BACKGROUND:Predictive analytics systems may improve perioperative care by enhancing preparation for, recognition of, and response to high-risk clinical events. Bradycardia is a fairly common and unpredictable clinical event with many causes; it may be benign or become associated with hypotension requiring aggressive treatment. Our aim was to build models to predict the occurrence of clinically significant intraoperative bradycardia at 3 time points during an operative course by utilizing available preoperative electronic medical record and intraoperative anesthesia information management system data. METHODS:The analyzed data include 62,182 scheduled noncardiac procedures performed at the University of Washington Medical Center between 2012 and 2017. The clinical event was defined as severe bradycardia (heart rate <50 beats per minute) followed by hypotension (mean arterial pressure <55 mm Hg) within a 10-minute window. We developed models to predict the presence of at least 1 event following 3 time points: induction of anesthesia (TP1), start of the procedure (TP2), and 30 minutes after the start of the procedure (TP3). Predictor variables were based on data available before each time point and included preoperative patient and procedure data (TP1), followed by intraoperative minute-to-minute patient monitor, ventilator, intravenous fluid, infusion, and bolus medication data (TP2 and TP3). Machine-learning and logistic regression models were developed, and their predictive abilities were evaluated using the area under the ROC curve (AUC). The contribution of the input variables to the models were evaluated. RESULTS:The number of events was 3498 (5.6%) after TP1, 2404 (3.9%) after TP2, and 1066 (1.7%) after TP3. Heart rate was the strongest predictor for events after TP1. Occurrence of a previous event, mean heart rate, and mean pulse rates before TP2 were the strongest predictor for events after TP2. Occurrence of a previous event, mean heart rate, mean pulse rates before TP2 (and their interaction), and 15-minute slopes in heart rate and blood pressure before TP2 were the strongest predictors for events after TP3. The best performing machine-learning models including all cases produced an AUC of 0.81 (TP1), 0.87 (TP2), and 0.89 (TP3) with positive predictive values of 0.30, 0.29, and 0.15 at 95% specificity, respectively. CONCLUSIONS:We developed models to predict unstable bradycardia leveraging preoperative and real-time intraoperative data. Our study demonstrates how predictive models may be utilized to predict clinical events across multiple time intervals, with a future goal of developing real-time, intraoperative, decision support.

Opioids play a critical role in acute postoperative pain management. Our objective was to develop machine learning models to predict postoperative opioid requirements in patients undergoing ambulatory surgery. To develop the models, we used a perioperative dataset of 13,700 patients (≥ 18 years) undergoing ambulatory surgery between the years 2016-2018. The data, comprising of patient, procedure and provider factors that could influence postoperative pain and opioid requirements, was randomly split into training (80%) and validation (20%) datasets. Machine learning models of different classes were developed to predict categorized levels of postoperative opioid requirements using the training dataset and then evaluated on the validation dataset. Prediction accuracy was used to differentiate model performances. The five types of models that were developed returned the following accuracies at two different stages of surgery: 1) Prior to surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 67%, Neural Network: 30%, Random Forest: 72%, Extreme Gradient Boost: 71% and 2) End of surgery-Multinomial Logistic Regression: 71%, Naïve Bayes: 63%, Neural Network: 32%, Random Forest: 72%, Extreme Gradient Boost: 70%. Analyzing the sensitivities of the best performing Random Forest model showed that the lower opioid requirements are predicted with better accuracy (89%) as compared with higher opioid requirements (43%). Feature importance (% relative importance) of model predictions showed that the type of procedure (15.4%), medical history (12.9%) and procedure duration (12.0%) were the top three features contributing to model predictions. Overall, the contribution of patient and procedure features towards model predictions were 65% and 35% respectively. Machine learning models could be used to predict postoperative opioid requirements in ambulatory surgery patients and could potentially assist in better management of their postoperative acute pain.

