Background Ileus is common after elective colorectal surgery, and is associated with increased adverse events and prolonged hospital stay. The aim was to assess the role of non‐steroidal anti‐inflammatory drugs (NSAIDs) for reducing ileus after surgery. Methods A prospective multicentre cohort study was delivered by an international, student‐ and trainee‐led collaborative group. Adult patients undergoing elective colorectal resection between January and April 2018 were included. The primary outcome was time to gastrointestinal recovery, measured using a composite measure of bowel function and tolerance to oral intake. The impact of NSAIDs was explored using Cox regression analyses, including the results of a centre‐specific survey of compliance to enhanced recovery principles. Secondary safety outcomes included anastomotic leak rate and acute kidney injury. Results A total of 4164 patients were included, with a median age of 68 (i.q.r. 57–75) years (54·9 per cent men). Some 1153 (27·7 per cent) received NSAIDs on postoperative days 1–3, of whom 1061 (92·0 per cent) received non‐selective cyclo‐oxygenase inhibitors. After adjustment for baseline differences, the mean time to gastrointestinal recovery did not differ significantly between patients who received NSAIDs and those who did not (4·6 versus 4·8 days; hazard ratio 1·04, 95 per cent c.i. 0·96 to 1·12; P = 0·360). There were no significant differences in anastomotic leak rate (5·4 versus 4·6 per cent; P = 0·349) or acute kidney injury (14·3 versus 13·8 per cent; P = 0·666) between the groups. Significantly fewer patients receiving NSAIDs required strong opioid analgesia (35·3 versus 56·7 per cent; P < 0·001). Conclusion NSAIDs did not reduce the time for gastrointestinal recovery after colorectal surgery, but they were safe and associated with reduced postoperative opioid requirement.

Total N Recipient Sex Male Female p-Value Donor Age (Years) 25140 50 (38-59) 49 (37-59) 0.020 Donor Sex (Male) 25140 8403 (53.6%) (53.0%) 0.418 Donor BMI 24449 25.6 (22.9-28.7) 25.4 (22.9-28.7) 0.077 Donor Ethnicity 25099 0.214 White 15084 (96.3%) (96.0%) Asian 264 (1.7%) (1.9%) Black 162 (1.0%) 86 (0.9%) Mixed/Other 158 (1.0%) (1.2%) Donor Terminal Creatinine 23382 75 (59-97) 75 (59-98) 0.701 Donor Cause of Death 25140 0.063 Intracranial haemorrhage 9845 (62.7%) (63.3%) Hypoxic brain damage 2624 (16.7%) (16.5%) Trauma 1752 (11.2%) (11.7%) Other 1469 (9.4%) (8.5%) Donor Type (DBD) 25140 11181 (71.3%) (75.2%) <0.001 Donor History of Diabetes 24485 823 (5.4%) (4.9%) 0.123 Donor History of Hypertension 24238 3650 (24.2%) (23.2%) 0.084 Donor Smoking History 24414 7364 (48.4%) (47.8%) 0.376 Donor HCV (Positive) 25053 21 (0.1%) 9 (0.1%) 0.451 Donor CMV (Positive) 24833 7668 (49.5%) (48.9%) 0.359 Donor Risk Index 23371 1.04 (0.96-1.44) 1.04 (0.96-1.43) 0.229 Recipient Age (Years) 25139 50 (38-59) 49 (37-59) <0.001 Recipient BMI 14402 25.9 (23.0-29.3) 25.3 (21.8-29.2) <0.001 Recipient Ethnicity 24934 0.007 White 12290 (79.0%) (77.5%) Asian 2050 (13.2%) (14.7%) Black 1004 (6.4%) (6.5%) Mixed/Other 222 (1.4%) (1.3%) Recipient Diabetes 25140 1276 (8.1%) (6.0%) <0.001 Recipient HCV (Positive) 18823 80 (0.7%) 52 (0.7%) 0.721 Recipient CMV (Positive) 22424 6828 (48.6%) (56.0%) <0.001 Recipient Waiting Time (Days) 25116 718 (292-1299) 811 (323-1458) <0.001 Recipient Dialysis at Transplant 22506 <0.001 Haemodialysis 10028 (71.2%) (64.5%) Peritoneal Dialysis 4025 (28.6%) (35.3%) Not on Dialysis 26 (0.2%) 19 (0.2%) Transplant Year 25140 0.050* 2000-2004 4014 (25.6%) (26.1%) 2005-2009 3906 (24.9%) (26.3%) 2010-2013 4139 (26.4%) (24.7%) 2014-2016 3631 (23.1%) (22.8%) CIT (Mins) 24834 954 (758-1174) 963 (780-1174) 0.002 Mismatch Level 25138 <0.001* 1 2042 (13.0%) (16.2%) 2 6023 (38.4%) (39.0%) 3 6502 (41.4%) (39.0%) 4 1122 (7.2%) (5.7%) AITX 25140 <0.001* No 15588 (99.3%) (98.8%) HLAi 99 (0.6%) (1.2%) ABOi 3 (0.0%) 1 (0.0%) CRF at Transplant 25139 <0.001* 0% 11446 (73.0%) 5234 (55.4%) 1-85% 3309 (21.1%) 3023 (32.0%) >85% 934 (6.0%) 1193 (12.6%) Continuous factors are reported as medians and interquartile ranges, with p-values from Mann-Whitney tests. Categorical factors are reported as N (%), with p-values from Fisher's exact test for those with two categories, or Chi-square tests where there are more categories, unless stated otherwise. Bold p-values are significant at p<0.

AimsThe increased use of social media creates opportunity for new, effective methods of delivering medical and clinical education. Twitter is a popular social media platform where users can post frequent updates and create threads containing related content using hashtags. This study aims to investigate and analyse the type of content relating to orthopaedic surgery that is being posted on the platform of Twitter.MethodsA retrospective search was performed for tweets containing the words ‘orthopaedic surgery’ or ‘orthopedic surgery’ or the use of the hashtag ‘#OrthoTwitter’ between November 2018 to November 2019. A total of 5243 tweets were included.ResultsTweets containing ‘orthopaedic surgery’ or ‘orthopedic surgery’ most frequently contained promotional or marketing content (30% promotional, 21% marketing), and private organisations were the category of author to which the greatest number of tweets belonged (30%). Tweets containing educational or research content were the least common among all tweets containing ‘orthopaedic surgery’ or ‘orthopedic surgery’ (11%). In contrast, of the tweets containing the hashtag ‘#OrthoTwitter’, 44% contained educational or research content, 15% contained promotional content and no tweets containing marketing content. Furthermore, 87% of all tweets using the hashtag ‘#OrthoTwitter’ were from orthopaedic surgeons, and the least number of tweets were from private organisations (2%).ConclusionTwitter is a widely used social media platform regarding orthopaedic surgery. We propose that the hashtag ‘#OrthoTwitter’ can be used to create an online community of orthopaedic surgeons where members can assist one another through sharing reliable and educational content.

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