The effects of preoperative sugarless gum chewing on gastric fluid volume and pH

There is a lack of consensus regarding the preoperative fasting period on gum chewing due to conflicting evidence. Our study aimed to determine the effects of preoperative sugarless gum chewing on gastric fluid volume and pH. This prospective randomised study was performed on patients who underwent elective operation under general anaesthesia. Sixty patients who fasted overnight were randomly assigned to either “non-chewing gum” as control or “chewing gum group” as intervention group who chewed one piece of gum upon the call to the operating theatre. They were further enquired whether gum chewing helped cope with hunger and reduced anxiety. Gastric volume and pH were determined immediately after induction of anaesthesia. Gum chewing had no significant effect on gastric fluid volume measured [30.4+13.6 (control) vs 29.1+11.6 ml (intervention), p = 0.693] and gastric fluid pH [1.4+0.4 (control) vs 1.6+0.4 (intervention), p = 0.05] while managing to reduce anxiety in 90.3% patients who chewed gum. Pre-operative gum-chewing did not increase in gastric fluid volume or a further reduction in gastric fluid acidity and was able to reduce anxiety in patients in the preoperative period

Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review

Background subtraction is a widely used operation in the video surveillance, aimed at separating the expected scene (the background) from the unexpected entities (the foreground). There are several problems related to this task, mainly due to the blurred boundaries between background and foreground definitions. Therefore, background subtraction is an open issue worth to be addressed under different points of view. In this paper, we propose a comprehensive review of the background subtraction methods, that considers also channels other than the sole visible optical one (such as the audio and the infrared channels). In addition to the definition of novel kinds of background, the perspectives that these approaches open up are very appealing: in particular, the multisensor direction seems to be well-suited to solve or simplify several hoary background subtraction problems. All the reviewed methods are organized in a novel taxonomy that encapsulates all the brand-new approaches in a seamless way

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society