The Future of Social Media, Anesthesiology, and the Perioperative Physician.

Social media has rapidly developed in the past decade to become a powerful and influential force for patients, physicians, health systems, and the academic community. While the use of social media in health care has produced many positive changes, such as rapid dissemination of information, crowd-sourced sharing of knowledge, learning, and social interaction, social media in health care has also negative effects. Recent examples of negative impacts of social media include rapid and unchecked information dissemination leading to patient misinformation and inadvertent reputational harm for health care professionals due to engaging in controversial topics on public platforms. Members of the anesthesiology community, like other medical specialties, have rapidly adopted social media at many different levels. However, most anesthesiologists, health systems, and academic communities have little education, preparation, and guidelines on optimizing the use of social media technology while minimizing the risks of social media. Anesthesiology has been and will continue to be impacted by the forces of technology and the cultural influences of social media for the foreseeable future. The purpose of this article was to examine the recent history of social media adoption in anesthesiology and perioperative medicine, understand the current impact of social media across our specialty, and consider how the future development of technology and evolving social and cultural dynamic influences of social media will have on anesthesiology over the next quarter century.

Immediate postdelivery airway management of neonates with prenatally diagnosed micrognathia: A retrospective observational study.

INTRODUCTION: Micrognathic neonates are at risk for upper airway obstruction, and many require intubation in the delivery room. Ex-utero intrapartum treatment is one technique for managing airway obstruction but poses substantial maternal risks. Procedure requiring a second team in the operating room is an alternative approach to secure the obstructed airway while minimizing maternal risk. The aim of this study was to describe the patient characteristics, airway management, and outcomes for micrognathic neonates and their mothers undergoing a procedure requiring a second team in the operating room at a single quaternary care children's hospital. METHODS: This was a retrospective descriptive study. Subjects had prenatally diagnosed micrognathia and underwent procedure requiring a second team in the operating room between 2009 and 2021. Collected data included infant characteristics, delivery room airway management, critical events, and medications. Follow-up data included genetic testing and subsequent procedures within 90 days. Maternal data included type of anesthetic, blood loss, and incidence of transfusion. RESULTS: Fourteen deliveries were performed via procedure requiring a second team in the operating room during the study period. 85.7% were male, and 50% had a genetic syndrome. Spontaneous respiratory efforts were observed in 93%. Twelve patients (85.7%) required an endotracheal tube or tracheostomy. Management approaches varied. Medications were primarily a combination of atropine, ketamine, and dexmedetomidine. Oxygen desaturation was common, and three patients experienced bradycardia. There were no periprocedural deaths. Follow-up at 90 days revealed that 78% of patients underwent at least one additional procedure, and one patient died due to an unrelated cause. All mothers underwent cesarean deliveries under neuraxial anesthesia. Median blood loss was 700 mL [IQR 700 mL, 800 mL]. Only one mother required a blood transfusion for pre-procedural placental abruption. DISCUSSION: Procedure requiring a second team in the operating room is a safe and effective approach to manage airway obstruction in micrognathic neonates while minimizing maternal morbidity. CONCLUSIONS: Though shown to be safe and effective, more data are needed to support the use of procedure requiring a second team in the operating room as an alternative to ex-utero intrapartum treatment for micrognathia outside of highly specialized maternal-fetal centers.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.

AIM OF THE REVIEW: The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models. METHODS: Systematic search of medical literature from PubMed and engineering literature from Compendex up to June 2, 2023. One reviewer screened studies that used EEG-based ML models to predict the neurologic outcomes after cardiac arrest. Four reviewers validated that the studies met selection criteria. Nine variables were manually extracted. The top-five common EEG features were calculated. We evaluated each study's risk of bias using the Quality in Prognosis Studies guideline. RESULTS: Out of 351 identified studies, 17 studies met the inclusion criteria. Random Forest (RF) (n = 7) was the most common ML model in the conventional ML category (n = 11), followed by Convolutional Neural Network (CNN) (n = 4) in the DNN category (n = 6). The AUCs for RF ranged between 0.8 and 0.97, while CNN had AUCs between 0.7 and 0.92. The top-three commonly used EEG features were band power (n = 12), Shannon's Entropy (n = 11), burst-suppression ratio (n = 9). CONCLUSIONS: RF and CNN were the two most common ML models with the highest AUCs for predicting the neurologic outcomes after cardiac arrest. Using a multimodal model that combines EEG features and electronic health record data may further improve prognostic performance.

