The CAMELS data set:Catchment attributes and meteorology for large-sample studies

We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: Topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al

Swiss Validation of the Enhanced Recovery After Surgery (ERAS) Database.

Enhanced recovery after surgery (ERAS) pathways have considerably improved postoperative outcomes and are in use for various types of surgery. The prospective audit system (EIAS) could be a powerful tool for large-scale outcome research but its database has not been validated yet. Swiss ERAS centers were invited to contribute to the validation of the Swiss chapter for colorectal surgery. A monitoring team performed on-site visits by the use of a standardized checklist. Validation criteria were (I) coverage (No. of operated patients within ERAS protocol; target threshold for validation: ≥ 80%), (II) missing data (8 predefined variables; target ≤ 10%), and (III) accuracy (2 predefined variables, target ≥ 80%). These criteria were assessed by comparing EIAS entries with the medical charts of a random sample of patients per center (range 15-20). Out of 18 Swiss ERAS centers, 15 agreed to have onsite monitoring but 13 granted access to the final dataset. ERAS coverage was available in only 7 centers and varied between 76 and 100%. Overall missing data rate was 5.7% and concerned mainly the variables "urinary catheter removal" (16.4%) and "mobilization on day 1" (16%). Accuracy for the length of hospital stay and complications was overall 84.6%. Overall, 5 over 13 centers failed in the validation process for one or several criteria. EIAS was validated in most Swiss ERAS centers. Potential patient selection and missing data remain sources of bias in non-validated centers. Therefore, simplified validation of other centers appears to be mandatory before large-scale use of the EIAS dataset

Impact of weekday surgery on application of enhanced recovery pathway: a retrospective cohort study.

To compare the enhanced recovery after surgery (ERAS) protocol compliance and clinical outcomes depending on the weekday of surgery. Cohort of consecutive non-selected patients undergoing elective colorectal surgery from January 2012 to March 2015. This retrospective analysis of our prospective database compared patients operated early in the week (Monday and Tuesday) with patients operated in the second half (late: Thursday, Friday). Compliance with the ERAS protocol, functional recovery, complications and length of stay. Demographic and surgical details were similar between the early (n=352) and late groups (n=204). Overall compliance with the ERAS protocol was 78% vs 76% for the early and late groups, respectively (p=0.009). Significant differences were notably prolonged urinary drainage and intravenous fluid infusion in the late group. Complication rates and length of stay, however, were not different between surgery on Monday or Tuesday and surgery on Thursday or Friday. Application of the ERAS protocol showed only minor differences for patients operated on early or late during the week, and clinical outcomes were similar. A fully implemented ERAS programme appears to work also over the weekend

Enhanced Recovery after Elective Colorectal Surgery - Reasons for Non-Compliance with the Protocol.

Enhanced recovery after surgery (ERAS) protocols for elective colorectal surgery reduce the intensity of postoperative complications, hospital stays and costs. Improvements in clinical outcome are directly proportional to the adherence to the recommended pathway (compliance). The aim of the present study was to analyze reasons for the non-compliance of colorectal surgeries with the ERAS protocol. A consecutive cohort of patients undergoing elective colorectal surgery was prospectively analyzed with regards to the surgery's compliance with the ERAS protocol. The reason for every single protocol deviation was documented and the decision was categorized based on whether it was medically justified or not. During the 8-month study period, 76 patients were included. The overall compliance with 22 ERAS items was 76% (96% in the preoperative, 82% in the perioperative, and 63% in the postoperative period). The decision to deviate from the clinical pathway was mainly a medical decision, while patients and nurses were responsible in 26 and 14% of the cases, respectively. However, reasons for non-compliance were medically justified in 78% of the study participants. 'Non-compliance' with the ERAS protocol was observed mostly in the postoperative period. Most deviations from the pathway were decided by doctors and in a majority of cases it appeared that they were due to a medical necessity rather than non-compliance. However, almost a quarter of deviations that were absolutely required are still amenable to improvement

Mapping (dis)agreement in hydrologic projections

Hydrologic projections are of vital socio-economic importance. However, they are also prone to uncertainty. In order to establish a meaningful range of storylines to support water managers in decision making, we need to reveal the relevant sources of uncertainty. Here, we systematically and extensively investigate uncertainty in hydrologic projections for 605 basins throughout the contiguous US. We show that in the majority of the basins, the sign of change in average annual runoff and discharge timing for the period 2070–2100 compared to 1985–2008 differs among combinations of climate models, hydrologic models, and parameters. Mapping the results revealed that different sources of uncertainty dominate in different regions. Hydrologic model induced uncertainty in the sign of change in mean runoff was related to snow processes and aridity, whereas uncertainty in both mean runoff and discharge timing induced by the climate models was related to disagreement among the models regarding the change in precipitation. Overall, disagreement on the sign of change was more widespread for the mean runoff than for the discharge timing. The results demonstrate the need to define a wide range of quantitative hydrologic storylines, including parameter, hydrologic model, and climate model forcing uncertainty, to support water resource planning

A ranking of hydrological signatures based on their predictability in space

Hydrological signatures are now used for a wide range of purposes, including catchment classification, process exploration and hydrological model calibration. The recent boost in the popularity and number of signatures has however not been accompanied by the development of clear guidance on signature selection. Here we propose that exploring the predictability of signatures in space provides important insights into their drivers, their sensitivity to data uncertainties, and is hence useful for signature selection. We use three complementary approaches to compare and rank 15 commonly‐used signatures, which we evaluate in 671 US catchments from the CAMELS data set (Catchment Attributes and MEteorology for Large‐sample Studies). Firstly, we employ machine learning (random forests) to explore how attributes characterizing the climatic conditions, topography, land cover, soil and geology influence (or not) the signatures. Secondly, we use simulations of a conceptual hydrological model (Sacramento) to benchmark the random forest predictions. Thirdly, we take advantage of the large sample of CAMELS catchments to characterize the spatial auto‐correlation (using Moran's I) of the signature field. These three approaches lead to remarkably similar rankings of the signatures. We show i) that signatures with the noisiest spatial pattern tend to be poorly captured by hydrological simulations, ii) that their relationship to catchments attributes are elusive (in particular they are not correlated to climatic indices) and iii) that they are particularly sensitive to discharge uncertainties. We suggest that a better understanding of their drivers and better characterization of their uncertainties would increase their value in hydrological studies

Prevalence and risk of Down syndrome in monozygotic and dizygotic multiple pregnancies in Europe: implications for prenatal screening.

