Pulmonary congestion is a critical finding in patients with heart failure (HF) that can be quantified by lung ultrasound (LUS) through B-line quantification, the latter of which can be easily ...measured by all commercially-available probes/ultrasound equipment. As such, LUS represents a useful tool for the assessment of patients with both acute and chronic HF. Several imaging protocols have been described in the literature according to different clinical settings. While most studies have been performed with either the 8 or 28 chest zone protocol, the 28-zone protocol is more time-consuming while the 8-zone protocol offers the best trade-off with no sizeable loss of information. In the acute setting, LUS has excellent value in diagnosing acute HF, which is superior to physical examination and chest X-ray, particularly in instances of diagnostic uncertainty. In addition to its diagnostic value, accumulating evidence over the last decade (mainly derived from ambulatory settings or at discharge from an acute HF hospitalisation) suggests that LUS can also represent a useful prognostic tool for predicting adverse outcome in both HF with reduced (HFrEF) and preserved ejection fraction (HFpEF). It also allows real-time monitoring of pulmonary decongestion during treatment of acute HF. Additionally, LUS-guided therapy, when compared with usual care, has been shown to reduce the risk of HF hospitalisations at short- and mid-term follow-up. In addition, studies have shown good correlation between B-lines during exercise stress echocardiography and invasive, bio-humoral and echocardiographic indices of haemodynamic congestion; B-lines during exercise are also associated with worse prognosis in both HFrEF and HFpEF. Altogether, LUS represents a reliable and useful tool in the assessment of pulmonary congestion and risk stratification of HF patients throughout their entire journey (i.e., emergency department/acute settings, in-hospital management, discharge from acute HF hospitalisation, monitoring in the outpatient setting), with considerable diagnostic and prognostic implications.
A substantial proportion of patients with heart failure (HF) receive suboptimal guideline-recommended therapy. We aimed to identify the factors leading to suboptimal drug prescription in HF and ...according to HF phenotypes. This retrospective, single-centre observational cohort study included 702 patients admitted for worsening HF (HF with a reduced ejection fraction HFrEF, n = 198; HF with a mildly reduced EF HFmrEF, n = 122; and HF with a preserved EF HFpEF, n = 382). A score based on the prescription and dose percentage of ACEi/ARBs, β-blockers, and MRAs at discharge was calculated (a total score ranging from zero to six). Approximately 70% of patients received ACEi/ARBs/ARNi, 80% of patients received β-blockers, and 20% received MRAs. The mean HF drug dose was approximately 50% of the recommended dose, irrespective of the HF phenotype. Ischaemic heart disease was associated with a higher prescription score (ranging from 0.4 to 1) compared to no history of ischaemic heart disease, irrespective of the left ventricular EF (LVEF) level. A lower prescription score was associated with older age and male sex in HFrEF and diabetes in HFmrEF. The overall ability of the models to predict the optimal drug dose, including key HF variables (including natriuretic peptides at admission), was poor (R2 < 0.25). A higher prescription score was associated with a lower risk of re-hospitalization and death (HR: 0.75 (0.57−0.97), p = 0.03), irrespective of phenotype (p-interaction = 0.41). Despite very different HF management guidelines according to LVEF, the prescription pattern of HF drugs is poorly related to LVEF and clinical characteristics, thus suggesting that physician-driven factors may be involved in the setting of therapeutic inertia. It may also be related to drug intolerance or clinical stability that is not predicted by the patients’ profiles.
Physical activity (PA) is a complex multidimensional human behaviour. Currently, there is no standardised approach for measuring PA using wearable accelerometers in health research. The total volume ...of PA is an important variable because it includes the frequency, intensity and duration of activity bouts, but it reduces them down to a single summary variable. Therefore, analytical approaches using accelerometer raw time series data taking into account the way PA are accumulated over time may provide more clinically relevant features of physical behaviour. Advances on these fields are highly needed in the context of the rapid development of digital health studies using connected trackers and smartwatches. The objective of this review will be to map advanced analytical approaches and their multidimensional summary variables used to provide a comprehensive picture of PA behaviour.
