Colorectal Endoscopic Stenting Trial (CReST) for obstructing left-sided colorectal cancer: randomized clinical trial

Background Colorectal cancer often presents with obstruction needing urgent, potentially life-saving decompression. The comparative efficacy and safety of endoluminal stenting versus emergency surgery as initial treatment for such patients is uncertain. Methods Patients with left-sided colonic obstruction and radiological features of carcinoma were randomized to endoluminal stenting using a combined endoscopic/fluoroscopic technique followed by elective surgery 1–4 weeks later, or surgical decompression with or without tumour resection. Treatment allocation was via a central randomization service using a minimization procedure stratified by curative intent, primary tumour site, and severity score (Acute Physiology And Chronic Health Evaluation). Co-primary outcome measures were duration of hospital stay and 30-day mortality. Secondary outcomes were stoma formation, stenting completion and complication rates, perioperative morbidity, 6-month survival, 3-year recurrence, resource use, adherence to chemotherapy, and quality of life. Analyses were undertaken by intention to treat. Results Between 23 April 2009 and 22 December 2014, 245 patients from 39 hospitals were randomized. Stenting was attempted in 119 of 123 allocated patients (96.7 per cent), achieving relief of obstruction in 98 of 119 (82.4 per cent). For the 89 per cent treated with curative intent, there were no significant differences in 30-day postoperative mortality (3.6 per cent (4 of 110) versus 5.6 per cent (6 of 107); P = 0.48), or duration of hospital stay (median 19 (i.q.r. 11–34) versus 18 (10–28) days; P = 0.94) between stenting followed by delayed elective surgery and emergency surgery. Among patients undergoing potentially curative treatment, stoma formation occurred less frequently in those allocated to stenting than those allocated to immediate surgery (47 of 99 (47.5 per cent) versus 72 of 106 (67.9 per cent); P = 0.003). There were no significant differences in perioperative morbidity, critical care use, quality of life, 3-year recurrence or mortality between treatment groups. Conclusion Stenting as a bridge to surgery reduces stoma formation without detrimental effects. Registration number: ISRCTN13846816 (http://www.controlled-trials.com)

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio