BackgroundThere is currently conflicting evidence surrounding the effects of obesity on postoperative outcomes. Previous studies have found obesity to be associated with adverse events, but others have found no association. The aim of this study was to determine whether increasing body mass index (BMI) is an independent risk factor for development of major postoperative complications.MethodsThis was a multicentre prospective cohort study across the UK and Republic of Ireland. Consecutive patients undergoing elective or emergency gastrointestinal surgery over a 4‐month interval (October–December 2014) were eligible for inclusion. The primary outcome was the 30‐day major complication rate (Clavien–Dindo grade III–V). BMI was grouped according to the World Health Organization classification. Multilevel logistic regression models were used to adjust for patient, operative and hospital‐level effects, creating odds ratios (ORs) and 95 per cent confidence intervals (c.i.).ResultsOf 7965 patients, 2545 (32·0 per cent) were of normal weight, 2673 (33·6 per cent) were overweight and 2747 (34·5 per cent) were obese. Overall, 4925 (61·8 per cent) underwent elective and 3038 (38·1 per cent) emergency operations. The 30‐day major complication rate was 11·4 per cent (908 of 7965). In adjusted models, a significant interaction was found between BMI and diagnosis, with an association seen between BMI and major complications for patients with malignancy (overweight: OR 1·59, 95 per cent c.i. 1·12 to 2·29, P = 0·008; obese: OR 1·91, 1·31 to 2·83, P = 0·002; compared with normal weight) but not benign disease (overweight: OR 0·89, 0·71 to 1·12, P = 0·329; obese: OR 0·84, 0·66 to 1·06, P = 0·147).ConclusionOverweight and obese patients undergoing surgery for gastrointestinal malignancy are at increased risk of major postoperative complications compared with those of normal weight.

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

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