Use of hydroxocobalamin to treat intraoperative vasoplegic syndrome refractory to vasopressors and methylene blue during liver transplantation.

INTRODUCTION: For patients with catecholamine-resistant vasoplegic syndrome (VS) during liver transplantation (LT), treatment with methylene blue (MB) and/or hydroxocobalamin (B12) has been an acceptable therapy. However, data on the effectiveness of B12 is limited to case reports and case series. METHODS: We retrospectively reviewed records of patients undergoing LT from January 2016 through March 2022. We identified patients with VS treated with vasopressors and MB, and abstracted hemodynamic parameters, vasopressor requirements, and B12 administration from the records. The primary aim was to describe the treatment efficacy of B12 for VS refractory to vasopressors and MB, measured as no vasopressor requirement at the conclusion of the surgery. RESULTS: One hundred one patients received intraoperative VS treatment. For the 35 (34.7%) patients with successful VS treatment, 14 received MB only and 21 received both MB and B12. Of the 21 patients with VS resolution after receiving both MB and B12, 17 (89.5%) showed immediate, but transient, hemodynamic improvements at the time of MB administration and later showed sustained response to B12. CONCLUSION: Immediate but transient hemodynamic response to MB in VS patients during LT supports the diagnosis of VS and should prompt B12 administration for sustained treatment response.

Intraoperative hypotension and postoperative acute kidney injury: A systematic review.

BACKGROUND: There is no consensus regarding safe intraoperative blood pressure thresholds that protect against postoperative acute kidney injury (AKI). This review aims to examine the existing literature to delineate safe intraoperative hypotension (IOH) parameters to prevent postoperative AKI. METHODS: PubMed, Cochrane Central, and Web of Science were systematically searched for articles published between 2015 and 2022 relating the effects of IOH on postoperative AKI. RESULTS: Our search yielded 19 articles. IOH risk thresholds ranged from <50 to <75 â€‹mmHg for mean arterial pressure (MAP) and from <70 to <100 â€‹mmHg for systolic blood pressure (SBP). MAP below 65 â€‹mmHg for over 5 â€‹min was the most cited threshold (N â€‹= â€‹13) consistently associated with increased postoperative AKI. Greater magnitude and duration of MAP and SBP below the thresholds were generally associated with a dose-dependent increase in postoperative AKI incidence. CONCLUSIONS: While a consistent definition for IOH remains elusive, the evidence suggests that MAP below 65 â€‹mmHg for over 5 â€‹min is strongly associated with postoperative AKI, with the risk increasing with the magnitude and duration of IOH.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.

