Although anaesthesiologists strive to avoid hypoxemia during surgery, reliably predicting future intraoperative hypoxemia is not currently possible. Here, we report the development and testing of a machine-learning-based system that, in real time during general anaesthesia, predicts the risk of hypoxemia and provides explanations of the risk factors. The system, which was trained on minute-by-minute data from the electronic medical records of over fifty thousand surgeries, improved the performance of anaesthesiologists when providing interpretable hypoxemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxemia events, with this system’s assistance they would anticipate 30% of them, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics.

SummaryThis study sought to determine whether using the Resuscitation Council UK's iResus Ó application on a smart phone improves the performance of doctors trained in advanced life support in a simulated emergency. Thirty-one doctors (advanced life support-trained within the previous 48 months) were recruited. All received identical training using the smart phone and the iResus application. The participants were randomly assigned to a control group (no smart phone) and a test group (access to iResus on smart phone). Both groups were tested using a validated extended cardiac arrest simulation test (CASTest) scoring system. The primary outcome measure was the overall cardiac arrest simulation test score; these were significantly higher in the smart phone group ( Every year, approximately 30 000 people in the UK have an unexpected cardiac arrest in hospital. Despite significant advances in resuscitation research, survival to hospital discharge following cardiac arrest in adults remains poor [1]. The survival benefit of wellperformed cardiopulmonary resuscitation (CPR) is well documented. Recent evidence from both resuscitation training and in-hospital cardiac arrest suggests that CPR quality is suboptimal [2,3].Human factors affect the quality of CPR and disparity exists between resuscitation theory and its practical application -even experienced teams often perform sub-optimally in simulated resuscitation scenarios [4]. Possible explanations for this include the high-stress environment resulting in poor leadership behaviour, failure to delegate tasks explicitly, poor recall of knowledge and inevitable skill decay [5][6][7].

One Sentence Summary: We present a new machine learning based system called Prescience that provides interpretable real-time predictions to help anesthesiologists prevent hypoxemia during surgery.Abstract: Hypoxemia causes serious patient harm, and while anesthesiologists strive to avoid hypoxemia during surgery, anesthesiologists are not reliably able to predict which patients will have intraoperative hypoxemia. Using minute by minute EMR data from fifty thousand surgeries we developed and tested a machine learning based system called Prescience that predicts real-time hypoxemia risk and presents an explanation of factors contributing to that risk during general anesthesia. Prescience improved anesthesiologists' performance when providing interpretable hypoxemia risks with contributing factors. The results suggest that if anesthesiologists currently anticipate 15% of events, then with Prescience assistance they could anticipate 30% of events or an estimated additional 2.4 million annually in the US, a large portion of which may be preventable because they are attributable to modifiable factors. The prediction explanations are broadly consistent with the literature and anesthesiologists' prior knowledge. Prescience can also improve clinical understanding of hypoxemia risk during anesthesia by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics. Making predictions of complex medical machine learning models (such as Prescience) interpretable has broad applicability to other data-driven prediction tasks in medicine.peer-reviewed)

Background The success rates and related complications of various techniques for intubation in children with difficult airways remain unknown. The primary aim of this study is to compare the success rates of fiber-optic intubation via supraglottic airway to videolaryngoscopy in children with difficult airways. Our secondary aim is to compare the complication rates of these techniques. Methods Observational data were collected from 14 sites after management of difficult pediatric airways. Patient age, intubation technique, success per attempt, use of continuous ventilation, and complications were recorded for each case. First-attempt success and complications were compared in subjects managed with fiber-optic intubation via supraglottic airway and videolaryngoscopy. Results Fiber-optic intubation via supraglottic airway and videolaryngoscopy had similar first-attempt success rates (67 of 114, 59% vs. 404 of 786, 51%; odds ratio 1.35; 95% CI, 0.91 to 2.00; P = 0.16). In subjects less than 1 yr old, fiber-optic intubation via supraglottic airway was more successful on the first attempt than videolaryngoscopy (19 of 35, 54% vs. 79 of 220, 36%; odds ratio, 2.12; 95% CI, 1.04 to 4.31; P = 0.042). Complication rates were similar in the two groups (20 vs. 13%; P = 0.096). The incidence of hypoxemia was lower when continuous ventilation through the supraglottic airway was used throughout the fiber-optic intubation attempt. Conclusions In this nonrandomized study, first-attempt success rates were similar for fiber-optic intubation via supraglottic airway and videolaryngoscopy. Fiber-optic intubation via supraglottic airway is associated with higher first-attempt success than videolaryngoscopy in infants with difficult airways. Continuous ventilation through the supraglottic airway during fiber-optic intubation attempts may lower the incidence of hypoxemia.

