Garbage In, Garbage Out? Negative Impact of Physiological Waveform Artifacts in a Hospital Clinical Data Warehouse.

Hospitals around the world are deploying increasingly advanced systems to collect and store high-resolution physiological patient data for quality improvement and research. However, data accuracy, completeness, consistency, and contextual validity remain issues. This report highlights a data artifact known as waveform clipping in our hospital's physiological data capture system that went unnoticed for years, limiting data analysis and delaying several research projects.

Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes.

Objective: To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.

Materials And Methods: We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data.

Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists.

Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed.

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.

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.

Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.

Objectives: To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs).

Materials And Methods: We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development.

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects.

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty.

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).

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.

Longitudinal Outcomes of Cumulative Impact Exposure on Oculomotor Functioning in Professional Motorsport Drivers.

Importance: Professional motorsport drivers are regularly exposed to biomechanical forces comparable with those experienced by contact and collision sport athletes, and little is known about the potential short-term and long-term neurologic sequelae.

Objective: To determine whether cumulative impact exposure is associated with oculomotor functioning in motorsport drivers from the INDYCAR professional open-wheel automobile racing series.

Design, Setting, And Participants: This is a longitudinal retrospective cohort study conducted across 3 racing seasons (2017-2019).

Examining Implicit Bias Differences in Pediatric Surgical Fellowship Letters of Recommendation Using Natural Language Processing.

Objective: We analyzed the prevalence and type of bias in letters of recommendation (LOR) for pediatric surgical fellowship applications from 2016-2021 using natural language processing (NLP) at a quaternary care academic hospital.

Design: Demographics were extracted from submitted applications. The Valence Aware Dictionary for sEntiment Reasoning (VADER) model was used to calculate polarity scores.

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.

"P": 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.

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.

Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data.

Objective: To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.

Methods: We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record.

Quality Initiative Using Theory of Change and Visual Analytics to Improve Controlled Substance Documentation Discrepancies in the Operating Room.

Background: Discrepancies in controlled substance documentation are common and can lead to legal and regulatory repercussions. We introduced a visual analytics dashboard to assist in a quality improvement project to reduce the discrepancies in controlled substance documentation in the operating room (OR) of our free-standing pediatric hospital.

Methods: Visual analytics were applied to collected documentation discrepancy audit data and were used to track progress of the project, to motivate the OR team, and in analyzing where further improvements could be made.

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.

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.

Interactive pediatric emergency checklists to the palm of your hand - How the Pedi Crisis App traveled around the world.

Background: Cognitive aids help clinicians manage critical events and have been shown to improve outcomes by providing critical information at the point of care. Critical event guidelines, such as the Society of Pediatric Anesthesia's Critical Events Checklists described in this article, can be distributed globally via interactive smartphone apps. From October 1, 2013 to January 1, 2014, we performed an observational study to determine the global distribution and utilization patterns of the Pedi Crisis cognitive aid app that the Society for Pediatric Anesthesia developed.

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.