Importance: Predicting postoperative complications has the potential to inform shared decisions regarding the appropriateness of surgical procedures, targeted risk-reduction strategies, and postoperative resource use. Realizing these advantages requires that accurate real-time predictions be integrated with clinical and digital workflows; artificial intelligence predictive analytic platforms using automated electronic health record (EHR) data inputs offer an intriguing possibility for achieving this, but there is a lack of high-level evidence from prospective studies supporting their use. Objective: To examine whether the MySurgeryRisk artificial intelligence system has stable predictive performance between development and prospective validation phases and whether it is feasible to provide automated outputs directly to surgeons' mobile devices. Design, Setting, and Participants: In this prognostic study, the platform used automated EHR data inputs and machine learning algorithms to predict postoperative complications and provide predictions to surgeons, previously through a web portal and currently through a mobile device application. All patients 18 years or older who were admitted for any type of inpatient surgical procedure (74 417 total procedures involving 58 236 patients) between June 1, 2014, and September 20, 2020, were included. Models were developed using retrospective data from 52 117 inpatient surgical procedures performed between June 1, 2014, and November 27, 2018. Validation was performed using data from 22 300 inpatient surgical procedures collected prospectively from November 28, 2018, to September 20, 2020. Main Outcomes and Measures: Algorithms for generalized additive models and random forest models were developed and validated using real-time EHR data. Model predictive performance was evaluated primarily using area under the receiver operating characteristic curve (AUROC) values. Results: Among 58 236 total adult patients who received 74 417 major inpatient surgical procedures, the mean (SD) age was 57 (17) years; 29 226 patients (50.2%) were male. Results reported in this article focus primarily on the validation cohort. The validation cohort included 22 300 inpatient surgical procedures involving 19 132 patients (mean [SD] age, 58 [17] years; 9672 [50.6%] male). A total of 2765 patients (14.5%) were Black or African American, 14 777 (77.2%) were White, 1235 (6.5%) were of other races (including American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander, and multiracial), and 355 (1.9%) were of unknown race because of missing data; 979 patients (5.1%) were Hispanic, 17 663 (92.3%) were non-Hispanic, and 490 (2.6%) were of unknown ethnicity because of missing data. A greater number of input features was associated with stable or improved model performance. For example, the random forest model trained with 135 input features had the highest AUROC values for predicting acute kidney injury (0.82; 95% CI, 0.82-0.83); cardiovascular complications (0.81; 95% CI, 0.81-0.82); neurological complications, including delirium (0.87; 95% CI, 0.87-0.88); prolonged intensive care unit stay (0.89; 95% CI, 0.88-0.89); prolonged mechanical ventilation (0.91; 95% CI, 0.90-0.91); sepsis (0.86; 95% CI, 0.85-0.87); venous thromboembolism (0.82; 95% CI, 0.81-0.83); wound complications (0.78; 95% CI, 0.78-0.79); 30-day mortality (0.84; 95% CI, 0.82-0.86); and 90-day mortality (0.84; 95% CI, 0.82-0.85), with accuracy similar to surgeons' predictions. Compared with the original web portal, the mobile device application allowed efficient fingerprint login access and loaded data approximately 10 times faster. The application output displayed patient information, risk of postoperative complications, top 3 risk factors for each complication, and patterns of complications for individual surgeons compared with their colleagues. Conclusions and Relevance: In this study, automated real-time predictions of postoperative complications with mobile device outputs had good performance in clinical settings with prospective validation, matching surgeons' predictive accuracy.

Accessing Artificial Intelligence for Clinical Decision-Making.

Advancements in computing and data from the near universal acceptance and implementation of electronic health records has been formative for the growth of personalized, automated, and immediate patient care models that were not previously possible. Artificial intelligence (AI) and its subfields of machine learning, reinforcement learning, and deep learning are well-suited to deal with such data. The authors in this paper review current applications of AI in clinical medicine and discuss the most likely future contributions that AI will provide to the healthcare industry. For instance, in response to the need to risk stratify patients, appropriately cultivated and curated data can assist decision-makers in stratifying preoperative patients into risk categories, as well as categorizing the severity of ailments and health for non-operative patients admitted to hospitals. Previous overt, traditional vital signs and laboratory values that are used to signal alarms for an acutely decompensating patient may be replaced by continuously monitoring and updating AI tools that can pick up early imperceptible patterns predicting subtle health deterioration. Furthermore, AI may help overcome challenges with multiple outcome optimization limitations or sequential decision-making protocols that limit individualized patient care. Despite these tremendously helpful advancements, the data sets that AI models train on and develop have the potential for misapplication and thereby create concerns for application bias. Subsequently, the mechanisms governing this disruptive innovation must be understood by clinical decision-makers to prevent unnecessary harm. This need will force physicians to change their educational infrastructure to facilitate understanding AI platforms, modeling, and limitations to best acclimate practice in the age of AI. By performing a thorough narrative review, this paper examines these specific AI applications, limitations, and requisites while reviewing a few examples of major data sets that are being cultivated and curated in the US.

Administering RiaSTAP for Congenital Afibrinogenemia During Liver Transplant.

Liver transplant has occasionally been performed in the presence of congenital afibrinogenemia and has been rarely used to treat it. Historically, to safely manage coagulopathy during transplant, these patients have been administered a combination of fresh frozen plasma and cryoprecipitate. In this case report, we discuss the first reported use of recombinant fibrinogen to treat such a patient and the decision-making process considered to balance the thrombotic and hemorrhagic risks.

Fluid Management During Kidney Transplantation: A Consensus Statement of the Committee on Transplant Anesthesia of the American Society of Anesthesiologists.

