bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development and implementation of a guideline-based decision support system for etiological diagnosis of Sustained Monomorphic Ventricular Tachycardia (SMVT)
Authors: Zheng S , Xu X W , Hu Z , Wang M , Yao Y , Li J 
Published: October 2024

Abstract

Background
Guideline (GL)-based decision support systems (DSSs) play a significant role in supporting physicians in their decision-making processes by providing structured clinical guidance. While these systems have shown significant success in enhancing practitioner performance and patient outcomes in general practice, their application in the context of ventricular arrhythmias remains limited. Moreover, the clinical etiological diagnosis of ventricular arrhythmias presents various challenges for healthcare professionals, such as adherence to updated diagnostic criteria and understanding of complex arrhythmia mechanisms.

Purpose
In this study, we aim to design, implement, and evaluate a guideline-based decision support systems for etiological diagnosis of sustained monomorphic ventricular tachycardia (SMVT), which could potentially address critical challenges associated with the disease diagnosis and management.

Methods
To comprehensively capture the semantics embedded in clinical guidelines, we first developed a dedicated knowledge graph tailored to the clinical decision-making. This involved extracting entities and relationships pertinent to key decision procedures from ESC guidelines for ventricular arrhythmias, clinical textbooks or literatures. Subsequently, we translated instances within the knowledge graph into archetypes within openEHR, amplifying the interoperability of clinical information. To enhance the interpretability of the DSS, we established executable diagnostic and treatment processes to offer visual representation of clinical steps in diagnosis and treatment. Ultimately, we constructed a decision support engine to implement the GL-DSS, and its effectiveness was evaluated with anonymous real-world data.

Results
The proposed GL-DSS was found to be quite feasible and could provide etiological diagnosis for SMVT. The knowledge base for the constructed GL-DDS included more than 2000 entities and relations, which played a vital role in representing disease pathogenesis. The proposed system can map the clinical entities to a set of openEHR archetypes, thus mitigate the interoperability issues of the hospital information systems and CDSS data integration. Also, this module improved system usability and ensured that guidelines could be easily translated and updated into decision support tools. Real-word medical records from 20 patient were collected for model evaluation, the results showed that the GL-DSS covered most of the entities and relations between diseases and diagnostic evidence recorded in clinical texts and achieved a diagnosis accuracy of 80%.

Conclusions
The results demonstrated that our proposed GL-DSS could completely and accurately capture the diagnostic information under the concrete EHR data. Thus, the implement decision support system could have the potential to improve clinical decision-making, patient care, and outcomes in this specialized medical domain.
Machine learning to predict incident atrial fibrillation in patients with acute stroke: FIND-AF STROKE
Authors: Nadarajah R , Wu J , Nakao Y M , Hsu J C , Helbitz A , Nakao K , Lin L Y , Gale C P 
Published: October 2024

Abstract

Background
Atrial fibrillation (AF)-related strokes have a high rate of recurrence, and are associated with substantial morbidity, disability, healthcare expenditure, and mortality.(1-3) Detection of AF promises opportunity to prevent recurrent stroke through conversion of antiplatelet to anticoagulant therapy,(4,5) but use of extended monitoring has cost implications.(6) Thus accurately identifying patients with stroke at high risk for AF is important but previous previous models have been developed in small, unrepresentative cohorts.(7)

Purpose
To develop and validate a simple novel decision support tool for prediction of incident AF after hospitalisation with stroke through machine learning.

Methods
We developed the model (FIND-AF STROKE) in UK secondary care and death certificate-linked primary care health record data from individuals aged ≥30 years without known AF hospitalized with a stroke or transient ischaemic attack (Jan 2, 1998 to Feb 28, 2022), randomly divided into training (80%) and testing (20%) datasets. We evaluated logistic regression and random forest models for prediction of 2-year AF risk in the testing dataset with internal bootstrap validation, and compared against the CHA2DS2VASC score. Youden index was used to set a threshold for higher and lower risk.

