bluuebunny/crossref_metadata_2025_split huggingface dataset:
Importance and limits of cardiovascular risk factors on the prediction of SCD using machine learning approach
Authors: Chocron R , Laurenceau T , Youssfi Y , Bougouin W , Empana J P , Chopin N , Jouven X 
Published: October 2024

Abstract

Background
SCD is mainly due to lethal ventricular arrhythmias and occurs often among patients with ischemic heart disease. SCD and ischemic heart disease share a similar pattern of cardiovascular risk factors (CVRF). Commonly developed risk scores have been focused on cardiac-related risk factors. We aimed to investigate whether non-CVRF variables could enhance predictive performance beyond standard CVRF of SCD.

Method
We compared 2 different strategies for variable inclusion. First, we built a prediction model containing EHR data restricted to cardiovascular diseases and risk factors (CVD MR model) that occurred up to 5 years before SCD. We selected all available main cardiovascular variables as surrogate markers for coronary artery disease, stroke, diabetes, hypertension, smoking status, obesity, lipid disorders and chronic renal failure. On the other hand, our global approach (ALL MR model) includes all medical records codes, without any prior selection. To estimate the risk of SCD over three months, we trained a machine learning model on EHR data representing 8,566,229 drug prescriptions and 801,352 hospital diagnoses up to five years prior to SCD. The data were obtained from a cohort of 12,338 SCD in France and 12,338 controls from 2011 to 2015. We then validated the results on two external cohorts: one temporal in the same area between 2016 and 2020 with 11,620 SCD and 11,620 controls and one geographical from the USA with 892 SCD and 892 controls from 2013 to 2021.

Results
The CVD MR + ML model (CatBoost algorithm) yielded moderate performances with an AUC of 0.68 (95%CI 0.65-0.68), a sensitivity of 45%.(95%CI 42-47) and a specificity of 82% (95%CI 80-84). We found that the EHR model with all medical records (All MR + ML model) offered better performances. In the derivation cohort, this model achieved an AUC of 0.80 (95% CI: 0.78–0.82) with a sensitivity of 67% (95% CI 64-69), a specificity of 80% (95% CI 78-82) (Figure 1). The logistic regression (All MR + LR) applied on all medical records performed better than the CatBoost algorithm when restricted to CVD medical records (Figure 1).

Conclusion
The inclusion of all potential variables beyond the usual CVRF significantly improves the performances of SCD prediction models, independently of the methods (AI and logistic regression).
Figure 1 : Receiver operating characteristic curves of the different models (ALL MR + ML, ALL MR + LR, CVD MR + ML and CVD MR + LR) in the derivation cohort and two validation cohorts (temporal and geographical)Figure 1.ROC curves
Performance of apple watch for detecting atrial fibrillation among a known atrial fibrillation population
Authors: Piccini J , Lande J , Kanwar R , Johnson L , Passman R 
Published: October 2024

Abstract

Background
Photoplethysmography (PPG) based consumer accessible devices with algorithms for irregular rhythm detection are gaining popularity as an opportunistic screening tool for atrial arrhythmias, and are FDA cleared for use in patients without a history of atrial fibrillation (AF). However, these devices are increasingly used for the management of AF, a purpose for which some algorithms are not intended.

Purpose
To compare the performance of the Apple Watch Irregular Rhythm Notification (IRN) with an implantable cardiac monitor (ICM) for the detection of atrial fibrillation (AF) in patients with known AF from the DEFINE AFib study.

Methods
Patients with known AF, a LINQ or LINQ II ICM, and an Apple Watch with PPG-based IRN activated were included in the analysis. ICM detected AF episodes served as the standard to which Apple Watch IRNs were compared. All AF episodes detected by the ICM device were manually adjudicated by two trained, independent reviewers with a third independent reviewer in situations of disagreement. Only AF episodes ≥60 minutes during a 75-minute window in which the Apple Watch was monitoring were included in accordance with the known Apple Watch algorithm detection parameters. To eliminate oversampling of ICM detected episodes compared to Apple Watch IRNs, 1,000 simulations of one randomly selected episode per subject was done to avoid single-subject bias.