BACKGROUND: Intraoperative fluid management may affect the outcome after kidney transplantation. However, the amount and type of fluid administered, and monitoring techniques vary greatly between institutions and there are limited prospective randomized trials and meta-analyses to guide fluid management in kidney transplant recipients. METHODS: Members of the American Society of Anesthesiologists (ASA) committee on transplantation reviewed the current literature on the amount and type of fluids (albumin, starches, 0.9% saline, and balanced crystalloid solutions) administered and the different monitors used to assess fluid status, resulting in this consensus statement with recommendations based on the best available evidence. RESULTS: Review of the current literature suggests that starch solutions are associated with increased risk of renal injury in randomized trials and should be avoided in kidney donors and recipients. There is no evidence supporting the routine use of albumin solutions in kidney transplants. Balanced crystalloid solutions such as Lactated Ringer are associated with less acidosis and may lead to less hyperkalemia than 0.9% saline solutions. Central venous pressure is only weakly supported as a tool to assess fluid status. CONCLUSIONS: These recommendations may be useful to anesthesiologists making fluid management decisions during kidney transplantation and facilitate future research on this topic.

Multiobjective optimization challenges in perioperative anesthesia: A review.

Physicians use perioperative decision-support tools to mitigate risks and maximize benefits to achieve the most successful outcome for patients. Contemporary risk-assessment practices augment surgeons' judgement and experience with decision-support algorithms driven by big data and machine learning. These algorithms accurately assess risk for a wide range of postoperative complications by parsing large datasets and performing complex calculations that would be cumbersome for busy clinicians. Even with these advancements, large gaps in perioperative risk assessment remain; decision-support algorithms often cannot account for risk-reduction therapies applied during a patient's perioperative course and do not quantify tradeoffs between competing goals of care (eg, balancing postoperative pain control with the risk of respiratory depression or balancing intraoperative volume resuscitation with the risk for complications from pulmonary edema). Multiobjective optimization solutions have been applied to similar problems successfully but have not yet been applied to perioperative decision support. Given the large volume of data available via electronic medical records, including intraoperative data, it is now feasible to successfully apply multiobjective optimization in perioperative care. Clinical application of multiobjective optimization would require semiautomated pipelines for analytics and reporting model outputs and a careful development and validation process. Under these circumstances, multiobjective optimization has the potential to support personalized, patient-centered, shared decision-making with precision and balance.

Added Value of Intraoperative Data for Predicting Postoperative Complications: The MySurgeryRisk PostOp Extension.

BACKGROUND: Models that predict postoperative complications often ignore important intraoperative events and physiological changes. This study tested the hypothesis that accuracy, discrimination, and precision in predicting postoperative complications would improve when using both preoperative and intraoperative data input data compared with preoperative data alone. METHODS: This retrospective cohort analysis included 43,943 adults undergoing 52,529 inpatient surgeries at a single institution during a 5-y period. Random forest machine learning models in the validated MySurgeryRisk platform made patient-level predictions for seven postoperative complications and mortality occurring during hospital admission using electronic health record data and patient neighborhood characteristics. For each outcome, one model trained with preoperative data alone; one model trained with both preoperative and intraoperative data. Models were compared by accuracy, discrimination (expressed as area under the receiver operating characteristic curve), precision (expressed as area under the precision-recall curve), and reclassification indices. RESULTS: Machine learning models incorporating both preoperative and intraoperative data had greater accuracy, discrimination, and precision than models using preoperative data alone for predicting all seven postoperative complications (intensive care unit length of stay >48 h, mechanical ventilation >48 h, neurologic complications including delirium, cardiovascular complications, acute kidney injury, venous thromboembolism, and wound complications), and in-hospital mortality (accuracy: 88% versus 77%; area under the receiver operating characteristic curve: 0.93 versus 0.87; area under the precision-recall curve: 0.21 versus 0.15). Overall reclassification improvement was 2.4%-10.0% for complications and 11.2% for in-hospital mortality. CONCLUSIONS: Incorporating both preoperative and intraoperative data significantly increased the accuracy, discrimination, and precision of machine learning models predicting postoperative complications and mortality.

Feasibility and Rationale for Incorporating Frailty and Cognitive Screening Protocols in a Preoperative Anesthesia Clinic.

