptom conditions, AF and symptom perception, and QOL were compared based on their genders. In addition, we accessed the prediction factors of QOL.

Methods

We included cases of having been diagnosed with AF from the cardiology department of a medical centre. Research tools mainly included AF symptoms and conditions, perceptions of AF and its symptoms (modified from AF Illness Perception Questionnaire-Revised, AF IPQ-R), and QOL (Atrial Fibrillation Effect on QualiTy-of-life, AFEQT). A two-tailed test was adopted, with 0.05 being statistically significant. A multiple regression analysis was conducted to analyze predictive factors for QOL.

Results

The 110 cases included 71 men (64.5%) and 39 women (35.5%). They averaged 64.0±0.8 (33-87) years old. Regarding the negative perception of AF and its symptoms, in 3 items, women scored higher than men did (p<0.05): "AF is very unpredictable" (3.85±0.78，3.47±1.06; t=2.11), "When I think about my AF I get upset" (2.95±1.15，2.74±0.91，t=2.24) and "My AF makes me feel frustrated" (2.74±1.07，2.23±0.76，t=2.65). Regarding QOL, men scored poor QOL than women did in 2 items (p=0.03): "difficulty in walking briskly" (2.69±1.68, 3.47±1.91, t=2.21) and "difficulty in walking briskly uphill or carrying groceries or other items, up a flight of stairs without stopping" (3.35±1.84，4.18±2.01，t=2.18). QOL had five significant predictive factors; their total variance explained was 63% (Table 1). The item with the most vital explanatory factor was "total score of consequences due to AF", followed by "total score of emotional representations due to AF", "total number of symptoms", "taking Amiodarone", and "total score of treatment control of AF".

Conclusions

When patients were compared based on gender, women were more than men in the negative perceptions of AF as unpredictable, which frustrated them. Men were poor QOL related to more physical constraints. Regarding predictive factors for QOL, patients who strongly felt the consequences caused by AF, had negative emotional representations, had a negative sense of treatment control, perceived more symptoms, and took Amiodarone had lower QOL. The health provider should pay more attention to the gender differences and influencing factors of QOL and consider these individual and essential factors in health care.
Performance-based outcome measures for physical capacity and functional ability among patients with pulmonary embolism
Authors: Rolving N , Caspersen C K , Ingemann-Molden S , Grove E L , Andreasen J , Klok F A , Hoejen A A
Published: November 2023

Introduction

Pulmonary embolism (PE) is a serious health condition with up to 50 % of patients experiencing persisting symptoms with shortness of breath, pain, reduced physical performance and psychological distress, also known as post-PE syndrome. This condition has received increasing attention, including recommendations of assessing physical capacity to identify patients who may benefit from rehabilitation interventions to increase functional ability and health. However, as the PE population is heterogeneous compared to other cardiac patient groups, outcome measures for assessing physical capacity traditionally used in cardiac rehabilitation may not be reliable. This scoping review aims to map the performance-based outcome measures (PBOM) used in research literature to measure physical capacity in a PE population, to map the nature of the research and investigate the psychometric properties of the identified instruments in a PE population.

Methods

The methodology of the review process was guided by the Joanna Briggs Institute (JBI) framework for scoping reviews and reporting followed the PRISMA guidelines.

Results

The search identified a total of 4585 potentially relevant studies of which 234 studies met the inclusion criteria. A substantial part of studies (190 studies) was on a CTEPH population (chronic thromboembolic pulmonary hypertension), a condition developed by just 1-2% of patients with PE. The majority of included research articles originated from Europe (51%) and Asia (36%). Ten different PBOMs were identified for measuring physical capacity in a PE/CTEPH population, with 6MWT and CPET as the far most commonly used and the Bruce protocol/Modified Bruce protocol and Incremental Shuttle Walk test (ISWT) as third and fourth. No studies were identified reporting on psychometric properties of the 10 PBOMs in a PE / CTEPH population.

Conclusions

No studies were identified reporting on validity or reliability of any of the 10 identified PBOMs in a PE/CTEPH population. Thus, there is a need for conducting studies that establish the reliability, validity and responsiveness of PBOMs in a PE population, to enable identification of at-risk patients and to monitor effects of treatment and rehabilitation interventions.
A fast, novel technique for detection of left ventricular hypertrophy by electrocardiography
Authors: Somani S , Hughes J W , Ashley E A , Witteles R M , Perez M V
Published: November 2023

Background

Electrocardiography (ECG) is a widely used non-invasive tool for detecting left ventricular hypertrophy (LVH), but existing techniques suffer from complex rules and limited sensitivity.

