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.

Effects of Cumulative Head Impact Exposure in Adolescent Male Contact and Collision Sport Student Athletes.

OBJECTIVE: This prospective cohort study aimed to investigate the association between head impact exposure (HIE) and neuropsychological sequelae in high school football and ice hockey players over 1 year. SETTING: Community sample. PARTICIPANTS: A cohort of 52 adolescent American football and ice hockey players were enrolled in the study, with a final study sample of 35 included in analyses. DESIGN: The study followed a prospective cohort design, with participants undergoing neuropsychological screening and accelerometer-based measurement of HIE over 1 season. MAIN MEASURES: Changes in cognition, emotions, behavior, and reported symptoms were assessed using standardized neuropsychological tests and self-reported questionnaires. RESULTS: Cumulative HIE was not consistently associated with changes in cognition, emotions, behavior, or reported symptoms. However, it was linked to an isolated measure of processing speed, showing inconsistent results based on the type of HIE. History of previous concussion was associated with worsened verbal memory recognition (ImPACT Verbal Memory) but not on a more robust measure of verbal memory (California Verbal Learning Test [CVLT]). Reported attention-deficit/hyperactivity disorder history predicted improved neurocognitive change scores. No associations were found between reported history of anxiety/depression or headaches/migraines and neuropsychological change scores. CONCLUSION: Overall, our findings do not support the hypothesis that greater HIE is associated with an increase in neuropsychological sequelae over time in adolescent football and ice hockey players. The results align with the existing literature, indicating that HIE over 1 season of youth sports is not consistently associated with significant neuropsychological changes. However, the study is limited by a small sample size, attrition over time, and the absence of performance validity testing for neurocognitive measures. Future studies with larger and more diverse samples, longer follow-up, and integration of advanced imaging and biomarkers are needed to comprehensively understand the relationship between HIE and neurobehavioral outcomes. Findings can inform guidelines for safe youth participation in contact sports while promoting the associated health and psychosocial benefits.

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. We developed an initial rules-based pipeline (Match Pipeline) using 2 keyword sets for each social needs domain. We performed rule-based keyword matching for distinct lexicons and tested the algorithm using an annotated dataset comprising 192 patients. Starting with a set of expert-identified keywords, we tested the adjustments by evaluating false positives and negatives identified in the labeled dataset. We assessed the performance of the algorithm using measures of precision, recall, and F1 score. Results: The algorithm for identifying residential instability had the best overall performance, with a weighted average for precision, recall, and F1 score of 0.92, 0.84, and 0.92 for identifying patients with homelessness and 0.84, 0.82, and 0.79 for identifying patients with housing insecurity. Metrics for the food insecurity algorithm were high but the transportation issues algorithm was the lowest overall performing metric. Discussion: The NLP algorithm in identifying social needs at JHHS performed relatively well and would provide the opportunity for implementation in a healthcare system. Conclusion: The NLP approach developed in this project could be adapted and potentially operationalized in the routine data processes of a healthcare system.

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. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

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.

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). Statistical analyses were conducted in November 2021. Data were retrieved from a secondary care setting associated with the INDYCAR series. INDYCAR series drivers who participated in 3 professional level racing seasons and were involved in at least 1 contact incident (ie, crash) in 2 of the 3 seasons were included in the study. Exposure: Cumulative acceleration and deceleration forces and total contact incidents (ie, crashes) measured via accident data recorder third generation chassis and ear accelerometers. Main Outcomes and Measures: Postseries oculomotor performance, including predictive saccades, vergence smooth pursuit, and optokinetic nystagmus, was measured annually with a head-mounted, clinical eye tracking system (Neurolign Dx 100). Results: Thirteen drivers (mean [SD] age, 29.36 [7.82] years; all men) sustained median resultant acceleration forces of 38.15 g (observed range, 12.01-93.05 g; 95% CI, 30.62-65.81 g) across 81 crashes. A 2-way multivariate analysis of variance did not reveal a statistically significant association between ear and chassis average resultant g forces, total number of contact incidents, and racing season assessed (F9,12 = 0.955; P = .54; Wilks Λ = 0.44). Conclusions and Relevance: In this cohort study of professional drivers from the INDYCAR series, there were no statistically significant associations among cumulative impact exposure, racing season assessed, and oculomotor performance. Longitudinal studies across racing seasons using multidimensional examination modalities (eg, neurocognitive testing, advanced imaging, biomarkers, and physical examination) are critical to understand potential neurological and neurobehavioral sequelae and long-term consequences of cumulative impact exposure.

