bluuebunny/crossref_metadata_2025_split huggingface dataset:
Excess mortality and hospitalizations associated with seasonal influenza in patients with heart failure
Authors: Modin D , Claggett B , Johansen N D , Solomon S D , Trebbien R , Krause T G , Jensen J U S , Martel C J M , Andersen M P , Gislason G , Biering-Soerensen T 
Published: October 2024

Abstract

Background
Influenza virus may cause severe infection in patients with heart failure. It is accepted that influenza infection is associated with increased morbidity and mortality in patients with heart failure. However, less is known about the excess burden of morbidity and mortality caused by influenza infection in patients with heart failure at a population level.

Purpose
To estimate the excess burden of morbidity and mortality as determined by annual excess number of deaths and hospitalizations associated with influenza infection in patients with heart failure.

Methods
We collected nationwide data on weekly number of deaths and hospitalizations among patents with heart failure in Denmark and weekly estimates of influenza circulation as determined by the proportion of positive influenza samples analyzed at all Danish Hospitals. These data were correlated in a time series linear regression model and this model was used to estimate the annual excess number of deaths and hospitalizations attributable to influenza circulation among patients with heart failure in Denmark.

Results
Data were available from 2010 to 2018 encompassing 8 influenza seasons with an annual mean of 25180 samples tested for influenza. Among an annual mean of 70570 patients with heart failure, our model estimated that influenza activity was associated with an annual excess of 250 all cause deaths (95%CI 144-489) corresponding to 2.6% of all all-cause deaths (95%CI 1.5% - 5.1%). Similarly, influenza activity was associated with an annual excess of 115 cardiovascular deaths (95%CI 62-244) corresponding to 2.9% of all cardiovascular deaths (95%CI 1.5% - 6.1%) among patients with heart failure. Influenza activity was also associated with an annual excess of 251 hospitalizations for pneumonia or influenza (95%CI 107-533) corresponding to 5.0% of all hospitalizations for pneumonia or influenza.

Conclusion
Our results indicate that influenza activity likely causes substantial morbidity and mortality among patients with heart failure. Notably, our study suggests that approximately 2.6% of all deaths and 5.0% of all hospitalizations with influenza or pneumonia may be attributed to influenza in patients with heart failure.
Explainable machine learning and multi-modal data for predicting readmission in patients with heart failure
Authors: Chae R , Zhou J , Car J , Zhu T , Lu L 
Published: October 2024

Abstract

Background
Cardiovascular diseases (CVDs) are among the leading causes of mortality and morbidity globally, with an estimated 1 million hospital admissions for CVD in England in 2019/20, leading to 5.5 million bed days. In addition, patients with CVDs have a notably high rate of hospital readmission, with approximately one in four patients experiencing heart failure (HF) being readmitted within one month of discharge. We explored whether machine learning models trained on electrocardiogram (ECG) and patient electronic health records (EHRs) can support decision-making and risk management and ultimately improve health outcomes for HF patients.

Purpose
The project aims to develop a machine learning model to predict health outcomes for HF patients. To test the hypothesis, we trained and tested an explainable machine learning model to predict the risk of mortality and hospital readmission rate for HF patients.

Methods
We used EHR data and tabulated ECG records to train an XGBoost model to predict the risk of 30-day mortality and 30-day hospital readmission for HF patients. EHR data from a cohort of 2,868 patients with their ECG records were used for model development. We extracted a total of 78 features from the ECG data, such as heart rate variability (HRV), P-wave durations, and QT intervals. These ECG features were combined with coded and free-text EHR data (e.g., demographic information, diagnosis, medication history, and lab results) and used as tabular inputs to the XGBoost model. Of the 2,868 patients, an internal set of 574 patients stratified by age, sex, and health outcomes (30-day mortality and 30-day hospital readmission) were held out for testing.