Results
In total, 97 subjects were analyzed of which 42 had an AF episode ≥60 minutes. Applying the 75-minute window of Apple Watch monitoring to episodes ≥60 minutes, 20 subjects had qualifying, adjudicated ICM detected AF. These 20 subjects accounted for 481 qualifying AF episodes. Of the ICM detected episodes, 191 (40%) occurred while an Apple Watch was not being worn. In total, 11/20 (55%) subjects had an IRN generated by the Apple Watch. Adjudication of the ICM episodes resulted in 290 true positive AF episodes ≥60 minutes during a 75-minute window while the watch was actively monitoring, with 74 (26%) of those episodes triggering an IRN resulting in a per episode sensitivity of 24 ± 7% and PPV of 91 ± 4% (Figure 1A). The efficacy of Apple Watch to detect AF episodes of differing lengths was assessed for episodes between 1-6 hours, 6-12 hours, 12-24 hours, and &gt; 24 hours with sensitivities of 19 ± 19%, 38 ± 8%, 32 ± 10%, and 31 ± 8%, respectively (Figure 1B). The Apple Watch generated an annualized false positive rate of 2.25 IRNs for every patient year of monitoring. At the subject level, Apple Watch had a sensitivity of 48%, specificity of 100%, PPV of 100%, and NPV of 71% (Figure 1A).

Conclusions
In patients with a history of AF, the Apple Watch IRN was most sensitive at the individual subject level while watch performance was lower by episode. The current IRN algorithm favors longer episode durations. The limited sensitivity, dependence on user wear-time, and reliance on long-durations of AF limit the use of Apple Watch for AF management.
Clinical value of multiple sensors contributing to a composite heart failure ICD monitoring index
Authors: Compagnucci P , D'onofrio A , Vitulano G , Calo' L , Bertini M , Santini L , Savarese G , Santobuono V E , Lavalle C , Viscusi M , Amellone C , Casella M , Valsecchi S , Capucci A , Dello Russo A 
Published: October 2024

Abstract

Background
The HeartLogic algorithm integrates data from various implantable defibrillator (ICD) sensors to predict impending heart failure (HF) decompensation. The algorithm computes worsening in its sensors and weighs them based on a risk level. These sensors include accelerometer-based first (S1) and third (S3) heart sounds, intrathoracic impedance (TI), respiration rate (RR), the ratio of respiration rate to tidal volume (RSBI), and night heart rate (NHR).

Objective
This study assessed the relative worsening of ICD sensors at the onset of HeartLogic alerts, their association with patient characteristics and subsequent outcomes.

Methods
The HeartLogic feature was activated in 568 ICD patients (410 with CRT-D) across 26 centers, with a median follow-up of 26 months [25th–75th percentile: 16–37].

Results
During the follow-up, 1200 HeartLogic alerts were recorded in 370 patients. The sensor with the highest worsening (SHW) at the alert onset was S3 (27% of alerts), followed by S3/S1 (25%), TI (16%), RR (15%), and NHR (11%), RSBI (6%). Patients with atrial fibrillation (AF) at implantation and those with chronic kidney disease (CKD) had higher alert prevalence (AF 84% vs. no-AF 58%, CKD 72% vs. no-CKD 59%; both p &lt;0.05) and higher alert rates (AF 1.51/patient-year vs. no-AF 0.88/patient-year, CKD 1.30/patient-year vs. no-CKD 0.89/patient-year; both p &lt;0.05). AF patients had alerts with every sensor as SHW, while CKD patients had alerts primarily with TI, RR, and RSBI as SHW (Figure). In 85% of cases among 247 patients with &gt;1 alert, the SHW changed between successive alerts. Of the 88 (7%) alerts resulting in HF hospitalizations or deaths, a greater proportion featured RR or RSBI (11%) and NHR (11%) as SHW, followed by heart sounds (5%) (both p &lt;0.05). Clinical events were more common with the first alert (12.6%) than subsequent alerts (5.2%, p &lt;0.001).

Conclusion
HeartLogic alerts are predominantly associated with the highest worsening in heart sounds. Nevertheless, recurrent alerts often involve other sensors, suggesting varied HF progression mechanisms and potential divergent outcomes. The frequency of alerts and the pattern of worsening in ICD sensors are linked to patient characteristics.
Electronic consultation from primary care to a cardiology department improves outcomes in patients with cancer
Authors: Garcia Vega D , Cinza-Sanjurjo S , Mazon-Ramos P , Rey-Aldana D , Portela-Romero M , Rodriguez-Manero M , Sestayo-Fernandez M , Lage-Fernandez R , Lopez-Lopez R , Gonzalez-Juanatey J R 
Published: October 2024

Abstract

Background
The number of cancer patients with known cardiovascular (CV) disease is increasing due to improved cancer prognosis.

