A machine learning-based lung ultrasound algorithm for the diagnosis of acute heart failure.

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.

Prognostic implication of lung ultrasound in heart failure: pooled analysis of international cohorts.

BACKGROUND: Lung ultrasound (LUS) is often used to assess congestion in heart failure (HF). In this study, we assessed the prognostic role of LUS in HF patients at admission and hospital discharge, and in an out-patient setting and explored whether clinical factors (age, sex, left ventricular ejection fraction (LVEF) and atrial fibrillation) impact the prognostic value of LUS findings. Further, we assessed the incremental prognostic value of LUS on top of AHEAD and MAGGIC clinical risk scores. METHODS AND RESULTS: We pooled data of patients hospitalized for HF or followed-up in out-patient clinics from international cohorts. We enrolled 1,947 patients, at admission (n=578), discharge (n=389) and in out-patient clinic (n=980). Total LUS B-line count was calculated for the 8-zone scanning protocol. The primary outcome was a composite of re-hospitalization for HF and all-cause death. Compared to those in the lower tertiles of B-lines, patients in the highest tertile were older, more likely to have signs of HF and higher NT-proBNP levels. A higher number of B-lines was associated with increased risk of primary outcome at discharge (Tertile3 vs Tertile1: adjustedHR= 5.74 (3.26- 10.12), p<0.0001) and in out-patients (Tertile3 vs Tertile1: adjustedHR= 2.66 (1.08- 6.54), p=0.033). Age and LVEF did not influence the prognostic capacity of LUS in different clinical settings. Adding B-line count to MAGGIC and AHEAD scores improved net reclassification significantly in all three clinical settings. CONCLUSION: A higher number of B-lines in patients with HF was associated with increased risk of morbidity and mortality, regardless of the clinical setting.

Poor in-hospital congestion improvement in acute heart failure patients classified according to LVEF: prognostic implications.

BACKGROUND: 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: 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. RESULTS: 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. CONCLUSIONS: 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.

Multi-modality assessment of congestion in acute heart failure: Associations with left ventricular ejection fraction and prognosis.

BACKGROUND: Integrating clinical examination with ultrasound measures of congestion could improve risk stratification in patients hospitalized with acute heart failure (AHF). AIM: To investigate the prevalence of clinical, echocardiographic and lung ultrasound (LUS) signs of congestion according to left ventricular ejection fraction (LVEF) and their association with prognosis in patients with AHF. METHODS: We pooled the data of four cohorts of patients (N = 601, 74.9±10.8 years, 59 % men) with AHF and analysed six features of congestion at enrolment: clinical (peripheral oedema and respiratory rales), biochemical (BNP/NT-proBNP≥median), echocardiographic (inferior vena cava (IVC)≥21 mm, pulmonary artery systolic pressure (PASP)≥40 mmHg, E/e'≥15) and B-lines ≥25 (8-zones) in those with reduced (<40 %, HFrEF), mildly reduced (40-49 %, HFmrEF and preserved (≥50 %HFpEF) LVEF. RESULTS: Compared to patients with HFmrEF (n = 110) and HFpEF (n = 201), those with HFrEF (N = 290) had higher natriuretic peptides, but prevalence of clinical (39 %), echocardiographic (IVC≥21 mm: 56 %, E/e'≥15: 57 %, PASP≥40 mmHg: 76 %) and LUS (48 %) signs of congestion was similar. In multivariable analysis, clinical (HR: 3.24(2.15-4.86), p < 0.001), echocardiographic [(IVC≥21 mm (HR:1.91, 1.21-3.03, p=0.006); E/e'≥15 (HR:1.54, 1.04-2.28, p = 0.031)] and LUS (HR:2.08, 1.34-3.24, p = 0.001) signs of congestion were significantly associated with all-cause mortality and/or HF re-hospitalization. Adding echocardiographic and LUS features of congestion to a model than included age, sex, systolic blood pressure, clinical congestion and natriuretic peptides, improved prediction at 90 and 180 days. CONCLUSIONS: Clinical and ultrasound signs of congestion are highly prevalent in patients with AHF, regardless of LVEF and their combined assessment improves risk stratification.

Early changes in estimated glomerular filtration rate post-initiation of empagliflozin in EMPEROR-Preserved.

AIMS: Renal function (estimated glomerular filtration rate [eGFR]) changes early after the introduction of empagliflozin have not been described in heart failure with preserved ejection fraction (HFpEF). The aim of this study was to describe early eGFR changes, assess its determinants and its clinical impact on cardiovascular and renal outcomes in patients with HFpEF enrolled in EMPEROR-Preserved. METHODS AND RESULTS: Estimated glomerular filtration rate changes (absolute and relative) from randomization to week 4 were calculated and landmark analyses performed. Initial eGFR change was available in 5836 patients (97.5% of the population). Empagliflozin induced a mean eGFR change of -3.2 ml/min/1.73 m2 versus placebo from baseline to week 4. After week 4, in the empagliflozin group, the risk of the primary outcome (composite of heart failure hospitalization or cardiovascular death), cardiovascular, all-cause mortality and sustained ≥50% eGFR decrease or end-stage renal disease (ESRD) did not differ by eGFR change levels. In contrast, in the placebo group, patients included in the tertile with most profound eGFR decrease (i.e. ≥5.1% from baseline) had a higher risk of the primary outcome (hazard ratio [HR] 1.46, 95% confidence interval [CI] 1.17-1.82), cardiovascular mortality (HR 1.38, 95% CI 1.01-1.89) and sustained ≥50% eGFR decrease or ESRD (HR 2.20, 95% CI 1.20-4.04) versus tertile with eGFR increase. CONCLUSION: An initial relatively small eGFR decrease may be expected after empagliflozin initiation. Such small eGFR decrease was not associated with adverse cardiovascular outcomes with empagliflozin. In contrast, eGFR decrease was associated with poor cardiovascular outcomes with placebo.