Results
Among 2,868 HF patients with 10-year follow-up, 1065 patients were reported for mortality or hospital readmission within 30 days of discharge. When the machine learning model was trained on ECG data in addition to EHR data, the model was able to predict patient mortality and hospital readmission rate in the test set with an area under the curve (AUC) of 0.91 and 0.70, respectively. For mortality prediction, the model achieved a sensitivity and specificity of 0.81 and 0.82, respectively, when employing an operating point with minimum difference between the sensitivity and specificity. When the threshold was adjusted to favor sensitivity at 0.91, the model specificity decreased to 0.74. The machine learning model also identified high sensitive troponin levels, QT dispersion, and lactate dehydrogenase levels as features of high importance for predicting the outcomes for HF patients.

Conclusions
Our developed machine learning model was able to predict mortality and readmission risks in HF patients using multi-modal data. Upon further validation, the machine learning model has the potential to be tested for supporting clinical decision-making and risk management for HF patients.ROC CurveFeature Importance
Assessment of quality of life using EuroQol 5-Dimension questionnaire level sum score in patients with HFrEF, HFmrEF, and HFpEF: a patient-level meta-analysis of DAPA-HF and DELIVER
Authors: Yang M , Kondo T , Talebi A , Jhund P S , Vaduganathan M , Hernandez A F , Lam C S P , Martinez F A , De Boer R A , Kosiborod M N , Kober L , Ponikowski P , Sabatine M S , Solomon S D , Mcmurray J J V 
Published: October 2024

Abstract

Background
The impact of SGLT2 inhibitors on generic quality of life in heart failure (HF) has not been evaluated.

Purpose
To quantify individual dimension scores and the Level Sum Score of the EQ-5D-5L (EQ-5D-LSS) in patients with HFrEF and HFmrEF/HFpEF, the association between these scores and outcomes, and the impact of treatment with dapagliflozin on these scores.

Methods
The 5 dimensions of the descriptive section of the EQ-5D-5L include mobility ("walking around"), self-care ("washing or dressing self"), usual activities ("e.g., work, study, housework, family or leisure activities"), pain/discomfort ("pain or discomfort"), and anxiety/depression ("anxious or depressed"). Each dimension is divided into 5 levels (1-5) i.e., no problem, slight problem, moderate problem, severe problem, and extreme problem (cannot do activity/extreme symptom). All dimensions and severity levels contribute equally when calculating the LSS. The individual dimension scores and the LSS were analyzed in patients with HFrEF and HFmrEF/HFpEF in the DAPA-HF and DELIVER trials, which compared dapagliflozin to placebo.

Results
Scores varied markedly by question with 37/30/33% of patients reporting no, a slight, or a moderate or greater problem for mobility; 67/20/13% for self-care; 40/33/27% for usual activities; 45/32/23% for pain/discomfort; and 57/27/16% for anxiety/depression. Compared to lower (better) LSS, patients with higher (worse) LSS were more often female, had more comorbidities, and had worse HF status. Compared to patients free from any problem across all dimensions (i.e. LSS of 5), the hazard ratios (95% CI) for time to first cardiovascular death or worsening HF were 1.27 (1.10-1.47), 1.70 (1.46-1.98), and 2.31 (1.88-2.85) in those with LSSs of 6-10, 11-15, 16-25 points, respectively. Patients randomized to dapagliflozin experienced greater improvement and less worsening across all EQ-5D-5L dimensions except pain/discomfort (Figure).

Conclusions
The EQ-5D-5L individual dimension questions revealed problems not often associated with (pain) or quantified in (anxiety/depression) HF and demonstrated the marked limitation of ordinary activities experienced by many patients with HF. Most EQ-5D-5L domain questions, including anxiety/depression, improved with dapagliflozin. The LSS, incorporating these domains was associated with clinical outcomes.
Prevalence of stage B heart failure in healthy adults and people with type 2 diabetes
Authors: Cowley A C , Dattani A , Yeo J L , Brady E M , Marsh A , Sian M , Parke K S , Wormleighton J , Arnold J R , Singh A , Mccann G P , Gulsin G S 
Published: October 2024

Abstract

Introduction
People with Type 2 Diabetes (T2D) are at increased risk of heart failure, and as such are classified as having Stage A Heart Failure (SAHF). Those with additional evidence of raised filling pressures, biochemical or structural cardiac changes, without heart failure symptoms, are deemed to have Stage B heart failure (SBHF). Identifying patients with SBHF may help determine those at highest risk of disease progression.