Purpose
The objective of this study is to assess the long-term results of cancer patients referred to a cardiology department by primary care using electronic consultation (e-consultation).

Methods
We analysed the patients with previous diagnosis of cancer referred to a cardiology department (CD) from 2010 to 2021 (n=6.889), selected from a total of 68.518 patients (61.629 without cancer), and compared two assistance models: in-person practice (from 2010 to 2012) and e-consultation (from 2013 to 2021). In the first period, all the patients referred by primary care physicians (PCPs) were seen in an in-person consultation and in the e-consultation model the cardiologist reviewed the electronic records (e-consultation) and decided to solve without any visit or, if it was necessary, with an in-person consultation. The outcomes between both periods were analysed using an interrupted time series regression model: 1) delay time to cardiology assistance; 2) all-cause and cardiovascular hospital admissions, and 3) all-cause and cardiovascular mortality during the first year after the initial consultation or e-consultation to the CD.

Results
Among cancer patients referred to cardiology consultation, the introduction of e-consultation decreased waiting times to cardiology care by 51,8% (95% CI: 51,7%-51,9%) (Fig 1). Over 60% of referrals were resolved in less than 8 days, with this proportion significantly higher after the e-consultation program was introduced (p&lt;0,001). Furthermore, we observed a reduction in the one-year rate of outcomes, with an incidence rate ratio (iRR) [IC 95%] of 0,75 [0,73-0,77] for CV-related hospitalizations (Fig 2b), 0,43 [0,42-0,44] for all-cause hospitalization (Fig 2a), and 0,87 [0,86-0,88] for all-cause mortality. In the multivariate analysis, the delay in receiving cardiology care was significantly associated with an independent increase in 1-year mortality (OR [95% CI] 1,39 [1,06-1,83]) and hospital admission (OR [95% CI] 1,25 [1,02-1,52]). Furthermore, the implementation of the e-consult model was associated with a reduction in hospital admission rates (OR [95% CI] 0,19 [0,14-0,25]).

Conclusions
In comparison to the in-person consultation period, an outpatient care program that incorporates e-consultation for cancer patients significantly reduced waiting times for cardiology care and shown to be safe, associated with lower rates of hospital admissions.Figure 1Figure 2
Prevention of dementia using mobile phone applications (PRODEMOS): a multinational randomised controlled effectiveness-implementation trial
Authors: Moll Van Charante E , Hoevenaar-Blom M P , Song M , Andrieu S , Coley N , Georges J , Van Gool W A , Handels R , Wang Y , Wimo A , Brayne C B , Wang W , Richard E 
Published: October 2024

Abstract

Background
The expected increase of dementia prevalence in the coming decades will mainly be within low-and middle-income countries and those with low socioeconomic status (SES) in high income countries. Forty percent of dementia is associated with modifiable, lifestyle-related cardiovascular risk factors. Mobile health interventions with remote coaching can help reach underserved high-risk populations globally.

Methods
This open label, blinded endpoint, hybrid effectiveness-implementation randomised controlled trial (RCT) investigated whether a coach-supported mHealth intervention can reduce dementia risk in persons aged 55-75 years of low SES in UK or from the general population in China with at least two dementia risk factors. The primary effectiveness outcome was change in CAIDE dementia risk score (including age, education, sex, systolic blood pressure, total cholesterol, BMI, physical exercise) after 12-18 months of intervention. Main implementation outcomes were coverage, acceptability, appropriateness, adoption, feasibility, sustainability, fidelity, and costs using a mixed methods approach.

Findings
Between 15th January 2021 and 18th April 2023, 1,488 persons were included and randomised (734 intervention, 754 control), with 1,229 (82·6%) available for analysis of the primary effectiveness outcome. After a mean follow-up of 16 months, the mean CAIDE score improved by 0·16 points in the intervention group vs. 0·01 in the control group (Mean Difference (MD) 0·16, 95% Confidence Interval (CI) 0·03-0·29). The effect was larger in those who adhered to the intervention (MD 0·27, 95%CI 0·12-0·44) and those planning to change lifestyle (MD 0·33, 95%CI 0·10-0·57). Ten percent of those invited participated; of these, 81% adopted the intervention and 50% continued active participation throughout the study. For the individual components of the CAIDE score, there were no statistically significant differences for systolic blood pressure, total cholesterol and BMI, but in the intervention group physical inactivity was reduced more than in the control group (MD 6·7%, 95%CI 2·1 to 11·2) and more participants quit smoking (MD -3·1%, 95%CI -4·8 to -1·5).