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.

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. The National Research Council dataset was used for emotion and intensity analysis.  The Kruskal-Wallis H-test was used to determine statistical significance.  SETTING: This study took place at a single, academic, free standing quaternary care children's hospital with an ACGME accredited pediatric surgery fellowship. PARTICIPANTS: Applicants to a single pediatric surgery fellowship were selected for this study from 2016 to 2021. A total of 182 individual applicants were included and 701 letters of recommendation were analyzed. RESULTS: Black applicants had the highest mean polarity (most positive), while Hispanic applicants had the lowest.  Overall differences between polarity distributions were not statistically significant.   The intensity of emotions showed that differences in "anger" were statistically significant (p=0.03).  Mean polarity was higher for applicants that successfully matched in pediatric surgery. DISCUSSION: This study identified differences in LORs based on racial and gender demographics submitted as part of pediatric surgical fellowship applications to a single training program. The presence of bias in letters of recommendation can lead to inequities in demographics to a given program. While difficult to detect for humans, natural language processing is able to detect bias as well as differences in polarity and emotional intensity. While the types of emotions identified in this study are highly similar among race and gender groups, the intensity of these emotions revealed differences, with "anger" being most significant. CONCLUSION: From this work, it can be concluded that bias in LORs, as reflected as differences in polarity, which is likely a result of the intensity of the emotions being used and not the types of emotions being expressed.   Natural language processing shows promise in identification of subtle areas of bias that may influence an individual's likelihood of successful matching.

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.

Use of Polar Heliostats to Improve Levels of Natural Lighting inside Buildings with Little Access to Sunlight.

The growing need to increase environmental and energy sustainability in buildings (housing, offices, warehouses, etc.) requires the use of solar radiation as a renewable source of energy that can help to lower carbon footprint, making buildings more efficient and thereby contributing to a more sustainable planet, while enhancing the health and wellbeing of its occupants. One of the technologies deployed in the use of solar energy in buildings is heliostats. In this context, this paper presents an analysis of the performance of a heliostat illuminator to improve illumination in a classroom at the Campus of Rabanales of the University of Cordoba (Spain). A design of a system in charge of monitoring and measuring daylighting variables using Arduino hardware technology and free software is shown. This equipment develops the communications, programming and collection of lighting data. In parallel, installation of an artificial lighting system complementary to the natural lighting system is implemented. Finally, an analysis of the impact of the proposed solution on the improvement of energy efficiency is presented. Specifically, it is estimated that up to 64% of savings in artificial lighting can be achieved in spaces with heliostatic illuminators compared to those without them.

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.

Assessing Preseason Baseline Cognitive Functioning in High School Student Athletes: A "Hybrid" Approach.

OBJECTIVE: International consensus statements highlight the value of neuropsychological testing for sport-related concussion. Computerized measures are the most frequently administered assessments of pre-injury baseline and post-injury cognitive functioning, despite known measurement limitations. To our knowledge, no studies have explored the convergent validity of computerized Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) and traditional, well-validated paper and pencil (P&P) neuropsychological tests in high school student athletes. This study aimed to assess a "hybrid" adolescent test battery composed of ImPACT and P&P measures to determine the extent of shared variance among ImPACT and P&P tests to inform comprehensive yet streamlined assessment. METHOD: Participants included male and female high school student athletes in the Southeastern United States participating in American football, hockey, and soccer who completed a battery of ImPACT and P&P tests (N = 69). RESULTS: We performed principal component analysis with ProMax rotation to determine components of the hybrid battery that maximally accounted for observed variance of the data (Kaiser-Meyer-Olkin factor adequacy = 0.71). Our analysis revealed four independent factors (Verbal Learning and Memory, ImPACT Memory and Speed, Verbal Processing Speed/Executive Functions, and Nonverbal Processing Speed/Executive Functions) explaining 75% of the variance. CONCLUSIONS: Findings of this study in adolescent student athletes support those from the adult literature demonstrating the independence of ImPACT and P&P tests. Providers should be aware of limitations in using standalone ImPACT or P&P measures to evaluate cognitive functioning after concussion. If confirmed in a larger, clinical sample, our findings suggest that a hybrid battery of computerized and P&P measures provides a broad scope of adolescent cognitive functioning to better inform recovery decisions, including return to play after concussion.