SummaryTraditional teaching of laryngoscopy is difficult due to the trainer and trainee lacking a shared view. The Karl Storz BERCI DCI Ò Video Laryngoscope provides a video image for the trainer and a direct view identical to that of a standard laryngoscope for the trainee. Forty-nine novice subjects were randomly assigned to a control group (n = 24) taught using a standard Macintosh laryngoscope or a study group (n = 25) taught using the Video Laryngoscope. Following training all subjects were assessed using a standard laryngoscope. Under simulated difficult airway conditions the study group performed better in terms of number of attempts (p = 0.02), number of repositioning manoeuvres required (p = 0.046) and teeth trauma (p = 0.034). The study group were more confident of the success of their tube placement (p = 0.035), found it easier than the control group (p = 0.042) and had improved knowledge of airway anatomy (p = 0.011). We conclude that video laryngoscopy confers benefits in the teaching of tracheal intubation.

Pain management following pediatric tonsillectomy and adenotonsillectomy surgery is challenging and traditionally involves perioperative opioids. However, the recent national opioid shortage compelled anesthesiologists at Bellevue Surgery Center to identify an alternative perioperative analgesic regimen that minimizes opioids yet provides effective pain relief. We assembled an interdisciplinary quality improvement team to trial a series of analgesic protocols using the Plan-Do-Study-Act cycle.Initially, we replaced intraoperative morphine and acetaminophen (M/A protocol) with intraoperative dexmedetomidine and preoperative ibuprofen (D/I protocol).However, when results were not favorable, we rapidly transitioned to intraoperative ketorolac and dexmedetomidine (D/K protocol). The following measures were evaluated using statistical process control chart methodology and interpreted using Shewhart's theory of variation: maximum pain score in the postanesthesia care unit, postoperative morphine rescue rate, postanesthesia care unit length of stay, total anesthesia time, postoperative nausea and vomiting rescue rate, and reoperation rate within 30 days of surgery. There were 333 patients in the M/A protocol, 211 patients in the D/I protocol, and 196 patients in the D/K protocol. With the D/I protocol, there were small increases in maximum pain score and postanesthesia care unit length of stay, but no difference in morphine rescue rate or total anesthesia time compared to the M/A protocol. With the D/K protocol, postoperative pain control and postanesthesia care unit length of stay were similar compared to the M/A protocol. Both the D/I and D/K protocols had reduced nausea and vomiting rescue rates. Reoperation rates were similar between groups. In summary, we identified an intraoperative anesthesia protocol for pediatric tonsillectomy and adenotonsillectomy surgery utilizing dexmedetomidine and ketorolac that provides effective analgesia without increasing recovery times or reoperation rates. K E Y W O R D S analgesics, anesthesia, dexmedetomidine, ketorolac, quality improvement, tonsillectomy | 683 FRANZ et Al.

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Recent work by our group has shown that an exopolysaccharide (EPS)-producing starter pair, Streptococcus thermophilusMR-1C and Lactobacillus delbrueckii subsp.bulgaricus MR-1R, can significantly increase moisture retention in low-fat mozzarella (D. B. Perry, D. J. McMahon, and C. J. Oberg, J. Dairy Sci. 80:799–805, 1997). The objectives of this study were to determine whether MR-1C, MR-1R, or both of these strains are required for enhanced moisture retention and to establish the role of EPS in this phenomenon. Analysis of low-fat mozzarella made with different combinations of MR-1C, MR-1R, and the non-EPS-producing starter culture strains S. thermophilus TA061 andLactobacillus helveticus LH100 showed that S. thermophilus MR-1C was responsible for the increased cheese moisture level. To investigate the role of the S. thermophilus MR-1C EPS in cheese moisture retention, theepsE gene in this bacterium was inactivated by gene replacement. Low-fat mozzarella made with L. helveticusLH100 plus the non-EPS-producing mutant S. thermophilusDM10 had a significantly lower moisture content than did cheese made with strains LH100 and MR-1C, which confirmed that the MR-1C capsular EPS was responsible for the water-binding properties of this bacterium in cheese. Chemical analysis of the S. thermophilus MR-1C EPS indicated that the polymer has a novel basic repeating unit composed of d-galactose, l-rhamnose, andl-fucose in a ratio of 5:2:1.