Continuous glucose monitoring in pregnant women with type 1 diabetes (CONCEPTT): a multicentre international randomised controlled trial.

BACKGROUND: Pregnant women with type 1 diabetes are a high-risk population who are recommended to strive for optimal glucose control, but neonatal outcomes attributed to maternal hyperglycaemia remain suboptimal. Our aim was to examine the effectiveness of continuous glucose monitoring (CGM) on maternal glucose control and obstetric and neonatal health outcomes. METHODS: In this multicentre, open-label, randomised controlled trial, we recruited women aged 18-40 years with type 1 diabetes for a minimum of 12 months who were receiving intensive insulin therapy. Participants were pregnant (≤13 weeks and 6 days' gestation) or planning pregnancy from 31 hospitals in Canada, England, Scotland, Spain, Italy, Ireland, and the USA. We ran two trials in parallel for pregnant participants and for participants planning pregnancy. In both trials, participants were randomly assigned to either CGM in addition to capillary glucose monitoring or capillary glucose monitoring alone. Randomisation was stratified by insulin delivery (pump or injections) and baseline glycated haemoglobin (HbA1c). The primary outcome was change in HbA1c from randomisation to 34 weeks' gestation in pregnant women and to 24 weeks or conception in women planning pregnancy, and was assessed in all randomised participants with baseline assessments. Secondary outcomes included obstetric and neonatal health outcomes, assessed with all available data without imputation. This trial is registered with ClinicalTrials.gov, number NCT01788527. FINDINGS: Between March 25, 2013, and March 22, 2016, we randomly assigned 325 women (215 pregnant, 110 planning pregnancy) to capillary glucose monitoring with CGM (108 pregnant and 53 planning pregnancy) or without (107 pregnant and 57 planning pregnancy). We found a small difference in HbA1c in pregnant women using CGM (mean difference -0·19%; 95% CI -0·34 to -0·03; p=0·0207). Pregnant CGM users spent more time in target (68% vs 61%; p=0·0034) and less time hyperglycaemic (27% vs 32%; p=0·0279) than did pregnant control participants, with comparable severe hypoglycaemia episodes (18 CGM and 21 control) and time spent hypoglycaemic (3% vs 4%; p=0·10). Neonatal health outcomes were significantly improved, with lower incidence of large for gestational age (odds ratio 0·51, 95% CI 0·28 to 0·90; p=0·0210), fewer neonatal intensive care admissions lasting more than 24 h (0·48; 0·26 to 0·86; p=0·0157), fewer incidences of neonatal hypoglycaemia (0·45; 0·22 to 0·89; p=0·0250), and 1-day shorter length of hospital stay (p=0·0091). We found no apparent benefit of CGM in women planning pregnancy. Adverse events occurred in 51 (48%) of CGM participants and 43 (40%) of control participants in the pregnancy trial, and in 12 (27%) of CGM participants and 21 (37%) of control participants in the planning pregnancy trial. Serious adverse events occurred in 13 (6%) participants in the pregnancy trial (eight [7%] CGM, five [5%] control) and in three (3%) participants in the planning pregnancy trial (two [4%] CGM and one [2%] control). The most common adverse events were skin reactions occurring in 49 (48%) of 103 CGM participants and eight (8%) of 104 control participants during pregnancy and in 23 (44%) of 52 CGM participants and five (9%) of 57 control participants in the planning pregnancy trial. The most common serious adverse events were gastrointestinal (nausea and vomiting in four participants during pregnancy and three participants planning pregnancy). INTERPRETATION: Use of CGM during pregnancy in patients with type 1 diabetes is associated with improved neonatal outcomes, which are likely to be attributed to reduced exposure to maternal hyperglycaemia. CGM should be offered to all pregnant women with type 1 diabetes using intensive insulin therapy. This study is the first to indicate potential for improvements in non-glycaemic health outcomes from CGM use. FUNDING: Juvenile Diabetes Research Foundation, Canadian Clinical Trials Network, and National Institute for Health Research

General anaesthetic and airway management practice for obstetric surgery in England: a prospective, multi-centre observational study