Results
Of 35 530 UK individuals (mean age 72.1 (SD 12.7) years, 50.9% women), 1742 (4.8%) had developed incident AF at 2 years. Using feature importance a parsimonious version of the model was created requiring only age, sex, ethnicity (white versus other) and five comorbidities.
In the testing dataset prediction performance for the random forest algorithm (area under the receiver operating characteristic (AUROC) 0.710, 95% CI 0.685 – 0.736) was superior to the CHA2DS2VASC score (0.645, 95% CI 0.617-0.672) (Figure 1).
The threshold for higher risk using the Youden index was 4.81% which resulted in a negative predictive value of 97.6%, sensitivity 0.712, specificity 0.583. Of the post-stroke patients 44% were labelled at higher risk of AF, who were older, had a higher prevalence of comorbidities, and a 3.3-fold higher incidence of AF post-stroke, than those identified as lower risk (Table 1).

Conclusions
The machine learning FIND-AF STROKE decision support tool can accurately identify individuals at risk of incident AF after stroke to enable a targeted and cost-effective approach to extended monitoring. Validation within cohorts that have undergone extended monitoring post-stroke will give a more accurate estimation of yield at different risk thresholds.Figure 1
Metaverse-based cardiac magnetic resonance simulation to overcome claustrophobia (META-MRI): a feasibility trial
Authors: Skalidis I , Arangalage D , Kachrimanidis I , Antiochos P , Olivotto I , Maurizi N 
Published: October 2024

Abstract

Background
Cardiac Magnetic Resonance Imaging (CMR) is crucial for diagnosing and stratifying cardiac disease, but up to 10% of patients face challenges due to claustrophobia. Conventional methods for overcoming claustrophobia have limited success. The metaverse, an immersive 3D environment using virtual and augmented reality, is under investigation for healthcare applications. A metaverse-based VR CMR simulation program was developed to assist patients with claustrophobia, but its clinical effectiveness is unexplored.

Methods
The META-MRI study involved 450 patients screened, with 32 enrolled. They were randomly assigned to intervention (Metaverse-based simulation + standard care) or control (standard care alone). The Metaverse program used a VR headset within the Aimedis metaverse platform. Training sessions occurred before scheduled CMR exams, with anxiolytic treatment when necessary. Primary endpoints included successful CMR completion, anxiety assessment using the State-Trait Anxiety Inventory (STAI), and usability through a questionnaire.

Results
Patients in the intervention group (n=16) completed Metaverse training successfully. During simulations, anxiety decreased (STAI scores: 73 ± 8 to 51 ± 11, p=0.02). Intervention group completion rate was significantly higher (81%) than the control group (37%) during real CMR exams (p=0.01). Usability scores indicated moderate satisfaction.

Discussion
The metaverse-based program significantly improved CMR completion rates and reduced anxiety levels in patients with severe claustrophobia compared to standard care alone. Traditional methods for claustrophobia have limitations, making the metaverse a promising tool. Despite promising results, further research is necessary, considering identified usability issues and economic implications.

Conclusion
This feasibility trial demonstrates the effectiveness of a metaverse-based simulation program in enhancing compliance with CMR exams for patients with severe claustrophobia. The strategy, combined with standard care, led to reduced anxiety and increased CMR completion. Further investigation with larger cohorts is required to validate these findings, addressing usability concerns and assessing the cost-effectiveness of incorporating the metaverse into routine clinical practice.Clinical and demographic characteristics
Digital devices for heart rhythm monitoring in atrial fibrillation patients scheduled for elective electrical cardioversion. Kardiover study
Authors: Benezet Mazuecos J , Alonso P , Lozano J M , Salas J , Gonzalez Lorenzo O , Rodriguez-Manero M , Narvaez I , Lozano A , Miracle A , Crosa J , Barrio I 
Published: October 2024

Abstract

Introduction
the management of patients with recent-onset atrial fibrillation (AF) is to achieve sinus rhythm (SR) by electrical or pharmacological cardioversion (CV). However, frequently SR is spontaneously restored, making scheduled admission and/or transesophageal echocardiogram (TEE) unnecessary and therefore causing misutilization of healthcare resources. Emerging medical technology has allowed for accurate heart rhythm monitoring.

