The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Assessment of a regional physical–biogeochemical stochastic ocean model. Part 1: Ensemble generation

International audienceIn this article, Part 1 of a two-part series, we run and evaluate the skill of a regional physical–biogeochemical stochastic ocean model based on NEMO. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. We generate model ensembles based on assumptions about errors in the atmospheric forcing, the ocean model parameterizations and in the sources and sinks of the biogeochemical variables. The resulting errors are found to be mainly driven by the wind forcing uncertainties, with the rest of the perturbed forcing and parameters locally influencing the ensemble spread. Biogeochemical uncertainties arise from intrinsic ecosystem model errors and from errors in the physical state. Uncertainties in physical forcing and parameterization are found to have a larger impact on chlorophyll spread than uncertainties in ecosystem sources and sinks. The ensembles undergo quantitative verification with respect to observations, focusing on upper-ocean properties. Despite a tendency for ensembles to be generally under-dispersive, they appear to be reasonably consistent with respect to sea surface temperature data. The largest statistical sea-level biases are observed in coastal regions. These biases hint at the presence of high-frequency error sources currently unaccounted for, and suggest that the ensemble-based uncertainties are unfit to model error covariances for assimilation. Model ensembles for chlorophyll appear to be consistent with ocean colour data only at times. The stochastic model is qualitatively evaluated by analysing its ability at generating consistent multivariate incremental model corrections. Corrections to physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. Mesoscale features imprint their signature on temperature and sea-level corrections, as well as on chlorophyll corrections due to the vertical velocities associated with vortices. Small scale local corrections are visible over the shelves. Chlorophyll information has measurable impact on physical variables

Assessment of a regional physical-biogeochemical stochastic ocean model. Part 1: Ensemble generation

In this article, Part 1 of a two-part series, we run and evaluate the skill of a regional physical-biogeochemical stochastic ocean model based on NEMO. The domain covers the Bay of Biscay at 1/36 degrees resolution, as a case study for open-ocean and coastal shelf dynamics. We generate model ensembles based on assumptions about errors in the atmospheric forcing, the ocean model parameterizations and in the sources and sinks of the biogeochemical variables. The resulting errors are found to be mainly driven by the wind forcing uncertainties, with the rest of the perturbed forcing and parameters locally influencing the ensemble spread. Biogeochemical uncertainties arise from intrinsic ecosystem model errors and from errors in the physical state. Uncertainties in physical forcing and parameterization are found to have a larger impact on chlorophyll spread than uncertainties in ecosystem sources and sinks. The ensembles undergo quantitative verification with respect to observations, focusing on upper-ocean properties. Despite a tendency for ensembles to be generally under-dispersive, they appear to be reasonably consistent with respect to sea surface temperature data. The largest statistical sea-level biases are observed in coastal regions. These biases hint at the presence of high-frequency error sources currently unaccounted for, and suggest that the ensemble-based uncertainties are unfit to model error covariances for assimilation. Model ensembles for chlorophyll appear to be consistent with ocean colour data only at times. The stochastic model is qualitatively evaluated by analysing its ability at generating consistent multivariate incremental model corrections. Corrections to physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. Mesoscale features imprint their signature on temperature and sea-level corrections, as well as on chlorophyll corrections due to the vertical velocities associated with vortices. Small scale local corrections are visible over the shelves. Chlorophyll information has measurable impact on physical variables