There are no current descriptions of general anaesthesia characteristics for obstetric surgery, despite recent changes to patient baseline characteristics and airway management guidelines. This analysis of data from the direct reporting of awareness in maternity patients' (DREAMY) study of accidental awareness during obstetric anaesthesia aimed to describe practice for obstetric general anaesthesia in England and compare with earlier surveys and best-practice recommendations. Consenting patients who received general anaesthesia for obstetric surgery in 72 hospitals from May 2017 to August 2018 were included. Baseline characteristics, airway management, anaesthetic techniques and major complications were collected. Descriptive analysis, binary logistic regression modelling and comparisons with earlier data were conducted. Data were collected from 3117 procedures, including 2554 (81.9%) caesarean deliveries. Thiopental was the induction drug in 1649 (52.9%) patients, compared with propofol in 1419 (45.5%). Suxamethonium was the neuromuscular blocking drug for tracheal intubation in 2631 (86.1%), compared with rocuronium in 367 (11.8%). Difficult tracheal intubation was reported in 1 in 19 (95%CI 1 in 16-22) and failed intubation in 1 in 312 (95%CI 1 in 169-667). Obese patients were over-represented compared with national baselines and associated with difficult, but not failed intubation. There was more evidence of change in practice for induction drugs (increased use of propofol) than neuromuscular blocking drugs (suxamethonium remains the most popular). There was evidence of improvement in practice, with increased monitoring and reversal of neuromuscular blockade (although this remains suboptimal). Despite a high risk of difficult intubation in this population, videolaryngoscopy was rarely used (1.9%)

OceanParcels/parcels: Parcels v1.0.4

<p>Parcels v1.0.4 builds on the previous v1.0.3 release. Major changes since then</p> <ul> <li>Support for <code>FieldLists</code> (#393), which allows Kernels like <code>AdvectionRK4</code> to work on multiple <code>Fields</code> at once. See also the <a href="http://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_FieldLists.ipynb">tutorial</a></li> <li>Changing the plotting routines from <code>Basemap</code> to <code>cartopy</code> (#401). Note that this means you will need to uninstall <code>Basemap</code> and install <code>cartopy</code>, as they are conflicting</li> <li>Interpolation of velocities on C-grids. This also means that the angle file is not needed anymore in <code>FieldSet.from_nemo()</code> (#394)</li> <li>Much smaller output files by controlling the NetCDF chunk size (#366)</li> <li>Displaying a <code>Progressbar</code> for long (> 10 seconds) runs of <code>ParticleSet.execute()</code> (#381 and #418)</li> <li>Possibility to initialise custom <code>Variables</code> directly in <code>ParticleSet.from_list()</code> (#397)</li> <li>Numerous bug fixes</li> </ul> <p>As always, please let us know if anything isn't working as expected.</p&gt

OceanParcels/parcels: Parcels v2.0.0-beta: a Lagrangian Ocean Analysis tool for the petascale age

This is the beta-release of Parcels v2. Compared to the last v1.1.1 release, there are three important changes 1) The order of arguments for Field interpolation has changed. This is now field[time, depth, lat, lon], which is consistent with the dimension order in which data is stored in the field.data numpy array (#503 and #276). 2) The dt argument has been dropped from Kernel definitions, so that the only arguments allowed in a Kernel are def kernelfunc(fieldset, particle, time) (#503) 3) Interpolation for C-grids is now done in a fluxes framework, instead of a velocity framework. The details of this will be presented in a manuscript, to be submitted soon (#499 and #494) Note that 1) and 2) above mean that Kernels written for Parcels v1 will break in this Parcels v2. If you're updating to this v2.0.0beta, therefore please update your custom Kernels. Other updates since v1.1.1 are: New FieldSet.from_xarray_dataset() method to directly read xarray.DataSet objects (#476) An optional argument in Field.show() to control which depth level to plot (#478) ParticleSet.from_field() now also implemented for Curvilinear Fields (#496) And numerous small bug fixesThis is the beta-release of Parcels v2. Compared to the last v1.1.1 release, there are three important changes 1) The order of arguments for Field interpolation has changed. This is now field[time, depth, lat, lon], which is consistent with the dimension order in which data is stored in the field.data numpy array (#503 and #276). 2) The dt argument has been dropped from Kernel definitions, so that the only arguments allowed in a Kernel are def kernelfunc(fieldset, particle, time) (#503) 3) Interpolation for C-grids is now done in a fluxes framework, instead of a velocity framework. The details of this will be presented in a manuscript, to be submitted soon (#499 and #494) Note that 1) and 2) above mean that Kernels written for Parcels v1 will break in this Parcels v2. If you're updating to this v2.0.0beta, therefore please update your custom Kernels. Other updates since v1.1.1 are: New FieldSet.from_xarray_dataset() method to directly read xarray.DataSet objects (#476) An optional argument in Field.show() to control which depth level to plot (#478) ParticleSet.from_field() now also implemented for Curvilinear Fields (#496) And numerous small bug fixes2.0.