Purpose

The goal of this study was to design and evaluate a new technique for rapid assessment of LVH by ECG.

Methods

The WS (Witteles-Somani) technique for LVH detection is based on recognizing the visual overlap between the S-wave of a precordial lead and the R-wave of any subsequent precordial lead. To evaluate its performance, ECGs from all patients at Stanford Healthcare with associated transthoracic echocardiograms (TTEs) between 2006 and 2018 were collected for analysis. ECGs with pre-existing bundle branch blocks or paced rhythms were excluded. A multiple adaptive thresholding technique was used to segment all QRS complexes from each ECG, with which algorithms to implement LVH detection criteria (i.e. WS, Sokolow-Lyon, Cornell, Peguero-Lo Presti) assessed performance over all ECGs. Associations between raw WS score (e.g. maximum S-wave and R-wave pair overlap value) and cardiovascular mortality, incident cardiovascular disease, and incident heart failure using age- and gender-adjusted Cox regression was also investigated.

Results

A total of 123,021 ECGs and 35,293 TTEs from 30,645 patients were collected. Of these ECGs, 36.1% belonged to patients with TTE-diagnosed LVH. The WS technique was more sensitive (46.1%) and had a higher F1-score (0.443) than other clinical criteria (Table 1), although with comparable calibration (AUROC 0.56 for WS vs. 0.54 for Sokolow-Lyon, 0.60 for Cornell, and 0.61 for Peguero-Lo Presti). Higher WS scores were associated with all-cause mortality (HR 1.08 [95% CI, 1.06, 1.11], P<0.005), incident cardiovascular disease (HR 1.15 [95% CI, 1.14, 1.17], P<0.005), and incident heart failure (HR 1.17 [95% CI, 1.16, 1.18], P<0.005).

Conclusion

The proposed WS technique is a much simpler and more sensitive technique than standard metrics to detect LVH from ECG, with important clinical implications in the development of future cardiovascular disease.Cumulative Incidence of DiseaseCriteria Classification Performance
Electrocardiographic artificial intelligence model for timely detection of preeclampsia
Authors: Butler L , Gunturkun F , Karabayir I , Chinthala L , Batu B , Davis R L , Akbilgic O
Published: November 2023

Background/Introduction

Preeclampsia (PE) is a major concern for maternal and fetal health, affecting about 5%-8% of women worldwide. Assessing PE early remains an obstetric challenge. PE increases risk of cardiovascular diseases such as heart failure and ischemic and hypertensive heart disease. This is more important when the disorder persists or newly occurs within the postpartum period (postpartum preeclampsia; PPPE). Timely detection and intervention from low cost and accessible data modalities is the key to reduce the burden of preeclampsia.

Purpose

The aim of this research was to develop ECG-based models that can predict risk of PE and PPPE before onset by using raw 12-lead ECGs, and a comparative model that includes simple demographic features.

Methods

10s 12-lead digital supine ECG and demographic data was obtained from the electronic health records at a university health science center. Case data was extracted using ICD9/10 codes. Controls were matched by age and race. The data was split 80%-20% with the 20% retained as holdout data used in any way during training, while five-fold cross validation was performed on the remaining 80%. The ECG-AI model, a modified ResNet convolutional neural network (CNN) with 1D ECG signals as inputs, was used to predict incidence of PE/PPPE. The ECG-AI output, together with age, race and gestational age at time of delivery were inputted into a Cox Proportional Hazard Model (ECG-AI-Cox). Models were evaluated using the area under the receiver operator characteristics curve (ROCAUC). We also assessed time-dependent AUC at time intervals at 10 days before PE onset and 7 days into PPPE.

Results

A total of 316 (Controls=190, Cases=126) women and a total of 374 ECGs were included in the study. The patient cohort had an average age of 27.76±4.76 of which 71% were African American, 24% were of White race. The mean gestational age at time of delivery was 32.76±4.45 weeks. From the 126 cases, 114 were diagnosed with PE while 12 developed de novo PPPE from 24 hours to a maximum of 14 days after delivery. AUC(95%CI) for ECG-AI was 81.44(0.71-0.92) with Sensitivity/Specificity=0.86/074 and time-dependent AUC>0.85 for ECG-AI-Cox (Table 1).