BACKGROUND:Many hospitals have implemented surgical safety checklists based on the World Health Organization surgical safety checklist, which was associated with improved outcomes. However, the execution of the checklists is frequently incomplete. We reasoned that aviation-style computerized checklist displayed onto large, centrally located screen and operated by the anesthesia provider would improve the performance of surgical safety checklist. METHODS:We performed a prospective before and after observational study to evaluate the effect of a computerized surgical safety checklist system on checklist performance. We created checklist software and translated our 4-part surgical safety checklist from wall poster into an aviation-style computerized format displayed onto a large, centrally located screen and operated by the anesthesia provider. Direct observers recorded performance of the first part of the surgical safety checklist that was initiated before anesthetic induction, including completion of each checklist item, provider participation and distraction level, resistance to use of the checklist, and the time required for checklist completion before and after checklist system implementation. We compared trends of the proportions of cases with 100% surgical safety checklist completion over time between pre- and postintervention periods and assessed for a jump at the start of intervention using segmented logistic regression model while controlling for potential confounding variables. RESULTS:A total of 671 cases were observed before and 547 cases were observed after implementation of the computerized surgical safety checklist system. The proportion of cases in which all of the items of the surgical safety checklist were completed significantly increased from 2.1% to 86.3% after the computerized checklist system implementation (P < .001). Before computerized checklist system implementation, 488 of 671 (72.7%) cases had <75% of checklist items completed, whereas after a computerized checklist system implementation, only 3 of 547 (0.5%) cases had <75% of checklist items completed. CONCLUSIONS:The implementation of a computerized surgical safety checklist system resulted in an improvement in checklist performance.

Via the emergence of new bronchoscopic technologies and techniques, there is enormous growth in the number of procedures being performed in nonoperating room settings. This, coupled with a greater focus from the Centers for Medicare and Medicaid Services for mandated anesthesiology oversight of procedural sedation for bronchoscopy by the pulmonologists has led to a more frequent working partnership between interventional pulmonologists and anesthesiologists. This article offers the interventional pulmonologist insight into how the anesthesiologist thinks and approaches anesthetic care delivery.

OBJECTIVE:To examine if attending surgeon presence at the preinduction briefing is associated with a shorter time to incision.STUDY DESIGN:Retrospective cohort study and survey.SETTING:Tertiary academic medical center.SUBJECTS AND METHODS:A retrospective cohort study was conducted of 22,857 operations by 141 attending surgeons across 12 specialties between August 3, 2016, and June 21, 2018. The independent variable was attending surgeon presence at the preinduction briefing. Linear regression models compared time from room entry to incision overall, by service line, and by surgeon. We hypothesized a shorter time to incision when the attending surgeon was present and a larger effect for cases with complex surgical equipment or positioning. A survey was administered to evaluate attending surgeons' perceptions of the briefing, with a response rate of 68% (64 of 94 attending surgeons).RESULTS:Cases for which the attending surgeon was present at the preinduction briefing had a statistically significant yet operationally minor reduction in mean time to incision when compared with cases when the attending surgeon was absent. After covariate adjustment, the mean time to incision was associated with an efficiency gain of 1.8 ± 0.5 minutes (mean ± SD; P < .001). There were no statistically significant differences in the subgroups of complex surgical equipment and complex positioning or in secondary analysis comparing service lines. The surgeon was the strongest confounding variable. Survey results demonstrated mild support: 55% of attending surgeons highly prioritized attending the preinduction briefing.CONCLUSION:Attending surgeon presence at the preinduction briefing has only a minor effect on efficiency as measured by time to incision.

Background: The "weekend effect," whereby surgeries performed during weekend haven been associated with poorer postoperative outcomes. We explored whether Saturday elective procedures at our hospital were associated with poorer post-operative outcomes when compared with weekday surgeries. Methods: A retrospective cohort study of patients undergoing elective surgery on the abdomen or perineum from 2008 to 2015 was performed. Procedures were classified by day (Group 1: Monday, Tuesday, Wednesday; Group 2: Saturday). Multivariate regression analyses were performed to determine group differences in procedure duration, length-of-stay (LOS) and complications. Results: In adjusted analyses, there were no statistically significant differences between Group 1 (n = 816) and Group 2 (n = 269) procedures in terms of procedure duration (Group 2 - Group 1 = 13.6 min, p =.19), LOS (Group 2 - Group 1 = 1.9 days, p =.14) and complications (OR 0.58, p=.46). Conclusion: Saturday elective procedures were not associated with poorer outcomes. (C) 2019 Elsevier Inc. All rights reserved.

BACKGROUND:We implemented a previously described barcode-based drug safety system in all of our anesthetizing locations. Providers were instructed to scan the barcode on syringes using our Anesthesia Information Management System before drug administration, but the rate of provider adherence was low. We studied an implementation intervention intended to increase the rate of scanning. METHODS:Using our Anesthesia Information Management System and Smart Anesthesia Manager software, we quantified syringe drug administrations by anesthesia providers with and without barcode scanning. We use an anesthesia team model in which an attending anesthesiologist is paired with a certified registered nurse anesthetist (CRNA) or a resident. Our system identified the pair of providers associated with a particular drug administration, but did not distinguish which providers actually administered the drug. Therefore, the rate of barcode scanning for a particular case was assigned to both providers equally. A baseline rate of scanning was established over a period of 17 months. An audit and feedback intervention was then performed that consisted of monthly performance reports sent by email to individual providers along with coffee gift card awards for top performers. The coffee gift cards were awarded in only the first 2 months of the intervention, while the email performance reports continued on a monthly basis. The coffee card awards were made public. The monthly emails reported the individual provider's rank order of performance relative to other providers, but was otherwise anonymous. The baseline rate of scanning was compared to the rate of scanning after the intervention for a period of 7 months. RESULTS:From November 2014 to March 2017, we accumulated 60,197 cases performed by 88 attending anesthesiologists, 65 CRNAs, and 148 residents. The total number of syringe drug administrations was 653,355. Average scanning performance improved from 8.7% of syringe barcodes scanned during the baseline period from November 2014 to February 2016 to 64.4% scanned during the period September 2016 to March 2017 (P < .001). Variation in performance among individuals was marked, ranging from 0% to 100% of syringes scanned. The performance of some individuals showed marked oscillation over time. There was greater variation in performance attributable to residents than in performance attributable to CRNAs. CONCLUSIONS:Feedback of individual provider performance data from the anesthesia information system to providers can be used in conjunction with other measures to improve performance. Despite improved average performance, there was marked variation in performance between individuals, and some individuals had marked oscillation of their performance over time.

Bartek, Matthew A. MD, MPH1; Saxena, Rajeev C. MD, MBA1; Solomon, Stuart MD1; Fong, Christine T. MS1; Lang, John D. MD1; Nair, Bala G. PhD1; Behara, Lakshmana D. MS2; Venigandla, Ravitheja MS2; Velagapudi, Kalyani PhD2 Author Information

BACKGROUND: Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards. STUDY DESIGN: We developed models to predict case-time duration using linear regression and supervised machine learning. For each of these models, we generated an all-inclusive model, service-specific models, and surgeon-specific models. In the latter 2 approaches, individual models were created for each surgical service and surgeon, respectively. Our data set included 46,986 scheduled operations performed at a large academic medical center from January 2014 to December 2017, with 80% used for training and 20% for model testing/validation. Predictions derived from each model were compared with our institutional standard of using average historic procedure times and surgeon estimates. Models were evaluated based on accuracy, overage (case duration > predicted thorn 10%), underage (case duration < predicted -10%), and the predictive capability of being within a 10% tolerance threshold. RESULTS: The machine learning algorithm resulted in the highest predictive capability. The surgeon-specific model was superior to the service-specific model, with higher accuracy, lower percentage of overage and underage, and higher percentage of cases within the 10% threshold. The ability to predict cases within 10% improved from 32% using our institutional standard to 39% with the machine learning surgeon-specific model. CONCLUSIONS: Our study is a notable advancement toward statistical modeling of case-time duration across all surgical departments in a large tertiary medical center. Machine learning approaches can improve case duration estimations, enabling improved operating room scheduling, efficiency, and reduced costs. (C) 2019 Published by Elsevier Inc. on behalf of the American College of Surgeons.