OceanParcels/parcels: Parcels v2.1.0: a Lagrangian Ocean Analysis tool for the petascale age

<p>Parcels v2.1.0 builds on previous versions v2.0.0. The major changes of v2.1.0 are:</p> <ol> <li>Parcels has a parallel MPI version! While working on multiple processors, the particles are spread over the processors for an efficient integration. (#625). See <a href="https://oceanparcels.org#parallel_install">https://oceanparcels.org#parallel_install</a> for instructions on how to install.</li> <li>For an efficient loading of the <code>Fieldset</code>, the <code>Field</code> objects are now loaded by chunks, controlled by the parameter <code>field_chunksize</code> (#632). This results in lower memory usage and faster simulation. It is also a fundamental part of the parallel implementation, since for low number of particles per processor, the computation time is dominated by the loading of the data. A more efficient parallel version will be dynamically balancing the particles between the processors such to minimise the number of chunks loaded per processor. See <a href="https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/documentation_MPI.ipynb">this document</a> for further background on the implementation.</li> <li>An efficient writing of the particleset. For a quicker export of the data, particles are now dumped into npy files during simulation. The pickles are gathered into one single file at the end of the simulation. (#614)</li> <li>A proper management of <code>particle.dt</code> modified by the kernel. If the kernel modifies <code>particle.dt</code>, the kernel will automatically be restarted with the updated <code>dt</code>. If you want to simply updates the <code>dt</code> for next kernel call, use <code>particle.update_next_dt(new_dt)</code>. (#657)</li> <li>New particles can now be added to the <code>ParticleSet</code> only via a temporary <code>ParticleSet</code> object. This enables a proper control of the <code>particle.id</code> in parallel (#629)</li> <li><code>Field.gradient()</code> function is not available anymore. This functionality was providing spurious results on curvilinear grids and was conflicting with the use of chunked fields. Users can still obtain easily an accurate field gradient (see example proposed in #633) </li> <li>Numerous bug fixes</li> </ol> <p>Note that Parcels v2.1.0 is the last version to officially support Python 2.7. While all functionalities currently work with both Python 2 and 3, new development and code dependencies will progressively lead to incompatibility with Python 2. We strongly advice the users to switch to Python 3.</p&gt

OceanParcels/parcels: Parcels v2.2.0: a Lagrangian Ocean Analysis tool for the petascale age

<p>Parcels v2.2.0 builds upon previous versions of Parcels and adds a number of new features. most importantly</p> <ol> <li>Improved advection-diffusion kernels, as detailed in <a href="https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_diffusion.ipynb">this tutorial</a> by @daanreijnders (#823)</li> <li>Support for time-evolving sigma-grids (#660)</li> <li>New interpolation scheme for tracers near coastlines, as detailed in <a href="https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_interpolation.ipynb">this tutorial</a>, thanks to @pierrick-giffard. (#815)</li> <li>Addition of Kernels to calculate the TEOS-10 equation of state, thanks to @pdnooteboom (#816)</li> <li>Partial implementation of AnalyticalAdvection Kernel following <a href="https://www.geosci-model-dev.net/10/1733/2017/">Döös et al 2017</a>, see also <a href="https://nbviewer.jupyter.org/github/OceanParcels/parcels/blob/master/parcels/examples/tutorial_analyticaladvection.ipynb">this tutorial</a></li> <li>Support for dimensions of length-1. This greatly simplifies creating FieldSets that are constant in longitude and/or latitude (#817)</li> </ol> <p>Plus a large number of minor bug fixes</p&gt