bluuebunny/crossref_metadata_2025 huggingface dataset:
Selective use of implatable loop recorders improves diagnostic potential for unexplained syncope
Authors: Archontakis S , Oikonomou E , Sideris K , Venetsanos D , Tsalamandris S , Trantalis G , Dourvas P , Milaras N , Kostakis P , Klogkeri T , Paraskevopoulou D , Triantafyllou E , Gika A , Gatzoulis K , Skevos S
Published: October 2024

Background

Syncope remains a common medical problem with significant socio-economic consequences. Recently, the role of dedicated Syncope Units (SU) and implantable loop recorders (ILR) has emerged, in the investigation of unexplained syncope.

Purpose

To investigate the possibilities for a more rational and targeted use of various diagnostic tools.

Methods

In this retrospective study, 196 patients with Unexplained Syncope admitted in a single SU, between March 2019 and February 2023 were included. Various diagnostic tools were utilised during the investigational process, according to clinical judgement. Patients were retrospectively allocated into Group A including those who, among other tests, underwent ILR insertion and Group B including patients investigated without ILR implantation. Data were also compared with Group C, including patients assessed prior to SU establishment.

Results

There was no difference between Group A (n=133) and Group B (n=63) in the diagnostic yield (74% vs. 76%, p=0.22). There was significant difference between Group A and B regarding age (67.3 ± 16.9 years vs. 48.3 ± 19.1 years, p<0.001) and regarding cause of syncope (cardiogenic in 69% of group A and reflex syncope in 77% of group B, p<0.001). The diagnosis was based on electrocardiography (ECG) in 55% of group A and in 19% of group B (p<0.001). Time to diagnosis was 4.2 ± 2.7 months in patients with ILR implantation and 7.5 ± 5.6 months in Group B, p<0.001. In Group C, the diagnostic yield was 57.9% and the ECG-based diagnostic yield was 18.3%.

Conclusions

A selective use of ILR according to the clinical and electrocardiographic characteristics represents an acceptable diagnostic pathway that increases the effectiveness of the structured SU-approach by further preserving financial resources
Multi-modality deep learning prediction of heart age: insights from UK-Biobank
Authors: Siontis G , Le Goallec A , Prost J B , Collin S , Diai S , Vincent T , Patel C
Published: October 2024

Background

Identification of apparently healthy individuals at high cardiovascular risk who may benefit from targeted preventive strategies remains challenging. Predicted heart age, the chronological age of individuals with similar absolute cardiovascular disease (CVD) risk but with no risk factors, has been proposed to appropriately describe the absolute CVD risk over conventional prediction tools.

Purpose

To develop multi-modality based deep learning predictors for heart age in UK-Biobank individuals.

Methods

We used deep learning to develop a heart age predictor based on videos from heart magnetic resonance imaging (MRI) (anatomical dimension), electrocardiograms (ECG) (electrical dimension) or both from 45000 individuals of the UK Biobank cohort (age range 45-81 years) (Figure). We estimated the heritability of heart aging and identified single-nucleotide polymorphisms associated with accelerated heart aging. We performed X-wide association study to identify non-genetic factors potentially associated with accelerated heart aging.

Results

We predicted age with a mean absolute error (MAE) of 2.5±0.03 years (R2=85.6±0.6%) and found that accelerated heart aging is heritable at more than 35%. MRI-based anatomical features predicted age better than ECG-based electro-physiological features (MAE=2.29±0.03 years versus 4.90±.0.08 years), and heart anatomical and electrical aging were weakly correlated (Pearson correlation=0.249±0.002). Our attention maps highlighted the aorta, the mitral valve, and the interventricular septum as key anatomical features driving heart age prediction in videos that capture the entire heartbeat cycle. We found accelerated anatomical and electrical heart aging to be genetically correlated (Pearson correlation=0.508±0.089). The most significant locus was in TTN (Titin) and it was associated with heart anatomical aging. Cardiovascular diseases, general health status, and mental health disorders were highly associated with accelerated heart aging.

Conclusions

A multi-modality based deep learning approach was highly predictive of heart age. Accelerated heart age has a complex biological basis with genetic and environmental correlates. Leveraging of the described pipeline to predict survival and the onset of age-related cardiovascular diseases may shed light on heart aging role in health outcomes and effectively guide personalized preventive strategies.
A deep learning model for syncope recognition in the emergency department
Authors: Giaj Levra A , Dipaola F , Gatti M , Mene' R , Furlan R
Published: October 2024

Introduction

Syncope is a common presenting symptom in the emergency departments (EDs) and poses challenges in its recognition and differentiation from alternative causes of transient loss of consciousness. Natural Language Processing (NLP) is an artificial intelligence technique that enables the analysis of natural language, such as clinical notes and medical history within electronic medical records (EMRs). The application of NLP into the medical field is surging but its use for the recognition of syncope is still underexplored.

Purpose

We aimed to develop a NLP-based deep learning model for syncope recognition in the ED.

Methods

Two models, based on the Italian and Multilingual Bidirectional Encoder Representations from Transformers (BERT) language model, were developed and evaluated using consecutive EMRs from Humanitas Research Hospital ED. The first "triage" model was only based on textual data contained in the "triage" section of the EMR. The second "anamnesis" model also considered textual data contained in the "medical history" section. Performance metrics including area under the curve (AUC), Matthew’s correlation coefficient (MCC) and Brier’s score (BS) were used to compare the discriminative capabilities of different BERT models.

Results

The models were developed and tested on 15,098 and 15,222 EMRs, respectively. The Multilanguage BERT discriminated patients with syncope with an AUC of 0.85 in the triage model and of 0.92 in the anamnesis model. The Italian BERT identified patients with syncope with a MCC of 0.50 and a BS of 0.02 in the triage model and of 0.64 and 0.01, respectively, in the anamnesis model.

Conclusions

We propose two NLP-based models that identify syncope patients with a high discriminative capability. The presented algorithm may provide a starting point for the management of syncope patients using machine learning techniques.ROC and calibration curves of modelsFramework for model implementation
Machine learning for prediction of all-cause mortality in acute coronary syndrome
Authors: Zaka A , Gupta A , Mustafiz C , Mutahar D , Parvez R , Stretton B , Kovoor J , Mridha N , Bacchi S
Published: October 2024

Background

Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the Global Registry of Acute Coronary Events (GRACE) and the Thrombolysis In Myocardial Infarction (TIMI) risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This study aims to compare machine learning models with traditional risk scores for predicting all-cause mortality in patients with ACS.

Methods

PubMed, EMBASE, Web of Science and Cochrane databases were searched until 1st November 2023 for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals between ML models and traditional methods in estimating the risk of all-cause mortality.

Results

Ten studies were included (239627 patients). The summary C-statistic of all ML models across all endpoints was 0.89 (95% CI, 0.86-0.92), compared to traditional methods 0.82 (95% CI, 0.79-0.85). The difference in C-statistic between all ML models and traditional methods was 0.07 (p<0.05). Three models undertook external validation, and calibration was inconsistently reported.

Conclusion

ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared to traditional risk scores. Despite outperforming well-established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the methodological limitations of existing models and the need for greater validation.
Assessing the impact of implantable loop recorder placement on anxiety level
Authors: Bakhshaei S , Bagheri S , Mohan A , Reddy S , Vu D , Du N , Singh B , Mehta D , Reinherz I , Ho A
Published: October 2024

Background

Anxiety is a prolonged state of apprehension or worry that can be an independent predictor of cardiovascular events, associated with a 48% increased risk of cardiac death. About one-third of patients with arrhythmias present with high anxiety levels, and subsequently worse quality of life. In our experience of patient care, many patients feel anxious about the possibility of an arrhythmia that could lead to their demise, especially if their symptoms are undiagnosed.

Purpose

Our project aims to explore the change in patients’ anxiety levels following implantable loop recorder (ILR) placement.

Methods

This is a survey-based observational study that was performed in an outpatient clinic and hospital using validated General Anxiety Disorder-7 (GAD-7) questionnaires. Individuals who met indications to undergo ILR implantation including palpitations, syncope, subclinical atrial fibrillation, and cryptogenic stroke underwent baseline assessment of anxiety prior to implantation. Individuals had repeat assessments at 6 weeks and 12 weeks post-implantation to evaluate the effect of ILR placement.

Results

Our study included 50 individuals. Out of the initial subjects, 10/25 females and 11/25 males were lost to follow-up. The average initial GAD-7 score was 3.3 (Range 0, 15), average 6-week score was 1.6 (Range 0, 14), and average 12-week score was 0.9 (Range 0, 10). Total decrease in GAD-7 score after 6 weeks of having ILR was 50%. Total decrease in GAD-7 score after 12 weeks of ILR was statistically significant at 73% (P=0.03). Females showed greater improvement of GAD-7 scores in comparison to males at 6 weeks (67% vs 36%) and 12 weeks (81% vs 66%).