Objective
to evaluate the diagnostic ability of a smart device ECG monitor to detect spontaneous conversion to SR in AF patients, preventing unnecessary admission and providing an earlier management.

Methods
A prospective multicenter study (Kardiover study) was designed including patients with paroxysmal, persistent or first diagnosed AF scheduled for elective electrical CV in cardiology departments of 7 hospitals in Spain. Patients were asked to submit ECG recordings to a central Corelab daily, twice a day and whenever they present symptoms, until CV (spontaneous or scheduled) and a week afterwards. Whenever a spontaneous conversion to SR was detected, investigators were contacted to confirm SR and abort admission, likewise when AF recurrence after CV was detected. Patients’ satisfaction was evaluated using a test for perceived utility and value, convenience, and accessibility.

Results
A total of 74 patients were enrolled (age 62 ± 10 years). Twenty-two patients (30%) showed spontaneous conversion to SR correctly detected by the devices. 86% of the spontaneous CV occurred during the first week. Among 52 patients admitted for CV, 45 (88%) were discharged in SR. During follow up after conversion to SR (spontaneous or electrical), recurrences of AF occurred in 24 patients (34%). At the end of the follow-up 51 patients (69%) remained in SR. Due to the early detection of spontaneous conversion to SR, a total of 22 admissions and 16 TEE were prevented and these resources used for other patients. Patients’ adherence to the protocol in regard to ECG recording transmissions was high, the CoreLab received 93% of the expected transmissions. Patient’s overall satisfaction was also high with a punctuation of 9.1 over 10 in the feedback test.

Conclusion
Digital devices for heart rhythm monitoring can optimize the management of AF patients scheduled for CV, preventing unnecessary admissions and providing a more rational use of healthcare resources.Baseline characteristicsStudy design and results
Assessing adherence to remote monitoring recommendations for cardiovascular implantable electronic devices: a retrospective analysis
Authors: Bertini M , Panchetti L , Lavalle C , La Greca C , Amellone C , Dello Russo A , Calo' L , Cocchiara L , Santobuono V E , Pepi P , Savarese G , Taravelli E , Santini L , Valsecchi S , D'onofrio A 
Published: October 2024

Abstract

Background
The recently published Expert Consensus Statement on the Practical Management of Remote Device Clinic offers evidence-based recommendations for managing patients with cardiovascular implantable electronic devices (CIEDs).

Purpose
This study evaluates adherence to remote monitoring (RM) recommendations in current clinical practice using data from a RM database.

Methods
According to RM guidelines, patient connectivity should be maintained. If continuous connection is ensured, it is reasonable to schedule in-person visits every 24 months, with alert-based RM potentially replacing structured intermittent device follow-up. Data from 6553 CIED patients followed on the LATITUDE (Boston Scientific) remote network at 26 Italian centers between 2010 and 2023 were analyzed. Median RM duration was 40 months (25th-75th percentile: 23-67).

Results
Enrollment of patients at centers significantly increased over the observation period (Figure). As of the January 2024 data extraction, 4723 patients had transmitted data in the last 12 months. Among these, 639 (14%) were NOT MONITORED (i.e. interrupted connectivity), with no significant differences among CIED types (Pacemaker: 13%; ICD: 12%; CRT-P: 7%; CRT-D: 13%; S-ICD: 18%). Scheduled device transmissions occurred at least once every 3 months in 96% of patients. The volume of in-office device interrogations and remote transmissions is detailed in Figure. In 2023, among the 4084 MONITORED patients, 6600 in-office device interrogations were conducted, and 64296 remote transmissions were reviewed. Of these, 34267 (53%) were scheduled, 15091 (24%) were patient-initiated, and only 14938 (23%) were triggered by alerts.

