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.

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.

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.

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.

Feasibility of Conducting Long-term Health and Behaviors Follow up in Adolescents (Preprint)

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.

[Interdisciplinary approach to endogenous neonatal endophthalmitis. Case report]

Neonatal endogenous endophthalmitis is a rare condition that can cause serious eye injuries. It can manifest in patients with comorbidities, such as preterm birth, low birth weight, postsurgical perinatal complications, or sepsis. This case report documents a preterm patient who underwent multiple abdominal surgeries. During her hospitalization, she developed sepsis, meningitis and neonatal endogenous endophthalmitis. The extremely low frequency of endogenous endophthalmitis at this age, the importance of preserving the patient's visual health, and the interdisciplinary approach are important learning points in this case.

Audit of blood product utilization in the care of injured children

BACKGROUND

Blood product utilization in injured children is poorly characterized; the decision to prepare products or transfuse patients can be difficult due to a lack of reliable evidence of transfusion needs across pediatric age-groups and injury types. We conducted an audit of transfusion practices in pediatric trauma based on age, injuries, and mechanism of injury.

METHODS

We reviewed and cross-referenced blood product transfusion practice data from the trauma registry and the anesthesia transfusion record database at a level 1 pediatric trauma center over a 10-year period. Demographic data, injury severity scores, and survival statistics were obtained from the trauma registry. Transfusion rates are reported separately for hospital admission and for intraoperative transfusions for procedures performed during the first two hospital days. Descriptive statistical analysis was used to compare specific groups based on age, injury type, and mechanism of injury.

RESULTS

We report 14 569 trauma admissions of 14 606 patients. The transfusion rate during the admission was 1.56% (227/14 569). 4591 (30.9%) admissions had surgical interventions in first two days of hospitalization with an intraoperative transfusion rate of 2.98%. Patients younger than one year had the highest transfusion rate during admission (2.8%), and the highest transfusion rate during surgical procedures performed in the first two days of the admission (18.87%). Admissions due to vascular injuries had the highest transfusion rates in infancy followed by hollow visceral injuries in adolescents (71.4% and 25%, respectively). Vascular injuries in most age-groups also had high transfusion rates ranging from 11% in 5- to 9-year age-group to 71% in infants. Mechanisms with the highest transfusion rates were firearm wounds in patients older than one year and vehicular accidents for patients younger than one year.

CONCLUSIONS

The overall blood product needs in the pediatric trauma population are low (1.56%). Selected populations requiring higher rates of need include infants younger than one year, and children with thoracic and vascular injuries. Understanding transfusion patterns is important to optimize resource allocation.

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.

Artificial intelligence, machine learning and the pediatric airway

Artificial intelligence and machine learning are rapidly expanding fields with increasing relevance in anesthesia and, in particular, airway management. The ability of artificial intelligence and machine learning algorithms to recognize patterns from large volumes of complex data makes them attractive for use in pediatric anesthesia airway management. The purpose of this review is to introduce artificial intelligence, machine learning, and deep learning to the pediatric anesthesiologist. Current evidence and developments in artificial intelligence, machine learning, and deep learning relevant to pediatric airway management are presented. We critically assess the current evidence on the use of artificial intelligence and machine learning in the assessment, diagnosis, monitoring, procedure assistance, and predicting outcomes during pediatric airway management. Further, we discuss the limitations of these technologies and offer areas for focused research that may bring pediatric airway management anesthesiology into the era of artificial intelligence and machine learning.

SCOSY: A Biomedical Collaboration Recommendation System

Finding relevant scientific articles and collaborators is a time-consuming and challenging task in today's information-rich environment. Despite this challenge, the study and development of recommendation systems, based on the authors' collaboration network, productivity and area of research, as topics of interest, have not been practically deployed in healthcare organizations. To address this known practice gap and to promote collaboration, Schosy was developed. This system collects publication metadata from PubMed, as the data source, and combining Collaborative and ContentBased Filtering techniques coupled with the Latent Dirichlet Allocation Topic Modeling algorithm, it recommends collaborators based on the authors' work, collaboration among the authors, Medical Subject Headings (MeSH) terms and the productivity of relevant researchers. As a result, this system provides an interpretable latent structure for collaborators and biomedical databases in order to enhance the experience of finding collaboration, for and by researchers and non-technical users.

Safety Huddle Intervention for Reducing Physiologic Monitor Alarms: A Hybrid Effectiveness-Implementation Cluster Randomized Trial

BACKGROUND

Monitor alarms occur frequently but rarely warrant intervention.

OBJECTIVE

This study aimed to determine if a safety huddle-based intervention reduces unit-level alarm rates or alarm rates of individual patients whose alarms are discussed, as well as evaluate implementation outcomes.

DESIGN

Unit-level, cluster randomized, hybrid effectiveness-implementation trial with a secondary patient-level analysis.

SETTING

Children's hospital.