Conclusion

Studies have suggested an overall decreased level of anxiety in patients receiving implantable devices that have the potential to treat arrhythmias such as implantable cardiac defibrillators and pacemakers. Our results suggest that the benefit may extend to implantable devices that seek to provide further diagnostic information and aid in the prognostication of disease burden. As seen in our study population, GAD-7 scores showed dramatic improvement with greater percentage in female than male population. After the placement of loop recorders, many patients experience a sense of security with having a device that monitors them. Our study is the first to address the association between ILR and anxiety. Future studies evaluating this association would help increase the power of the study.Figure 1:Bar graph of GAD-7 Scores
Time-series clustering of raw recordings of 24-hour ambulatory blood presssure for risk stratification in general population
Authors: Ntalianis E , Sabovcik F , Cauwenberghs N , Kuznetsova T
Published: October 2024

Objective

24-hour ambulatory blood pressure measurements (ABPM) is the best out-of-office BP measurement used to refine cardiovascular (CV) risk stratification. Currently, only summary ABPM indexes are used in the clinical practice while the temporal characteristics of the raw BP recordings have not been explored. Therefore, in this study we employed an unsupervised machine learning (ML) algorithm suitable for multivariate time-series analysis to identify distinct BP and heart rate (HR) profiles associated with the incidence of adverse CV events.

Design and Method

In 1391 community-dwelling individuals (mean age, 46.9 years; 50.97% women), we acquired 24-hour ABPM and clinical data and collected CV events (n=399) on average 22 years later. We employed multivariate dynamic time warping and k-medoids algorithm on the raw recordings of systolic and diastolic BP and HR. To assess the clinical significance of the derived clusters, we compared the clinical characteristics and the incidence of adverse events. We validated the trained model using 24-hour ABPM obtained from external population cohort (n=1137).

Results

The silhouette score and Dunn index showed that the optimal number of clusters was 4. Across all clusters we observed significant difference in age and sex distribution. Cluster 4 had the worst CV risk factors profiling, with higher office BP and anti-hypertensive medication intake compared to the other clusters. We observed differences in the incidence of adverse events between clusters (Figure), with cluster 1 representing the lowest risk group and cluster 4 the highest. After adjusting for traditional CV risk factors, including office systolic BP, cluster 1 had the lowest risk (HR:0.92; 95%CI:0.84-0.99; P=0.036) and cluster 4 the highest (HR:1.13; 95%CI:1.03-1.24; P=0.006) as compared to the average population risk. The analysis of the external validation cohort revealed similar ABPM patterns and clinical characteristics.

Conclusion

Employing an unsupervised ML model on the raw ABPM recordings we identified clinically meaningful clusters associated with an increasing cumulative incidence of CV events. This ML analysis paves the way towards utilization of temporal BP and HR information and subsequently optimization of CV risk stratification.
Using deep learning to predict cardiovascular magnetic resonance findings from echocardiography videos
Authors: Sahashi Y , Ouyang D , Kwan A
Published: October 2024

Background

Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning.

Purpose

To assess the performance of a deep learning model applied to echocardiography to detect CMR-based parameters.

Methods

In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including WMA presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training.

Results

The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]).

Conclusions

Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos.Central Figure -Study Pipeline-
Rates of therapeutic interventions in a large real-world cohort with insertable cardiac monitors
Authors: De Melis M , Margetta J , Rosemas S C , Zhou J , Saha S
Published: October 2024

Background

Insertable cardiac monitors (ICMs) provide up to several years of continuous monitoring and have demonstrated high diagnostic yields for arrhythmia detection. However, the rates of arrhythmia-related therapeutic interventions following ICM implant have not been comprehensively characterized across indicated patient populations.

Purpose

To quantify rates of arrhythmia therapies in a large, real-world U.S. cohort of ICM patients.

Methods

De-identified patients receiving a Reveal LINQ ICM between Oct 1, 2016 – Sept 30, 2020 with no prior cardiac implantable electronic devices (CIEDs) and ≥1 year of follow-up in the Optum Clinformatics® Data Mart claims database were identified (N=17,037). Patients were stratified by indication for ICM placement: N=431 (2.5%) of patients were indicated for AF ablation monitoring, 2,193 (12.9%) for AF management, N=4,852 (28.5%) Cryptogenic stroke, N=1,252 (7.3%) Palpitations, 1,346 (7.9%) Suspected AF, 6,716 (39.4%) Unexplained syncope, and 247 (1.4%) Ventricular tachycardia (VT). Each patient was followed in the claims database after ICM insertion until end of continuous claims enrollment, end of database, or death. Therapeutic interventions for arrhythmias were characterized during follow-up, including procedures (CIED implants, cardioversion, and ablation) and arrhythmia medication initiation (antiarrhythmics, rate-control medications, and oral anticoagulants).

Results

Mean(SD) follow-up in the claims database post ICM placement was 3.4(1.3) years; 52.4% of patients were female, and mean age was 70.8(11.9) years. Overall, 54.5% of patients received a therapeutic intervention during follow-up, with 25.0% of patients receiving a therapeutic procedure and 44.0% initiating an arrhythmia-related medication (table). The highest procedure rates included implantable pulse generator (IPG) implants in the Unexplained syncope population (21.4% of patients) and cardiac ablations in the populations receiving ICM for known AF (27.8% of AF ablation monitoring patients and 23.5% of AF management patients). Medication initiation rates were highest for oral anticoagulants in the Cryptogenic stroke population (31.0% of patients). The mean time from ICM insertion to therapeutic action was 13(13) months for procedures and 7(11) months for medication initiation.