SGLT2 Inhibitors in Heart Failure with Reduced Ejection Fraction: A Paradigm Shift Toward Dual Cardio-Renal Protection.

Sodium-glucose transport inhibitors (SGLT2i) have been found to be effective in preventing heart failure in patients with diabetes or chronic kidney disease with or without cardiovascular disease. Recent evidence suggests that SGLT2i substantially improve cardiovascular and renal outcomes in patients with heart failure with reduced ejection fraction (HFrEF). In this review, we discuss the combined cardio-renal benefits of SGLT2i in patients with HFrEF. In addition, we discuss the impact of renoprotection in the midterm management of HFrEF and possible implementation strategies for initiating SGLT2i in routine care of HFrEF.

Comparing and contrasting risk factors for heart failure in patients with and without history of myocardial infarction: data from HOMAGE and the UK Biobank.

AIMS: Myocardial infarction (MI) is among the commonest attributable risk factors for heart failure (HF). We compared clinical characteristics associated with the progression to HF in patients with or without a history of MI in the HOMAGE cohort and validated our results in UK Biobank. METHODS AND RESULTS: During a follow-up of 5.2 (3.5-5.9) years, 177 (2.4%) patients with prior MI and 370 (1.92%) patients without prior MI experienced HF onset in the HOMAGE cohort (n = 26 478, history of MI: n = 7241). Older age, male sex and higher heart rate were significant risk factors of HF onset in patients with and without prior MI. Lower renal function was more strongly associated with HF onset in patients with prior MI. Higher body mass index (BMI), systolic blood pressure and blood glucose were significantly associated with HF onset only in patients without prior MI (all p for interactions <0.05). In the UK Biobank (n = 500 001, history of MI: n = 4555), higher BMI, glycated haemoglobin, diabetes and hypertension had a stronger association with HF onset in participants without prior MI compared to participants with MI (all p for interactions <0.05). CONCLUSION: The importance of clinical risk factors associated with HF onset is dependent on whether the patient has had a prior MI. Diabetes and hypertension are associated with new-onset HF only in the absence of MI history. Patients may benefit from targeted risk management based on MI history.

Impact of smoking on cardiovascular risk and premature ageing: Findings from the STANISLAS cohort.

BACKGROUND AND AIMS: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

The Prescription Pattern of Heart Failure Medications in Reduced, Mildly Reduced, and Preserved Ejection Fractions.

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, ß-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 ß-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.

Antiangiogenic activity of zinc and zinc-sorafenib combination using the chick chorioallantoic membrane assay: A descriptive study.

AIM: Zinc, a trace element, is known for downregulating several proangiogenic growth factors and cytokines. However, its antiangiogenic activity is not adequately studied. The present study was aimed to evaluate the possible antiangiogenic activity of zinc via the chick chorioallantoic membrane (CAM) assay. Furthermore, the antiangiogenic activity of the combination therapy of zinc with various doses of sorafenib, a tyrosine kinase inhibitor, was evaluated. MATERIALS AND METHODS: A pilot study was initially conducted so as to select suitable doses of zinc and sorafenib. The antiangiogenic activity after combining zinc 2.5 µg/embryo with sorafenib 1 and 2 µg/embryo was also evaluated. The antiangiogenic activity was quantified in terms of total length of blood vessels, number of junctions, number of branching points, and mean length of the blood vessels. RESULTS: Zinc 2.5 µg/embryo showed significant (P < 0.05) antiangiogenic activity, as compared to the control group. However, its effect was not comparable to that of sorafenib 2 µg/embryo. The combination of zinc 2.5 µg/embryo with sorafenib 2 µg/embryo did not show an additive/synergistic effect. The combination of zinc 2.5 µg/embryo with sorafenib 1 µg/embryo produced an antiangiogenic activity which was comparable (P > 0.05) to that of sorafenib 2 µg/embryo. CONCLUSION: Zinc caused significant antiangiogenic activity in the CAM assay. The lack of addition/synergism in the zinc-sorafenib combination could have been due to the variability in the dose/ratio selection. Addition of zinc to sorafenib therapy could improve treatment tolerability, reduce cost of therapy, and reduce the emergence of drug resistance. Future mechanistic studies could identify the exact pharmacodynamics of zinc as an angiogenesis inhibitor.

Advanced analytical methods to assess physical activity behaviour using accelerometer raw time series data: a protocol for a scoping review.

BACKGROUND: 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. METHODS: 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. DISCUSSION: 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. SCOPING REVIEW REGISTRATION: This protocol has been registered with the Open Science Framework (OSF): https://osf.io/yxgmb .