bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract

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 &lt; .001) (Figure 1, A), and an increase of OAC use (61% [95% IC: 19.6%–102.4%], p &lt; .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

Abstract

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

Abstract

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 &gt;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
Predicting ventricular arrhythmia inducibility in ajmaline-induced brugada type I pattern: validation of the dST-Tiso interval
Authors: Iacopino S , Sorrenti P , Petretta A , Colella J , Di Vilio A , Statuto G , Filannino P , Artale P , Giacopelli D , Camastra D , Peluso G , Fabiano G , Campagna G , Fabiano E , Cecchini F 
Published: November 2023

Abstract

Background
The dST-Tiso is the interval between the onset of the coved ST-segment elevation and its termination at the isoelectric line in V1 and V2 electrocardiographic (ECG) leads during manifest Brugada (BrS) type I pattern. A dST-Tiso &gt;300 ms was proposed as a predictor of ventricular arrythmia (VA) inducibility in patients with ajmaline-induced pattern.

Purpose
The aim of this study was to validate this criterion in an independent data set.

Methods
Consecutive patients with a BrS type I ECG pattern found after ajmaline administration underwent programmed ventricular stimulation (PVS). dST-Tiso was measured in all patients and tested as a predictor of positive VA inducibility.

Results
In our institution, a total of 128 patients (median age 43 years, 59% male) with drug-induced BrS Type I ECG pattern underwent PVS between January 2021 and December 2022. Thirty-two patients (25.0%) had VA inducibility that required defibrillation. Compared to non-inducible subjects, those with positive PVS were more frequently male (81% vs 51%, p=0.003), had longer PQ (165 vs. 160 ms, p=0.016) and dST-Tiso (310 vs 230 ms, p&lt;0.001) intervals, and shorter QT interval (412 vs. 420 ms, p=0.022). The figure shows the distribution of dST-Tiso intervals for patients with not inducible and inducible VA. As a continuous variable, dST-Tiso confirmed significant association with VA inducibility with an adjusted odds ratio of 1.02 (95% CI 1.01-1.03, p&lt;0.001) for each 1 ms increase in duration. A dST-Tiso interval &gt;300 ms yielded a sensitivity of 75%, a specificity of 86%, positive and negative predictive values of 69% and 91%, respectively.

Conclusion
The validation of the estimation model based on the dST-Tiso interval &gt;300 ms confirmed its high accuracy in predicting VA inducibility in drug-induced BrS type I pattern. This simple ECG marker might be related to the extent of the electrical substrate of the disease.Distribution of dST-Tiso intervals
Virtual cardiac hospital in the home following cardiac surgery delivers superior patient outcomes, reduces costs, and increases bed availability
Authors: Martin L K , Naismith C , Nguyen S , Patching K , Shaw M , Ma R , Yeoh J , Wong G , Seevanaygam S , Horrigan M C G 
Published: November 2023

Abstract

Introduction
Increasing patient complexity and unprecedented bed access constraints are important challenges in contemporary delivery of post-operative cardiac surgical care. Bed constraints lead to inefficient ICU discharge, excessive hospital Length Of Stay (LOS), and ineffective transitional care which results in suboptimal patient experience and hospital readmissions. The Cardiac Hospital in the Home (cHITH) model was designed to provide an early supported discharge in a virtual ward environment by an expert team of advanced practice cardiac nurses, pharmacists and cardiologists. The model utilizes wearable technology to monitor cardiac rhythm, oxygen saturation and vital signs in conjunction with daily virtual ward rounds using a standard tele health platform. Care is focused on coaching around mobilization and medical therapy, detection and management of complications and liaison with community pharmacies and providers of sub acute care. Escalation pathways for clinical deterioration were designed to support patient safety.

Results
Between March and October 2022, 123 of 197 patients were referred to cHITH of whom 103 (84%) were recruited. Where appropriate, results were compared with those from the same calendar period in 2021 and dichotomized to detect early trends in care. The cHITH group had a mean age of 62 ± 4 years with 82% males, 62% underwent isolated CABG surgery and there were no mortalities. Compared with historical results, inpatient LOS was reduced 1.6 days with 35% of care delivered in the virtual cardiac hospital. Readmission at 30 days was reduced by 42% (p&lt;0.05)* and patient satisfaction was &gt;90% (net promoter score). Important trends included an increase in cHITH LOS and a reduction in Total LOS.

