bluuebunny/crossref_metadata_2025 huggingface dataset:
The correlation between the confusion assessment method for intensive care units 7-item, the intensive care delirium screening checklist and adverse outcomes after post-cardiac surgery delirium
Authors: Hsieh H , Chou P
Published: October 2024

Background

Postoperative Delirium(POD) and Subsyndromal Delirium(SSD) remain prevalent, particularly among the cardiovascular surgical population due to the utilization of cardiopulmonary bypass. Adverse outcomes, such as prolonged ICU stay, extended length of stay, and increased incidence of adverse events, are associated with individuals diagnosed with Postoperative Delirium or those experiencing fluctuations between Postoperative Delirium and Subsyndromal Delirium during ICU stays. Therefore, early delirium screening is of utmost importance. The Intensive Care Delirium Screening Checklist(ICDSC) and the Confusion Assessment Method for the Intensive Care Unit 7-item(CAM-ICU-7) are validated delirium screening instruments for critical patients.

Purpose

This study aims to determine whether the ICDSC or the CAM-ICU-7 is the superior predictor for adverse outcomes following Postoperative Delirium among the cardiovascular population.

Methods

In this prospective cohort study, 84 patients who underwent elective cardiovascular surgery underwent a total of 833 paired assessments(ICDSC and CAM-ICU-7) twice daily by the researcher. The correlation between the results of ICDSC and CAM-ICU-7 was evaluated. Multiple linear regression was employed to explore associations between ICDSC and CAM-ICU-7 and length of ICU stay, length of overall stay, and the total number of adverse events. The best model was selected after testing with Pearson's correlation coefficient, Hotelling's t, and Steiger's Z tests. Logistic regression explored associations between ICDSC and CAM-ICU-7 and adverse events.

Results

Postoperative Delirium incidence evaluated by the ICDSC and CAM-ICU-7 was 47.61% and 45.23%, respectively. The incidence of fluctuations between Postoperative Delirium and Subsyndromal Delirium evaluated by the ICDSC and CAM-ICU-7 was 34.52% and 21.43%, respectively. The agreement between the ICDSC and CAM-ICU-7 is high(kappa = 0.835, p<0.001) in detecting Postoperative Delirium but fair in detecting Subsyndromal Delirium(kappa = 0.213, p<0.001). Our research revealed that the length of ICU stay can be predicted by POD(detected by ICDSC and CAM-ICU-7) and fluctuated POD and SSD(detected by ICDSC but not CAM-ICU-7). However, only fluctuated POD and SSD detected by ICDSC could predict hospital length of stay. The total number of adverse events and incidence could be predicted by POD either detected by ICDSC or CAM-ICU-7 but only fluctuated POD and SSD detected by ICDSC, not CAM-ICU-7, could predict the total number of adverse events.

Conclusions

Both ICDSC and CAM-ICU-7 can detect Postoperative Delirium with high agreement. However, ICDSC could identify more individuals with both Postoperative Delirium and Subsyndromal Delirium, thus predicting more adverse outcomes than CAM-ICU-7. Therefore, ICDSC emerges as the superior delirium screening instrument for the cardiovascular population.
Multimodal analgesics after adult cardiac surgery; a systematic review
Authors: Wynne R , Jedwab R , Gjeilo K H , Magboo R , Goudarzi M , Okeefe Mccarthy S , Keeping Burke L , Murfin J , Killackey T , Bruneau J , Phillips E , Bowden T , Fredericks S , Sanders J , Lie I
Published: October 2024

Background

Moderate to severe pain is frequently reported during the acute recovery phase following adult cardiac surgery. Multimodal analgesics are recommended for adult patients post cardiac surgery, but the optimal combination of multimodal analgesics is unknown.

Purpose

To synthesise the best available evidence about the effectiveness of multimodal analgesics on pain after adult cardiac surgery.

Methods

A systematic review was conducted according to the PRISMA-P statement and the published review protocol. Inclusion criteria were full-text papers or abstracts with adequate reporting, in any language, of randomised controlled trials (RCTs) in adult patients undergoing cardiac surgery that investigated the effect of multimodal postoperative analgesic regimens, compared to a control group. The highest mean level of patient self-reported pain intensity at rest, captured using a validated pain assessment tool, was the primary outcome measure.

