Identifying prognostic factors for clinical outcomes and costs in four high-volume surgical treatments using routinely collected hospital data

Identifying prognostic factors (PFs) is often costly and labor-intensive. Routinely collected hospital data provide opportunities to identify clinically relevant PFs and construct accurate prognostic models without additional data-collection costs. This multicenter (66 hospitals) study reports on associations various patient-level variables have with outcomes and costs. Outcomes were in-hospital mortality, intensive care unit (ICU) admission, length of stay, 30-day readmission, 30-day reintervention and in-hospital costs. Candidate PFs were age, sex, Elixhauser Comorbidity Score, prior hospitalizations, prior days spent in hospital, and socio-economic status. Included patients dealt with either colorectal carcinoma (CRC, n = 10,254), urinary bladder carcinoma (UBC, n = 17,385), acute percutaneous coronary intervention (aPCI, n = 25,818), or total knee arthroplasty (TKA, n = 39,214). Prior hospitalization significantly increased readmission risk in all treatments (OR between 2.15 and 25.50), whereas prior days spent in hospital decreased this risk (OR between 0.55 and 0.95). In CRC patients, women had lower risk of in-hospital mortality (OR 0.64), ICU admittance (OR 0.68) and 30-day reintervention (OR 0.70). Prior hospitalization was the strongest PF for higher costs across all treatments (31–64% costs increase/hospitalization). Prognostic model performance (c-statistic) ranged 0.67–0.92, with Brier scores below 0.08. R-squared ranged from 0.06–0.19 for LoS and 0.19–0.38 for costs. Identified PFs should be considered as building blocks for treatment-specific prognostic models and information for monitoring patients after surgery. Researchers and clinicians might benefit from gaining a better insight into the drivers behind (costs) prognosis

Основы теории действий в условиях неопределенности

Цель исследования: рассмотреть теорию непредвиденных обстоятельств, включая взаимосвязь между непредвиденными ситуациями и управленческими показателями, на основе которых можно будет вывести алгоритм действий в условиях неопределенности для каждого предприятия в отдельности (так как не существует универсальных систем, которые можно применять в любой организации)

Bulk Fermi surface and momentum density in heavily doped La2−x_{2-x}Srx_xCuO4_4 using high resolution Compton scattering and positron annihilation spectroscopies

We have observed the bulk Fermi surface (FS) in an overdoped ($x$=0.3) single crystal of La$_{2-x}$Sr$_x$CuO$_4$ by using Compton scattering. A two-dimensional (2D) momentum density reconstruction from measured Compton profiles yields a clear FS signature in the third Brillouin zone along [100]. The quantitative agreement between density functional theory (DFT) calculations and momentum density experiment suggests that Fermi-liquid physics is restored in the overdoped regime. In particular the predicted FS topology is found to be in good accord with the corresponding experimental data. We find similar quantitative agreement between the measured 2D angular correlation of positron annihilation radiation (2D-ACAR) spectra and the DFT based computations. However, 2D-ACAR does not give such a clear signature of the FS in the extended momentum space in either the theory or the experiment.Comment: 9 pages, 8 figure

Non-destructive Assessment of Quality and Yield for Grass-Breeding

Selection of cultivars has, until now, been based mainly on dry matter (DM) yields because of the high costs of sampling and chemical analysis. Imaging spectroscopy could reduce costs by limiting sampling and harvesting of individual plots to reference samples (Schut et al., accepted). In this study, the prediction accuracy of DM yields and chemical composition with imaging spectroscopy is evaluated for cultivar selection purposes

Ex Ante Scale Dynamics Analysis in the Policy Debate on Sustainable Biofuels in Mozambique

In this paper, we explore how ex ante scale dynamics analysis can contribute to better understanding of interactions between scales and levels, and how these interactions influence solution space in policy processes. In so doing, we address opportunities and challenges of conducting ex ante scale dynamics analysis as part of an action-oriented social science research approach that seeks to enhance its contribution to more scale-sensitive policy development. The policy debate on sustainable biofuels in Mozambique provides the empirical context in which we analyze interactions across administrative, institutional, and economic scales and levels, and how these interactions influence the space in which policy solutions can be explored and designed. On the basis of the analysis, we conclude that ex ante scale dynamics analysis can contribute to: (1) increasing awareness of interactions between scales and levels, and their implications for policy, (2) identifying immediate and potential matches and mismatches between scales and levels, and developing (adaptive) capacity to address them, and (3) identifying stakeholders and their scale- and level-related interests that can provide the basis for collaborative multi-stakeholder learning. Consequently, ex ante scale dynamics analysis can provide an important contribution to balancing and harmonizing interactions across different scales and levels, from which innovative and scale-sensitive policy responses can emerge. As part of an action-oriented, social science research approach, careful attention needs to be paid to processes of scale and level inclusion and exclusion when conducting scale dynamics analysis