Projections
Modelling of total hospital costs and bed-day savings was performed using real data from the medical centre. Assuming that 75% of patients would be managed in the cHITH for 3.5 days, the projected cost of cHITH care was $24.2M comparing favorably with $27.8M for usual care. When readmitted days are subtracted from total virtual care days the cHITH model of care is projected to save 975 inpatient bed days.

Conclusions
This virtual model of care has demonstrated safety, efficiency and high levels of patient satisfaction. Ongoing analysis is required to understand the broader applications of this model and to further evaluate quality, safety and efficiency metrics.Table 1.Results
AI remote ECG monitoring improves arrhythmia detection
Authors: Rosengarten J , Hart E , Ojo M , Baker M 
Published: November 2023

Abstract

Background
Ambulatory ECG monitoring is needed to investigate intermittent heart rhythm symptoms. Traditional monitoring approaches are resource intensive, requiring multiple patient hospital visits and time-consuming analysis, using fixed capacity monitoring equipment. Low diagnostic yield means tests are often repeated.

Purpose
We carried out a large pilot to compare ambulatory cardiac monitoring with an AI-led remote ECG monitoring service (Zio XT) with Holter monitoring. Patients were fitted with single-use Zio monitoring patches, which they wore for 14 days of continuous monitoring and then returned it by Freepost for outsourced analysis and reporting.

Methods
Over the course of 12 months, 3505 patients were involved in evaluation of Zio. Of these, we reviewed the detailed rhythm data of 200 consecutive patients, and compared this with a retrospective cohort of 200 consecutive standard Holter monitored patients, fitted prior to the evaluation period. All patients had been referred to cardiology for investigation of heart rhythm symptoms.

Results
The total Zio group had a mean age of 63.8 years. The 400 participants used in the analysis groups matched this (Table) and there was no significant difference in the age ranges between the two groups. Holter duration was for: 24-hr (45%), 48-hr (8%), 72-hr (20%), 5-day (2%), 7-day (26%). Significant arrhythmias (defined as SVT ≥ 30s, VT ≥ 8bts, AV Block Mob 2⁰ or 3⁰, Pause ≥ 3s, Paroxysmal AF) were detected in 49 (25.8%) Zio patients compared with 18 (9.0%) Holter patients (P&lt;0.00001). Total number of arrhythmia findings was greater in Zio compared with Holter; P&lt;0.05 (Table). The arrhythmia findings in Zio were comparable with those in the pilot’s total cohort. In Holter, 41% of significant arrhythmias were detected in patients monitored for 24 hrs, comparable with Zio where 46% of arrhythmias were detected within the first 24 hours of wear-time. In Zio, 15% of significant arrhythmias were detected post 7 days of the wear-time.

Conclusions
Arrhythmia detection with Zio was significantly greater compared with Holter; detecting significant arrhythmias in nearly 3 times as many patients. Longer detection enabled greater arrhythmia detection, with 3 in every 20 patients having episodes occurring after 7 days. Even when adjusting for wear time, arrhythmia detection within 7 days was superior in the Zio group. Over half (59%) of arrhythmias were detected in patients monitored for 48 hours or longer, suggesting that for an efficient service longer monitoring is needed, particularly when symptoms are intermittent. For Holter monitoring this has considerable resource utilisation implications, relating to staff (cardiac physiologist) time and capital equipment (Holter monitoring stock). Also, greater detection of arrhythmia across the wear time suggests a higher sensitivity than Holter for detecting cardiac arrhythmias.
Detection of cardiac arrhythmias following acute coronary syndrome using a connected continuous rhythm monitoring device
Authors: Charleux P , Zeitouni M , Procopi N , Devos P , Barthelemy O , Guedeney P , Granger C , Ferrante A , Laredo M , Kerneis M , Silvain J , Collet J P , Montalescot G 
Published: November 2023

Abstract

Background
The incidence of cardiac arrhythmias following acute coronary syndrome (ACS) is not well-known.

Purpose
To describe the incidence of clinically relevant cardiac arrhythmias following low-risk ACS, using a connected continuous rhythm monitoring device.