BACKGROUND: Advanced age, frailty, low education level, and impaired cognition are generally reported to be associated with postoperative cognitive complications. To translate research findings into hospital-wide preoperative assessment clinical practice, we examined the feasibility of implementing a preoperative frailty and cognitive assessment for all older adults electing surgical procedures in a tertiary medical center. We examined associations among age, education, frailty, and comorbidity with the clock and 3-word memory scores, estimated the prevalence of mild to major cognitive impairment in the presurgical sample, and examined factors related to hospital length of stay. METHODS: Medical staff screened adults ≥65 years of age for frailty, general cognition (via the clock-drawing test command and copy, 3-word memory test), and obtained years of education. Feasibility was studied in 2 phases: (1) a pilot phase involving 4 advanced nurse practitioners and (2) a 2-month implementation phase involving all preoperative staff. We tracked sources of missing data, investigated associations of study variables with measures of cognition, and used 2 approaches to estimate the likelihood of dementia in our sample (ie, using extant data and logistic regression modeling and using Mini-Cog cut scores). We explored which protocol variables related to hospital length of stay. RESULTS: The final implementation phase sample included 678 patients. Clock and 3-word memory scores were significantly associated with age, frailty, and education. Education, clock scores, and 3-word scores were not significantly different by surgery type. Likelihood of preoperative cognitive impairment was approximately 20%, with no difference by surgery type. Length of stay was significantly associated with preoperative comorbidity and performance on the clock copy condition. CONCLUSIONS: Frailty and cognitive screening protocols are feasible and provide information for perioperative care planning. Challenges to clinical adaptation include staff training, missing data, and additional administration time. These challenges appear minimal relative to the benefits of identifying frailty and cognitive impairment in a group at risk for negative postoperative cognitive outcome.

Considerations for Clock Drawing Scoring Systems in Perioperative Anesthesia Settings.

The Clock Drawing Test is a cognitive screening tool gaining popularity in the perioperative setting. We compared 3 common scoring systems: (1) the Montreal Cognitive Assessment; (2) the Mini-Cog; and (3) the Libon scale. Three novice raters acquired interrater and intrarater reliability for each scoring system and then scored 738 preoperative clock drawings with each scoring system. Final scores correlated with each other but with notable discrepancies, indicating the need to attend to interrater and intrarater reliability when implementing any scoring approach in a clinical setting.

Should Percussion Pacing Have a Role in Perioperative Advanced Cardiac Life Support?: A Case Report.

Percussion pacing involves using one's fist to repeatedly strike a patient's left sternal border in a rhythmic manner. The resulting increase in ventricular pressure can trigger myocardial depolarization and subsequent contraction. We describe the successful treatment of acute preoperative symptomatic sinus bradycardia with percussion pacing in a 63-year-old patient scheduled for placement of a gastric feeding tube after trauma involving spinal cord injury. Although no longer included in current advanced cardiovascular life support guidelines, percussion pacing may be a suitable alternative to chest compressions in multitrauma cases where the force of compressions could cause further complications.

Break Scrub to Take That Phone Call?

BACKGROUND: The American College of Surgeons reports that 60% of the hundreds of thousands of surgical site infections occurring annually are preventable. The practice of surgeons taking phone calls while remaining sterile in the operating field is often accomplished by interposing a sterile disposable towel between the phone and their glove. After completing the call, surgeons resume operating. The purpose of our study was to test the conceptual idea of whether bacteria transmit from an inanimate object, such as a telephone, to the gloves of a surgeon through a sterile disposable towel. STUDY DESIGN: Glo Germ (Glo Germ Co), an ultraviolet light-enhanced particle powder sized to mimic bacteria, was placed on an inanimate surface and held with a sterile disposable operating room towel covering a sterile surgical glove. The glove was then inspected for Glo Germ using an ultraviolet light. Additionally, 18 operating room telephones were cultured and then held with a Sterile Disposable OR Towel (Medline Industries Inc) covering a sterile surgical glove. The surgical gloves were then cultured to determine if bacteria had transmitted from the telephone through the towel and onto the sterile glove. RESULTS: The Glo Germ powder readily transmitted through the towel to the gloves. Median colony-forming units (CFU) on the cultured telephones for the 17 samples was 10, ranging from 1 to 35 CFUs. Of these 17 samples, 47% had transmission from the telephone to the glove, which was significantly greater than 0% (95% CI 26% to 69%, p < 0.001). CONCLUSIONS: Sterile disposable operating room towels do not provide an effective barrier between bacteria present on operating room telephones and the otherwise sterile gloves of a surgeon.