Interpretation
A coach-supported mHealth intervention to improve lifestyle is modestly effective in reducing dementia risk factors in those with low SES in the UK and any SES in China. Implementation is challenging in these populations, but those reached actively participated. Whether this will result in less cognitive decline and dementia requires a larger RCT with long follow-up.
Design and implementation of digital home blood pressure monitoring based on Bluetooth and WeChat App in the FAIR-Pilot study
Authors: Wang B N , Liu X , Li Y X , Liu Z H , Chang Y X , Jia J , Zhang Y , Zhang Y , Li J P 
Published: October 2024

Abstract

Background
Home blood pressure monitoring (HBPM) could be an alternative way to office blood pressure and ambulatory blood pressure monitoring for hypertensive patient management. However, the measurement protocol of HBPM still requires standardization, which may be improved by digital technology.

Purpose
To investigate the use of digital HBPM management in hypertensive patients and its application in clinical practice.

Methods
Fractional Flow Reserve to Determine the Appropriateness of Percutaneous Renal Artery Intervention in Atherosclerosis Renal Hypertension Patients (FAIR)-pilot study is a multicenter pilot randomized controlled trial exploring the application of fractional flow reserve (FFR) to guide the renal artery stenting in patients with renovascular hypertension. After randomization and FFR measurement, participants were divided into stenting with FFR≥ 0.8 (Stent group), no stent implantation with FFR≥ 0.8 (No stent group), and stenting with FFR＜ 0.8 (Controlled group). Participants of the FAIR-pilot study enrolled from February 1st, 2023 to February 25th, 2024 were included. A validated Bluetooth blood pressure monitor was given to every participant at the time of enrollment. Participants were taught to undertake standardized HBP and were advised to take morning and night HBPs every day. HBPM data were automatically uploaded to a WeChat App developed by FAIR investigators and could be reviewed by both participants and investigators. Twice-daily blood pressure reports were automatically generated and sent to investigators for participant compliance monitoring and reminding (Figure 1). HBPM data and blood pressure variability were analyzed.

Results
A total of 93 participants with 58 males (62.4%) and a mean age of 59.97 ± 16.18 years, including 26,802 HBP readings were analyzed. The average numbers of daily HBP measurement were similar between three groups and were all less than twice a day. Systolic HBP decreased from 143.11 ± 14.91 to 133.01 ± 13.01mmHg after the procedure in the Controlled group, which was more than the other two groups (140.51 ± 13.38 to 138.53 ± 17.30 mmHg and 136.20 ± 15.67 to 130.84 ± 11.04 mmHg). During the follow-up after the procedure, the HBP variability (BPV) of overall participants was 13.78 ± 4.87 and 5.46 ± 3.14 for systolic and diastolic BP, respectively. The systolic BPV of No stent group was higher than the other two groups (No stent vs. Stent vs. Controlled: 33.00 ± 9.49 vs. 8.34 ± 2.86 vs. 9.12 ± 2.71, P=0.125), however without statistical significance. Similar was found for morning and night systolic BPVs. (Table 1).

Conclusion
Digital HBPM management via novel information technology is feasible in the hypertension field and could be a helpful tool in clinical practice.
Do physicians and patients speak different languages: quantitative evidence from linguistic analysis
Authors: Whittaker P 
Published: October 2024

Abstract

Background
Misunderstanding and miscommunication between physicians and patients are well-accepted sources of poor therapy adherence and poor outcomes. Some of these issues are suspected to occur because these two groups speak different "languages". A likely component of such language difference is vocabulary; specifically, the use of medical terminology. Nonetheless, there are other psychological aspects of language such as authoritative and emotional use. The text analysis programme, Linguistic Inquiry Word Count (LIWC), was used to assess such features. LIWC is an established tool that analyses the frequency of words that fall into various linguistic, psychological, and emotional categories. The aim was to compare text from both physicians and patients. The context was the use of anticoagulation therapy in patients with atrial fibrillation.