Surgical neonates: A retrospective review of procedures and postoperative outcomes at a quaternary children's hospital.

INTRODUCTION: Neonates have a high incidence of respiratory and cardiac perioperative events. Disease severity and indications for surgical intervention often dovetail with an overall complex clinical course and predispose these infants to adverse long-term neurodevelopmental outcomes and increased length of stay. Our aims were to describe severe and nonsevere early postoperative complications to establish a baseline of care outcomes and to identify subgroups of surgical neonates and procedures for future prospective studies. METHODS: Electronic health record data were examined retrospectively for a cohort of patients who had general anesthesia from January 26, 2015 to August 31, 2018. Inclusion criteria were full-term infants with postmenstrual age less than 44 weeks or premature infants less than 60 weeks postmenstrual age undergoing nonimaging, noncardiac surgery. Severe postoperative complications were defined as mortality, reintubation, positive blood culture, and surgical site infection. Nonsevere early postoperative outcomes were defined as hypoglycemia, hyperglycemia, hypothermia, hyperthermia, and readmission within 30 days. RESULTS: About 2569 procedures were performed in 1842 neonates of which 10.9% were emergency surgeries. There were 120 postoperative severe complications and 965 nonsevere postoperative outcomes. Overall, 30-day mortality was 1.8% for the first procedure performed, with higher mortality seen on subgroup analysis for patients who underwent exploratory laparotomy (10.3%) and congenital lung lesion resection (4.9%). Postoperative areas for improvement included hyperglycemia (13.9%) and hypothermia (7.9%). DISCUSSION: The mortality rate in our study was comparable to other studies of neonatal surgery despite a high rate of emergency surgery and a high prevalence of prematurity in our cohort. The early outcomes data identified areas for improvement, including prevention of postoperative glucose and temperature derangements. CONCLUSIONS: Neonates in this cohort were at risk for severe and nonsevere adverse postoperative outcomes. Future studies are suggested to improve mortality and adverse event rates.

Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital.

Objectives: Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium. Materials and Methods: We performed a single-center retrospective cohort study using electronic health record data from February 2015 to December 2019. We built and evaluated 4 commonly used ML models for predicting emergence delirium: least absolute shrinkage and selection operator, ridge regression, random forest, and extreme gradient boosting. The primary outcome was the occurrence of emergence delirium, defined as a Watcha score of 3 or 4 recorded at any time during recovery. Results: The dataset included 54 776 encounters across 43 830 patients. The 4 ML models performed similarly with performance assessed by the area under the receiver operating characteristic curves ranging from 0.74 to 0.75. Notable variables associated with increased risk included adenoidectomy with or without tonsillectomy, decreasing age, midazolam premedication, and ondansetron administration, while intravenous induction and ketorolac were associated with reduced risk of emergence delirium. Conclusions: Four different ML models demonstrated similar performance in predicting postoperative emergence delirium using a large pediatric dataset. The prediction performance of the models draws attention to our incomplete understanding of this phenomenon based on the studied variables. The results from our modeling could serve as a first step in designing a predictive clinical decision support system, but further optimization and validation are needed. Clinical trial number and registry URL: Not applicable.

Refocusing on work-based hazards for the anaesthesiologist in a post-pandemic era.

The coronavirus pandemic has raised public awareness of one of the many hazards that healthcare workers face daily: exposure to harmful pathogens. The anaesthesia workplace encompasses the operating room, interventional radiology suite, and other sites that contain many other potential occupational and environmental hazards. This review article highlights the work-based hazards that anaesthesiologists and other clinicians may encounter in the anaesthesia workplace: ergonomic design, physical, chemical, fire, biological, or psychological hazards. As the anaesthesia work environment enters a post-COVID-19 pandemic phase, anaesthesiologists will do well to review and consider these hazards. The current review includes proposed solutions to some hazards and identifies opportunities for future research.

Anesthesia and Sedation Exposure and Neurodevelopmental Outcomes in Infants Undergoing Congenital Cardiac Surgery: A Retrospective Cohort Study.

BACKGROUND: Children undergoing complex cardiac surgery are exposed to substantial cumulative doses of sedative medications and volatile anesthetics and are more frequently anesthetized with ketamine, compared with healthy children. This study hypothesized that greater exposure to sedation and anesthesia in this population is associated with lower neurodevelopmental scores at 18 months of age. METHODS: A secondary analysis was conducted of infants with congenital heart disease who participated in a prospective observational study of environmental exposures and neurodevelopmental outcomes to assess the impact of cumulative volatile anesthetic agents and sedative medications. Cumulative minimum alveolar concentration hours of exposure to volatile anesthetic agents and all operating room and intensive care unit exposures to sedative and anesthesia medications were collected before administration of Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley III), at 18 months of age. RESULTS: The study cohort included 41 (37%) single-ventricle and 69 (63%) two-ventricle patients. Exposures to volatile anesthetic agents, opioids, benzodiazepines, and dexmedetomidine were not associated with abnormal Bayley III scores. At 18-month follow-up, after adjusting for confounders, each mg/kg increase in ketamine exposure was associated with a 0.34 (95% CI, -0.64 to -0.05) point decrease in Bayley III motor scores (P = 0.024). CONCLUSIONS: Total cumulative exposures to volatile anesthetic agents were not associated with neurodevelopmental impairment in infants with congenital heart disease undergoing various imaging studies and procedures, whereas higher ketamine doses were associated with poorer motor performance.

Perioperative Maternal-Fetal Outcomes in the Setting of Minimally Invasive Fetal Therapy for Complex Monochorionic Pregnancies with Monitored Anesthesia Care.

INTRODUCTION: Fetoscopic selective laser photocoagulation (FSLPC) and selective cord occlusion with radiofrequency ablation (RFA) can improve fetal outcomes when vascular anastomoses between fetuses cause twin-to-twin transfusion syndrome (TTTS) or selective fetal growth restriction (sFGR) in multiple gestation pregnancies with monochorionic placentation. This study analyzed perioperative maternal-fetal complications and anesthetic management in a high-volume fetal therapy center over a 4-year period. METHODS: Included patients received MAC for minimally invasive fetal procedures for complex multiple gestation pregnancies between January 1, 2015, and September 20, 2019. Maternal and fetal complications, intraoperative maternal hemodynamics, medication usage, and reasons for conversion to general anesthesia, if applicable, were analyzed. RESULTS: A total of 203 (59%) patients underwent FSLPC and 141 (41%) had RFA. Four patients (2%; rate 95% CI: 0.00039, 0.03901) undergoing FSLPC had conversion to general anesthesia. No conversions to general anesthesia occurred in the RFA group. The incidence of maternal complications was higher in those who underwent FSLPC. No aspiration or postoperative pneumonia events were observed. Medication usage was similar in FSLPC and RFA groups. CONCLUSION: A low rate of conversion to general anesthesia and no serious adverse maternal events were observed in patients receiving MAC.

Patient and Process Outcomes among Pediatric Patients Undergoing Appendectomy during the COVID-19 Pandemic: An International Retrospective Cohort Study.