Lactic acidosis (LA) in end-stage liver disease (ESLD) patients has been recognized as one of the most complicated clinical problems and is associated with increased morbidity and mortality. Multiple-organ failure, associated with advanced stages of cirrhosis, exacerbates dysfunction of numerous parts of lactate metabolism cycle, which manifests as increased lactate production and impaired clearance, leading to severe LA-induced acidemia. These problems become especially prominent in ESLD patients, that undergo partial hepatectomy and, particularly, liver transplantation. Perioperative management of LA and associated severe acidemia is an inseparable part of anesthesia, post-operative and critical care for this category of patients, presenting a wide variety of challenges. In this review, lactic acidosis applied pathophysiology, clinical implications for ESLD patients, diagnosis, role of intraoperative factors, such as anesthesia-and surgery-related, vasoactive agents impact, and also current treatment options and modalities have been discussed.

Checklists are commonly used to structure the communication process between anesthesia nursing healthcare providers during the transfer of care, or handoff, of a patient after surgery. However, intraoperative information is often recalled from memory leading to omission of critical data or incomplete information exchange during the patient handoff. We describe the implementation of an electronic anesthesia information transfer tool (T2) for use in the handover of intubated patients to the intensive care unit. A pilot observational study auditing handovers against a pre-existing checklist was performed to evaluate information reporting and attendee participation. There was a modest improvement in information reporting on part of the anesthesia provider, as well as team discussions regarding the current hemodynamic status of the patient. While T2 was well-received, further evaluation of the tool in different handover settings can clarify its potential for decreasing adverse communication-related events.

Ischemia-reperfusion injury (IRI) continues to be a major contributor to graft dysfunction, thus supporting the need for therapeutic strategies focused on minimizing organ damage especially with growing numbers of extended criteria grafts being utilized which are more vulnerable to cold and warm ischemia. Nitric oxide (NO·) is highly reactive gaseous molecule found in air and regarded as a pollutant. Not surprising, it is extremely bioactive, and has been demonstrated to play major roles in vascular homeostasis, neurotransmission, and host defense inflammatory reactions. Under conditions of ischemia, NO· has consistently been demonstrated to enhance microcirculatory vasorelaxation and mitigate pro-inflammatory responses, making it an excellent strategy for patients undergoing organ transplantation. Clinical studies designed to test this hypothesis have yielded very promising results that includes reduced hepatocellular injury and enhanced graft recovery without any identifiable complications. By what means NO· facilitates extra-pulmonary actions is up for debate and speculation. The general premise is that they are NO· containing intermediates in the circulation, that ultimately mediate either direct or indirect effects. A plethora of data exists explaining how NO·-containing intermediate molecules form in the plasma as S-nitrosothiols (e.g., S-nitrosoalbumin), whereas other compelling data suggest nitrite to be a protective mediator. In this article, we discuss the use of inhaled NO· as a way to protect the donor liver graft against IRI in patients undergoing liver transplantation.

Nitric oxide (NO) has come far since being discovered serendipitously to relax vascular smooth muscle. Initially, administered to animals to reduce pulmonary artery pressures and improve oxygenation. It now enjoys FDA approval for administration to newborns with pulmonary hypertension but is used common place for other critical cardiopulmonary ailments. While never quite living up to expectations, newer applications show greater promise as a therapy especially in the area of ischemia-reperfusion. The following will give a clinical overview of inhaled nitric oxide as a gas, as applied to the pediatric patient population, and to those adults suffering with cardiopulmonary and hematologic disease. Lastly, due to more recent discoveries, the effects of how NO may be used to treat disorders such as ischemia-reperfusion, will also be reviewed.

