Explore the vast range of titles available.

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneity. Such exercise can be achieved with coupled leaf and canopy radiative transfer models (RTMs), whereby input variables can virtually simulate any vegetation scenario. With the intention of evaluating multiple VIs in an efficient way, this led us to the development of a global sensitivity analysis (GSA) toolbox dedicated to the analysis of VIs on their sensitivity towards RTM input variables. We identified VIs that are designed to be sensitive towards leaf chlorophyll content (LCC), leaf water content (LWC) and leaf area index (LAI) for common sensors of terrestrial Earth observation satellites: Landsat 8, MODIS, Sentinel-2, Sentinel-3 and the upcoming imaging spectrometer mission EnMAP. The coupled RTMs PROSAIL and PROINFORM were used for simulations of homogeneous and forest canopies respectively. GSA total sensitivity results suggest that LCC-sensitive indices respond most robust: for the great majority of scenarios, chlorophyll a + b content (Cab) drives between 75% and 82% of the indices’ variability. LWC-sensitive indices were most affected by confounding variables such as Cab and LAI, although the equivalent water thickness (Cw) can drive between 25% and 50% of the indices’ variability. Conversely, the majority of LAI-sensitive indices are not only sensitive to LAI but rather to a mixture of structural and biochemical variables.

This contribution showcases the Spanish Tier-1 and Tier-2s’ contribution to the computing of the ATLAS experiment at the LHC during the Run-3 period. The Tier-1 and Tier-2 Grid infrastructures, encompassing data storage, processing, and involvement in software development and computing tasks for the experiment, will undergo updates to enhance efficiency and visibility within the experiment. Central to our efforts is to engage actively with the various challenges inherent in research and development, in preparation for the upcoming, more intricate phase represented by the High-Luminosity LHC (HL-LHC). In tackling these issues, we capitalize on National High Performance Computers like MareNostrum, part of the Supercomputing Spanish Network. A new activity in this work is the development and implementation of what we call the “Facility for Interactive Distributed Analysis”. This initiative aims to facilitate data analysis work for physicists at Spanish centres (IFIC, UAM, and IFAE) by orchestrating the distributed nature of initial analysis phases with subsequent interactive phases involving reduced data files. The ultimate goal is to reduce the time to produce publishable physics results or contributions tailored for workshops and conferences. The ATLAS Tier-1 and Tier-2 sites in Spain have contributed and will continue to contribute significantly to research and development in computing.

The SHARE data collection has been funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193; COMPARE: CIT5-CT-2005-028857; SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA N°211909; SHARE-LEAP: GA N°227822; SHARE M4: GA N°261982; DASISH: GA N°283646), and Horizon 2020 (SHARE-DEV3: GA N°676536; SHARE-COHESION: GA N°870628; SERISS: GA N°654221; SSHOC: GA N°823782) and by DG Employment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2; P01_AG005842; P01_AG08291; P30_AG12815; R21_AG025169; Y1-AG-4553-01; IAG_BSR06-11; OGHA_04-064; HHSN271201300071C), and from various national funding sources is gratefully acknowledged. PC acknowledges support by the French National Agency for HIV, hepatitis and emerging infectious diseases research (ANRS / EMERGING INFECTIOUS DISEASES).

AimsAtrial fibrillation (AF) costs are expected to be substantial, but cost comparisons with the general population are scarce. Using data from the prospective Swiss-AF cohort study and population-based controls, we estimated the impact of AF on direct healthcare costs from the Swiss statutory health insurance perspective.MethodsSwiss-AF patients, enrolled from 2014 to 2017, had documented, prevalent AF. We analysed 5 years of follow-up, where clinical data, and health insurance claims in 42% of the patients were collected on a yearly basis. Controls from a health insurance claims database were matched for demographics and region. The cost impact of AF was estimated using five different methods: (1) ordinary least square regression (OLS), (2) OLS-based two-part modelling, (3) generalised linear model-based two-part modelling, (4) 1:1 nearest neighbour propensity score matching and (5) a cost adjudication algorithm using Swiss-AF data non-comparatively and considering clinical data. Cost of illness at the Swiss national level was modelled using obtained cost estimates, prevalence from the Global Burden of Disease Project, and Swiss population data.ResultsThe 1024 Swiss-AF patients with available claims data were compared with 16 556 controls without known AF. AF patients accrued CHF5600 (EUR5091) of AF-related direct healthcare costs per year, in addition to non-AF-related healthcare costs of CHF11100 (EUR10 091) per year accrued by AF patients and controls. All five methods yielded comparable results. AF-related costs at the national level were estimated to amount to 1% of Swiss healthcare expenditure.ConclusionsWe robustly found direct medical costs of AF patients were 50% higher than those of population-based controls. Such information on the incremental cost burden of AF may support healthcare capacity planning.