Results

Of 3754 citations identified in the initial search to the 12th of December 2022, there were 1605 duplicates leaving 2149 citations for title and abstract screening, of which 2027 were irrelevant. There were 122 full-text papers assessed and 29 included in the review. Data were independently extracted by 2 reviewers in Covidence®. Participants (N = 2360), aged 59.5 + 8.2 years, were primarily male (N = 1561, 66.1%), weighing 78.1 +9.2 kilograms. Risk of bias was high and reporting quality was poor. Pain at rest reported in 18 (62%) studies was 3.5 (SD 1.2) in the control and 2.7 (SD 0.9) in the intervention groups respectively. Nurse-led versus patient-controlled analgesic administration was tested in 14 (48.3%) trials. All trials tested different analgesic combinations, none of which compared a combination of non-opioids, opioid agonist-antagonist agents, or full opioid agonists.

Conclusions

Poorly managed pain is associated with postoperative complications, unplanned readmissions, and the development of chronic pain. Robust RCTs are urgently needed to inform optimal combinations of multimodal analgesics to optimise patient recovery after adult cardiac surgery.Risk of Bias
Factors associated with sleeping difficulties in patients with heart failure
Authors: Klompstra L , Jaarsma T , Stromberg A
Published: October 2024

Background

Sleeping difficulties is prevalent in heart failure (HF) and a predictor of cardiac events in patients with HF.

Purpose

To describes the prevalence of sleeping difficulties in patients with HF and factors associated.

Method

This study used data of a multicentre RCT entitled Tele-Yoga study (clincialtrials.gov:NCT03703609). Patients were included in the analysis when they were diagnosed with HF and completed a questionnaire on sleeping difficulties. Sleeping difficulties were measured with the Minimal Insomnia Symptom Scale (MISS), patients with a score a 6 or higher patients were characterised as having sleeping difficulties. Through a literature search we identified factors that could be associated with sleeping difficulties. Demographic and clinical background variables (age, sex, BMI, marital status, working status, having atrial fibrillation and/or diabetes), anxiety and depressive symptoms (Hospital Anxiety and Depression scale) and Health related Quality of Life (EQ-5D index score) were considered. Logistic regression was performed with backwards elimination of the factors considered associated with having or not having sleeping difficulties.

Results

In total 244 patients were included (mean age 67±10, 28% women), whereas 27% had sleeping difficulties (n = 68, a score a 6 or higher on the MISS); 28% had moderate till very severe difficulties initiating sleep (n=77), 53% had moderate till very severe problems by waking up at night (n=130), 38% had moderate till very severe problems by not feeling refreshed by sleep (n=92).

The logistic regression showed that patients with sleeping difficulties were significantly more often single (27% vs. 18%, OR 0.336 P-value=0.023), more often not working (72% vs. 69%, OR 2.524, P-value=0.047) more often experienced symptoms of anxiety (45% vs. 10%, OR 4.882, P-value<001) and had a lower quality of life, (0.637±0.261 vs. 0.786±0.203, OR 0.042 P-Value=0.001) compared to patients with no sleeping difficulties.

Conclusion

Patients having symptom of anxiety were at five times greater risk of experiencing sleeping difficulties. Being in a relationship and being employed were associated with less sleeping difficulties. Additionally, patients with sleeping problems reported lower quality of life.
An ACNAP survey assessing the cardiology multidisciplinary learning needs to support lipid management
Authors: Barrowcliff A , Neubeck L , Khatib R
Published: October 2024

Background/ Introduction: Reducing LDL cholesterol is a high priority in cardiovascular care but requires knowledge and skills to deliver effectively. Nurses and allied professionals (NAPs) can play a major role in supporting lipid optimisation. Most NAPs will work together with patients prescribed lipid lowering therapies, and some may be more closely involved in initiation and monitoring.

The Association of Cardiovascular Nursing and Allied Professionals (ACNAP) wanted to gauge the level of understanding, confidence and learning needs of cardiology professionals to better implement European Society of Cardiology (ESC) lipid lowering guidelines.

Purpose

The aim of the project was to highlight gaps in knowledge to better inform needs for training and educational programmes developed by ACNAP.

Methods

A 29 question electronic survey was developed by expert consensus, exploring previous experience, clinical knowledge, and perceptions of learning needs. It was launched at the EuroHeartCare conference in June 2023, on social media, and members of the ESC were emailed inviting participation.

Results

123 people responded. 29.5% of the respondents were physicians and 70.5% NAPs. Most respondents were involved in lipid optimisation of some description with only 7.0% of respondents having no direct involvement in their patient cohort.

A higher percentage of respondents responded "totally agree" when asked it they knew what cholesterol targets to aim for in secondary prevention, compared to primary prevention (61% v 41.5%). 94% of respondents felt comfortable when to initiate statins and and 82% ezetimibe. This reduced to 45% for inclisiran and 40% for bempedoic acid. Similarly, the majority felt comfortable counselling a patient newly initiated on statins or ezetimibe, but significantly fewer the newer lipid lowering therapies.

Experience and understanding of familial hypercholesterolaemia (FH) was also explored. Only half the respondents would be comfortable in identifying and diagnosing FH and a further 25% unsure. A large percentage (37%) were unaware of the Simon Broome criteria or the Dutch Lipid Criteria Network (49%).

When considering learning needs, 71% of NAP respondents needed extra support to help make lipid lowering decisions. This rises to 84% and 74% when looking at nurses and pharmacists separately, respectively. Nearly 76% would prefer any learning to be delivered online, with interactive online training packages being the most popular method.

Conclusions

The three main gaps identified by NAPs were: management in primary prevention (initiation and targets), the newer innovative medicines (bempedoic acid, inclisiran and PCSK9 monoclonal antibodies) and FH. Interestingly, these are the same gaps highlighted by physician responders. These findings can help guide ACNAP and ESC in the development of educational programmes with the aim to improve better lipid management overall.
A multilabel deep learning model for the detection of conduction and rhythm disorders from PDF outputs of a widely available portable ECG Device
Authors: Sangha V , Dhingra L S , Khera R
Published: October 2024

Background

Wearable and portable devices with ECG capabilities can provide broad access to screening for cardiovascular disorders. However, most are approved for the detection of only atrial fibrillation. Most algorithms for detecting rhythm and conduction disorders are trained and tested on 12-lead ECGs or the lead I of clinical ECGs, with an unclear role for extending the diagnostic range of wearable and portable devices. Moreover, no current algorithms enable cardiovascular diagnosis on data available to end users, who largely have access to the PDF outputs of their data.

Purpose

To validate a novel artificial intelligence (AI) model trained to identify conduction disorders and arrhythmias on synthetic ECG outputs from lead I data of clinical ECGs, to detect the same disorders on cardiologist-annotated PDF outputs from the Alivecor KardiaMobile single-lead ECGs (Kardia ECGs).

Methods

We extracted lead I data from 3,076,352 clinical ECGs across 5 hospitals of a large U.S.-based hospital system from 2000-2024 and simulated wearable PDF outputs from commercial devices using a novel plotting approach that replicated the variations in plotted formats. Gold-standard cardiologist written diagnosis statements were processed to generate 8 clinical labels spanning conduction and rhythm disorders, and we developed a multilabel model to predict these labels using an EfficientNet-B3 convolutional neural network. We examined the performance of the model on 24,808 Kardia ECGs annotated by cardiologists.

Results

The model achieved a high performance on clinical ECGs in a held-out subset of the synthetic lead I ECG printouts resembling wearable outputs (AUC > 0.9 for all 8 labels). In the independent 24,808 Kardia ECGs, annotated by experts, the model achieved high performance across all labels. For the rhythm disorders atrial fibrillation or atrial flutter, premature atrial contraction, and premature ventricular contraction, the model had AUROCs of 0.99, 0.91, and 0.95, respectively. For the conduction disorders 1st-degree AV block and 2nd or 3rd-degree AV blocks, the model had AUROCs of 0.94 and 0.99, respectively. Finally, for supraventricular tachycardias, wide QRS, and paced ECGs, the model had AUROCs of 0.998, 0.98, and 0.98, respectively.

Conclusions

We demonstrate that a deep learning model trained to identify rhythm and conduction disorders based on synthetic ECGs where lead I data from clinical 12 lead ECGs are used to replicate real-world PDF outputs of wearable ECGs, generalizes to real-world wearable and handheld single-lead devices.
Predicting the severity of valvular heart diseases using ai integrated with ECG and anthropometric data
Authors: Nakayama M , Ohkawauchi T , Simura T , Matsuo H , Kobori Y , Uchiyama T
Published: October 2024

Background

AI has been used in studies to predict the severity of aortic stenosis (AS) from electrocardiography (ECG), but the application of AI to predict the severity of aortic regurgitation (AR), mitral regurgitation (MR) and tricuspid regurgitation (TR) has not been widely explored. This study aimed to determine whether deep learning can predict the severity of valvular heart disease (AS, AR, MR and TR) assessed by echocardiography using ECG data.