This scoping review will be guided by the Arksey and O'Malley methodological framework. A search for relevant publications will be undertaken in MEDLINE (PubMed), Embase and Web of Science databases. The selection of articles will be limited to studies published in English from January 2010 onwards. Studies including analytical methods that go beyond total PA volume, average daily acceleration and the conventional cut-point approaches, involving tri-axial accelerometer data will be included. Two reviewers will independently screen all citations, full-text articles and extract data. The data will be collated, stored and charted to provide a descriptive summary of the analytical methods and outputs, their strengths and limitations and their association with different health outcomes.
This protocol describes a systematic method to identify, map and synthesise advanced analytical approaches and their multidimensional summary variables used to investigate PA behaviour and identify potentially clinically relevant features. The results of this review will be useful to guide future research related to analysing PA patterns, investigate their association with health conditions and suggest appropriate recommendations for changes in PA behaviour. The results may be of interest to sports scientists, clinical researchers, epidemiologists and smartphone application developers in the field of PA assessment.
This protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb .
Abstract Aims Residual congestion in acute heart failure (AHF) is associated with poor prognosis. However, there is a lack of data on the prognostic value of changes in a combined assessment of ...in-hospital congestion. The present study sought to assess the association between in-hospital congestion changes and subsequent prognosis according to left ventricular ejection fraction (LVEF) classification. Methods and results Patients (N = 244, 80.3 ± 7.6 years, 50.8% male) admitted for acute HF in two European tertiary care centres underwent clinical assessment (congestion score included dyspnoea at rest, rales, third heart sound, jugular venous distention, peripheral oedema, and hepatomegaly; simplified congestion score included rales and peripheral oedema), echocardiography, lung ultrasound, and natriuretic peptides (NP) measurement at admission and discharge. The primary outcome was a composite of all-cause mortality and/or HF re-hospitalization. In the 244 considered patients (95 HF with reduced EF, 57 HF with mildly reduced EF, and 92 HF with preserved EF), patients with limited improvement in clinical congestion score (hazard ratio 2.33, 95% CI 1.51–3.61, P = 0.0001), NP levels (2.29, 95% CI 1.55–3.38, P < 0.0001), and the number of B-lines (6.44, 95% CI 4.19–9.89, P < 0.001) had a significantly higher risk of outcome compared with patients experiencing more sizeable decongestion. The same pattern of association was observed when adjusting for confounding factors. A limited improvement in clinical congestion score and in the number of B-lines was related to poor prognosis for all LVEF categories. Conclusion In AHF, the degree of congestion reduction assessed over the in-hospital stay period can stratify the subsequent event risk. Limited reduction in both clinical congestion and B-lines number are related to poor prognosis, irrespective of HF subtype.
Residual congestion in acute heart failure (AHF) is associated with poor prognosis. However, there is a lack of data on the prognostic value of changes in a combined assessment of in-hospital ...congestion. The present study sought to assess the association between in-hospital congestion changes and subsequent prognosis according to left ventricular ejection fraction (LVEF) classification.
Patients (N=244, 80.3±7.6 years, 50.8% male) admitted for acute HF in two European tertiary care centers underwent clinical assessment (congestion score included dyspnea at rest, rales, third heart sound, jugular venous distention, peripheral edema and hepatomegaly; simplified congestion score included rales and peripheral edema), echocardiography, lung ultrasound (LUS) and natriuretic peptides (NP) measurement at admission and discharge. The primary outcome was a composite of all-cause mortality and/or HF re-hospitalization.