Managing the Complex Issues of Pediatric Nonaccidental Trauma: A Simulation-Based Case of a Critically Injured Child.

INTRODUCTION: Trainees generally have insufficient training in managing critically ill and injured pediatric patients due to limited exposure to such patients. Patient simulation experiences allow trainees to learn management skills needed in such a crisis. Herein, we describe a case regarding a critically injured pediatric patient. This case requires trainees to use teamwork skills, medical knowledge, and technical skills to manage the patient. METHODS: We developed a team-based simulation regarding the resuscitation of a critically injured child-a toddler with multiple injuries, all requiring emergent care. The case was developed for senior medical students and residents and can be completed in a single 1-hour session, including a debriefing period. We also address psychosocial issues of managing a critically injured child by having the mother and her boyfriend present for part of the case. The team must address the underlying issue of suspected nonaccidental trauma while managing a medical resuscitation. RESULTS: We have performed this scenario with a cohort of 100 trainees. Through direct observations, all teams have been able to manage the patient successfully. The average response to the effectiveness of the case in terms of developing pediatric resuscitation skills was very positive, with scores of 6.7 on a scale of 1 to 7. DISCUSSION: Medical simulation has been demonstrated to be a valuable tool for assessing the knowledge and skills of trainees. This pediatric simulation improved learners' general understanding of managing a pediatric resuscitation. Accordingly, this case has been incorporated as part of resident and medical student training.

Hyperkalemic Arrest: Developing Team Cognition.

INTRODUCTION: Critical events are frequently managed by individuals with different skill sets, funds of knowledge, and experiences who form ad hoc teams on a daily basis without any previous practice together. Such groups' spontaneity of formation puts a premium on individuals' ability to understand team cognition and work together. Team cognition can be thought of as an analogue of individual cognition and is revealed during functional interactions of team members working interdependently on a shared goal. This simulation helps trainees develop and practice team-training skills in order to better form ad hoc teams and manage critical events. METHODS: This simulation can be applied to senior medical students and residents and focuses on the management of an accidental administration of potassium leading to hyperkalemic arrest. The simulation takes 10 minutes to complete and, when coupled with a debriefing session, can be accomplished in under 45 minutes. RESULTS: Twenty-two trainees, consisting of five teams of four to five residents, participated in this simulation. Each team showed varying levels of team cognition, and most successfully managed the hyperkalemic arrest; both of these points were reviewed at length during the debriefing. The trainees gave the simulation high ratings in terms of its effectiveness for team training, with a score of 6.7 on a scale of 1-7. DISCUSSION: Medical simulations have been very productive in providing learners with opportunities to manage critical events. With the exploding practice of interdisciplinary medicine, we believe simulation-based training should be implemented to develop team cognition and practice team training.

Locating the optimal internal jugular target site for central venous line placement.

STUDY OBJECTIVE: Historically, the placement of internal jugular central venous lines has been accomplished by using external landmarks to help identify target-rich locations in order to steer clear of dangerous structures. This paradigm is largely being displaced, as ultrasound has become routine practice, raising new considerations regarding target locations and risk mitigation. Most human anatomy texts depict the internal jugular vein as a straight columnar structure that exits the cranial vault the same size that it enters the thoracic cavity. We dispute the notion that the internal jugulars are cylindrical columns that symmetrically descend into the thoracic cavity, and purport that they are asymmetric conical structures. DESIGN: The primary aim of this study was to evaluate 100 consecutive adult chest and neck computed tomography exams that were imaged at an inpatient hospital. We measured the internal jugular on the left and right sides at three different levels to look for differences in size as the internal jugular descends into the thoracic cavity. MAIN RESULTS: We revealed that as the internal jugular descends into the thorax, the area of the vessel increases and geometrically resembles a conical structure. We also reconfirmed that the left internal jugular is smaller than the right internal jugular. CONCLUSIONS: Understanding that the largest target area for central venous line placement is the lower portion of the right internal jugular vein will help to better target vascular access for central line placement. This is the first study the authors are aware of that depicts the internal jugular as a conical structure as opposed to the commonly depicted symmetrical columnar structure frequently illustrated in anatomy textbooks. This target area does come with additional risk, as the closer you get to the thoracic cavity, the greater the chances for lung injury.