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.

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.

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.

STBUR: Sleep trouble breathing and unrefreshed questionnaire: Evaluation of screening tool for postanesthesia care and disposition.

BACKGROUND: The Snoring, Trouble Breathing, and Un-Refreshed (STBUR) questionnaire is a five-question screening tool for pediatric sleep-disordered breathing and risk for perioperative respiratory adverse events in children. The utility of this questionnaire as a preoperative risk-stratification tool has not been investigated. In view of limited availability of screening tools for preoperative pediatric sleep-disordered breathing, we evaluated the questionnaire's performance for postanesthesia adverse events that can impact postanesthesia care and disposition. METHODS: The retrospective study protocol was approved by the institutional research board. The data were analyzed using two different definitions for a positive screening based on a five-point scale: low threshold (scores 1 to 5) and high threshold (score of 5). The primary outcome was based on the following criteria: (a) supplemental oxygen therapy following postanesthesia care unit (PACU) stay until hospital discharge, (b) greater than two hours during phase 1 recovery, (c) anesthesia emergency activation in the PACU, and (d) unplanned hospital admission. RESULTS: About 6025 patients completed the questionnaire during the preoperative evaluation. And 1522 patients had a low threshold score and 270 had a high-threshold score. We found statistically significant associations in three outcomes based on the low threshold score: supplemental oxygen therapy (negative-predictive value [NPV] 0.97, 95% CI 0.97-98), PACU recovery time (NPV 0.99, 95% CI 0.99-0.99) and escalation of care (NPV 0.98, 95% CI 0.97-0.98). Positive-predictive values were statistically significant for all outcomes except anesthesia emergency in the PACU. CONCLUSION: The Snoring, Trouble Breathing, and Un-Refreshed questionnaire identified patients at higher risk for prolonged phase 1 recovery, oxygen therapy requirement, and escalation of care. The questionnaire's high-negative predictive value and specificity may make it useful as a screening tool to identify patients at low risk for prolonged stay in PACU.

The Migration of Caudally Threaded Thoracic Epidural Catheters in Neonates and Infants.

BACKGROUND: The migration of pediatric thoracic epidural catheters via a thoracic insertion site has been described. We assessed the migration of caudally threaded thoracic epidural catheters in neonates and infants at our institution. METHODS: The anesthesia records and diagnostic imaging studies of neonates and infants who had caudal epidural catheters placed during a 26-month period at our hospital were analyzed. Imaging studies were reviewed for changes in epidural catheter tip position. RESULTS: Eighty-five patients 1-325 days of age (median, 51 days; interquartile range, 39-78 days) and weights of 2.5-9.5 kg (median, 5 kg; interquartile range, 4.3-5.8 kg) met the study criteria. Fifty-four (64%) of the patients (95% CI, 52%-73%) experienced catheter migration of 1 or more vertebral levels (range, 3 levels caudad [outward] to 3 levels cephalad [inward]), and 23 (27%) of the patients (95% CI, 18%-38%) experienced catheter migration to the T4 level or higher. Migration of 2 or more vertebral levels occurred only in children who weighed <6 kg and were under 73 days of age. CONCLUSIONS: Epidural catheter migration occurs commonly in neonates and infants. Postoperative imaging is crucial to confirm catheter tip location after epidural catheter placement, as failure to assess catheter migration might result in suboptimal analgesia or other undesirable outcomes.

A retrospective study of the impact of supraglottic airway devices on the appearance of neck masses in children undergoing serial magnetic resonance imaging.

BACKGROUND: General anesthesia and sedation are used routinely for magnetic resonance imaging (MRI) studies in children to optimize image quality. Airway devices such as supraglottic airways (SGAs) can alter the appearance of cervical soft tissue regions on an MRI and increase the risk of misdiagnosis. This phenomenon has not been well described in vivo. AIMS: We conducted this retrospective study to determine how often SGAs affected the appearance of neck masses in children who received multiple anesthetics for MRIs with and without an SGA. METHODS: We retrieved data on children 17 years old and younger who had multiple MRIs between January 2005 and January 2015. Inclusion criteria were patients with neck masses who had a SGA for at least one MRI and either a natural airway or endotracheal tube (ETT) for another MRI. We reviewed MRI images and imaging reports to determine if SGAs affected the appearance of neck masses. RESULTS: Twelve of the 921 patients who received anesthesia for neck MRIs during the study period met the inclusion criteria. SGAs affected the appearance of the neck mass in 11 of the 12 patients. CONCLUSIONS: Supraglottic airways can significantly alter the appearance of neck masses in children undergoing MRIs and affect radiologists' ability to assess those masses. Communication with the radiologist prior to the induction of anesthesia is crucial when using supraglottic devices in this patient population. It may be more prudent to use a different airway device and/or anesthetic technique when MRIs of these neck masses are undertaken.

Design and Implementation of a Visual Analytics Electronic Antibiogram within an Electronic Health Record System at a Tertiary Pediatric Hospital.

BACKGROUND: Hospitals use antibiograms to guide optimal empiric antibiotic therapy, reduce inappropriate antibiotic usage, and identify areas requiring intervention by antimicrobial stewardship programs. Creating a hospital antibiogram is a time-consuming manual process that is typically performed annually. OBJECTIVE: We aimed to apply visual analytics software to electronic health record (EHR) data to build an automated, electronic antibiogram ("e-antibiogram") that adheres to national guidelines and contains filters for patient characteristics, thereby providing access to detailed, clinically relevant, and up-to-date antibiotic susceptibility data. METHODS: We used visual analytics software to develop a secure, EHR-linked, condition- and patient-specific e-antibiogram that supplies susceptibility maps for organisms and antibiotics in a comprehensive report that is updated on a monthly basis. Antimicrobial susceptibility data were grouped into nine clinical scenarios according to the specimen source, hospital unit, and infection type. We implemented the e-antibiogram within the EHR system at Children's Hospital of Philadelphia, a tertiary pediatric hospital and analyzed e-antibiogram access sessions from March 2016 to March 2017. RESULTS: The e-antibiogram was implemented in the EHR with over 6,000 inpatient, 4,500 outpatient, and 3,900 emergency department isolates. The e-antibiogram provides access to rolling 12-month pathogen and susceptibility data that is updated on a monthly basis. E-antibiogram access sessions increased from an average of 261 sessions per month during the first 3 months of the study to 345 sessions per month during the final 3 months. CONCLUSION: An e-antibiogram that was built and is updated using EHR data and adheres to national guidelines is a feasible replacement for an annual, static, manually compiled antibiogram. Future research will examine the impact of the e-antibiogram on antibiotic prescribing patterns.

Extubation in the operating room after pediatric liver transplant: A retrospective cohort study.

BACKGROUND: Early extubation immediately following liver transplantation is increasingly common in adult practice. Some pediatric institutions have begun to adopt this strategy. Careful patient selection is essential in minimizing risk. METHODS: This retrospective cohort study evaluated infants and children who underwent liver transplantation between July 2011 and December 2014. Our primary objective was to determine early extubation rate. Secondary objectives were to identify clinical factors associated with successful early extubation compared with delayed extubation and to examine significant postoperative complications, intensive care unit length of stay, and hospital length of stay. RESULTS: The early extubation rate was 57.8% (37/64, confidence interval [CI] 44.8%-70.1%) over this 3.5-year period, increasing from 42% in 2012 to 58% by the end of 2014. The patients in the early extubation group were more likely to be older than the delayed extubation group (mean [SD], 7 [5.3] years vs 3.5 [5.5] years, difference between the mean [95% CI], 3.5 [0.8, 6.2] years); were to have come from home on the day of surgery (78.4% vs 25.9%); and were less likely to be listed as United Network for Organ Sharing status 1A (2.7% vs 25.9%). The early extubation group received less packed red blood cell volume (mean [SD], 9 [13.2] mL/kg vs 40.6 [48.5] mL/kg, difference between the mean [95% CI], 31.6 [95% CI 14.9, 48.3] mL/kg) and fresh-frozen plasma (mean 2.7 [SD 9.5] vs 13.3 [SD15.1], difference between the mean [95% CI], 10.5 [4.4,16.7] mL/kg). None of the patients in the early extubation group required reintubation in the first 24 hours following transplant and none experienced hepatic artery thrombosis. The early extubation group had a shorter average postoperative PICU stay (mean 3.8 [SD 2.1] days vs 17.6 [SD 31.3] days, difference between the mean [95% CI], 9.5 [4.3, 14.7] days) and a shorter postoperative hospital stay overall (mean 10.7 [SD 4.3] days vs 29.7 [SD 43.1] days, difference between the mean [95% CI], 19.1 [8.6, 29.6] days). CONCLUSION: More than half of our pediatric liver transplant patients were successfully extubated in the operating room immediately following surgery. We believe early extubation to be safe when employed in selected subpopulations of pediatric patients undergoing liver transplantation.