Conclusions

In a large sample of real-world ICM patients, more than half received a therapeutic intervention after ICM placement. Time to intervention was lengthy and showed substantial variability amongst patients, underscoring the importance of long-term continuous monitoring to uncover clinically-relevant arrhythmia findings.
Artificial intelligence derived electrocardiograms: a comparative study of network architectures predicting sex and left ventricular dysfunction
Authors: Cohen-Shelly M , Hochstein D , Barda N , Bleiweiss A , Aflalo E , Bar N , Zimlichman E , Klang E , Rahman N , Klempfner R , Maor E , Beinart R , Segev A , Raanani E , Sabbag A
Published: October 2024

Background

Within cardiovascular medicine, AI-driven electrocardiogram (ECG) analysis has emerged as a powerful tool, capable of predicting clinically significant abnormalities. The increasing growth of wearable technologies for ECG monitoring underscores the importance of advancing AI methodologies. While Convolutional Neural Networks (CNNs) have traditionally been the go-to choice for ECG analysis, the rise of Vision Transformers (ViTs), a novel computer vision paradigm, raises the question of their potential superiority.

Purpose

To compare the performance of CNNs and ViTs in ECG analysis, and to assess the efficiency of lean models using truncated ECG recordings.

Methods

We trained a model using 12-lead ECGs as input for the binary classification of two outputs: Sex (Male or Female) and Left ventricular dysfunction (LVD, defined as below 35%), determined by echocardiography within 2 weeks of the ECG. Models were developed using full standard ECGs, single lead and truncated recordings (1,2 and 4 10 seconds). We compared CNNs (PyTorch) and ViTs (HuggingFace) performance. We analyzed the explainability of our findings, representing the AI model's focus of interest on ECGs from normal cases versus those with reduced ejection fraction, thereby validating its diagnostic discernment (Fig 1)

Results

We identified 150,691 and 29,422 patients with valid ECG tests for the Sex and LVD prediction models, respectively. For the Sex outcome, the AUROCs were 0.911 (95% CI: 0.908-0.914) and 0.898 (95% CI: 0.894-0.901) for the CNN-based model and the ViT-based models, respectively, with the difference statistically significant (P-Value < 0.001). For the LVD outcome, the AUROCs were comparable at 0.878 (95% CI: 0.864-0.892) and 0.866 (95% CI: 0.853-0.879) for the CNN-based model and the ViT-based models, respectively, (P-Value = 0.056). Furthermore, we found that 98.8% for the maximal predictive power of the models was achieved within a 2 second time frame of a 12 lead ECG. Similarly, a single lead based model achieved a relatively high AUC for the prediction of LVD.

Conclusions

AI-ECG models developed with ViT architecture did not surpass CNN-based models in sex classification and LVD identification. Our study highlights the significance of lean models allowing for rapid prediction within 2-second tracings or using a single lead, demonstrates the potential for streamlined AI-ECG applications in clinical practice.
Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination
Authors: De La Linde Valdes A , Rios-Munoz G , Lopez-Doriga Costales J , Carta-Bergaz A , Gonzalez-Torrecilla E , Atienza F , Arenal A , Calvo D , Gomez-Sanchez R , Bermejo Thomas J , Avila Alonso P
Published: October 2024

Background

Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis.

Purpose

The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical decision-making.

Methods

We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC).

Results

The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2).

Conclusions

This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.Figure 1Figure 2
A smartphone based AI model to detect left ventricular systolic dysfunction on 12-lead ECG
Authors: Demolder A , Herman R , Vavrik B , Martonak M , Boza V , Herman M , Palus T , Kresnakova V , Bahyl J , Iring A , Hatala R , Bartunek J , Vanderheyden M , Heggermont W , Penicka M
Published: October 2024

Background

Despite echocardiography being essential for cardiac assessment, a point-of-care tool for rapid left ventricular ejection fraction (LVEF) evaluation is lacking in current clinical practice, highlighting the need for a widescale, easily deployable method to assess LV function.

Purpose

To develop and validate two artificial intelligence (AI) models that accurately detect reduced LVEF on a single 12-lead ECG using a smartphone, facilitating early identification of patients at risk for heart failure who may benefit from more detailed echocardiographic evaluation.

Methods

A large collection of ECGs and transthoracic echocardiograms (TTEs) was sourced from our hospital data vault from 2011 to 2021. ECGs were paired to TTEs within a 24h time window and the resulting pairs were randomly assigned to the model development dataset (50%) and validation dataset (50%). Two distinct AI-ECG models were developed: one to detect LVEF≤40% and another for LVEF<50%, derived from the ESC definition of heart failure. Both AI-ECG models were paired with smartphone based ECG digitization technology. Key performance metrics include area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score.