Methods and Results

We analyzed data from 31,787 adult patients who underwent ECG and echocardiography within three days of each other between 2014 and 2019 at two hospitals. Valvular heart diseases, including AS, AR, MR, and TR, were classified into categories; normal, mild, moderate, and severe; according to guidelines. ECGs, along with patient age, gender, height, and weight, were inputs to a LightGBM machine learning model. The model was trained on 80.6% of the data, with the remaining 19.4% used for validation. The sensitivity for predicting moderate or severe valvular heart disease was 85% for AS, 88% for AR, 85% for MR, and 85% for TR. The negative predictive values for AS, AR, MR, and TR were all 100%. Prediction accuracies for the severity of each disease were AS at 0.857, AR at 0.871, MR at 0.855, and TR at 0.890. Predictions based on AS diagnostic parameters; mean pressure gradient, valve area, and maximum blood flow velocity also showed favorable accuracies at 0.891, 0.821, and 0.877, respectively.

Conclusion

Integrating AI with standard clinical parameters, including ECG data, height, and weight, showed potential in accurately predicting the severity of various valvular heart diseases. Further researches across diverse populations and settings are necessary to validate the approach's effectiveness and explore its impact on patient care.
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

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

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 > 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

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 <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 <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 >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 <0.05). Clinical events were more common with the first alert (12.6%) than subsequent alerts (5.2%, p <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

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<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

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

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

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

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.
Amiodarone-related thyroid dysfunction and associated outcomes in patients with heart failure - a matched case-control study
Authors: Sam Aiyad S A A , Selmer C S , Ersboll M K E , Butt J G B , Rorth R R , Westergaard L M W , Ostergaard L M O , Jons C J , Gustafsson F G , Fosbol E L F , Kober L K , Kristensen S L K
Published: October 2024

Aim

The association between amiodarone-related thyroid dysfunction and the risk of worsening heart failure (HF) or death has been reported with conflicting results.

Methods

The study cohort was patients with HF and without a history of thyroid dysfunction (defined by medical treatment or diagnosis) who initiated treatment with amiodarone in the period 1996-2021. Within this cohort, those who developed thyroid dysfunction (cases) were identified and matched 1:3 on age, sex, calendar year, duration of HF, and time since initiation of amiodarone with patients (controls) from the same cohort and followed for the primary outcome of HF hospitalization or all-cause death at 1-year, assessed by Kaplan Meier curves and Cox regression models adjusted for ischemic heart disease, chronic kidney disease, malignancy, atrial fibrillation and ventricular tachycardia.

Results

The study cohort comprised 21,947 patients, of whom 4,137 (19%) developed thyroid dysfunction following initiation of amiodarone. After matching, the study population consisted of 4,063 cases (mean age 69 years, 68% men, 41.6% developed thyroid dysfunction within the first year after initiation of amiodarone) and 10,940 matched controls, (Figure 1). The index date was the date of thyroid dysfunction for cases, and for matched controls a date at a similar distance since amiodarone initiation. At 1-year follow-up, the primary outcome occurred in 1,543 (38.0%) cases and 3,681 (33.6%) matched controls (reference), adjusted hazard ratio (HR) 1.13 (95%, confidence interval CI 1.07-1.20), although this was solely driven by HF hospitalization (28.9% vs 21.8%; HR 1.32 (95% CI 1.23-1.41), whereas all-cause death was lower among cases (18.2% vs. 20.4%, HR 0.85 (95% CI 0.78-0.93), (Figure 2).

Conclusions

Among HF patients treated with amiodarone, the development of thyroid dysfunction was associated with an increased risk of HF hospitalization or all-cause death, but this was solely driven by the excess risk of HF hospitalization, whereas it was associated with a lower risk of all-cause death. The absence of excess mortality indicates that thyroid complications are manageable in this vulnerable population of patients with HF.Figure 1 Flowchart of the populationFigure 2 Cumulative risk
Effects of short-term exposures to lower air temperature and cold spells on myocardial infarction hospital admissions: a nationwide study in Sweden
Authors: Ni W , Stafoggia M , Zhang S , Ljungman P , Breitner S , Bont J D , Jernberg T , Atar D , Agewall S , Schneider A
Published: October 2024

Background

Exposures to lower air temperature and cold spells have been associated with an increased risk of various diseases. However, the short-term effect of lower air temperature and cold spells on myocardial infarction (MI) remains incompletely understood.

Purpose

We aimed to evaluate the short-term effects of lower air temperature and cold spells on MI hospital admissions within the nationwide SWEDEHEART registry.