Anesthesia Information Management Systems.

Anesthesia information management systems (AIMS) have evolved from simple, automated intraoperative record keepers in a select few institutions to widely adopted, sophisticated hardware and software solutions that are integrated into a hospital's electronic health record system and used to manage and document a patient's entire perioperative experience. AIMS implementations have resulted in numerous billing, research, and clinical benefits, yet there remain challenges and areas of potential improvement to AIMS utilization. This article provides an overview of the history of AIMS, the components and features of AIMS, and the benefits and challenges associated with implementing and using AIMS. As AIMS continue to proliferate and data are increasingly shared across multi-institutional collaborations, visual analytics and advanced analytics techniques such as machine learning may be applied to AIMS data to reap even more benefits.

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool's sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia.

Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.

Using High-Technology Simulators to Prepare Anesthesia Providers Before Implementation of a New Electronic Health Record Module: A Technical Report.

Learning to use a new electronic anesthesia information management system can be challenging. Documenting anesthetic events, medication administration, and airway management in an unfamiliar system while simultaneously caring for a patient with the vigilance required for safe anesthesia can be distracting and risky. This technical report describes a vendor-agnostic approach to training using a high-technology manikin in a simulated clinical scenario. Training was feasible and valued by participants but required a combination of electronic and manual components. Further exploration may reveal simulated patient care training that provides the greatest benefit to participants as well as feedback to inform electronic health record improvements.

The timing and prevalence of intraoperative hypotension in infants undergoing laparoscopic pyloromyotomy at a tertiary pediatric hospital.

BACKGROUND: Intraoperative hypotension may be associated with adverse outcomes in children undergoing surgery. Infants and neonates under 6 months of age have less autoregulatory cerebral reserve than older infants, yet little information exists regarding when and how often intraoperative hypotension occurs in infants. AIMS: To better understand the epidemiology of intraoperative hypotension in infants, we aimed to determine the prevalence of intraoperative hypotension in a generally uniform population of infants undergoing laparoscopic pyloromyotomy. METHODS: Vital sign data from electronic records of infants who underwent laparoscopic pyloromyotomy with general anesthesia at a children's hospital between January 1, 1998 and October 4, 2013 were analyzed. Baseline blood pressure (BP) values and intraoperative BPs were identified during eight perioperative stages based on anesthesia event timestamps. We determined the occurrence of relative (systolic BP <20% below baseline) and absolute (mean arterial BP <35 mmHg) intraoperative hypotension within each stage. RESULTS: A total of 735 full-term infants and 82 preterm infants met the study criteria. Relative intraoperative hypotension occurred in 77%, 72%, and 58% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Absolute intraoperative hypotension was seen in 21%, 12%, and 4% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Intraoperative hypotension occurred primarily during surgical prep and throughout the surgical procedure. Preterm infants had higher rates of absolute intraoperative hypotension than full-term infants. CONCLUSIONS: Relative intraoperative hypotension was routine and absolute intraoperative hypotension was common in neonates and infants under 91 days of age. Preterm infants and infants under 61 days of age experienced the highest rates of absolute and relative intraoperative hypotension, particularly during surgical prep and throughout surgery.