BACKGROUND: COVID-19 forced healthcare systems to make unprecedented changes in clinical care processes. The authors hypothesized that the COVID-19 pandemic adversely impacted timely access to care, perioperative processes, and clinical outcomes for pediatric patients undergoing primary appendectomy. METHODS: A retrospective, international, multicenter study was conducted using matched cohorts within participating centers of the international PEdiatric Anesthesia COVID-19 Collaborative (PEACOC). Patients younger than 18 yr old were matched using age, American Society of Anesthesiologists Physical Status, and sex. The primary outcome was the difference in hospital length of stay of patients undergoing primary appendectomy during a 2-month period early in the COVID-19 pandemic (April to May 2020) compared with prepandemic (April to May 2019). Secondary outcomes included time to appendectomy and the incidence of complicated appendicitis. RESULTS: A total of 3,351 cases from 28 institutions were available with 1,684 cases in the prepandemic cohort matched to 1,618 in the pandemic cohort. Hospital length of stay was statistically significantly different between the two groups: 29 h (interquartile range: 18 to 79) in the pandemic cohort versus 28 h (interquartile range: 18 to 67) in the prepandemic cohort (adjusted coefficient, 1 [95% CI, 0.39 to 1.61]; P < 0.001), but this difference was small. Eight centers demonstrated a statistically significantly longer hospital length of stay in the pandemic period than in the prepandemic period, while 13 were shorter and 7 did not observe a statistically significant difference. During the pandemic period, there was a greater occurrence of complicated appendicitis, prepandemic 313 (18.6%) versus pandemic 389 (24.1%), an absolute difference of 5.5% (adjusted odds ratio, 1.32 [95% CI, 1.1 to 1.59]; P = 0.003). Preoperative SARS-CoV-2 testing was associated with significantly longer time-to-appendectomy, 720 min (interquartile range: 430 to 1,112) with testing versus 414 min (interquartile range: 231 to 770) without testing, adjusted coefficient, 306 min (95% CI, 241 to 371; P < 0.001), and longer hospital length of stay, 31 h (interquartile range: 20 to 83) with testing versus 24 h (interquartile range: 14 to 68) without testing, adjusted coefficient, 7.0 (95% CI, 2.7 to 11.3; P = 0.002). CONCLUSIONS: For children undergoing appendectomy, the COVID-19 pandemic did not significantly impact hospital length of stay.

Standardized Anesthesia InductioN Tool (SAINT) - The development and international adoption of an integrated electronic tool for documenting the induction of anesthesia in children.

BACKGROUND: The induction of anesthesia in children poses a challenge for the anesthesiologist, the parent and child. Anxiety and negative behaviours and strategies that effectively mitigate should be documented accurately and be available for future patient encounters. To address the need for a structured and standardized electronic documentation tool. AIMS: Our aim was to develop a comprehensive electronic tool to capture and report behaviours during induction of anesthesia. METHODS: We performed a literature search on existing validated tools for documenting behaviours during anesthesia induction. We used the nominal group technique to achieve agreement on the components to include. We used Agile software development techniques to design and review the integrated electronic tool. Twelve international hospitals informed the development of the tool. RESULTS: We developed an electronic tool, the Standardized Anesthesia InductioN Tool (SAINT). SAINT incorporates validated scales for documenting key stages of the anesthesia induction journey (separation from caregivers, mask acceptance, induction behaviour, parental presence, the use of adjuncts and their effectiveness). In addition, the standardised data elements used in SAINT allow for local reporting, quality metrics and can assist in data across multi-centre trials. To date the tool has been adopted by 133 institutions across four countries and is freely available. CONCLUSION: We show that collaborative development and rapid adoption of the comprehensive induction tool SAINT has led to its rapid adoption in the routine practice of pediatric anesthesiology across several countries. Further studies on how the SAINT is being used for quality improvement or research are warranted.

A Narrative Review of Multiinstitutional Data Registries of Pediatric Congenital Heart Disease in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies. While the care of children with CHD has improved over recent decades, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. Electronic health record systems have enabled institutions to combine data on the management and outcomes of children with CHD in multicenter registries. The application of descriptive analytics methods to these data can improve clinicians' understanding and care of children with CHD. This narrative review covers efforts to leverage multicenter data registries relevant to pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Early prediction of clinical deterioration using data-driven machine-learning modeling of electronic health records.