OBJECTIVE: To determine risk of Down syndrome (DS) in multiple relative to singleton pregnancies, and compare prenatal diagnosis rates and pregnancy outcome. DESIGN: Population-based prevalence study based on EUROCAT congenital anomaly registries. SETTING: Eight European countries. POPULATION: 14.8 million births 1990-2009; 2.89% multiple births. METHODS: DS cases included livebirths, fetal deaths from 20 weeks, and terminations of pregnancy for fetal anomaly (TOPFA). Zygosity is inferred from like/unlike sex for birth denominators, and from concordance for DS cases. MAIN OUTCOME MEASURES: Relative risk (RR) of DS per fetus/baby from multiple versus singleton pregnancies and per pregnancy in monozygotic/dizygotic versus singleton pregnancies. Proportion of prenatally diagnosed and pregnancy outcome. STATISTICAL ANALYSIS: Poisson and logistic regression stratified for maternal age, country and time. RESULTS: Overall, the adjusted (adj) RR of DS for fetus/babies from multiple versus singleton pregnancies was 0.58 (95% CI 0.53-0.62), similar for all maternal ages except for mothers over 44, for whom it was considerably lower. In 8.7% of twin pairs affected by DS, both co-twins were diagnosed with the condition. The adjRR of DS for monozygotic versus singleton pregnancies was 0.34 (95% CI 0.25-0.44) and for dizygotic versus singleton pregnancies 1.34 (95% CI 1.23-1.46). DS fetuses from multiple births were less likely to be prenatally diagnosed than singletons (adjOR 0.62 [95% CI 0.50-0.78]) and following diagnosis less likely to be TOPFA (adjOR 0.40 [95% CI 0.27-0.59]). CONCLUSIONS: The risk of DS per fetus/baby is lower in multiple than singleton pregnancies. These estimates can be used for genetic counselling and prenatal screening

CAMELS-GB : a large sample, open-source, hydro-meteorological dataset for Great Britain

Data underpins our knowledge and understanding of the hydrological system; they are used to drive, test and evaluate hydrological models and advance our understanding of hydrological processes and dynamics. With the increasing availability of observational datasets, the integration of information from many catchments for data and modelling analyses is becoming increasingly common. The production of new, open source, datasets for large samples of catchments is vital to advance knowledge on hydrological processes and to ensure hydrological research is reusable and reproducible through the use of common datasets and code. However, the availability of open source, large-sample catchment datasets is notably sparse. In this study, we present CAMELS-GB, the first large sample, open-source, hydro-meteorological catchment dataset for Great Britain (GB). CAMELS-GB integrates a wealth of different datasets derived from national, continental and global products based on observational, satellite and modelled data. The dataset consists of hydro-meteorological timeseries, catchment attributes and catchment boundaries for >800 catchments that cover a wide range of climatic, hydrological, landscape and human management characteristics across GB. Long daily timeseries is provided for a range of hydro-meteorological data (including rainfall, potential-evapotranspiration, temperature, radiation, humidity and flow) from 1970-2015 covering several major hydrological events. A comprehensive set of catchment attributes are provided describing a range of catchment characteristics including topography, climate, hydrology, land cover, soils and (hydro)-geology. Importantly, we also derive human impact attributes (including abstraction returns, percentage urban and gauge distance from reservoir), as well as attributes describing the quality of the flow data (including discharge uncertainty estimates and out of bank flow). The dataset and code used to derive the data will be made open source and provided with comprehensive metadata to allow its use in a wide range of hydro-meteorological data and environmental modelling analyses

Consensus on Training and Implementation of Enhanced Recovery After Surgery: A Delphi Study.

Enhanced Recovery After Surgery (ERAS) is widely accepted in current surgical practice due to its positive impact on patient outcomes. The successful implementation of ERAS is challenging and compliance with protocols varies widely. Continual staff education is essential for successful ERAS programmes. Teaching modalities exist, but there remains no agreement regarding the optimal training curriculum or how its effectiveness is assessed. We aimed to draw consensus from an expert panel regarding the successful training and implementation of ERAS. A modified Delphi technique was used; three rounds of questionnaires were sent to 58 selected international experts from 11 countries across multiple ERAS specialities and multidisciplinary teams (MDT) between January 2016 and February 2017. We interrogated opinion regarding four topics: (1) the components of a training curriculum and the structure of training courses; (2) the optimal framework for successful implementation and audit of ERAS including a guide for data collection; (3) a framework to assess the effectiveness of training; (4) criteria to define ERAS training centres of excellence. An ERAS training course must cover the evidence-based principles of ERAS with team-oriented training. Successful implementation requires strong leadership, an ERAS facilitator and an effective MDT. Effectiveness of training can be measured by improved compliance. A training centre of excellence should show a willingness to teach and demonstrable team working. We propose an international expert consensus providing an ERAS training curriculum, a framework for successful implementation, methods for assessing effectiveness of training and a definition of ERAS training centres of excellence