Methods
In a prospective, single arm and open label, observational study, patients in sinus rhythm and with a left ventricular ejection fraction &gt; 40% discharged home after an ACS were equipped with a rhythm monitoring tee-shirt, providing a high-quality continuous 15-lead electrocardiogram signals. Patients were prompted to wear the device as much as possible, for a maximum length of 30 days. Recorded data were reviewed by two independent cardiologists. The primary endpoint was the occurrence of clinically relevant arrhythmias – defined as either new atrial fibrillation, ventricular tachycardia, accelerated idioventricular rhythm or frequent premature ventricular contractions (≥ 500/day) – detected by the device over the first 30 days after hospital discharge following the ACS.

Results
From January 2020 to August 2022, 99 patients were discharged home with a functional rhythm monitoring device, among whom 45 (55%) were ST-segment elevation myocardial infarction. The median recording duration was 13 days (range: 1-30), with a median workable signal of 9 days. Recorded data were interpretable in 82 patients. At least one clinically relevant arrhythmia occurred in 10 patients (12.2%) during follow-up. Median time to first arrhythmia was 16,5 days (IQR: 8,5-23). Paroxysmal atrial fibrillation was detected in 3 patients (3.7%), non-sustained ventricular tachycardia in 4 patients (4.9%), accelerated idioventricular rhythm in 3 patients (3.7%) and frequent premature ventricular contractions in 5 patients (6.1%) (Figure 1). None of these arrhythmias were symptomatic. No sustained ventricular tachycardia was detected.

Conclusions
Asymptomatic cardiac arrhythmias were frequent after ACS in patients considered at low risk for further events. The long-term clinical risk of these arrhythmias remains to be studied.
Correlation between non-invasive measurements and intracardiac pressures using machine learning techniques
Authors: Van Ravensberg A E , Scholte N T B , Omar Khader A , Brugts J J , Bruining N , Van Der Boon R M A 
Published: November 2023

Abstract

Background
Solutions targeting early recognition of congestion in heart failure (HF) patients have the potential to prevent readmissions and can thus significantly reduce the burden on HF care. A gold standard measure of congestion is the invasively measured pulmonary capillary wedge pressure (PCWP). However, the invasive nature and accessibility of this measurement limits its clinical use. Non-invasive approximation of the PCWP using biosensing wearables could be a promising replacement for HF monitoring.

Purpose
The primary aim of this retrospective study was to create a model that estimates the PCWP based on non-invasive measurements of vital signs using both traditional statistics and machine learning (ML) techniques.

Methods
The study cohort consisted of right-sided heart catheterizations between 23/6/2017 and 19/8/2022. The following models were used: linear regression or classification, k-nearest neighbors, random forest, gradient boosting and multilayer perceptron. The outcome measure for the regression models was the continuous PCWP as measured during the catheterization. The two outcome classes for the classification models were low (&lt;12 mmHg) and high (≥12 mmHg) PCWP. Non-invasive mean arterial blood pressure (MAP), saturation, heart rate, weight and temperature measured at most 72 hours before or after the catheterization were collected as the features for the models. Additionally, ECG-signals acquired during the catheterization were used to calculate the heart rate variability (HRV). The data was split in a validation (20%) and training (80%) data set. The models were built based on the training set and then applied on the validation set to determine the coefficient of determination (R2) for the regression models and the AUC of the ROC for the classification models.

Results
A total of 859 catheterizations were included of which 31.2% had HF as primary diagnosis. Average age of the cohort was 59 ± 14 years and 52.2% were male. In the HF group, the features with the highest correlation with the PCWP were the HRV and gender (Table 1). All the used regression techniques resulted in low R2 values of up to 0.10 and the classification techniques in AUC of the ROC values of up to 0.61 (Table 2).

Conclusion
In the current study, PCWP could not be approximated with non-invasive measurements using traditional statistics and ML techniques. These findings support the notion that traditional measures for monitoring HF are poorly correlated with hemodynamic parameters. Perhaps repeated measurements over time could overcome the shortcomings of a single vital signs measurement for congestion evaluation. Future prospective research is needed to evaluate the potential of wearable devices measuring trends over a longer period and their potential in predicting hemodynamics.