Prognostic Assessment of HLM Score in Heart Failure Due to Ischemic Heart Disease: A Pilot Study.

Background: Ischemic heart disease (IHD) represents the main cause of heart failure (HF). A prognostic stratification of HF patients with ischemic etiology, particularly those with acute coronary syndrome (ACS), may be challenging due the variability in clinical and hemodynamic status. The aim of this study is to assess the prognostic power of the HLM score in a population of patients with ischemic HF and in a subgroup who developed HF following ACS. Methods: This is an observational, prospective, single-center study, enrolling consecutive patients with a diagnosis of ischemic HF. Patients were stratified according to the four different HLM stages of severity, and the occurrence of CV death, HFH, and worsening HF events were evaluated at 6-month follow-up. A sub-analysis was performed on patients who developed HF following ACS at admission. Results: The study included 146 patients. HLM stage predicts the occurrence of CV death (p = 0.01) and CV death/HFH (p = 0.003). Cox regression analysis confirmed HLM stage as an independent predictor of CV death (OR: 3.07; 95% IC: 1.54-6.12; p = 0.001) and CV death/HFH (OR: 2.45; 95% IC: 1.43-4.21; p = 0.001) in the total population of patients with HF due to IHD. HLM stage potentially predicts the occurrence of CV death (p < 0.001) and CV death/HFH (p < 0.001) in patients with HF following ACS at admission. Conclusions: Pathophysiological-based prognostic assessment through HLM score is a potentially promising tool for the prediction of the occurrence of CV death and CV death/HFH in ischemic HF patients and in subgroups of patients with HF following ACS at admission.

Telemedicine: an Effective and Low-Cost Lesson From the COVID-19 Pandemic for the Management of Heart Failure Patients.

PURPOSE: The purpose of this review is to explore the benefits and controversies that telemedicine (TM), applied to patients with heart failure (HF), can provide in terms of diagnosis, therapeutic management, and prognosis improvement. During the coronavirus disease 19 (COVID-19) outbreak, TM emerged as the most effective and feasible method available to ensure continuous care for chronic diseases. Among these, HF, characterized by high mortality, morbidity, and the need for frequent visits, may benefit of the TM role. HF patients are affected by frequent exacerbations undergoing a progressive prognosis impoverishment, strongly depending on the disease's management. A precise clinical handling is always required, with a constant optimization of the therapy, a continuous control of risk factors, and a sensitive attention to any change in symptoms, clinical signs, and laboratory tests. In this context, TM has shown to improve therapy adherence and HF: patients' self-care, impacting the prognosis even if specific results are controversial. Major evidence shows that TM may allow an adequate primary prevention, reducing the impact of the main cardiovascular risk factors. TM can also be useful for the secondary prevention, early detecting a likely HF exacerbation before it becomes clinically manifest, thereby lowering the need for hospitalization. Moreover, an optimal up-titration of the therapy and an increase in treatment adherence are feasible by using TM. However, some studies did not show unambiguous results, and uncertainties still remain.

HeartLogic™: real-world data-efficiency, resource consumption, and workflow optimization.

Heart failure (HF) is a major and still growing medical problem and is characterized by episodes of acute decompensation that are associated with a negative prognosis and a significant burden on the patients, doctors, and healthcare resources. Early detection of incipient HF may allow outpatient treatment before patients severely decompensate, thus reducing HF hospitalizations and related costs. The HeartLogic™ algorithm is an automatic, remotely managed system combining data directly related to HF pathophysiology into a single score, the HeartLogic™ index. This index proved to be effective in predicting the risk of incipient HF decompensation, allowing to redistribute resources from low-risk to high-risk patients in a timely and cost-saving manner. The alert-based remote management system seems more efficient than the one based on scheduled remote transmission in terms of caregivers' workload and alert detection timing. The widespread application of the HeartLogic™ algorithm requires the resolution of logistical and financial issues and the adoption of a pre-defined, functional workflow. In this paper, we reviewed general aspects of remote monitoring in HF patients, the functioning and pathophysiological basis of the HeartLogic index, its efficiency in the management of HF patients, and the economic effects and the organizational revolution associated with its use.

Prevalence, Management, and Outcome of Atrial Fibrillation and Other Supraventricular Arrhythmias in COVID-19 Patients.

COVID-19 mainly affects the respiratory system but has been correlated with cardiovascular manifestations such as myocarditis, heart failure, acute coronary syndromes, and arrhythmias. Cardiac arrhythmias are the second most frequent complication affecting about 30% of patients. Several mechanisms may lead to an increased risk of cardiac arrhythmias during COVID-19 infection, ranging from direct myocardial damage to extracardiac involvement. The aim of this review is to describe the role of COVID-19 in the pathogenesis of cardiac arrhythmias and provide a comprehensive guidance for their monitoring and management.

Incidence of Device-Related Thrombosis in Watchman Patients Undergoing a Genotype-Guided Antithrombotic Strategy.

