Background Large studies comparing totally minimally invasive oesophagectomy (TMIE) with laparoscopically assisted (hybrid) oesophagectomy are lacking. Although randomized trials have compared TMIE invasive with open oesophagectomy, daily clinical practice does not always resemble the results reported in such trials. The aim of the present study was to compare complications after totally minimally invasive, hybrid and open Ivor Lewis oesophagectomy in patients with oesophageal cancer. Methods The study was performed using data from the International Esodata Study Group registered between February 2015 and December 2019. The primary outcome was pneumonia, and secondary outcomes included the incidence and severity of anastomotic leakage, (major) complications, duration of hospital stay, escalation of care, and 90-day mortality. Data were analysed using multivariable multilevel models. Results Some 8640 patients were included between 2015 and 2019. Patients undergoing TMIE had a lower incidence of pneumonia than those having hybrid (10.9 versus 16.3 per cent; odds ratio (OR) 0.56, 95 per cent c.i. 0.40 to 0.80) or open (10.9 versus 17.4 per cent; OR 0.60, 0.42 to 0.84) oesophagectomy, and had a shorter hospital stay (median 10 (i.q.r. 8–16) days versus 14 (11–19) days (P = 0.041) and 11 (9–16) days (P = 0.027) respectively). The rate of anastomotic leakage was higher after TMIE than hybrid (15.1 versus 10.7 per cent; OR 1.47, 1.01 to 2.13) or open (15.1 versus 7.3 per cent; OR 1.73, 1.26 to 2.38) procedures. Conclusion Compared with hybrid and open Ivor Lewis oesophagectomy, TMIE resulted in a lower pneumonia rate, a shorter duration of hospital stay, but higher anastomotic leakage rates. Therefore, no clear advantage was seen for either TMIE, hybrid or open Ivor Lewis oesophagectomy when performed in daily clinical practice.

Objective:To determine the relationship between BC, specifically low skeletal muscle mass (sarcopenia) and poor muscle quality (myosteatosis) and outcomes in emergency laparotomy patients.Background:Emergency laparotomy has one of the highest morbidity and mortality rates of all surgical interventions. BC objectively identifies patients at risk of adverse outcomes in elective cancer cohorts, however, evidence is lacking in emergency surgery.Methods:An observational cohort study of patients undergoing emergency laparotomy at ten English hospitals was performed. BC analyses were performed at the third lumbar vertebrae level using preoperative computed tomography images to quantify skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SM-RA). Sex-specific SMI and SM-RA were determined, with the lower tertile splits defining sarcopenia (low SMI) and myosteatosis (low SM-RA). Accuracy of mortality risk prediction, incorporating SMI and SM-RA variables into risk models was assessed with regression modeling.Results:Six hundred ten patients were included. Sarcopenia and myosteatosis were both associated with increased risk of morbidity (52.1% vs 45.1%, P = 0.028; 57.5% vs 42.6%, P = 0.014), 30-day (9.5% vs 3.6%, P = 0.010; 14.9% vs 3.4%, P < 0.001), and 1-year mortality (27.4% vs 11.5%, P < 0.001; 29.7% vs 12.5%, P < 0.001). Risk-adjusted 30-day mortality was significantly increased by sarcopenia [OR 2.56 (95% CI 1.12-5.84), P = 0.026] and myosteatosis [OR 4.26 (2.01-9.06), P < 0.001], similarly at 1-year [OR 2.66 (95% CI 1.57-4.52), P < 0.001; OR2.08 (95%CI 1.26-3.41), P = 0.004]. BC data increased discrimination of an existing mortality risk-prediction model (AUC 0.838, 95% CI 0.835–0.84).Conclusion:Sarcopenia and myosteatosis are associated with increased adverse outcomes in emergency laparotomy patients.

The aim of this study was to develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. Summary Background Data: For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging.This study investigated survival prediction after esophagectomy using a Random Survival Forest (RSF) model derived from routine data from a large, well-curated, national dataset. Methods: Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time-dependent area under the curve) were validated internally using bootstrap resampling. Results: The study analyzed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year time-dependent area under the receiver operator curve of 83.9% [95% confidence interval (CI) 82.6%-84.9%], compared to 82.3% (95% CI 81.1%-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, circumferential resection margin involvement (tumor at < 1 mm from cut edge) and age. There was a wide range of survival estimates even within TNM staging groups, with quintiles of prediction within Stage 3b ranging from 12.2% to 44.7% survival at 5 years. Conclusions: An RSF model for long-term survival after esophagectomy exhibited excellent discrimination and well-calibrated predictions. At a patient level, it provides more accuracy than TNM staging alone and could help in the delivery of tailored treatment and follow-up.

Background: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches.Methods: Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model.Results: A total of 812 patients were included. The recurrence rate at less than 1 year was 29⋅1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0⋅791 for ELR, 0⋅801 for RF, 0⋅804 for XGB, 0⋅805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0⋅804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25⋅7 per cent) and lymphovascular invasion (16⋅9 per cent). Conclusion:The model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients.

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Background There are a variety of surgical and endoscopic interventions available to treat gastroesophageal reflux disease. There is, however, no consensus on which approach is best. The aim of this national audit is to describe the current variation in the UK clinical practice in relation to anti-reflux surgery (ARS) and to report adherence to available clinical guidelines. Methods This national audit will be conducted at centers across the UK using the secure online web platform ALEA. The study will comprise two parts: a registration questionnaire and a prospective multicenter audit of ARS. All participating centers will be required to complete the registration questionnaire comprising details regarding pre-, peri-, and post-operative care pathways and whether or not these are standardized within each center. Following this, a 12-month multicenter prospective audit will be undertaken to capture data including patient demographics, predominant symptoms, preoperative investigations, surgery indication, intraoperative details, and postoperative outcomes within the first 90 days. Local teams will retain access to their own data to facilitate local quality improvement. The full dataset will be reported at national and international scientific congresses and will contribute to peer-reviewed publications and national quality improvement initiatives. Conclusions This study will identify and explore variation in the processes and outcomes following ARS within the UK using a collaborative cohort methodology. The results generated by this audit will facilitate local and national quality improvement initiatives and generate new possibilities for future research in anti-reflux interventions.

Oesophageal cancer (OC) is the ninth most common cancer worldwide. Patients receive neoadjuvant therapy (NAT) as standard of care, but less than 20% of patients with oesophageal adenocarcinoma (OAC) or a third of oesophageal squamous cell carcinoma (OSCC) patients, obtain a clinically meaningful response. Developing a method of determining a patient’s response to NAT before treatment will allow rational treatment decisions to be made, thus improving patient outcome and quality of life. (1) Background: To determine the use and accuracy of microRNAs as biomarkers of response to NAT in patients with OAC or OSCC. (2) Methods: MEDLINE, EMBASE, Web of Science and the Cochrane library were searched to identify studies investigating microRNAs in treatment naïve biopsies to predict response to NAT in OC patients. (3) Results: A panel of 20 microRNAs were identified as predictors of good or poor response to NAT, from 15 studies. Specifically, miR-99b, miR-451 and miR-505 showed the strongest ability to predict response in OAC patients along with miR-193b in OSCC patients. (4) Conclusions: MicroRNAs are valuable biomarkers of response to NAT in OC. Research is needed to understand the effects different types of chemotherapy and chemoradiotherapy have on the predictive value of microRNAs; studies also require greater standardization in how response is defined.

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