bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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&lt;0.0001, and electrical axis by gender R=0.37 & p&lt;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

Abstract

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

Abstract

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

Abstract

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.
What does routine healthcare data tell us about the diagnostic pathway for older people with heart failure in Wales: under-testing, problems with access, or poor coding?
Authors: Rees E , Best V , Akbari A , Harris D , Dicken B , Ionescu A , Jones G , Halcox J 
Published: October 2024

Abstract

Background
United Kingdom guidelines (NICE, NG106) for suspected chronic heart failure (HF) recommend that NT pro-BNP is performed as a first-line test. The result is also used to triage individuals; when NT pro-BNP is &lt;400ng/L, a diagnosis of HF is less likely, echocardiography (echo) and specialist assessment should be performed within 6 weeks when the result is 400-2000ng/L, and within 2 weeks if &gt;2000ng/L. How accurately this guidance is followed in clinical practice in Wales is not known.

Purpose
To examine how the current diagnostic pathway is recorded in routinely-collected electronic health record (EHR) data for older people in Wales with a diagnosis of heart failure.

Methods
We conducted a retrospective, population-level, observational study using linked anonymised EHR data in Wales (2015-22). We included patients aged 65y+ with a HF diagnosis in their primary or secondary care records and with a minimum of 12m pre and post-diagnosis data (for assessment of diagnostic pathway). We looked back from the point of HF diagnosis for codes associated with prior cardiovascular conditions (any time prior), symptoms (within 3 years), and key diagnostic tests (within 90 days).

Results
The final cohort comprised 51,020 individuals 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). HF diagnosis was first evident in secondary care records in 54.9%, in primary care 36.3%, and in both data sources on the same day in 8.8%. 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%). The most common recorded symptoms preceding diagnosis were breathlessness (41.4%), ankle oedema (9.8%), and fatigue (8.8%). Searched for symptoms were not recorded in 43.8%. In the 90 days preceding the diagnosis, 7-30% had recorded NT pro-BNP with the number of tests increasing throughout the study period (Figure 1). In the 90 days prior to HF diagnosis, 28-31% had an echo recorded. NT pro-BNP was &gt;2000ng/L in 51.8%, 400-2000ng/L in 38.6% and &lt;400ng/L in 9.6%.

Conclusions
In Wales, a greater number of individuals with heart failure appear to be diagnosed during hospital admission, compared with primary care. Many individuals with a code for HF in their records, do not have recorded objective evidence of cardiac dysfunction. The proportion of first-line and definitive tests appears low. It is unclear whether this is because of under-referral, difficulty accessing tests in primary care, or inaccurate coding of test referrals and results. These results are important in evaluating health service delivery and workforce planning.