Conclusion

Results from this research show that PE and PPPE can be predicted at moderate accuracy at least 10 days before childbirth and PE onset while PPPE can be predicted with high accuracy up to 1 week after delivery. The ECG-AI and ECG-AI-Cox models are easy to implement within clinical workflows due to their simple data requirement. There is also potential for the models’ integration within smart devices to help early detection for women that might not have access to healthcare.
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

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 < 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 < 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 < 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 < 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

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

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

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>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 < 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

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

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 >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 (<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

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 >100 mg/dL) at discharge between January 2018 and December 2021 in a tertiary medical center were retrospectively reviewed. The LDL-C <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 <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<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<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

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<0.001), 95% CI (0.30-0.63) within the group of residents and Kappa=0.519, (p<0.001), 95% CI (0.35-0.68) within the group of attendings. Overall Kappa value was 0.417 (p<0.001), 95% CI (0.34-0.49), indicating a significantly important moderate level of agreement (p<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
The impact on the elapsed time to care and outcomes of a clinician-to-clinician electronic consultation program in patients diagnosed or suspected atrial fibrillation in primary care
Authors: Garcia Vega D , Cinza-Sanjurjo S , Mazon-Ramos P , Rodriguez-Manero M , Portela-Romero M , Rigueiro-Veloso P , Rey-Aldana D , Gonzalez-Juanatey J R
Published: November 2023

Background

An early diagnosis and early initiation of oral anticoagulants (OAC) are main determinants for outcomes in patients with atrial fibrillation (AF). Clinician-to-clinician electronic consultations (e-consultations) program as the first step in the ambulatory care organization for the general practitioner (GPs) referrals to cardiologist may improve health care access by reducing the elapsed time for cardiology care compared with a face-to-face consultation for all the referrals.

Objective

To evaluate the effect on outcomes of a reduced elapsed time to care after a clinician-to-clinician e-consultations program implementation (2013–2019) in comparison with previous in-person consultation (2010–2012) in the outpatient health care management in a Cardiology Department.

Methodology

We included 10,488 patients referred by general care practitioners for cardiology consultation with confirmed or suspected AF from 1 January 2010, to 31 December 2019. Until 2012, all patients were attended with an in-person consultation (2010–2012). In 2013, we instituted an e-consult program (2013–2019) for all primary care referrals to cardiologists that preceded patient's in-person consultation when considered. The shared electronic patient dossier (EPD) was available between GP and cardiologist, and any clinical comment and change in therapy advice from cardiologist was directly implemented in this EPD.

Results

During the e-consultation period (2013–2019) were referred 6627 patients by GPs to cardiology versus 3861 during the in-person consultation (2010– 2012). The e-consultation implementation was associated with a reduction in the elapsed time to anticoagulation prescription (177.6 ±8.9 vs. 22.5 ±8.1 days,p < .001) (Figure 1, A), and an increase of OAC use (61% [95% IC: 19.6%–102.4%], p < .001) (Figure 1, B). The e-consult program implementation was associated with a reduction in the 1-year CV mortality (.48 [95% CI: .30–.75]) and all-cause mortality (.42 [95% CI: .29– .62]). The OAC reduces the stroke mortality (.15 [95% CI: .06–.39]) and CV mortality (.43 [95% CI: .29–.62]) and all-cause mortality (.23 [95% CI: .17–.31]) (Figure 2).

Conclusion

A shared EPD-based clinician-to-clinician e-consultation program significantly reduced the elapsed time for cardiology consultation and initiation of OAC. The implementation of this program was associated with a lower risk of stroke, cardiovascular and all-cause mortality.
Using natural language processing to generate a large-scale database of aortic stenosis with long-term follow-up: the CASPER (cogstack aortic stenosis patient electronic registry) database
Authors: Wu J , Biswas D , Seale T , Bean D , Fairhurst N , Kaye G , Dobson R , Chowienczyk P , Shah A , O'gallagher K
Published: November 2023

Background

Aortic stenosis (AS) is one of the most common heart valve diseases in the Western World, with a prevalence of approximately 4% in patients over 70 years old. High quality observational data can provide insight into characteristics that define patient trajectories and inform the design of appropriately powered randomised trials.