OBJECTIVES: This study sought to report the incidence of device-related thrombosis (DRT) and thromboembolic (TE) events when an alternative to clopidogrel is prescribed in loss-of-function (LOF) allele carriers of the cytochrome P450 2C19 (CYP2C19) gene. BACKGROUND: LOF polymorphisms of the CYP2C19 gene are associated with reduced hepatic bioactivation of clopidogrel. METHODS: A total of 1,002 Watchman patients were included. Six hundred forty-five patients underwent CYP2C19 genetic testing; among patients with clopidogrel resistance, clopidogrel was replaced by either prasugrel (pilot cohort) or half dose direct oral anticoagulant ([DOAC]/Group 1), both in combination with aspirin. We compared the incidence of DRT/TE events among genotyped patients and a control group which received standard dual antiplatelet therapy (DAPT) (Group 2; n = 357). All reported events occurred during a timeframe between 45- and 180-day follow-up transesophageal echocardiograms, when the 2 different antithrombotic strategies (genotype-guided vs standard DAPT) were adopted. RESULTS: In the pilot cohort (n = 244), bleeding events occurred in 10.2% of patients who received aspirin plus prasugrel, leading to early discontinuation of the prasugrel-based protocol. DOAC Group 1 patients (n = 401), 25.7% were reduced metabolizers, and clopidogrel was replaced by half dose direct oral anticoagulant. DRT was documented in 1 (0.2%) patient of Group 1 and 7 (1.96%) patients of Group 2 (log-rank P = 0.021). The composite endpoint of DRT/TE events was significantly lower among patients receiving a genotype-guided antithrombotic strategy (0.75% vs 3.10%; log-rank P = 0.017). CONCLUSIONS: In Watchman patients, a genotype-based antithrombotic strategy with aspirin plus half dose DOAC in reduced clopidogrel metabolizers was superior to standard DAPT with respect to DRT/TE events.

Clinical and economic impact of multipoint left ventricular pacing: A comparative analysis from the Italian registry on multipoint pacing in cardiac resynchronization therapy (IRON-MPP).

INTRODUCTION: Early evidence suggests that multipoint left ventricular pacing (MPP) may improve response to cardiac resynchronization therapy (CRT). It is unknown whether this benefit is sustained and cost-effective. We used real-world data to evaluate long-term impact of MPP-ON clinical status, heart failure hospitalizations (HFH) and costs. METHODS: The Italian registry on multipoint left ventricular pacing is a prospective, multicenter registry of patients implanted with MPP-enabled CRT devices. For this analysis, clinical and echocardiographic data were collected through 24 months and compared between patients with (MPP-ON) or without (MPP-OFF) early MPP activation at implant. The total cost of each HFH was estimated with national Italian reimbursement rates. RESULTS: The study included 190 MPP-OFF and 128 MPP-ON patients with similar baseline characteristics. At 1 and 2 years, the MPP-ON group had lower rates of HFH vs MPP-OFF (1-year hazard ratio [HR]: 0.14, P = .0014; 2-year HR: 0.38, P = .009). The finding persisted in a subgroup of patients with consistent MPP activation through follow-up (1-year HR: 0.19; P = .0061; 2-year HR: 0.39, P = .022). Total HFH per-patient costs were lower in the MPP-ON vs the MPP-OFF group at 1 year (€101 ± 50 vs €698 ± 195, P < .001) and 2 years (€366 ± 149 vs €801 ± 203, P = .038). More MPP-ON patients had ≥5% improvement in ejection fraction (76.8% vs 65.4%, P = .025) and clinical composite score (66.7% vs 47.5%, P = .01). CONCLUSIONS: In this multicenter clinical study, early MPP activation was associated with a significant reduction in cumulative HFH and related costs after 1 and 2 years of follow-up.

Development of European NO2 Land Use Regression Model for present and future exposure assessment: Implications for policy analysis.

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.

Examining lag effects between industrial land development and regional economic changes: The Netherlands experience.

In most empirical applications, forecasting models for the analysis of industrial land focus on the relationship between current values of economic parameters and industrial land use. This paper aims to test this assumption by focusing on the dynamic relationship between current and lagged values of the 'economic fundamentals' and industrial land development. Not much effort has yet been attributed to develop land forecasting models to predict the demand for industrial land except those applying static regressions or other statistical measures. In this research, we estimated a dynamic panel data model across 40 regions from 2000 to 2008 for the Netherlands to uncover the relationship between current and lagged values of economic parameters and industrial land development. Land-use regulations such as land zoning policies, and other land-use restrictions like natural protection areas, geographical limitations in the form of water bodies or sludge areas are expected to affect supply of land, which will in turn be reflected in industrial land market outcomes. Our results suggest that gross domestic product (GDP), industrial employment, gross value added (GVA), property price, and other parameters representing demand and supply conditions in the industrial market explain industrial land developments with high significance levels. It is also shown that contrary to the current values, lagged values of the economic parameters have more sound relationships with the industrial developments in the Netherlands. The findings suggest use of lags between selected economic parameters and industrial land use in land forecasting applications.

Estimating demand for industrial and commercial land use given economic forecasts.

Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on 'land use intensity' measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models' results were compared with estimations generated using the 'null model' (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the 'null model', but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models' performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions.