bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cardiovascular related polypharmacy and its association with liver and kidney impairment amongst individuals on lipid-lowering therapy: a cross-sectional study using primary care data
Authors: Trin C , Koh J W K , Orman Z O , Magliano D M , Zomer E Z , Ademi Z A , Moran C M , Talic S T 
Published: October 2024

Abstract

Background
Individuals with risk factors for cardiovascular disease (CVD) frequently require concurrent treatment with various medications, a phenomenon known as polypharmacy. This study aimed to investigate the association between CVD-related polypharmacy and the presence of liver and kidney impairment amongst patients receiving lipid-lowering therapy within the Australian primary care setting.

Methods
This study utilised electronic medical records from general practices retrieved between January 2013 and December 2023. Multivariate logistic regression identified independent risk factors associated with CVD-related polypharmacy, defined as the concurrent use of five or more CVD protective medications. The analysis included individuals on lipid lowering therapy who are at risk for CVD. The relationship between CVD-related polypharmacy and the presence of liver and kidney dysfunction was further assessed, defined by exceeding established reference ranges for relevant biomarkers (alanine aminotransferase [ALT], aspartate aminotransferase [AST], bilirubin, creatinine, and estimated glomerular filtration rate [eGFR]) as per Australian guidelines.

Results
A total of 13,568 participants (median age: 63 years [IQR 54-72], 54% males) were included. Independent factors associated with CVD-related polypharmacy included diabetes (OR=5.40, 95% CI: 4.96-5.93), chronic kidney disease (CKD) (OR=2.39, 95% CI: 2.00-2.86), hypertension (OR=1.91, 95% CI: 1.75-2.07), and smoking (OR=1.05, 95% CI: 0.97-1.14). Compared to the 18-35 age group, individuals aged ≥80 years displayed a significantly higher likelihood of CVD-related polypharmacy (OR=8.17, 95% CI: 5.47-12.21). While no significant association was observed between CVD-related polypharmacy and liver impairment (OR=1.19, 95% CI: 0.59-2.37), an independent positive association was identified with kidney impairment (OR=1.38, 95% CI: 1.24-1.54), adjusted for established CVD risk factors including age, sex, smoking, diabetes, hyperlipidaemia, hypertension, and CKD.

Conclusion
This study suggests a positive association between CVD-related polypharmacy and increased risk of kidney impairment in Australian primary care. Further research is warranted to explore potential causal mechanisms and evaluate the efficacy of medication review strategies, enhanced kidney function monitoring, and potential medication deprescribing in managing patients at risk of CVD who are on multiple medications.
The role of anticoagulation in atrial high-rate episodes: a meta-analysis
Authors: Ahmed H , Mahmoud Ismayl M I , Manvir Mangat M M , Anirudh Palicherla A P , Andrew Goldsweig A G , Nandan Anavekar N A , Ahmed Aboeata A A 
Published: October 2024

Abstract

Background
The role of anticoagulation in patients with atrial high-rate episodes (AHRE), who have no prior history of atrial fibrillation or atrial flutter, is being extensively studied.

Purpose
Given the increased thrombo-embolic risk in patients who have atrial high-rate episodes, our aim was to evaluate whether anticoagulation leads to enhanced clinical outcomes compared to no anticoagulation (1).

Methods
We searched PubMed, EMBASE, and the Cochrane Library, through February 2024, for studies evaluating outcomes in patients with atrial high-rate episodes managed with oral anticoagulation (OAC) compared to those not managed with oral anticoagulation. Outcomes assessed were ischemic stroke, thromboembolic events, major bleeding, and all-cause mortality. A fixed-effects model was used to calculate pooled risk risk ratios (RR) and their 95% confidence intervals (CI).

Results
A total of 4 studies comprising 6788 patients were included. Anticoagulation was associated with a significant reduction in the risks of ischemic stroke (RR 0.66; 95% CI 0.49 - 0.90; P = 0.0084) and thromboembolic events (RR 0.70; 95% CI 0.55 - 0.89; P = 0.0037). Compared to atrial high-rate episodes not managed with anticoagulation, there was a significantly increased risk of major bleeding with anticoagulation (RR 1.57; 95% CI 1.23 - 2.00; P = 0.0002), while the risk of all-cause mortality was similar (RR 1.07; 95% CI 0.95 - 1.21; P = 0.2437).

Conclusion
Our study suggests that anticoagulation in patients with atrial high-rate episodes decreases the risks of ischemic stroke and thromboembolic events, while increasing the risk of major bleeding, compared patients not managed with anticoagulation. Our study does not show a mortality benefit with the use of anticoagulation in patients with atrial high-rate episodes. Further studies are needed to confirm the safety and efficacy of anticoagulation in atrial high-rate episodes.Forest Plots of Outcomes Assessed
Modulation of p300 acetylation to protect against doxorubicin-induced cardiotoxicity
Authors: Lee J , Connelly K 
Published: October 2024

Abstract

Background
Doxorubicin (DOX) is a widely-used anti-neoplastic agent, but the most common adverse reaction to its use is cardiotoxicity(1). DOX acts by producing reactive oxygen species (ROSs) that cause DNA damage(1,2), which activates various proteins, including the tumour suppressor p53 and the acetyltransferase p300. When activated, p300 acetylates and increases the activity of p53 which leads to increased apoptotic activity(3). Cardiotoxicity results because the heart has limited mechanisms to dispose of ROSs(4), and because cardiomyocytes have a low turnover rate(5).

Purpose
We examined the p300 inhibitor Theracurmin (THR)(6,7) as a candidate to prevent DOX cardiotoxicity using an in-vivo mouse model, and sought to elucidate the underlying molecular mechanisms using in-vitro studies.

Methods
C57BL/6 mice were pre-treated with THR or vehicle (as control), then administered DOX or vehicle. Cardiac function was evaluated by echocardiography and cardiac catheterization. In vitro studies used cardiac fibroblasts (FBs) and cardiac endothelial cells (ECs) pre-treated with THR, then with DOX, as in the animal studies. Western blotting and rt-qPCR was used to quantify protein and mRNA levels of genes in the p53-p300 pathway or related to apoptosis, oxidative stress, and cell cycle control.

Results
DOX-treated mice showed significant decreases in left ventricular (LV) ejection fraction (LVEF, p=0.0004), fractional shortening (FS, p=0.0012), and a significant increase in end-systolic volume (ESV, p=0.0004). We also showed a significant decrease in anterior (LVAW;d, p=0.005) and posterior (LVPW;d, p=0.005) wall thickness of the LV in these mice. However, when mice were pre-treated with THR prior to DOX administration, we saw significant increases in LVEF (p=0.045) and LVPW;d (p=0.015), and a significant decrease in ESV (p=0.0047) compared to those that were not pre-treated. In-vitro analyses demonstrated a significant increase in cleaved caspase 3 protein expression in DOX-treated ECs (p=0.007), that was not prevented by THR pre-treatment. In DOX-treated FBs, we saw a reduction in AKT mRNA (p=0.017) and increases in BAX 9p=0.008), P21 (p=0.0001), and GADD45 (0.037) mRNA expression. In DOX-treated ECs, we also saw significant increases in BAX (p=0.015) and P21 (p=0.011) mRNA expression. These changes in mRNA expression were not reversed with THR pre-treatment in either cell type.

Conclusion
THR pre-treatment successfully mitigated functional deficits and structural deficits associated with DOX cardiotoxicity. In-vitro studies show that THR pre-treatment was not sufficient to significantly prevent changes in key molecular targets associated with apoptosis. Further studies are required to elucidate the mechanism behind the observed functional changes.Abstract OverviewAbstract Results
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

Abstract

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&lt;0.001) in detecting Postoperative Delirium but fair in detecting Subsyndromal Delirium(kappa = 0.213, p&lt;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

Abstract

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

Abstract

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&lt;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

Abstract
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

Abstract

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 &gt; 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

Abstract

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.