AimsDirect-acting oral anticoagulants (DOACs) have, to a substantial degree, replaced vitamin K antagonists (VKA) as treatments for stroke prevention in atrial fibrillation (AF) patients. However, evidence on the real-world causal effects of switching patients from VKA to DOAC is lacking. We aimed to assess the empirical incremental cost-effectiveness of switching patients to DOAC compared with maintaining VKA treatment.MethodsThe target trial approach was applied to the prospective observational Swiss-AF cohort, which enrolled 2415 AF patients from 2014 to 2017. Clinical data, healthcare resource utilisation and EQ-5D-based utilities representing quality of life were collected in yearly follow-ups. Health insurance claims were available for 1024 patients (42.4%). Overall survival, quality-of-life, costs from the Swiss statutory health insurance perspective and cost-effectiveness were estimated by emulating a target trial in which patients were randomly assigned to switch to DOAC or maintain VKA treatment.Results228 patients switching from VKA to DOAC compared with 563 patients maintaining VKA treatment had no overall survival advantage over a 5-year observation period (HR 0.99, 95% CI 0.45, 1.55). The estimated gain in quality-adjusted life years (QALYs) was 0.003 over the 5-year period at an incremental costs of CHF 23 033 (€ 20 940). The estimated incremental cost-effectiveness ratio was CHF 425 852 (€ 387 138) per QALY gained.ConclusionsApplying a causal inference method to real-world data, we could not demonstrate switching to DOACs to be cost-effective for AF patients with at least 1 year of VKA treatment. Our estimates align with results from a previous randomised trial.

ObjectiveEvidence on long-term costs of atrial fibrillation (AF) and associated factors is scarce. As part of the Swiss-AF prospective cohort study, we aimed to characterise AF costs and their development over time, and to assess specific patient clusters and their cost trajectories.MethodsSwiss-AF enrolled 2415 patients with variable duration of AF between 2014 and 2017. Patient clusters were identified using hierarchical cluster analysis of baseline characteristics. Ongoing yearly follow-ups include health insurance clinical and claims data. An algorithm was developed to adjudicate costs to AF and related complications.ResultsA subpopulation of 1024 Swiss-AF patients with available claims data was followed up for a median (IQR) of 3.24 (1.09) years. Average yearly AF-adjudicated costs amounted to SFr5679 (€5163), remaining stable across the observation period. AF-adjudicated costs consisted mainly of inpatient and outpatient AF treatment costs (SFr4078; €3707), followed by costs of bleeding (SFr696; €633) and heart failure (SFr494; €449). Hierarchical analysis identified three patient clusters: cardiovascular (CV; N=253 with claims), isolated-symptomatic (IS; N=586) and severely morbid without cardiovascular disease (SM; N=185). The CV cluster and SM cluster depicted similarly high costs across all cost outcomes; IS patients accrued the lowest costs.ConclusionOur results highlight three well-defined patient clusters with specific costs that could be used for stratification in both clinical and economic studies. Patient characteristics associated with adjudicated costs as well as cost trajectories may enable an early understanding of the magnitude of upcoming AF-related healthcare costs.

BackgroundPatients with atrial fibrillation (AF) face an increased risk of adverse cardiovascular events. Evidence suggests that early rhythm control including AF ablation may reduce this risk.MethodsTo compare the risks for cardiovascular events in AF patients with and without pulmonary vein isolation (PVI), we analysed data from two prospective cohort studies in Switzerland (n = 3968). A total of 325 patients who had undergone PVI during a 1-year observational period were assigned to the PVI group. Using coarsened exact matching, 2193 patients were assigned to the non-PVI group. Outcomes were all-cause mortality, hospital admission for acute heart failure, a composite of stroke, transient ischemic attack and systemic embolism (Stroke/TIA/SE), myocardial infarction (MI), and bleedings. We calculated multivariable adjusted Cox proportional-hazards models.ResultsOverall, 2518 patients were included, median age was 66 years [IQR 61.0, 71.0], 25.8% were female. After a median follow-up time of 3.9 years, fewer patients in the PVI group died from any cause (incidence per 100 patient-years 0.64 versus 1.87, HR 0.39, 95%CI 0.19–0.79, p = 0.009) or were admitted to hospital for acute heart failure (incidence per 100 patient-years 0.52 versus 1.72, HR 0.44, 95%CI 0.21–0.95, p = 0.035). There was no significant association between PVI and Stroke/TIA/SE (HR 0.94, 95%CI 0.52–1.69, p = 0.80), MI (HR 0.43, 95%CI 0.11–1.63, p = 0.20) or bleeding (HR 0.75, 95% CI 0.50–1.12, p = 0.20).ConclusionsIn our matched comparison, patients in the PVI group had a lower incidence rate of all-cause mortality and hospital admission for acute heart failure compared to the non-PVI group.ClinicalTrials.gov IdentifierNCT02105844, April 7th 2014.

Causal machine learning (ML) methods hold great promise for advancing precision medicine by estimating personalized treatment effects. However, their reliability remains largely unvalidated in empirical settings. In this study, we assessed the internal and external validity of 17 mainstream causal heterogeneity ML methods -- including metalearners, tree-based methods, and deep learning methods -- using data from two large randomized controlled trials: the International Stroke Trial (N=19,435) and the Chinese Acute Stroke Trial (N=21,106). Our findings reveal that none of the ML methods reliably validated their performance, neither internal nor external, showing significant discrepancies between training and test data on the proposed evaluation metrics. The individualized treatment effects estimated from training data failed to generalize to the test data, even in the absence of distribution shifts. These results raise concerns about the current applicability of causal ML models in precision medicine, and highlight the need for more robust validation techniques to ensure generalizability.