Natural airway as an alternative to intubation for pediatric endoscopic esophageal foreign body removal: A retrospective cohort study of 326 patients.

BACKGROUND: Anesthesia is required for endoscopic removal of esophageal foreign bodies (EFBs) in children. Historically, endotracheal intubation has been the de facto gold standard for airway management in these cases. However, as more elective endoscopic procedures are now performed under propofol sedation with natural airway, there has been a move toward using similar Monitored Anesthesia Care (MAC) for select patients who require endoscopic removal of an EFB. METHODS: In this single-center retrospective cohort study, we compared endoscopic EFB removal with either MAC or endotracheal intubation. Descriptive statistics summarized factors stratified by initial choice of airway technique, including intra- and postanesthesia complications and the frequency of mid-procedure conversion to endotracheal intubation in those initially managed with MAC. To demonstrate the magnitude of associations between these factors and the anesthesiologist's choice of airway technique, univariable Firth logistic and quantile regressions were used to estimate odds ratios (95% CI) and beta coefficients (95% CI). RESULTS: From the initial search, 326 patients were identified. Among them, 23% (n = 75) were planned for intubation and 77% (n = 251) were planned for MAC. Three patients (0.9%) who were initially planned for MAC required conversion to endotracheal intubation after induction. Two (0.6%) of these children were admitted to the hospital after the procedure and treated for ongoing airway reactivity. No patient experienced reflux of gastric contents to the mouth or dislodgement of the foreign body to the airway, and no patient required administration of vasoactive medications or cardiopulmonary resuscitation. Patients had higher odds that the anesthesiologist chose to utilize MAC if the foreign body was a coin (OR, 3.3; CI, 1.9-5.7, p < .001) or if their fasting time was >6 h. Median total operating time was 15 min greater in intubated patients (11 vs. 26 min, p < .001). CONCLUSIONS: This study demonstrates that MAC may be considered for select pediatric patients undergoing endoscopic removal of EFB, especially those who have ingested coins, who do not have reactive airways, who have fasted for >6 h, and in whom the endoscopic procedure is expected to be short and uncomplicated. Prospective multi-site studies are needed to confirm these findings.

Psychological resilience, emotional symptoms, and recovery duration in adolescent sport-related concussion.

OBJECTIVE: This study aimed to explore the relation between resilience, emotional changes following injury, and recovery duration in sport-related concussion. METHODS: Thirty-one high school student-athletes (ages 14-18) with sports-related injuries (concussion, n = 17 orthopedic injury, n = 14) were recruited from a pediatric sports medicine clinic. Participants completed self-report resilience ratings and self- and parent-reported post-concussion symptoms as part of a neuropsychological test battery. Hierarchical regression analyses examined predictors of recovery duration, including: (1) injury group and sex, (2) self- and parent-reported emotional symptom changes, and (3) resilience score. RESULTS: Injury group and sex alone were not predictors of recovery duration (p = .60). When parent and patient reported emotional response to injury were added to the analysis, 35% of the variance in length of recovery was explained, making the model statistically significant (F (2.26) = 3.57, p = .019). Including resilience did not reach statistical significance (p = .443). Post hoc analysis revealed parent-report of emotional changes was significantly associated with recovery duration t(31) = 3.16, p < .01), while self-report was not (p = .54). CONCLUSIONS: Parent-reported emotional change plays a pivotal role in predicting recovery length among adolescents recovering from sport-related concussion and orthopedic injury. These pilot findings highlight the significance of caregiver input in the clinical exam and emphasize the potential for acute interventions supporting psychological resources to enhance recovery outcomes across adolescent sport-related injuries.

Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.

Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation. Data from both the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial and Extension study (SVR II), which extended cohort follow up for five years was used to develop ML-driven models predicting TFS. Models incrementally incorporated features corresponding to successive phases of care, from pre-Stage 1 palliation (S1P) through the stage 2 palliation (S2P) hospitalization. Models trained with features from Pre-S1P, S1P operation, and S1P hospitalization all demonstrated time-dependent area under the curves (td-AUC) beyond 0.70 through 5 years following S1P, with a model incorporating features through S1P hospitalization demonstrating particularly robust performance (td-AUC 0.838 (95% CI 0.836-0.840)). Machine learning may offer a clinically useful alternative means of providing individualized survival probability predictions, years following the staged surgical palliation of hypoplastic left heart syndrome.

Precision Anesthesia in 2050.

Over the last few decades, the field of anesthesia has advanced far beyond its humble beginnings. Today's anesthetics are better and safer than ever, thanks to innovations in drugs, monitors, equipment, and patient safety.1-4 At the same time, we remain limited by our herd approach to medicine. Each of our patients is unique, but health care today is based on a one-size-fits-all approach, while our patients grow older and more medically complex every year. By 2050, we believe that precision medicine will play a central role across all medical specialties, including anesthesia. In addition, we expect that health care and consumer technology will continually evolve to improve and simplify the interactions between patients, providers, and the health care system. As demonstrated by 2 hypothetical patient experiences, these advancements will enable more efficient and safe care, earlier and more accurate diagnoses, and truly personalized treatment plans.

Survival analysis for pediatric heart transplant patients using a novel machine learning algorithm: A UNOS analysis.

BACKGROUND: Impact of pretransplantation risk factors on mortality in the first year after heart transplantation remains largely unknown. Using machine learning algorithms, we selected clinically relevant identifiers that could predict 1-year mortality after pediatric heart transplantation. METHODS: Data were obtained from the United Network for Organ Sharing Database for years 2010-2020 for patients 0-17 years receiving their first heart transplant (N = 4150). Features were selected using subject experts and literature review. Scikit-Learn, Scikit-Survival, and Tensorflow were used. A train:test split of 70:30 was used. N-repeated k-fold validation was performed (N = 5, k = 5). Seven models were tested, Hyperparameter tuning performed using Bayesian optimization and the concordance index (C-index) was used for model assessment. RESULTS: A C-index above 0.6 for test data was considered acceptable for survival analysis models. C-indices obtained were 0.60 (Cox proportional hazards), 0.61 (Cox with elastic net), 0.64 (gradient boosting), 0.64 (support vector machine), 0.68 (random forest), 0.66 (component gradient boosting), and 0.54 (survival trees). Machine learning models show an improvement over the traditional Cox proportional hazards model, with random forest performing the best on the test set. Analysis of the feature importance for the gradient boosted model found that the top 5 features were the most recent serum total bilirubin, the travel distance from the transplant center, the patient body mass index, the deceased donor terminal Serum glutamic pyruvic transaminase/Alanine transaminase (SGPT/ALT), and the donor PCO2. CONCLUSIONS: Combination of machine learning and expert-based methodology of selecting predictors of survival for pediatric heart transplantation provides a reasonable prediction of 1- and 3-year survival outcomes. SHapley Additive exPlanations can be an effective tool for modeling and visualizing nonlinear interactions.

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method. AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase. CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

Pediatric intraoperative cardiopulmonary arrests: A survey to evaluate if Medical Emergency Teams are utilized in pediatric operating rooms.

BACKGROUND: Studies have shown that standardized code teams may improve outcomes following cardiac arrests. Pediatric intra-operative cardiac arrests are rare events and are associated with a mortality rate of 18%. There is limited data available regarding use Medical Emergency Team (MET) response to pediatric intra-operative cardiac arrest. The purpose of this study was to identify the use of MET in response to pediatric intraoperative cardiac arrest as an exploratory step in establishing evidence-based standardized practice across the hospital for training and management of this rare event. METHODS: An anonymous electronic survey was created and sent to two populations: The Pediatric Anesthesia Leadership Council, a section of the Society for Pediatric Anesthesia, and the Pediatric Resuscitation Quality Collaborative, a multinational collaborative group, which works to improve resuscitation care in children. Standard summary and descriptive statistics were used for survey responses. RESULTS: The overall response rate was 41%. The majority of respondents worked in a university affiliated, free-standing children's hospital. Ninety-five percent of respondents had a dedicated pediatric MET at their hospital. In 60% of responses from Pediatric Resuscitation Quality Collaborative and 18% of Pediatric Anesthesia Leadership Council hospitals, the MET responds to pediatric intra-operative cardiac arrest; however, the majority of times MET involvement is requested rather than automatic. The MET was found to be activated intraoperatively for situations other than cardiac arrest such as, massive transfusion events, need for extra staff, and for specialty expertise. In 65% of institutions, simulation-based training for cardiac arrest is supported but lacking pediatric intra-operative focus. CONCLUSIONS: This survey revealed heterogeneity in the composition and response of the medical response teams responding to pediatric intra-operative cardiac arrests. Improved collaboration and cross training among MET, anesthesia, and operating room nursing may improve outcomes of pediatric intra-operative code events.

Feasibility of Conducting Long-term Health and Behaviors Follow-up in Adolescents: Longitudinal Observational Study.

BACKGROUND: Machine learning uses algorithms that improve automatically through experience. This statistical learning approach is a natural extension of traditional statistical methods and can offer potential advantages for certain problems. The feasibility of using machine learning techniques in health care is predicated on access to a sufficient volume of data in a problem space. OBJECTIVE: This study aimed to assess the feasibility of data collection from an adolescent population before and after a posterior spine fusion operation. METHODS: Both physical and psychosocial data were collected. Adolescents scheduled for a posterior spine fusion operation were approached when they were scheduled for the surgery. The study collected repeated measures of patient data, including at least 2 weeks prior to the operation and 6 months after the patients were discharged from the hospital. Patients were provided with a Fitbit Charge 4 (consumer-grade health tracker) and instructed to wear it as often as possible. A third-party web-based portal was used to collect and store the Fitbit data, and patients were trained on how to download and sync their personal device data on step counts, sleep time, and heart rate onto the web-based portal. Demographic and physiologic data recorded in the electronic medical record were retrieved from the hospital data warehouse. We evaluated changes in the patients' psychological profile over time using several validated questionnaires (ie, Pain Catastrophizing Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Pediatric Quality of Life Inventory). Questionnaires were administered to patients using Qualtrics software. Patients received the questionnaire prior to and during the hospitalization and again at 3 and 6 months postsurgery. We administered paper-based questionnaires for the self-report of daily pain scores and the use of analgesic medications. RESULTS: There were several challenges to data collection from the study population. Only 38% (32/84) of the patients we approached met eligibility criteria, and 50% (16/32) of the enrolled patients dropped out during the follow-up period-on average 17.6 weeks into the study. Of those who completed the study, 69% (9/13) reliably wore the Fitbit and downloaded data into the web-based portal. These patients also had a high response rate to the psychosocial surveys. However, none of the patients who finished the study completed the paper-based pain diary. There were no difficulties accessing the demographic and clinical data stored in the hospital data warehouse. CONCLUSIONS: This study identifies several challenges to long-term medical follow-up in adolescents, including willingness to participate in these types of studies and compliance with the various data collection approaches. Several of these challenges-insufficient incentives and personal contact between researchers and patients-should be addressed in future studies.

Performance Characteristics of a Machine-Learning Tool to Predict 7-Day Hospital Readmissions.

OBJECTIVES: To develop an institutional machine-learning (ML) tool that utilizes demographic, socioeconomic, and medical information to stratify risk for 7-day readmission after hospital discharge; assess the validity and reliability of the tool; and demonstrate its discriminatory capacity to predict readmissions. PATIENTS AND METHODS: We performed a combined single-center, cross-sectional, and prospective study of pediatric hospitalists assessing the face and content validity of the developed readmission ML tool. The cross-sectional analyses used data from questionnaire Likert scale responses regarding face and content validity. Prospectively, we compared the discriminatory capacity of provider readmission risk versus the ML tool to predict 7-day readmissions assessed via area under the receiver operating characteristic curve analyses. RESULTS: Overall, 80% (15 of 20) of hospitalists reported being somewhat to very confident with their ability to accurately predict readmission risk; 53% reported that an ML tool would influence clinical decision-making (face validity). The ML tool variable exhibiting the highest content validity was history of previous 7-day readmission. Prospective provider assessment of risk of 413 discharges showed minimal agreement with the ML tool (κ = 0.104 [95% confidence interval 0.028-0.179]). Both provider gestalt and ML calculations poorly predicted 7-day readmissions (area under the receiver operating characteristic curve: 0.67 vs 0.52; P = .11). CONCLUSIONS: An ML tool for predicting 7-day hospital readmissions after discharge from the general pediatric ward had limited face and content validity among pediatric hospitalists. Both provider and ML-based determinations of readmission risk were of limited discriminatory value. Before incorporating similar tools into real-time discharge planning, model calibration efforts are needed.