In the 244 considered patients (95 HF with reduced EF, 57 HF with mildly reduced EF and 92 HF with preserved EF), patients with limited improvement in clinical congestion score (hazard ratio 2.33, 95%CI 1.51 to 3.61, p=0.0001), NP levels (2.29, 95%CI 1.55 to 3.38, p<0.0001) and the number of B-lines (6.44, 95%CI 4.19 to 9.89, p<0.001) had a significantly higher risk of outcome compared to patients experiencing more sizeable decongestion. The same pattern of association was observed when adjusting for confounding factors. A limited improvement in clinical congestion score and in the number of B-lines was related to poor prognosis for all LVEF categories.
In AHF, the degree of congestion reduction assessed over the in-hospital stay period can stratify the subsequent event risk. Limited reduction in both clinical congestion and B-lines number are related to poor prognosis, irrespective of HF subtype.
Lung ultrasound (LUS) is an effective tool for diagnosing acute heart failure (AHF). However, several imaging protocols currently exist and how to best use LUS remains undefined. We aimed at ...developing a lung ultrasound-based model for AHF diagnosis using machine learning. Random forest and decision trees were generated using the LUS data (via an 8-zone scanning protocol) in patients with acute dyspnea admitted to the Emergency Department (PLUME study, N = 117) and subsequently validated in an external dataset (80 controls from the REMI study, 50 cases from the Nancy AHF cohort). Using the random forest model, total B-line sum (i.e., in both hemithoraces) was the most significant variable for identifying AHF, followed by the difference in B-line sum between the superior and inferior lung areas. The decision tree algorithm had a good diagnostic accuracy area under the curve (AUC) = 0.865 and identified three risk groups (i.e., low 24%, high 70%, and very high-risk 96%) for AHF. The very high-risk group was defined by the presence of 14 or more B-lines in both hemithoraces while the high-risk group was described as having either B-lines mostly localized in superior points or in the right hemithorax. Accuracy in the validation cohort was excellent (AUC = 0.906). Importantly, adding the algorithm on top of a validated clinical score and classical definition of positive LUS scanning for AHF resulted in a significant improvement in diagnostic accuracy (continuous net reclassification improvement = 1.21, P < 0.001). Our simple lung ultrasound-based machine learning algorithm features an excellent performance and may constitute a validated strategy to diagnose AHF.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Smoking may lead to premature ageing, but the impact on the cardiovascular system and circulating proteins needs further investigation. In the present study, we aim to understand the impact of ...smoking on heart and vessels and circulating biomarkers of multiple domains including cardiovascular damage, premature ageing and cancer-related pathways.
The STANISLAS Cohort is a longitudinal familial cohort with detailed cardiovascular examination and biomarker assessment. This study included all the participants enrolled in the fourth visit of the STANISLAS Cohort for whom information on smoking habits was available (n = 1696). We assessed pulse wave velocity, intima-media thickness, echocardiographic parameters and a total of 460 proteins to study the association of circulating plasma proteins with smoking status (never vs. past vs. current smoking) while adjusting for potential confounders.
Current smokers were approximately 18 years younger but had higher left ventricular mass index (LVMi) and similar pulse wave velocity (PWV), carotid intima media thickness (cIMT), frequency of hypertension, diabetes and carotid plaques compared to the much older never smokers. After multivariate selection, 25 proteins were independently associated with current or past smoking. Current smoking was strongly associated with higher levels of EDIL-3, CCL11, TNFSF13B, KIT, and lower levels of IL-12B and PLTP (p < 0.0001) while past smoking was associated with FGF-21, CHIT1, and lower levels of CXCL10, IL1RL2 and RAGE (p < 0.01).
Current smoking is associated with signs of early onset of cardiovascular ageing and protein biomarkers that regulate inflammation, endothelial function, metabolism, oncological processes and apoptosis.
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•Cigarette smoking is associated with clinical markers of premature cardiovascular ageing.•Smokers had higher LVMi compared to much older non-smokers.•Current smoking was strongly associated with plasma proteins indicating inflammation, endothelial dysfunction and apoptosis.•Smokers had higher levels of EDIL-3, CCL11, TNFSF13B, KIT, and lower levels of IL-12B and PLTP compared to never smokers.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
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