Purpose
The aim of this work was to assess the prevalence of SBHF in a cohort of asymptomatic healthy volunteers and people with T2D characterised according to current American Heart Association/American College Cardiology/Heart Failure Society of America.

Methods
A single-centre, prospectively recruited cohort of middle-aged asymptomatic adults with T2D and healthy volunteers, with no history or signs of cardiovascular disease, underwent comprehensive cardiac phenotyping with transthoracic echocardiography and circulating brain natriuretic peptide. Healthy volunteers with risk factors for heart failure, including hypertension and obesity, were excluded from analysis. Prevalence of SBHF was determined by comparing cardiovascular data to proposed standards, left ventricular ejection fraction and global longitudinal strain parameters were omitted from analysis due to data availability.

Results
We included 383 T2Ds (diabetes duration 9(5,15) years) and 65 healthy volunteers (Table 1); 91% of those with T2D and 65% of healthy volunteers met ≥1 criteria for SBHF (Table 2). Median number of SBHF criteria met in the T2D cohort was 3 (2,4); in particular, a large proportion of the cohort met defined cut offs for relative wall thickness (70.6%), lateral e’ (62.2%), septal e’ (54.1%) and LV wall thickness (47.4%). Of the 65 healthy volunteers, median number of SBHF criteria met was 1 (0,2.5). In healthy volunteers, SBHF was most commonly defined by lateral e’ (40.0%), septal e’ (38.3%), relative wall thickness (32.2%), left atrial volume indexed (22.4%) and serum brain natriuretic peptide (23.3%). Of note, brain natriuretic peptide levels met current SBHF parameters in 18.8% of healthy volunteers and 9.9% people with T2D, which may be attributable to the exclusion of obesity in the healthy volunteer group.

Conclusion
Using currently proposed standards, a high proportion of middle-aged asymptomatic people with T2D, and healthy volunteers without any risk factors for heart failure, would be categorised as having SBHF. These data strongly suggest that the majority of the proposed parameters for the definition of SBHF are not specific for a UK population and if applied will incorrectly classify many asymptomatic people at high risk of developing heart failure.
Clinical correlates and prognostic impact of cognitive decline in patients with heart failure and preserved ejection fraction: insights from PARAGON-HF
Authors: Shen L , Dewan P , Ferreira J P , Jhund P S , Desai A S , Lam C S P , Martinez F , Packer M , Redfield M M , Rouleau J L , Veldhuisen D J , Zannad F , Zile M R , Solomon S D , Mcmurray J J V 
Published: October 2024

Abstract

Background
Cognitive decline is common in patients with heart failure (HF), but its clinical correlates and prognostic impact are not well understood in this population.

Purpose
To examine the baseline variables associated with cognitive decline and the associations between cognitive decline and HF outcomes in patients with heart failure and preserved ejection fraction (HFpEF).

Methods
We analyzed cognitive function, as measured by the Mini-Mental State Examination (MMSE), in 2895 patients with HFpEF enrolled in a pre-specified sub-study of PARAGON-HF. Logistic regression analysis was performed to determine the factors associated with cognitive decline i.e., a decrease in MMSE score of ≥3 points by 48 weeks. Time-updated Cox proportional hazards regressions and semiparametric proportional rates models were used to examine the subsequent risk of clinical outcomes after a decline in MMSE scores of ≥1-, 2- and 3-points during follow-up respectively, adjusted for known prognostic variables including NT-proBNP and baseline MMSE.