Results

A total of 1,205,370 ECGs (198,274 patients) and 291,433 TTEs (105,849 patients) were collected and paired, resulting in 109,809 ECG-TTE pairs (56,236 patients). The validation dataset consisted of 25,510 distinct TTE-ECG pairs (25,510 patients). Prevalence of LVEF≤40% and LVEF<50% was 5.4% and 7.9% respectively. The LVEF≤40% model demonstrated an AUC of 0.963 (95% CI: 0.959-0.966), sensitivity 0.924 (95% CI: 0.91-0.937), specificity 0.887 (95% CI: 0.883-0.891), and F1-score of 0.474 (95% CI: 0.457-0.490). PPV and NPV were 0.318 (95% CI: 0.304-0.333) and 0.995 (95% CI: 0.994-0.996) respectively. Performance of the LVEF <50% model shows an AUC of 0.952 (95% CI: 0.947-0.956), with a slightly lower sensitivity of 0.899 (95% CI: 0.886-0.912), specificity of 0.875 (95% CI: 0.871-0.879), PPV of 0.382 (95% CI: 0.368-0.395), NPV of 0.99 (95% CI: 0.989-0.992), and F1-score of 0.536 (0.521-0.55).

Conclusion

The AI models exhibit the ability to identify reduced LVEF from standard 12-lead ECGs using a smartphone. Results suggest potential clinical utility as a preliminary screening tool to select patients for confirmatory echocardiography. This AI application could potentially offer a timely and cost-effective method for identifying patients at risk for future development of heart failure.
Predicting mortality of type-2 diabetes mellitus by applying machine learning to electronic medical records in the west of scotland and hong kong
Authors: Kaur N , Deligianni F , Pellicori P , Tse G , Cleland J G F
Published: October 2024

Introduction

Type 2 diabetes mellitus (T2DM) is associated with accelerated development of atherosclerosis and a reduced life expectancy. Using machine learning (ML) to identify novel characteristics from electronic medical records (EMRs) associated with prognosis may increase prognostic precision and find new targets for investigation and treatment.

Aim

We used a novel ML approach to investigate the use of EMRs to predict all-cause mortality in two ethnically and geographically different populations; one from the West of Scotland (WoS) and the other from Hong Kong.

Methods

We obtained EMRs including demographics, prior comorbidities, laboratory measurements, medications, and mortality. Multivariable Cox regression and time-dependent random forest model were used to identify predictors of all-cause mortality in patients with T2DM. Subsequently, we applied a state-of-the-art ML interpretability method, to gain further insight into the key predictors.

Results

In WoS, 46,031 individuals received a new diagnosis of T2DM between 2009 and 2019. Their median age was 66 (IQR: 56 to 75) years. Within 10 years, 11,727 (25%) deaths were recorded. In Hong Kong, 273,876 patients with a first-attendance with T2DM at public hospitals or clinics were included, with follow-up until December 2019. The median age of the patients was 64 (IQR: 57 to 72) years. Within 10 years, 91,155 (33%) deaths were recorded. For both cohorts, the strongest predictor for all-cause mortality was prescription of loop diuretics (Figure 1). For the WoS, other important predictors were greater age, lower serum albumin, elevated alanine transaminase (ALT), increased alkaline phosphatase (ALP), and lower estimated glomerular function rate (eGFR) (c-index: 0.83; Brier score: 0.07). For Hong Kong, predictive variables were remarkably similar and included greater age, lower eGFR, lower haemoglobin and lymphocytes, lower serum albumin, and elevated ALP (c-index: 0.85; Brier score: 0.06). Multivariable Cox regression adjusting for age and sex showed a higher mortality amongst those prescribed loop diuretics compared to those who were not (WoS: hazard ratio: 1.549, 95% CI: 1.521 to 1.575; Hong Kong: hazard ratio: 1.745 (95% CI: 1.721 to 1.769). Only a minority of patients prescribed loop diuretics had a diagnosis of heart failure, end-stage renal disease or resistant hypertension.