Conclusions
In current clinical practice, RM adoption is increasing, alongside the volume of in-office and remote visits. Solutions must be implemented to ensure transmission continuity in a substantial percentage of patients. Additionally, there persists a significant reliance on frequent scheduled transmissions and in-office visits. Centers stand to gain from implementing the guideline-recommended alert-based RM strategy, reducing the considerable burden of nonactionable remote and in-office visits among patients with continuous connectivity.
Feasibility of novel robot-assisted, remotely-performed echocardiographic examination
Authors: Guppy-Coles K , Johnstone M , Armstrong J , Lo A , West C , Atherton J , Scott A C 
Published: October 2024

Abstract

Background
Access to echocardiography services outside metropolitan areas is persistently limited. We sought to determine the feasibility and efficiency of performing remotely-controlled, semi-automated robot-assisted echocardiographic examinations (robot-assisted echo) by cardiac sonographers as a potential avenue to provide standard echocardiographic specialist services to remote and rural areas as well as permit expedited diagnoses enabling earlier management of disease.

Methods
Two accredited cardiac sonographers (Cardiac Investigations Unit) undertook 6 hours virtual training, and 10 hours hands-on robot-assisted echo simulation training; using a novel, semi-automated robotic arm system to remotely manipulate an ultrasound probe to acquire echocardiographic images. Sonographers then performed remotely-performed robot-assisted echo examinations on staff volunteers, and consenting inpatients in whom echocardiography was clinically indicated. Cameras, probe pressure sensors and an ultrasound platform remote console were utilised to support remote operation. Traditional echocardiography (manipulation of probe by hand) was also performed as a reference. Feasibility to obtain a comprehensive robot-assisted echo was compared to traditional echocardiography. Sonographer learning curve and efficiency (image acquisition time and offline measurement time) was also investigated.

Results
Total cohort was n=78 (age 51±15yrs; 57% male) with 63% patients. All participants had robot-assisted echo and traditional echocardiograms performed within 72hrs. Average feasibility (%) of robot-assisted echo as (a) 11 averaged assessments and (b) 38 averaged measures was 92±9 (a) and 86±11 (b), compared to (a) 99±1 and (b) 97±7 by traditional examination (all respectively). Sonographer learning curve was identified via 1:1 block subcohort analysis, with averaged assessment feasibility (n=33:31) being 86±19 (first block) to 98±3 (last block) and averaged measurement feasibility (n=32:31) being 77±11 (first block) to 92±7 (last block), all respectively. In the staff participant cohort (n=31), average image acquisition efficiency in robot-assisted echo was poorer than that of traditional examination (47+9 minutes compared to 19+2 minutes; p&lt;0.0001), respectively. Average efficiency of offline measurement time was 13+7 minutes by robot-assisted echo; comparable (p=0.33) to traditional examination at 12+6 minutes. There was no learning curve effect on image acquisition efficiency; 42+10 vs 51+7 minutes (1:1; n=16:15; p=0.007).

Conclusion
Remotely-performed, robot-assisted echo using a commercially-available ultrasound platform performed by briefly-trained, cardiac sonographers is feasible, with reasonable efficiency even though less efficient in image acquisition compared to traditional echocardiograms. Further investigation of performance as a remote service is warranted in larger, clinically-indicated populations.
Smartphone motion sensor based detection of atrial fibrillation in populations referred to cardiology
Authors: Blomster J , Dehkordi K J , Tapiovaara T , Lahdenoja O , Hurnanen T , Taghavi F , Santalahti K , Pankaala M 
Published: October 2024

Abstract

Introduction
The motion sensors of a common smartphone when placed on chest can be used to detect vibrations caused by cardiac motion. The concept has been shown to detect atrial fibrillation (AFib) in clinical setting with 96% accuracy.

Purpose
We sought to examine how the smartphone motion sensor based AFib detection performs in various populations referred to cardiology pooled from 3 different studies addressing arrhythmias, heart failure or suspected coronary artery disease.