Results
A total of 450 (15.5% of the population) patients experienced a decrease in MMSE score of ≥3 points from baseline, at any follow-up visit. The corresponding number for a decrease in MMSE score of ≥2 points and ≥1 point was 755 (26.1%) and 1231 (42.5%), respectively. Independent predictors of cognitive decline at 48 weeks included older age, living in Latin America or the Asia/Pacific region, ischemic etiology, prior HF hospitalization, history of stroke/TIA, and lower serum albumin level (Figure 1). There was a graded relationship between the size of the decrease in MMSE score from baseline and the subsequent risk of mortality. Notably, a decrease of ≥3 points was associated with a 50% increase in the subsequent risk of death from any cause (adjusted HR 1.53, 95% CI 1.10-2.11) and a 90% elevation in risk of death from cardiovascular causes (1.89, 1.25-2.86), respectively, after extensive adjustment for known prognostic variables and baseline MMSE score. By contrast, cognitive decline during follow-up was not associated with a higher risk of HF hospitalization or the composite of HF hospitalization or CV death (Figure 2).

Conclusions
In patients with HFpEF, a decline in MMSE score during follow-up is independently associated with a graded increase in the risk for mortality, but not of HF hospitalization.The predictors for cognitive declineRisk of outcomes after a decline in MMSE
Assessing stroke and mortality risk in heart failure: the CHA2DS2-VASc score's prognostic value in patients with and without atrial fibrillation - a systematic review and meta analysis
Authors: Abouzid M , Kamel I , Saleh A , Williams L , Margenat A , Hariharan R 
Published: October 2024

Abstract

Background
Heart failure (HF) is a common cardiovascular disorder that is linked to substantial morbidity and mortality. The CHA2DS2-VASc score, originally designed to assess the risk of stroke in patients with atrial fibrillation, has demonstrated potential as a prognostic indicator for adverse outcomes in patients with heart failure. This systematic review and meta-analysis aimed to assess the predictive accuracy of the CHA2DS2-VASc score in determining the occurrence of stroke and increased mortality rates in patients with heart failure.

Methods
A thorough search of electronic databases, such as PubMed, EMBASE, and the Cochrane Library, was performed to find pertinent papers published until the search cutoff date. Included studies examined the correlation between the CHA2DS2-VASc score and the likelihood of stroke or death in patients with heart failure. The assessment of the quality of the included studies was conducted using recognized criteria, and data were collected for quantitative synthesis.

Findings
The meta-analysis encompassed four studies (45864 patients), revealing a consistent and substantial correlation between elevated CHA2DS2-VASc scores and heightened risks of both stroke and mortality in heart failure patients. More precisely, patients with CHA2DS2-VASc scores ≥4 had a greater risk of stroke compared to those with scores &lt;4 (odds ratio 0.38, 95% confidence interval 0.33-0.43, p-value &lt; 0.00001). The combined data showed homogeneity (p-value = 0.24, I2 = 29%). Similarly, patients with CHA2DS2-VASc scores ≥4 had a higher risk of mortality compared to those with scores &lt;4 (OR 0.49, 95% CI 0.30-0.80, P = 0.05). However, the combined data showed significant heterogeneity (P &lt; 0.00001, I2 = 96%).

Conclusion
The CHA2DS2-VASc score is a useful predictive tool for identifying heart failure patients who are hospitalized and at a high risk of both stroke and high mortality. The results of this study provide evidence for the practicality of using the CHA2DS2-VASc score for risk stratification in patients with heart failure. Additional investigation is necessary to confirm these findings and examine the incorporation of the CHA2DS2-VASc score into risk assessment algorithms for tailored patient management.Figure:Risk of strokeFigure:Risk of mortality
Utility of mobile phone app-assisted screening for heart failure and heart stress in primary care
Authors: Trifunovic-Zamaklar D , Mladenovic Z , Stefanovic M , Tadic S , Apostolovic S , Maric Kocijancic J , Vranic I , Petrovic O , Milic G , Milovancev A , Filipovic T , Popovic M , Paunovic I , Neskovic A N , Stankovic I 
Published: October 2024

Abstract

Background
As heart failure (HF) still has a similar prognosis to some of the most common cancers, a timely diagnosis of HF is of paramount importance. We sought to determine whether a mobile phone app can facilitate screening for HF in primary care.