How do we measure organisational wellness? Development of a comprehensive patient-centred and employee-centred visual analytical solution.

BACKGROUND: Dashboards are visual information systems frequently employed by healthcare organisations to track key quality improvement and patient safety performance metrics. The typical healthcare dashboard focuses on specific metrics, disease processes or units within a larger healthcare organisation. Here, we describe the development of a visual analytical solution (keystone dashboard) for monitoring an entire healthcare organisation. METHODS: The improvement team reviewed and assessed various data sources across the organisation and selected a group of patient and employee related metrics that afforded a broad overview of the organisation's well-being. Metrics spanned the organisation and included data from patient safety, quality improvement, human resources, risk management and medical staff affairs. Each metric was assigned a numeric weight that correlated with its impact. A visual model incorporating the various data fields was then constructed. RESULTS: The keystone dashboard incorporates a data heatmap and density visualisation to emphasis areas of higher density and/or weighted values. The heatmap is used to indicate the weight/magnitude of each metric within a data range in two dimensions: location and time. The visualisation 'heats up' depending on the combination of counts events and their assigned impact for the reporting month. Most data sources update in near real time. SUMMARY: The keystone dashboard serves as a comprehensive and collaborative integration of data from patient safety, quality improvement, human resources, risk management and medical staff affairs. This visual analytical solution incorporates and analyses metrics into a single view with the intent of providing valuable insight into the health of an entire organisation. This dashboard is unique as it provides a broad overview of a healthcare organisation by incorporating key metrics that span the organisation.

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.

Socioeconomic inequalities in low birth weight risk before and during the COVID-19 pandemic in Argentina: A cross-sectional study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic may have exacerbated existing socioeconomic inequalities in health. In Argentina, public hospitals serve the poorest uninsured segment of the population, while private hospitals serve patients with health insurance. This study aimed to assess whether socioeconomic inequalities in low birth weight (LBW) risk changed during the first wave of the COVID-19 pandemic. METHODS: This multicenter cross-sectional study included 15929 infants. A difference-in-difference (DID) analysis of socioeconomic inequalities between public and private hospitals in LBW risk in a pandemic cohort (March 20 to July 19, 2020) was compared with a prepandemic cohort (March 20 to July 19, 2019) by using medical records obtained from ten hospitals. Infants were categorized by weight as LBW < 2500 g, very low birth weight (VLBW) < 1500 g and extremely low birth weight (ELBW) < 1000 g. Log binomial regression was performed to estimate risk differences with an interaction term representing the DID estimator. Covariate-adjusted models included potential perinatal confounders. FINDINGS: Of the 8437 infants in the prepandemic cohort, 4887 (57•9%) were born in public hospitals. The pandemic cohort comprised 7492 infants, 4402 (58•7%) of whom were born in public hospitals. The DID estimators indicated no differences between public versus private hospitals for LBW risk (-1•8% [95% CI -3•6, 0•0]) and for ELBW risk (-0•1% [95% CI -0•6, 0•3]). Significant differences were found between public versus private hospitals in the DID estimators (-1•2% [95% CI, -2•1, -0•3]) for VLBW risk. The results were comparable in covariate-adjusted models. INTERPRETATION: In this study, we found evidence of decreased disparities between public and private hospitals in VLBW risk. Our findings suggest that measures that prioritize social spending to protect the most vulnerable pregnant women during the pandemic contributed to better birth outcomes. FUNDING: No funding was secured for this study.

"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.