Inhaled nitric oxide (iNO) has been used extensively to treat pulmonary hypertension primarily in newborns. This therapy is a safe and effective therapy to improve the matching between airway ventilation and blood oxygenation. A key conceptual component of iNO therapy is that effects are limited to the pulmonary compartment thereby avoiding unwanted systemic effects. The mechanism underlying this model is that any NO entering the blood stream is rapidly oxidized to nitrate, a relatively inert anion that is excreted. Mediating this oxidation is oxyhemoglobin that becomes oxidized to methemoglobin, accumulation of which is limited by erythrocyte methemoglobin reductase. In this article, we discuss studies that dismiss the notion that once in the blood stream iNO is inactivated and show that a surprising result of iNO therapy is the formation of stable NO-derived products that circulate and can elicit NO-dependent signaling in extra-pulmonary tissues. This pathway has the potential to open up new applications for iNO for treatment of systemic diseases associated with loss of NO signaling.

Ischemia-reperfusion plays a major role in the injury experienced by the liver during transplantation. Much work has been done recently investigating the role of redox species in hepatic ischemia-reperfusion. As animal models are better characterized and developed, and more insights are gained into the pathophysiology of hepatic ischemia reperfusion injury in humans the questions into exactly how oxidants participate in this injury are becoming more refined. These questions include effects of cellular location, timing of injury, and ability of therapeutics to access this site are increasing our appreciation of the complexity of ischemia reperfusion and improving attempts to ameliorate its effects. In this review, we aim to discuss the various methods to alter redox chemistry during ischemia reperfusion injury and future prospects for preventing organ injury during hepatic ischemia reperfusion.

Decreases in endothelial nitric oxide synthase derived nitric oxide (NO) production during liver transplantation promotes injury. We hypothesized that preemptive inhaled NO (iNO) would improve allograft function (primary) and reduce complications post-transplantation (secondary). Patients at two university centers (Center A and B) were randomized to receive placebo (n = 20/center) or iNO (80 ppm, n = 20/center) during the operative phase of liver transplantation. Data were analyzed at set intervals for up to 9-months post-transplantation and compared between groups. Patient characteristics and outcomes were examined with the Mann-Whitney U test, Student t-test, logistic regression, repeated measures ANOVA, and Cox proportional hazards models. Combined and site stratified analyses were performed. MELD scores were significantly higher at Center B (22.5 vs. 19.5, p<0.0001), surgical times were greater at Center B (7.7 vs. 4.5 hrs, p<0.001) and warm ischemia times were greater at Center B (95.4 vs. 69.7 min, p<0.0001). No adverse metabolic or hematologic effects from iNO occurred. iNO enhanced allograft function indexed by liver function tests (Center B, p<0.05; and p<0.03 for ALT with center data combined) and reduced complications at 9-months (Center A and B, p = 0.0062, OR = 0.15, 95% CI (0.04, 0.59)). ICU (p = 0.47) and hospital length of stay (p = 0.49) were not decreased. iNO increased concentrations of nitrate (p<0.001), nitrite (p<0.001) and nitrosylhemoglobin (p<0.001), with nitrite being postulated as a protective mechanism. Mean costs of iNO were $1,020 per transplant. iNO was safe and improved allograft function at one center and trended toward improving allograft function at the other. ClinicalTrials.gov with registry number 00582010 and the following URL:http://clinicaltrials.gov/show/NCT00582010.

General anesthesia is associated with impaired gas exchange mainly because of increased shunt due to atelectasis in the dependent regions of the lung. Postoperative atelectasis is associated with adverse clinical outcomes in terms of hypoxic respiratory failure requiring endotracheal intubation and pneumonia secondary to impairment of ciliary and lymphatic functions. Prevention of atelectasis and/or airway closure could be a mechanism by which positive end expiratory pressure (PEEP) improves oxygenation. Positive end expiratory pressure has been used intraoperatively as a part of open lung and protective lung ventilation strategies. However, it is unclear at the present time whether the intraoperative use of PEEP is associated with a decrease in mortality or in the incidence of other important clinical surrogates of outcome such as postoperative respiratory failure. The aim of this review is to review the physiologic effects and history of PEEP, to present some of the current uses in specific surgical populations and comment on potential benefits on postoperative mortality and pulmonary complications that may be ascribed to intraoperative PEEP use.