Methods
Three articles from English-speaking countries (Australia, Canada, and the UK) were examined. These articles presented transcribed verbal communications between interviewers and groups of physicians and patients. More than 80 separate comments, each ≥ 20 words, made by each group were extracted from the articles and analysed. The total number of words was 8,609 from physicians and 4,804 from patients. Data were collected for the following LIWC analysis categories: (1) the proportion of big words (words with ≥7 letters), (2) clout; high scores suggest that the speaker is confident, authoritative, and has a high level of social dominance. It implies that the individual is speaking from a position of strength or influence; and (3) emotion: this category is integral to understanding how people communicate feelings and emotions.

Results
As anticipated the number of big words was higher in physician interviews (19±1%) than for patients (12±1%; P=0.0013). The clout indicator also showed the physicians with a higher score (43±6 vs 16±6; P=0.028). In contrast, in the emotion category, patient scores (1.19±0.02) were higher than for physicians (0.58±0.15; P=0.016); primarily because of a three-fold higher negative emotion score (0.53±0.08 vs 0.16±0.11; P=0.048).

Conclusions
Physicians and patients used language differently. As expected, vocabulary differed; physicians used more big words. This is consistent with greater use of medical terminology, a known source of miscommunication. The higher physician clout score indicates the potential for a power imbalance. The greater use of emotion-related words by patients is consistent with their anxiety and concern. Human judgment and contextual analysis are required to fully understand and address the nuances of communication and associated understanding. Nevertheless, LIWC can play a supportive role. If we identify language patterns likely to result in miscommunication or misunderstanding, steps can be taken to mitigate the effects. LIWC provided quantitative insight into qualitative impressions and revealed subtle, but potentially vital, differences.
Listen to your heart: The effect of music on heart rate variability
Authors: Marsman E M J , Van Steijn N J , Pepplinkhuizen S , De Haas L M , Lamerigts K L , Kleer P S , Ten Berg S , Nieuwkerk A C , Kacuk I T , De Veld J A , Peltenburg P J , Van Trier T J , Winter M M 
Published: October 2024

Abstract

Background
Heart rate variability (HRV) is indicative of autonomic balance. High HRV during sinus rhythm is associated with reduced morbidity and mortality across a wide range of health disorders. Listening to music has been shown to induce psychological and physiological responses that increase HRV, a mechanism currently employed in music therapy to reduce anxiety and post-operative pain. However, it remains unclear which music characteristics have the most positive effect on HRV.

Purpose
This study aimed to assess the impact of different music genres, white noise, tempo, and personal music preferences on HRV.

Methods
Adults without atrial fibrillation or pacemaker dependency were included during the Netherlands largest three-day music festival. Participants were subjected to three-minute fragments of white noise, and subsequently a randomized order of classical, rock, electronic and the subject’s most-listened personal ("Spotify Wrapped 2022") music; all having an either fast (130-140 BPM) or slow tempo (70-80 BPM), which was randomized as well. Afterwards, participants ranked all music fragments in order of their preference. A tachogram was collected using the PPG sensor of an Apple Watch Series 8. Data was pre-processed to exclude non-sinus rhythm beats and measurement artefacts. HRV time-domain (SDNN, RMSSD, NN50, pNN50, triangular index) and frequency-domain (LF, LFn, HF, HFn, LF/HF) metrics were calculated from the tachograms using Python-software. HRV outcomes were corrected for age, sex, heart rate and music tempo group and analyzed using general linear mixed models in SPSS.

Results
In total 242 participants with a median age of 28 years (IQR 24-32) were included, of which 136 (56%) female. Classical music showed a significantly higher HRV response (RMSSD) compared to electronic (p=0.025) and the subject’s personal music (p=0.030, see figure). No other significant differences in HRV were found between the different genres and personal music. White noise showed a significantly higher HRV for all time domains, as well as LF and HF, compared to the different genres and personal music. A total of 181 (74.8%) participants ranked their personal music fragment as the most preferred. There was no difference between the fast and slow tempo group when performing an interaction analysis for tempo and the different music genres except for classical and personal music (RMSSD, p = 0.047).

Conclusions
This large study highlights that classical music enhances HRV more than other music, suggesting its therapeutic potential. Remarkably, white noise showed the greatest increase in HRV. Although these were bitter pills to swallow; white noise or classical music once a day might keep the doctor away.