Comparison of general endotracheal anesthesia versus sedation without endotracheal intubation during initial percutaneous endoscopic gastrostomy insertion for infants: A retrospective cohort study.

BACKGROUND: Critical airway incidents are a major cause of morbidity and mortality during anesthesia. Delayed management of airway obstruction quickly leads to severe complications due to the reduced apnea tolerance in infants and neonates. The decision of whether to intubate the trachea during anesthesia is therefore of great importance, particularly as an increasing number of procedures are performed outside of the operating room. AIM: In this retrospective cohort study, we evaluated airway management for infants below 6 months of age undergoing percutaneous endoscopic gastrostomy insertion. We compared demographic, procedural, and health outcome-related data for infants undergoing percutaneous endoscopic gastrostomy insertion under general endotracheal anesthesia (n = 105) to those receiving monitored anesthesia care (n = 44) without endotracheal intubation. METHODS: A retrospective chart review was completed for all infants <6 months of age who underwent percutaneous endoscopic gastrostomy insertion in our institution's endoscopy suite between January 2002 and January 2017. Descriptive statistics summarized numeric variables using medians and corresponding ranges (minimum-maximum), and categorical variables using frequencies and percentages. Differences in study outcomes between patients undergoing general anesthesia or monitored anesthesia care were evaluated with univariate quantile or Firth logistic regression for numerical and categorical outcomes, respectively. Results are presented as ß [95% confidence interval] or odds ratio [95% confidence interval] along with corresponding p-values. RESULTS: Both groups were similar in distribution of age, race, and gender. However, patients selected for general anesthesia had lower median body weights (3.9 kg [range: 2.0-6.7] vs. 4.4 kg [range: 2.6-6.9]), higher percentages of cardiac (95.2% vs. 84.1%), and/or neurologic comorbidities (74.3% vs. 56.8%) and were more frequently given American Society of Anesthesiologists level IV classifications (41.9% vs. 29.6%) indicating that these infants may have had more severe disease than patients selected for monitored anesthesia care. Three monitored-anesthesia-care patients required intraoperative conversion to general anesthesia. General anesthesia patients experienced greater odds of intraoperative hypoxemia (45.2% vs. 29.0%; odds ratio: 2.0 [0.9-4.3], p-value: .09) and required postoperative airway intervention more frequently than monitored-anesthesia-care patients (13.03% vs. 2.3%; odds ratio: 4.6 [0.8-25.6], p-value: .08). Procedure times were identical in both groups (6 min), but general anesthesia resulted in longer median anesthesia times (44 min [range: 22-292] vs. 12 min [range:19-136]; ß:13 [95% 6.9-19.1], p-value: < .001). CONCLUSION: Study results suggest that providers selected general anesthesia over monitored anesthesia care for infants and neonates with low body weights, cardiac comorbidities, and neurologic comorbidities. Increased rates of airway intervention, and increased length of stay may be at least partially related to more severe patient comorbidity, as indicated by higher American Society of Anesthesiologists classifications. However, due to the exploratory nature of these analyses, further confirmatory studies are needed to evaluate the impact of airway selection during PEG on postoperative patient outcomes.

Impact of Coronavirus-2019 On Pediatric and Adult Heart Transplantation Waitlist Activity and Mortality in The United States: A Descriptive Approach.

