Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Deep Learning Approach to Structure from Polarization

Imaging polarimeters are a type of imaging device that attempts to estimate the polarized Stokes vector at each point in an imaged scene. Polarization has shown the ability to reduce background clutter, defeat atmospheric scatterers, improve scene contrast within polarized regions, and provide shape information about polarized objects of interest. Measured angle of polarization imagery tends to be highly correlated with the azimuthal component of object surface normal vectors. Hence, while polarimetric images do not directly provide 3D scene information, our goal in this work is to investigate the applicability of deep learning approaches for estimation of 3D structure from polarimetric image data. Unlike other image modalities, no repositories of polarimetric training data are readily available for training and testing purposes. As part of this work, we design a set of laboratory-based data collection experiments under a controlled set of scene conditions to obtain a sufficient set of polarimetric training and testing data. We then develop a deep learning approach to structure from polarization based upon the Pix2Pix conditional generative adversarial network for image translation problems. Initial results from training and testing our approach are presented that demonstrate promise for obtaining pixel-wise 3D information from polarimetric image data

Kuluttajabarometri maakunnittain 2000, 2. neljännes

Suomen virallinen tilasto (SVT

Use of failure-to-rescue to identify international variation in postoperative care in low-, middle- and high-income countries: a 7-day cohort study of elective surgery

This was an investigator-initiated study funded by Nestle Health Sciences through an unrestricted research grant and by a National Institute for Health Research (UK) Professorship held by R.P. The study was sponsored by Queen Mary University of London