Variability in outcome and resource use in intensive care units

OBJECTIVE: To examine variability in outcome and resource use between ICUs. Secondary aims: to assess whether outcome and resource use are related to ICU structure and process, to explore factors associated with efficient resource use. DESIGN AND SETTING: Cohort study, based on the SAPS 3 database in 275 ICUs worldwide. PATIENTS: 16,560 adults. MEASUREMENTS AND RESULTS: Outcome was defined by standardized mortality rate (SMR). Standardized resource use (SRU) was calculated based on length of stay in the ICU, adjusted for severity of acute illness. Each unit was assigned to one of four groups: "most efficient" (SMR and SRU < median); "least efficient" (SMR, SRU > median); "overachieving" (low SMR, high SRU), "underachieving" (high SMR, low SRU). Univariate analysis and stepwise logistic regression were used to test for factors separating "most" from "least efficient" units. Overall median SMR was 1.00 (IQR 0.77-1.28) and SRU 1.07 (0.76-1.58). There were 91 "most efficient", 91 "least efficient", 47 "overachieving", and 46 "underachieving" ICUs. Number of physicians, of full-time specialists, and of nurses per bed, clinical rounds, availability of physicians, presence of emergency department, and geographical region were significant in univariate analysis. In multivariate analysis only interprofessional rounds, emergency department, and geographical region entered the model as significant. CONCLUSIONS: Despite considerable variability in outcome and resource use only few factors of ICU structure and process were associated with efficient use of ICU. This suggests that other confounding factors play an important role.

Tree phenology and carbon dioxide fluxes - use of digital photography for process-based interpretation at the ecosystem scale

Vegetation phenology is an important indicator of climate change and climate variability and it is strongly connected to biospheric–atmospheric gas exchange. We aimed to evaluate the applicability of phenological information derived from digital imagery for the interpretation of CO2 exchange measurements. For the years 2005–2007 we analyzed seasonal phenological development of 2 temperate mixed forests using tower-based imagery from standard RGB cameras. Phenological information was jointly analyzed with gross primary productivity (GPP) derived from net ecosystem exchange data. Automated image analysis provided reliable information on vegetation developmental stages of beech and ash trees covering all seasons. A phenological index derived from image color values was strongly correlated with GPP, with a significant mean time lag of several days for ash trees and several weeks for beech trees in early summer (May to mid-July). Leaf emergence dates for the dominant tree species partly explained temporal behaviour of spring GPP but were also masked by local meteorological conditions. We conclude that digital cameras at flux measurement sites not only provide an objective measure of the physiological state of a forest canopy at high temporal and spatial resolutions, but also complement CO2 and water exchange measurements, improving our knowledge of ecosystem processes.

Environmental variability promotes plant invasion

Global environmental change not only entails changes in mean environmental conditions but also in their variability. Changes in climate variability are often associated with altered disturbance regimes and temporal patterns of resource availability. Here we show that increased variability of soil nutrients strongly promotes another key process of global change, plant invasion. In experimental plant communities, the success of one of the world's most invasive plants, Japanese knotweed, is two- to four-fold increased if extra nutrients are not supplied uniformly, but in a single large pulse, or in multiple pulses of different magnitudes. The superior ability to take advantage of variable environments may be a key mechanism of knotweed dominance, and possibly many other plant invaders. Our study demonstrates that increased nutrient variability can promote plant invasion, and that changes in environmental variability may interact with other global change processes and thereby substantially accelerate ecological change

Methane emissions from floodplains in the Amazon Basin: challenges in developing a process-based model for global applications