Methods
From a total of 1 524 patient recordings 748 recordings were from men (49%). The mean age of the patients was 64 years (range 20-94 years) and mean BMI was 28.9kg/m2 (range 15.2-63.7kg/m2). The signals were collected by placing a smartphone on chest of the patients in supine position and then analyzed with the CardioSignal solution. From the recordings two 60 second strips were analyzed. A simultaneous 5-lead or 1-lead ECG data was collected for reference. The ECG signals were analyzed for quality and rhythm diagnostics by 2 physicians blinded for the motion sensor AFib detection result. Finally, a head-to-head comparison was conducted for the motion sensor AFib detection result and the ECG data.

Results
From the 1524 patient recordings the final analysis set included 237 individuals with AFib, whereas the rest had sinus rhythm. The sensitivity to detect AFib was 91.9% and specificity 98.7%. The positive predictive value was 91.0% (88.3% to 94.2%) and the negative predictive value 98.3% (95.2% to 99.1%). The accuracy was 97.0% (94.7% to 97.7%).

Conclusions
A smartphone-based assessment of AFib using the embedded motion sensors seem to yield very good detection results, especially specificity. The accuracy of the results is comparable to available ECG and PPG based AFib detection solutions.
Engagement of adults with congenital heart disease in research: overcoming barriers relating to social deprivation
Authors: Webb-Mitchell R , Constantine A , Van Bulck L , Goossens E , Kovacs A , Luyckx K , Moons P , Dimopoulos K , Coats L 
Published: October 2024

Abstract

Background
In adult congenital heart disease (ACHD), social deprivation is associated with adverse health outcomes, such as increased inpatient admissions and major adverse cardiovascular events (1). Research studies on this topic often rely on patients engaging with surveys, and the characteristics of non-respondents in relation to social deprivation remain unknown.

Purpose
We assessed the association between social deprivation and response to a research survey in the ACHD population.

Methods
We included all individuals in the UK who were invited to participate in the APPROACH-IS II study (2), which assessed patient-reported outcomes in ACHD. A validated index of relative deprivation was derived from household postcodes, based on the 2019 Index of Multiple Deprivation (IMD) scores for geographic units in England (3). A lower IMD decile indicates a greater degree of deprivation. We compared patient engagement rates between 2 centres utilising different recruitment approaches: a Southern centre (South) relying on recruitment during mainly telephone or video consultations, and a Northern centre (North) recruiting patients during face-to-face clinic appointment, with the help of an ACHD clinical and research nurse specialist.

Results
In total, 593 patients were asked to take part in APPROACH-IS II in the UK: 324 patients from the Southern centre and 269 patients from the Northern centre, of whom 130(40.1%) and 209(77.7%) responded, respectively (p&lt;0.0001). In the South, patients approached were homogeneously spread amongst IMD deciles (6.0[4.0-9.0]) and respondents were significantly less deprived (higher IMD decile) than non-respondents (7.0[5.0-9.0] vs 6.0[4.0-8.0], p=0.02). Indeed, the response rate in areas of higher deprivation (IMD decile&lt;=4) was significantly lower than in the least deprived areas (IMD &gt;=8) (31.3% vs 47.0%, p=0.03). In the North, the distribution of deprivation in patients was skewed towards lower IMD deciles, with a significantly higher proportion of patients from areas of higher deprivation (Figure A, 26 vs 99(30.6%) in the South, p&lt;0.0001). There was no difference in IMD decile between respondents and non-respondents in the North (Figure B, 5.0[2.0-7.0] vs 4.0[2.0-7.0], p=0.26). When comparing patients living in the most deprived areas (IMD &lt;=4), there was a large and significant difference in response rate between centres (p&lt;0.0001).

Conclusions
In our population, deprivation appears to have a significant impact on the likelihood of patients engaging with clinical research. An enhanced approach, involving face-to-face contact and the involvement of a dedicated clinical nurse specialist, can improve recruitment to research studies and abolish the effect of socioeconomic status on patient engagement.Figure 1A and B