Conclusion

Amongst patients with recent-onset T2DM, prescription of loop diuretics was the most important predictor of all-cause mortality in both WoS and Hong Kong. Prescription of loop diuretics might be a pharmacological marker of congestion and undiagnosed heart failure or loop diuretics might have deleterious effects on the outcome of patients with T2DM.Fig 1:Predictors of all-cause Mortality
ValveVision AI - a multimodal language model for qualitative reporting of the aortic valve in echocardiography
Authors: Gupta A , Bergman H , Penn J , Stojmenski A , Postolovski D , Sokol D , Pawar S , Makkar R , Sandler R , Sokol J
Published: October 2024

Background

The precise assessment of the aortic valve via echocardiography is critical for early detection and management of aortic valve diseases. Until recently, previous studies have examined machine learning models to estimate individual measurements and severity of aortic stenosis (AS) from echocardiographic images. These image processing algorithms, while precise in their narrow focus, fall short in mirroring the holistic and interconnected clinical judgment typical of human echocardiographers in producing a qualitative report. Large language models (LLMs), particularly image-to-text multi-modal LLMs, are a fundamental advance in the field of deep learning with implications for a host of applications in medical imaging. They promise to encapsulate not just discrete data points typical in traditional machine learning, but also the complex contextual interrelations in clinical diagnosis.

Methods

In this study, a large-scale heterogeneous database of echocardiographic images containing over 90,681 studies with textual descriptors of the aortic valve was used to train a single, image-to-text multimodal LLM called ValveVision AI. The ground truth textual summaries were drafted by level III echocardiographers in a clinical setting between 2015-2020. BLEU and ROUGE score was calculated. The models were retrospectively assessed on a holdout dataset. Reviewing physicians compared the generated summary to the ground truth and binarily agreed or rejected it. Receiver Operating Characteristics (ROC) for distinct pathologies were also assessed (Figure I).

Results

ValveVision AI performed with a BLEU score of 0.45 and a ROUGE score of 0.49. The performance of the model in reporting on classification of moderate/severe vs none/mild AS in concordance with the validation protocol described above achieved a specificity of 91.98% and a sensitivity of 83.89%, along with more precise qualitative description. Qualitatively, the model exhibited the capability of zero-shot learning in certain instances, however, this result remains an area of exploration.

Conclusion

This study represents to our knowledge, the first attempt at an image-representation to text-tokenizer deep learning model architecture to mimic the thought and subtlety of echocardiographic qualitative analysis of the aortic valve. The results suggest that this multimodal LLM has sufficient accuracy to create a preliminary textual summary of the aortic valve that, if paired with a point of care ultrasound (POCUS) device in a primary care setting, may facilitate case triage, increase efficiency, and determine a more precise care pathway for patients.
How does lead selection impact diagnosis through AI? Implications for continuous monitoring systems using convolutional neural networks
Authors: Ramirez E , Ruiperez-Campillo S , Castells F , Casado-Arroyo R , Millet J
Published: October 2024

Background and Objectives

Recently, there has been a surge in non-invasive diagnosing methods leveraging the dipolar 12-lead ECG information using Convolutional Neural Networks (CNNs). However, the traditional ECG contains 12 projections of the cardiac dipole in a three-dimensional space can result in redundancy. Furthermore, in continuous monitoring systems such as Holter and wearables, not all 12 leads are consistently available. This study aims to quantify redundancy and determine the best lead configuration for improved classification results using a CNN. It evaluates the level of redundancy in ECG leads and its impact on CNN classification.

Methods

The degree of redundancy was quantified by means of lead entropies (H) as depicted in Figure 1.A. The standard 12-lead ECG was chosen as the baseline input. From it, various subsets of leads were selected (6, 3, 1 leads) with the objective of attaining maximal orthogonality between leads while minimizing redundancy (Figure 1.B). A 2D-CNN model was trained for multiclass classification: Hypertension (HYP), ST-T interval Changes (STTC), Conduction Disturbances (CD), Myocardial Infarction (MI) and Normal (NORM). The designed inputs were employed to evaluate how redundancy impacts the performance of the CNN (see Figure 1.C).

Results

Reducing redundancy in input leads led to notable improvements in model prediction for all pathological conditions in terms of Area Under the Curve (AUC). Reducing to 6 and 8 leads were the most suitable input for the CNN (Table 1) increasing by 1.18% in HYP, 0.23% in STTC, 0.52% in CD, and 0.33% in MI. However, a significant reduction in redundancy may result in the loss of essential clinical information for classification, as observed in scenarios involving only 3 or 1 leads.

Conclusions

When diagnosing pathologies with systems featuring fewer leads, its combinations should aim for maximum orthogonality to reduce redundancy while preserving unique information. By strategically selecting a set of leads with reduced input redundancy, the effectiveness of the standard 12-lead ECG can be improved, thereby enhancing the utility of such continuous monitoring systems for automated diagnosis and also decreasing training and prediction times.
A population-based characterisation of the relationship between anatomical and electrical axes of the heart
Authors: Kayyali M , Minchole A , Qian S , Whitaker J , Bishop M , Lamata P
Published: October 2024

Introduction

Cardiac anatomy, including heart size, position and orientation within the torso, is known to impact ECG morphology. Understanding the relationship between cardiac anatomy and ECG morphology through their main axes has the potential to lead to more accurate and reliable ECG biomarkers. Nevertheless, there is no standardised method for the definition of the anatomical and electrical axes of the heart.

