Development and external validation of preoperative clinical prediction models for postoperative outcomes including preoperative aerobic fitness in patients approaching elective colorectal cancer surgery

Introduction: Preoperative aerobic fitness is associated with postoperative outcomes after elective colorectal cancer (CRC) surgery. This study aimed to develop and externally validate two clinical prediction models incorporating a practical test to assess preoperative aerobic fitness to distinguish between patients with and without an increased risk for 1) postoperative complications and 2) a prolonged time to in-hospital recovery of physical functioning after elective colorectal cancer (CRC) surgery. Materials and methods: Models were developed using prospective data from 256 patients and externally validated using prospective data of 291 patients. Postoperative complications were classified according to Clavien-Dindo. The modified Iowa level of assistance scale (mILAS) was used to determine time to postoperative in-hospital physical recovery. Aerobic fitness, age, sex, body mass index, American Society of Anesthesiologists (ASA) classification, neoadjuvant treatment, surgical approach, tumour location, and preoperative haemoglobin level were potential predictors. Areas under the curve (AUC), calibration plots, and Hosmer-Lemeshow tests evaluated predictive performance. Results: Aerobic fitness, sex, age, ASA, tumour location, and surgical approach were included in the final models. External validation of the model for complications and postoperative recovery presented moderate to fair discrimination (AUC 0.666 (0.598–0.733) and 0.722 (0.651–0.794), respectively) and good calibration. High sensitivity and high negative predictive values were observed in the lower predicted risk categories (&lt;40 %). Conclusion: Both models identify patients with and without an increased risk of complications or a prolonged time to in-hospital physical recovery. They might be used for improving patient-tailored preoperative risk assessment and targeted and cost-effective application of prehabilitation interventions.</p

Mining Social Interaction Data in Virtual Worlds

Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. This chapter presents techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world