Tropical wetlands are estimated to represent about 50% of the natural wetland methane (CH4) emissions and explain a large fraction of the observed CH4 variability on timescales ranging from glacial–interglacial cycles to the currently observed year-to-year variability. Despite their importance, however, tropical wetlands are poorly represented in global models aiming to predict global CH4 emissions. This publication documents a first step in the development of a process-based model of CH4 emissions from tropical floodplains for global applications. For this purpose, the LPX-Bern Dynamic Global Vegetation Model (LPX hereafter) was slightly modified to represent floodplain hydrology, vegetation and associated CH4 emissions. The extent of tropical floodplains was prescribed using output from the spatially explicit hydrology model PCR-GLOBWB. We introduced new plant functional types (PFTs) that explicitly represent floodplain vegetation. The PFT parameterizations were evaluated against available remote-sensing data sets (GLC2000 land cover and MODIS Net Primary Productivity). Simulated CH4 flux densities were evaluated against field observations and regional flux inventories. Simulated CH4 emissions at Amazon Basin scale were compared to model simulations performed in the WETCHIMP intercomparison project. We found that LPX reproduces the average magnitude of observed net CH4 flux densities for the Amazon Basin. However, the model does not reproduce the variability between sites or between years within a site. Unfortunately, site information is too limited to attest or disprove some model features. At the Amazon Basin scale, our results underline the large uncertainty in the magnitude of wetland CH4 emissions. Sensitivity analyses gave insights into the main drivers of floodplain CH4 emission and their associated uncertainties. In particular, uncertainties in floodplain extent (i.e., difference between GLC2000 and PCR-GLOBWB output) modulate the simulated emissions by a factor of about 2. Our best estimates, using PCR-GLOBWB in combination with GLC2000, lead to simulated Amazon-integrated emissions of 44.4 ± 4.8 Tg yr−1. Additionally, the LPX emissions are highly sensitive to vegetation distribution. Two simulations with the same mean PFT cover, but different spatial distributions of grasslands within the basin, modulated emissions by about 20%. Correcting the LPX-simulated NPP using MODIS reduces the Amazon emissions by 11.3%. Finally, due to an intrinsic limitation of LPX to account for seasonality in floodplain extent, the model failed to reproduce the full dynamics in CH4 emissions but we proposed solutions to this issue. The interannual variability (IAV) of the emissions increases by 90% if the IAV in floodplain extent is accounted for, but still remains lower than in most of the WETCHIMP models. While our model includes more mechanisms specific to tropical floodplains, we were unable to reduce the uncertainty in the magnitude of wetland CH4 emissions of the Amazon Basin. Our results helped identify and prioritize directions towards more accurate estimates of tropical CH4 emissions, and they stress the need for more research to constrain floodplain CH4 emissions and their temporal variability, even before including other fundamental mechanisms such as floating macrophytes or lateral water fluxes.

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations

Multi-objective optimization algorithms aim at finding Pareto-optimal solutions. Recovering Pareto fronts or Pareto sets from a limited number of function evaluations are challenging problems. A popular approach in the case of expensive-to-evaluate functions is to appeal to metamodels. Kriging has been shown efficient as a base for sequential multi-objective optimization, notably through infill sampling criteria balancing exploitation and exploration such as the Expected Hypervolume Improvement. Here we consider Kriging metamodels not only for selecting new points, but as a tool for estimating the whole Pareto front and quantifying how much uncertainty remains on it at any stage of Kriging-based multi-objective optimization algorithms. Our approach relies on the Gaussian process interpretation of Kriging, and bases upon conditional simulations. Using concepts from random set theory, we propose to adapt the Vorob’ev expectation and deviation to capture the variability of the set of non-dominated points. Numerical experiments illustrate the potential of the proposed workflow, and it is shown on examples how Gaussian process simulations and the estimated Vorob’ev deviation can be used to monitor the ability of Kriging-based multi-objective optimization algorithms to accurately learn the Pareto front.

The Neuropsychological Assessment of Cognitive Deficits Considering Measures of Performance Variability

Neuropsychologists often face interpretational difficulties when assessing cognitive deficits, particularly in cases of unclear cerebral etiology. How can we be sure whether a single test score below the population average is indicative of a pathological brain condition or normal? In the past few years, the topic of intra-individual performance variability has gained great interest. On the basis of a large normative sample, two measures of performance variability and their importance for neuropsychological interpretation will be presented in this paper: the number of low scores and the level of dispersion.We conclude that low scores are common in healthy individuals. On the other hand, the level of dispersion is relatively small. Here, base rate information about abnormally low scores and abnormally high dispersion across cognitive abilities are providedto improve the awareness of normal variability and to serve clinicians as additional interpretive measures in the diagnostic process.