Aims

To evaluate definitions of anatomical and electrical axes of the heart based on a derived metric in an undiseased population in the UKBioBank.

Methods

The anatomical and electrical axes of 2189 healthy subjects were computed separately in five different ways (Figure 1). Cardiac bi-ventricular anatomy was automatically segmented from cardiac MRI. Anatomical axes were computed from different combinations of anatomical landmarks (valves and apex) and axis of inertia. ECGs were transformed into vectorcardiograms (VCGs) using the Kors transformation. A range of combinations of dipole magnitudes, weighted averages and loop shape were used to define the electrical axes of the QRS loop. The anatomical-electrical relationship was quantified with the cosine of the angle between each of the 25 pairs of anatomical and electrical axes definitions. Spatial consistency was assessed by the standard deviation of this cosine across the cohort, where a higher level of spatial consistency corresponds to a lower standard deviation.

Results

Spatial consistency between anatomical and electrical axes exhibited a large range of standard deviation values from 0.61 for the worst pair to 0.14 for the best pair of definitions. The pair with the highest spatial consistency was defined by the valvular plane centre and the apex (VPA) for the anatomical axis, and the direction of maximum QRS dipole magnitude (maxQRS) in the VCG for the electrical axis (Panel A and B in Figure 2). The anatomical-electrical relationship was not significantly correlated with BMI, age or gender despite the individual anatomical and electrical axes showing significant correlation (Frontal plane: anatomical axis was most impacted by BMI, R=0.53 & p<0.0001, and electrical axis by gender R=0.37 & p<0.0001).

Conclusion

In this healthy population, the most spatially consistent definition of anatomical and electrical axes is defined by VPA and maxQRS. This spatial consistency is not affected by BMI, age or gender, suggesting an intrinsic relationship between the pair of measures, and represents a potential strategy to permit correction for patient anatomic factors when interpreting ECGs.Figure 1.Axes Definition MethodsFigure 2.Results
Response patterns in self-reported medical conditions: insights from the National Health and Nutrition Examination Survey (NHANES)
Authors: Siontis G , Patel C P
Published: October 2024

Background

In the era of large-scale experiments, self-reported information has become a useful approach for gathering information longitudinally. The extent of subjectivity in self-reported medical conditions can vary considerably and can subject to a large degree of individual interpretation.

Purpose

To assess potential clustering among non-related self-reported conditions in the National Health and Nutrition Examination Survey (NHANES) over a period of 20 years (1999-2018).

Methods

We considered adult individuals (older than 20 years old) at the time of recruitment in all 10 cycles of the NHANES spanning from 1999 to 2018. For each individual, we gathered information on demographics, socioeconomic, examination data, laboratory variables and self-reported conditions included in the questionnaire instruments being available for all cycles. We focused only on questionnaire variables that are valid questions for both men and women. We examined potential patterns in clustering of responses among individuals in the same survey cycle and subsequently among individuals of different survey cycles.

Results

The study sample consisted of 55081 adult individuals. Female and male participants were equally represented in the study sample (52% and 48% respectively). The mean age of individuals at screening was 50 years (standard deviation 18). Participants were mostly non-Hispanic white (44%), followed by non-Hispanic black (21%) and Mexican American (17%). 41 self-reported medical conditions were available across the 10 cycles of NHANES and were considered in this analysis. The percentage of affirmative answers and missing information in the level of self-reported medical condition did not differ considerably across the 10 cycles for the vast majority of the medical conditions; but the percentage of missing information differed considerably across the different self-reported conditions and cycles ranging between 0% to 97% (Figure).

Conclusions

Surveys based on self-reported medical conditions may suffer from clusters and secular trends in provided responses or missing information among non-related conditions. Potential clustering may bias the data analysis and interpretation of the findings. The risk of clustering in individuals responses should be taken into account in analysis of self-reported conditions.Figure
What does routinely-collected electronic health record data tell us about the use of heart failure medication among older people in Wales? Making progress but could do better
Authors: Rees E , Best V , Akbari A , Dicken B , Ionescu A , Jones G , Halcox J
Published: October 2024

Background

European Society of Cardiology 2021 guidelines recommend 4 pillars of treatment to reduce mortality for those with heart failure (HF) and reduced ejection fraction (HFrEF): pillar 1) angiotensin converting enzyme inhibitor (ACE-I)/angiotensin receptor blocker (ARB)/angiotensin receptor-neprilysin inhibitor (ARNI), pillar 2) beta-blocker (BB), pillar 3) mineralocorticoid receptor antagonist (MRA), pillar 4) sodium-glucose co-transporter 2 inhibitor (SGLT2i). When there is heart failure with preserved ejection fraction (HFpEF), guidelines recommend diuretics to alleviate congestion and treatment of risk factors such as hypertension and diabetes. Prior to 2021, United Kingdom guidelines did not include SGLT2i.