BACKGROUND: Transplant centers saw a substantial reduction in deceased donor solid organ transplantation since the beginning of the coronavirus 2019 (COVID-19) pandemic in the United States. There is limited data on the impact of COVID-19 on adult and pediatric heart transplant volume and variation in transplant practices. We hypothesized that heart transplant activity decreased during COVID-19 with associated increased waitlist mortality. METHODS: The United Network for Organ Sharing (UNOS) database was used to identify patients at the time of listing for heart transplant from 2017-2020. Patients were categorized as pediatric (<18 years) or adult (≥18 years) and as pre-COVID (2017-2019) or post-COVID (2020). Regional and statewide data were taken from United States Census Bureau. CovidActNow project was used to obtain COVID-19 mortality rates. FINDINGS: Among pediatric patients, average time on the waiting list decreased by 28 days. Even though the average number of pediatric transplants (n=39 per month) did not change significantly during 2020, there was a temporal decline in the first quarter of 2020 followed by a sharp increase. Overall absolute pediatric waitlist mortality decreased from 5•31 to 4•73, however female mortality increased by 2%. Regional differences in pediatric mortality were observed: Northeast, decreased by 7•5%; Midwest, decreased by 9%; West, increased by 3•5%; and South, increased by 13%. North Dakota (0•55), Oklahoma (0•21) and Hawaii (0•33) showed higher mortality than other states per 100,000. In adults, average time on waiting list increased by 40 days and there was an increase in the number of transplants from 242 to 266. Adult waitlist mortality had a larger decrease, 18•44 to 15•70, with an increase in female mortality of 7%. Regional differences in adult mortality were also observed: Northeast, decreased by 3%; Midwest, increased by 5•5%; West, increased by 4•5% and South, decreased by 5%. Iowa (0•37), Wyoming (0•22), Arkansas (0•18) and Vermont (0•19) had the highest mortality per 100,000 compared to the other states. INTERPRETATION: Pediatric heart transplant volume declined in early 2020 followed by a later increase, while adult transplant volume increased all year round. Although, overall pediatric waitlist mortality decreased, female waitlist mortality increased for both adults and pediatrics. Regional differences in waitlist mortality were observed for both pediatrics and adults. Future studies are needed to understand this initial correlation and to determine the impact of COVID-19 on heart transplant recipients. FUNDING: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

"P3": an adaptive modeling tool for post-COVID-19 restart of surgical services.

OBJECTIVE: To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. MATERIALS AND METHODS: Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. RESULTS: The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. CONCLUSIONS: Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

Airway Management for Initial PEG Insertion in the Pediatric Endoscopy Unit: A Retrospective Evaluation of 168 Patients.

PURPOSE: Percutaneous endoscopic gastrostomy (PEG) tube placements are commonly performed pediatric endoscopic procedures. Because of underlying disease, these patients are at increased risk for airway-related complications. This study compares patient characteristics and complications following initial PEG insertion with general endotracheal anesthesia (GETA) vs. anesthesia-directed deep sedation with a natural airway (ADDS). METHODS: All patients 6 months to 18 years undergoing initial PEG insertion within the endoscopy suite were considered for inclusion in this retrospective cohort study. Selection of GETA vs. ADDS was made by the anesthesia attending after discussion with the gastroenterologist. RESULTS: This study included 168 patients (GETA n=38, ADDS n=130). Cohorts had similar characteristics with respect to sex, race, and weight. Compared to ADDS, GETA patients were younger (1.5 years vs. 2.9 years, p=0.04), had higher rates of severe American Society of Anesthesiologists (ASA) disease severity scores (ASA 4-5) (21% vs. 3%, p<0.001), and higher rates of cardiac comorbidities (39.5% vs. 18.5%, p=0.02). Significant associations were not observed between GETA/ADDS status and airway support, 30-day readmission, fever, or pain medication in unadjusted or adjusted models. GETA patients had significantly increased length of stay (eß=1.55, 95% confidence interval [CI]=1.11-2.18) after adjusting for ASA class, room time, anesthesia time, fever, and cardiac diagnosis. GETA patients also had increased room time (eß=1.20, 95% CI=1.08-1.33) and anesthesia time (eß=1.50, 95% CI=1.30-1.74) in adjusted models. CONCLUSION: Study results indicate that younger and higher risk patients are more likely to undergo GETA. Children selected for GETA experienced longer room times, anesthesia times, and hospital length of stay.

Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.