OBJECTIVES: To develop and evaluate a high-dimensional, data-driven model to identify patients at high risk of clinical deterioration from routinely collected electronic health record (EHR) data. MATERIALS AND METHODS: In this single-center, retrospective cohort study, 488 patients with single-ventricle and shunt-dependent congenital heart disease <6 months old were admitted to the cardiac intensive care unit before stage 2 palliation between 2014 and 2019. Using machine-learning techniques, we developed the Intensive care Warning Index (I-WIN), which systematically assessed 1028 regularly collected EHR variables (vital signs, medications, laboratory tests, and diagnoses) to identify patients in the cardiac intensive care unit at elevated risk of clinical deterioration. An ensemble of 5 extreme gradient boosting models was developed and validated on 203 cases (130 emergent endotracheal intubations, 34 cardiac arrests requiring cardiopulmonary resuscitation, 10 extracorporeal membrane oxygenation cannulations, and 29 cardiac arrests requiring cardiopulmonary resuscitation onto extracorporeal membrane oxygenation) and 378 control periods from 446 patients. RESULTS: At 4 hours before deterioration, the model achieved an area under the receiver operating characteristic curve of 0.92 (95% confidence interval, 0.84-0.98), 0.881 sensitivity, 0.776 positive predictive value, 0.862 specificity, and 0.571 Brier skill score. Performance remained high at 8 hours before deterioration with 0.815 (0.688-0.921) area under the receiver operating characteristic curve. CONCLUSIONS: I-WIN accurately predicted deterioration events in critically-ill infants with high-risk congenital heart disease up to 8 hours before deterioration, potentially allowing clinicians to target interventions. We propose a paradigm shift from conventional expert consensus-based selection of risk factors to a data-driven, machine-learning methodology for risk prediction. With the increased availability of data capture in EHRs, I-WIN can be extended to broader applications in data-rich environments in critical care.

Thromboelastography Changes of Whole Blood Compared to Blood Component Transfusion in Infant Craniosynostosis Surgery.

ABSTRACT: Surgical treatment of craniosynostosis with cranial vault reconstruction in infants is associated with significant blood loss. The optimal blood management approach is an area of active investigation. Thromboelastography (TEG) was used to examine changes in coagulation after surgical blood loss that was managed by transfusion with either whole blood or blood components. Transfusion type was determined by availability of whole blood from the blood bank.This retrospective study examined differences in posttransfusion TEG maximum amplitude (MA), a measure of the maximum clot strength, for patients transfused with whole blood or blood components. We included all patients less than 24 months old who underwent cranial vault remodeling, received intraoperative transfusions with whole blood or blood components, and had baseline and posttransfusion TEG measured. Whole blood was requested for all patients and was preferentially used when it was available from the American Red Cross.Of 48 eligible patients, 30 received whole blood and 18 received blood components. All patients received an intraoperative antifibrinolytic agent. The posttransfusion MA in the whole blood group was 61.8 mm (IQR 59.1, 64.1) compared to 57.9 mm (IQR 50.5, 60.9) in the blood components group (P = 0.010). There was a greater posttransfusion decrease in MA for patients transfused with blood components (median decrease of 7.7 mm [IQR -3.4, 6.3]) compared with whole blood (median decrease of 2.1 mm [IQR -9.6, 7.5] P < 0.001).Transfusion with blood components was associated with a greater decrease in MA that was likely related to decreased postoperative fibrinogen in this group. Patients who received whole blood had higher postoperative fibrinogen levels.

An Environmental Scan of Anesthesia Information Management Systems in the American and Canadian Marketplace.

Anesthesia Information Management Systems are specialized forms of electronic medical records used by anesthesiologists to automatically and reliably collect, store, and present perioperative patient data. There are no recent academic publications that outline the names and features of AIMS in the current American and Canadian marketplace. An environmental scan was performed to first identify existing AIMS in this marketplace, and then describe and compare these AIMS. We found 13 commercially available AIMS but were able to describe in detail the features and functionalities of only 10 of these systems, as three vendors did not participate in the study. While all AIMS have certain key features, other features and functionalities are only offered by some of the AIMS. Features less commonly offered included patient portals for pre-operative questionnaires, clinical decision support systems, and voice-to-text capability for documentation. The findings of this study can inform AIMS procurement efforts by enabling anesthesia departments to compare features across AIMS and find an AIMS whose features best fit their needs and priorities. Future studies are needed to describe the features and functionalities of these AIMS at a more granular level, and also assess the usability and costs of these systems.