Purpose

To examine how HF treatment is recorded in routinely-collected electronic health record (EHR) data for older people in Wales, and to explore how that has changed since 2015.

Methods

We conducted a retrospective, population-level, observational study using linked anonymised EHR data in Wales (2015-22). 737,230 individuals were aged 65y+ in the study period (21.5% of population in 2022). Of these, 65,010 had a code for heart failure in their primary or secondary care records. We excluded 13,990 individuals with fewer than 12 months data pre- or post-diagnosis. We looked forward, from HF diagnosis, for codes associated with specific medications.

Results

The final cohort comprised 51,020 individuals aged 65y and over with a diagnosis of heart failure between 2015-2022. Incidence and prevalence of heart failure increased across the study period (1.4-1.5% and 8.5-9.1% respectively). Preceding cardiovascular diagnoses included hypertension (69.5%), atrial fibrillation (36.7%), myocardial infarction (30.5%), coronary artery disease (25.7%), and valve disease (17.7%). Diabetes (41.7%), chronic kidney disease (30.9%) and cancer (26.4%) were the most common comorbidities. The subtype of HF (HFrEF or HFpEF) was only evident in 5,850. Treatments recorded most often were loop diuretics, ACE-I, and BB (Table 1). SGLT2i codes increased in 2020/2021(Figure 1). At 10-12 months post diagnosis, the proportion with simultaneous pillar combinations was; 27.4% 1 + 2, 12.5% 1, 2 + 3, and only 2% 1, 2, 3 + 4.

Conclusions

In Wales, there is a need to improve the coding of HF subtype in primary care records. We suggest interpreting data with the assumption that approximately half of the cohort are likely to have HFrEF. While the cohort proportion on ACE-I or beta-blockers may be appropriate, the proportion with more than 1 simultaneous pillar of treatment recorded appears low. The use of SGLT2i in codes is increasing. These results are important in evaluating health service delivery and in establishing baseline data from which to monitor improvements.
Impact of heart failure diagnosis on recorded healthcare resource use by older people in Wales; is primary care shouldering a hidden burden?
Authors: Rees E , Best V , Akbari A , Harris D , Dicken B , Ionescu A , Jones G , Halcox J
Published: October 2024

Background

Heart failure (HF) is associated with high healthcare resource use and mortality. Most of the evidence focuses on hospital admission data. The impact on primary care interactions and outpatient attendance is less well described, but this information is important in understanding resource utilisation, patient pathways, and in planning services.

Purpose

To examine how a diagnosis of HF impacts healthcare resource use and outcomes for older people in Wales.

Methods

We conducted a retrospective, population-level, observational study using linked anonymised electronic health record (EHR) data in Wales (2015-22). 737,230 individuals were aged 65y+ in the study period in primary and secondary care data. Of these, 65,010 had a code for HF in their primary or secondary care records. To evaluate changes in resource use, we excluded 13,990 individuals with fewer than 12 months health data pre- and post-diagnosis. We summarised general practice (GP) interactions, emergency department (ED) attendance, outpatient (OP) attendance, inpatient (IP) episodes and days, time between HF diagnosis and death, and place of death.

Results

The final cohort comprised 51,020 individuals 65y and over with a diagnosis of HF between 2015-2022. Incidence and prevalence of HF increased across the study period (1.4-1.5% and 8.5-9.1% respectively). Figure 1 shows the percentage change in healthcare resource use for the cohort when HF was diagnosed (12 months pre-and post-diagnosis comparison). Table 1 shows the average number of healthcare utilisation events per person. After a diagnosis of HF, there are fewer ED attendances (-28.2%) and IP admissions (-9.3%) but more OP appointments (31%) and GP interactions (20.3%). When admissions occur, hospital stays tend to be longer (55.4%). Median time (IQR) from HF diagnosis to death from any cause was 247(34-840) days, and with HF listed as a cause 186(25-780) days. In this cohort, the place of death was recorded as NHS establishment (20,379, 67.8%), community (5,450, 18.1%), non-NHS establishment (3,789,12.6%) and unknown (450, 1.5%).

Conclusions

In Wales, a diagnosis of HF in those 65y+ increases primary care interactions and OP attendances. The change in volume of primary care interactions is considerable (increase from 1,047,920 to 1,315,080). Longer hospital stays may suggest difficulty in managing patients or discharging back to community settings. Majority of those with HF die in NHS establishments. It is unclear whether this reflects deficits in end-of-life care. These results are important in planning effective health service delivery and in establishing baseline data from which to monitor quality improvements.