BACKGROUND: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. METHODS: Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features. We developed classification models to predict an individual's overall need for transfusion and regression models to predict the number of blood product units to be ordered preoperatively. The study is reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist for prediction model development. RESULTS: The GBM performed best in both domains, with an area under receiver operating characteristic curve of 0.87 ± 0.03 (95% confidence interval) and F-score of 0.91 ± 0.04 for classification, and a mean squared error of 1.15 ± 0.12, R-squared (R) of 0.73 ± 0.02, and root mean squared error of 1.05 ± 0.06 for regression. GBM feature ranking determined that the following variables held the most information for prediction: platelet count, weight, preoperative hematocrit, surgical volume per institution, age, and preoperative hemoglobin. We then produced a calculator to show the number of units of blood that should be ordered preoperatively for an individual patient. CONCLUSIONS: Anesthesiologists and surgeons can use this continually evolving predictive model to improve clinical care of patients presenting for craniosynostosis surgery.

Deep Learning for Improved Risk Prediction in Surgical Outcomes.

The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients' families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.

Pediatric Airway Management in COVID-19 Patients: Consensus Guidelines From the Society for Pediatric Anesthesia's Pediatric Difficult Intubation Collaborative and the Canadian Pediatric Anesthesia Society.

The severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]) pandemic has challenged medical systems and clinicians globally to unforeseen levels. Rapid spread of COVID-19 has forced clinicians to care for patients with a highly contagious disease without evidence-based guidelines. Using a virtual modified nominal group technique, the Pediatric Difficult Intubation Collaborative (PeDI-C), which currently includes 35 hospitals from 6 countries, generated consensus guidelines on airway management in pediatric anesthesia based on expert opinion and early data about the disease. PeDI-C identified overarching goals during care, including minimizing aerosolized respiratory secretions, minimizing the number of clinicians in contact with a patient, and recognizing that undiagnosed asymptomatic patients may shed the virus and infect health care workers. Recommendations include administering anxiolytic medications, intravenous anesthetic inductions, tracheal intubation using video laryngoscopes and cuffed tracheal tubes, use of in-line suction catheters, and modifying workflow to recover patients from anesthesia in the operating room. Importantly, PeDI-C recommends that anesthesiologists consider using appropriate personal protective equipment when performing aerosol-generating medical procedures in asymptomatic children, in addition to known or suspected children with COVID-19. Airway procedures should be done in negative pressure rooms when available. Adequate time should be allowed for operating room cleaning and air filtration between surgical cases. Research using rigorous study designs is urgently needed to inform safe practices during the COVID-19 pandemic. Until further information is available, PeDI-C advises that clinicians consider these guidelines to enhance the safety of health care workers during airway management when performing aerosol-generating medical procedures. These guidelines have been endorsed by the Society for Pediatric Anesthesia and the Canadian Pediatric Anesthesia Society.

Post-operative blood loss is higher among African American neonates undergoing open-heart surgery with cardiopulmonary bypass for CHD.

BACKGROUND: Neonates are at high risk of bleeding after open-heart surgery. We sought to determine pre-operative and intra-operative risk factors for increased bleeding after neonatal open-heart surgery with cardiopulmonary bypass. METHODS: We conducted a retrospective cohort study of neonates (0-30 days old) who underwent open-heart surgery with cardiopulmonary bypass from January, 2009, to March, 2013. Cardiac diagnosis; demographic and surgical data; and blood products, haemostatic agents, and anti-thrombotic agents administered before, during, and within 24 hours after surgery were abstracted from the electronic health record and anaesthesia records. The outcome of interest was chest tube output (in ml/kg body weight) within 24 hours. Relationships between chest tube output and putative associated factors were evaluated by unadjusted and adjusted linear regression. RESULTS: The cohort consisted of 107 neonates, of whom 79% had a Society of Thoracic Surgeons-European Association for Cardio-Thoracic Surgery (STAT) Mortality Category of 4 or 5. Median chest tube output was 37 ml/kg (range 9-655 ml/kg). Age, African-American race, and longer durations of surgery and cardiopulmonary bypass each had statistically significant associations with increased chest tube output in unadjusted analyses. In multivariable analysis, African-American race retained an independent, statistically significant association with increased chest tube output; the geometric mean of chest tube output among African-American neonates was 71% higher than that of Caucasians (95% confidence interval, 29-125%; p = 0.001). CONCLUSION: Among neonates with CHD undergoing open-heart surgery with cardiopulmonary bypass, African-American race is independently associated with greater chest tube output over the first 24 hours post-operatively.