Material and methods
The national echocardiographic society conducted a multicenter screening programme for HF in which general practitioners from 13 primary care centres used a mobile phone app to decide whether patients without a prior HF diagnosis should be referred to one of the 6 university hospitals for transthoracic echocardiography (TTE) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) testing. The app would recommend further testing (TTE and NT-proBNP) to patients with symptoms potentially associated with HF, atrial fibrillation, pathologic electrocardiogram (ECG), previous myocardial infarction or exposure to cardiotoxic cancer therapies, or at least two other risk factors listed in Figure (left panel). Patients with signs of HF were not eligible for the programme. The algorithms proposed in the 2021 ESC guidelines were used to diagnose HF across the entire spectrum of left ventricular ejection fraction (EF).

Results
Of the 930 screened outpatients (mean age 66±11 years, age range 21-91 years, 61% female), 318 symptomatic patients (34.2%) were diagnosed with new HF (Figure). HF with preserved (HFpEF) was diagnosed more frequently than HF with mildy reduced (HFmrEF) and reduced EF (HFrEF). Heart stress, defined as elevated plasma NT-proBNP in asymptomatic individuals with risk factors irrespective of the presence or absence of structural heart disease or cardiac dysfunction, was found in 22.3% (Figure, right panel). In multivariate regression analysis, advanced age, atrial fibrillation, pathological ECG, long-standing history of arterial hypertension and chronic kidney disease were independently associated with HF, while other risk factors were not (Figure, middle panel).

Conclusions
Screening in primary care based on symptoms and risk factors, facilitated by a mobile phone app, resulted in a new HF diagnosis in approximately one third of screened patients, with the majority of them having HFpEF. Recalibration of the referral criteria might further improve the performance of the app for screening purposes.
Incidence of left ventricular dysfunction in the general population of the Amazon Basin and diagnostic potential of lung ultrasound
Authors: Holm A E , Wegener A , Gomes L C , Lima K O , Matos L O , Kaagard M D , Vieira I V M , Souza R M , Marinho C R F , Biering-Sorensen T , Silvestre O M , Brainin P 
Published: October 2024

Abstract

Background
B-lines by lung ultrasound (LUS) are associated with left ventricular (LV) dysfunction and may be useful for diagnostics in resource-limited settings.

Purpose
To assess the incidence of LV dysfunction in the general population of the Amazon Basin and to evaluate the diagnostic potential of LUS. We defined LV dysfunction as LV ejection fraction &lt;45% and LUS was assessed across eight chest zones. B-lines were calculated as the sum of all zones.

Methods and results
We conducted echocardiography and LUS in 514 individuals from a population sample in the Amazon Basin (mean age 41 years, 40% men). No patients had known heart failure, renal insufficiency or lung disease. A total of 14 individuals (3%) had LV dysfunction, corresponding to an incidence of 27.2 per 1,000 individuals. The mean number of B-lines in the general population was 0.2 (range 0 to 17 B-lines). Individuals with LV dysfunction presented with a significantly higher number of B-lines (P=0.006) and more frequently had &gt;=3 B-lines (8% vs 2%, P=0.018) compared to those with normal LV function. Identification of &gt;=3 B-lines yielded a sensitivity of 58% and specificity of 93% (AUC 0.72, negative predictive value 99%, positive predictive value 19%) for detecting LV dysfunction. Per 1 unit increase in B-lines, LV ejection fraction decreased by 9% (95%CI: -15% to -3%, P=0.010) and the relationship persisted to be significant after adjusting for age, sex, body mass index, heart rate and creatinine (-8%, 95%CI: -15% to -2%, P=0.016).

Conclusion
The incidence of LV dysfunction in the Amazon Basin was 27.2/1,000 individuals and B-lines by LUS was able to detect LV dysfunction with reasonable diagnostic accuracy. Moreover, a higher number of B-lines was associated with decreasing LV ejection fraction. Our findings indicate that LUS may supplement conventional cardiac diagnostics for assessing LV function, especially in rural environments with restricted access to imaging tools.B-lines and LV ejection fraction