Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Data underlying the paper: Spatiotemporal patterns of extreme sea levels along the western North-Atlantic coasts

ATSR (Atlantic Tide and Surge Reanalysis) provides time-series of tides and surges from 1988-2015 for the western North-Atlantic coastline. Simulations are based on the Global Tide and Surge Model version 2.0. Tropical cyclones are explicitly included based on the Extended Best Track dataset and parametric cyclone model (Holland, 1980)

Time-series of monthly mean steric sea levels

This dataset with monthly mean sea levels complements the Global Tide and Surge Reanalysis (GTSR) dataset. The GTSR time-series do not includes variations in mean sea level driven by changes in water mass and water density. We estimated the density effect by computing the monthly mean steric sea levels based on temperature and salinity data profiles. The algorithm is developed by Amiruddin et al., (2015; {https:/doi.org/10.1002/2015JC010923}). We use monthly means of global gridded temperature and salinity data from Ishii and Kimoto (2009). This data set ends in 2012, so we use the EN4.1.1 dataset from the UK MET Office for the period 2012-2014 (Good et al., 2013; Gouretski & Reseghetti, 2010)

Daily maxima of total water levels from the Global Tide and Surge Reanalysis (GTSR) dataset

GTSR (Global Tide and Surge Reanalysis) is the first global reanalysis of storm surges and extreme sea levels based on hydrodynamic modelling. GTSR covers the entire world's coastline and provides time-series of tides and storm surge from 1979-2014. Tides are simulated with FES2012, while storm surges are simulated by forcing the Global Tide and Surge Model (GTSM) with wind and pressure fields from ERA-Interim. In addition to timeseries, GTSR also provides estimates of extreme sea levels for various return period based on the Gumbel distributio

STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p

STORM Climate Change synthetic tropical cyclone tracks

UPDATE 22/06/2023: Tom Russell (Oxford University) and colleagues have created global .tiff maps for the return period datasets. You can find them here: https://zenodo.org/record/7438145Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al (2020)). The dataset is generated by extracting the climate change signal from each of the four general circulation models listed below, and adding this signal to the historical data from IBTrACS. This new dataset is then used as input for STORM, and resembles future-climate (2015-2050; RCP8.5/SSP5) conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones.Climate change information from the following models is used in this study (each model has its own 10.000 years of STORM data):1) CMCC-CM2-VHR42) CNRM-CM6-1-HR3) EC-Earth3P-HR4) HAdGEM3-GC31-HMSee Roberts et al (2020) for more information on these models.</p