Objective

The aim of this study is to develop a data pipeline to generate a large database containing information of an AS cohort in a hospital by analysing both structured and unstructured (free text) data in routinely collected electronic heath records (EHR). The unstructured data are processed using natural language processing (NLP, an artificial intelligence technique that allows machines to recognize clinical concepts in EHR with contextualisation and linked to SNOMED ontology).

Methods

The open-source CogStack retrieval system is used to collect clinical notes of AS patients. The natural language processing toolkit, MedCAT, is used to identify clinical concepts in the form of SNOMED terms in clinical notes. In addition to patient demographic data (including ethnicity and markers of social deprivation), a range of clinical characteristics, co-morbidities, medications and procedural interventions are extracted. Validation of the dataset was performed using 3 existing, independent data sources (structured echocardiogram reports, surgical aortic valve replacement (AVR) operation notes, transcatheter intervention (TAVI) reports).

Results

We identified 7,451 patients with AS from our EHR, of whom 5,754 had an echocardiogram at our centre. The mean duration of follow up was 5 years. Of those with an echocardiogram at our centre, we were able to extract data on AS severity in 91% and left ventricular function in 89%. Manual validation of procedural data suggests a sensitivity of 98% to detect TAVI and 97% to detect AVR procedures. Kaplan-Meier curves for automated detection of patient mortality are consistent with the known survival data for patients with AS.

Conclusion

We developed and validated an automated NLP-enabled data pipeline to identify and characterise a single retrospective centre cohort of AS patients from routinely collected EHR across the spectrum of severity, with long term mortality data. This resource has the potential to provide insights toward several outstanding questions relating to progression of AS, and the influence of ethnicity and social deprivation without the need for conventional dedicated databases. The EHR-based database also has the potential to guide the design of future randomised control trials.Data Pipeline
Early rule-out of major adverse cardiac events in acute chest pain patients using a risk score and self-reported computerised history taking
Authors: Brandberg H , Spaak J , Sundberg C J , Koch S , Zakim D , Kahan T
Published: November 2023

Introduction

Risk scores, such as the History, ECG, Age, Risk factors, and Troponin (HEART) score, Emergency Department Assessment of Chest Pain Score Accelerated Diagnostic Protocol (EDACS-ADP), and Troponin-only Manchester Acute Coronary Syndrome (T-MACS), are recommended for the evaluation of acute chest pain patients. Obtaining a structured medical history by self-reported computerised history taking (CHT) may provide an automated and improved risk assessment in the emergency department (ED).

Purpose

We aimed to determine the diagnostic accuracy of three well-established acute chest pain risk scores using CHT to predict 30-day major adverse cardiovascular event (MACE).

Methods

Prospective cohort of clinically stable patients aged ≥18 years presenting with a chief complaint of chest pain and an ECG not indicating an acute coronary syndrome (ACS), at a tertiary hospital ED during 2017–2019. Medical histories were self-reported on a tablet using a CHT program (Clinical Expert Operating System, CLEOS), owned by a public university. With >17,000 decision nodes, CLEOS mimics a physician interview and tailors each interview depending on previous answers. Data acquired is in a binary structured format and can be used for input in clinical decision support systems. The HEART score, EDACS-ADP, and T-MACS were calculated from the CHT and the electronic health records (EHR). Relevant EHR data, including medical history, vital signs, and lab data, was extracted manually by the research staff in a predetermined standardised way. The EHR was subsequently reviewed for ACS and intervention procedure International Classification of Diseases (ICD) codes for any 30-day 3-point MACE.

Results

Of the 1,000 consecutive participants enrolled (mean age 55±17 years; 54% women), the HEART score, EDACS-ADP, and T-MACS could be calculated in 65%, 65% and 68%, respectively using only CHT acquired data. Main reasons for premature termination of the CHT were early discharge from ED or getting tired. A 30-day MACE occurred in 7.2% of the total population. The sensitivity, specificity, positive and negative predictive values of any 30-day MACE are presented in Table 1. The diagnostic accuracy for each risk score using CHT was similar to previous studies, where physician-collected data was used.

Conclusions

CHT can provide relevant risk scores in a majority of acute chest pain patients, and allow a safe and early rule-out of a 30-day MACE. The use of CHT may enable automation and improve the management of a large proportion of low-risk acute chest pain patients in the ED.Example of a question in the CHT program