Methods

This population-based nationwide study included 120,380 MI cases admitted to hospitals in Sweden during the cold season (October to March) from 2005 to 2019. Daily mean air temperature was estimated at 1 km2 spatial resolution across Sweden using a three-stage machine learning approach and percentiles of daily temperatures experienced by individuals in the same municipality were employed as individual exposure indicators to account for potential geographic adaptation. Cold spells were defined as periods of at least two consecutive days with a daily mean temperature below the 10th percentile of the temperature distribution for each municipality. A time-stratified case-crossover design incorporating a conditional logistic regression model with distributed lag non-linear model using lag 0-1 and lag 2-6 days was employed to evaluate the short-term effects of lower air temperature and cold spells on total MI, non-ST-segment elevation MI (NSTEMI) and ST-segment elevation MI (STEMI).

Results

We found significant increases in risk for total MI, NSTEMI, and STEMI in association with a decrease of one percentile in temperature at a lag of 2-6 days, indicated by ORs (95% CI) of 1.099 (1.057-1.142), 1.110 (1.06-1.164), and 1.076 (1.004-1.153), respectively. Additionally, we found that cold spells were significantly associated with increased risks for total MI, NSTEMI, and STEMI at a lag of 2-6 days, with ORs (95% CI) of 1.077 (1.037-1.12), 1.069 (1.02-1.119), and 1.095 (1.023-1.172), respectively. Moreover, independent effects of lower air temperature and cold spells on MI occurrence were found after adjusting for each other. Conversely, we observed that lower air temperature and cold spells were associated with decreased risks for MI at a lag of 0-1 days.

Conclusions

This nationwide case-crossover study demonstrated a novel biphasic pattern in MI risk following exposure to lower air temperatures and cold spells, characterized by an initial transient reduction in risk at lags 0-1 day, followed by a significant increase in risk at lags 2-6 days. As climate change continues to induce complex shifts in regional weather patterns, enhancing protections to reduce future cold-related cardiac hospitalizations is crucial.
The health and wellbeing of cardiology trainees in the united kingdom
Authors: Jenner W , Brown O I , Camm C F
Published: October 2024

Background

Cardiology is a rewarding medical speciality. However, the intense workload can burden cardiologists with physical and psychological demands, and risks contributing to ill health. Cardiologists suffering poor health may increase absenteeism, stretching a workforce, and potentially reduce quality of patient care.

Purpose

We sought to describe the prevalence of physical and mental health conditions of cardiology trainees within the United Kingdom. We also aimed to identify factors associated with risk of burnout and negative drivers of psychological wellbeing.

Methods

The British Junior Cardiologists Association (BJCA) is a professional body which represents cardiology trainees in the UK. The BJCA conducts an annual survey of trainees, covering various aspects of working lives and all UK cardiology trainees are eligible to participate. For the 2023 survey, in addition to demographics and training questions, we asked questions on ill health, burnout, support for wellbeing, and invited responders to complete validated screening questionnaires for anxiety (GAD-7) and depression (PHQ-9). Responders were eligible for inclusion if they consented to take part in the health and wellbeing questions, and if they had completed four demographic questions. Anxiety and depression were deemed significant if scoring in the moderate or severe range (PHQ-9≥10; GAD7≥10). Significant burnout was defined as responders indicating being "somewhat" or "very" close to burnout. Logistic regression was used to determine odds ratios (OR) between univariate predictors of burnout, anxiety, and depression.

Results

Of the 398 responders, 212 fulfilled inclusion criteria (66% male, age 34±4 years, 42% had dependents). A physical health condition was reported by 11% of trainees, and 9% a reported a mental health condition. Significant burnout was reported by 76% of trainees. Those with dependents reported lower rates of burnout compared to those without (OR 0.35, 95% CI 0.18-0.67, p<0.01). Significant depression scores were reported by 25%, significant anxiety scores in 18%, and significant scores of either depression or anxiety in 29% (Fig 1). Scores from 17% of trainees were consistent with significant depression or anxiety and yet not known to have a mental health condition. Less Than Full Time trainees reported higher rates of anxiety (OR 4.38, 95% CI 1.37-14.10, p=0.01) and depression (OR 10.31, 95% CI 2.98-35.71, p<0.01). Training was reported to have a negative impact on physical health by 43% of trainees, and 47% reported a negative impact on mental health. Principal drivers of negative wellbeing included the amount of service provision; curriculum requirements, and lack of training opportunities (Fig 2).

Conclusions

The prevalence of anxiety, depression and burnout is high amongst cardiology trainees. Urgent action is needed by training bodies and employers to ensure training programmes consider the physical and mental wellbeing of trainees.Depression and Anxiety ScreeningNegative Drivers of Wellbeing