PATIENTS

Unit-level: all patients hospitalized on 4 control (n = 4177) and 4 intervention (n = 7131) units between June 15, 2015 and May 8, 2016. Patient-level: 425 patients on randomly selected dates postimplementation.

INTERVENTION

Structured safety huddle review of alarm data from the patients on each unit with the most alarms, with a discussion of ways to reduce alarms.

MEASUREMENTS

Unit-level: change in unit-level alarm rates between baseline and postimplementation periods in intervention versus control units. Patient-level: change in individual patients' alarm rates between the 24 hours leading up to huddles and the 24 hours after huddles in patients who were discussed versus not discussed in huddles.

RESULTS

Alarm data informed 580 huddle discussions. In unit-level analysis, intervention units had 2 fewer alarms/patient-day (95% CI: 7 fewer to 6 more, P = .50) compared with control units. In patient-level analysis, patients discussed in huddles had 97 fewer alarms/patientday (95% CI: 52-138 fewer, P < .001) in the posthuddle period compared with patients not discussed in huddles. Implementation outcome analysis revealed a low intervention dose of 0.85 patients/unit/day.

CONCLUSIONS

Safety huddle-based alarm discussions did not influence unit-level alarm rates due to low intervention dose but were effective in reducing alarms for individual children.

Reducing Unnecessary Postoperative Complete Blood Count Testing in the Pediatric Intensive Care Unit

CONTEXT

Complete blood count (CBC) testing commonly occurs to determine the need for blood transfusions after surgical procedures. Many clinicians believe postoperative CBCs are "routine."

OBJECTIVE

To decrease unnecessary routine CBC testing in a low-risk cohort of postoperative patients in the pediatric intensive care unit (PICU) at The Children's Hospital of Philadelphia by 50% in 6 months.

DESIGN

Quality-improvement study. Data from our institution regarding frequency of ordering laboratory studies and transfusion requirements were collected for prior quality-improvement work demonstrating the safety and feasibility of avoiding routine postoperative CBCs in this cohort. Baseline survey data were gathered from key stakeholders on attitudes about and utilization of routine postoperative laboratory testing. Patient and clinician data were shared with all PICU clinicians. Simple Plan-Do-Study-Act cycles involving education, audit, and feedback were put into place.

MAIN OUTCOME MEASURES

Percentage of postoperative patients receiving CBCs within 48 hours of PICU admission. Balancing measures were hemoglobin level below 8 g/dL in patients for whom CBCs were sent and blood transfusions up to 7 days postoperatively for any patients in this cohort.

RESULTS

Sustained decreases below our 50% goal were seen after our interventions. There were no hemoglobin results below 8 g/dL or surgery-related blood transfusions in this cohort within 7 days of surgery. Estimated hospital charges related to routine postoperative CBCs decreased by 87% during 6 postintervention months.

CONCLUSION

A simple approach to a systemic problem in the PICU of unnecessary laboratory testing is feasible and effective. By using local historical data, we were able to identify a cohort of patients for whom routine postoperative CBC testing is unnecessary.

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.

Association Between Ibuprofen Use and Severity of Surgically Managed Posttonsillectomy Hemorrhage

Importance

Ibuprofen used in postoperative management of pain after tonsillectomy has not been shown to increase the overall risk for posttonsillectomy hemorrhage (PTH). The severity of bleeding is difficult to quantify but may be a more important outcome to measure.

Objective

To evaluate the association between ibuprofen use and severity of PTH using transfusion events as a marker of severity.

Design, Setting, and Participants

This retrospective cohort study identified 8868 patients who underwent tonsillectomy from January 20, 2011, through June 30, 2014, at the tertiary academic Children's Hospital of Philadelphia. Of these patients, 6710 met the inclusion criteria. Data were collected using electronic database acquisition and query.

Main Outcomes and Measures

Multivariate analysis was performed to identify independent prognostic factors for PTH and receipt of transfusion.

Results

Of the 6710 patients who met criteria for analysis (3454 male [51.5%] and 3256 female [48.5%]; median age, 5.4 years [interquartile range, 3.7-8.2 years]), 222 (3.3%) presented with PTH that required surgical control (sPTH). A total of 15 of the 8868 patients required transfusion for an overall risk for transfusion after tonsillectomy of 0.2%. Fifteen of 222 patients undergoing sPTH (6.8%) received transfusions. No significant independent increased risk for sPTH was associated with use of ibuprofen (adjusted odds ratio [OR], 0.90; 95% CI, 0.68-1.19). A significant independent association was found in the risk for sPTH in patients 12 years or older (adjusted OR, 2.74; 95% CI, 1.99-3.76) and in patients with a history of recurrent tonsillitis (adjusted OR, 1.52; 95% CI, 1.12-2.06). When using transfusion rates as a surrogate for severity of sPTH, transfusion increased by more than 3-fold among ibuprofen users compared with nonusers (adjusted OR, 3.16; 95% CI, 1.01-9.91), and the upper limit of the 95% CI suggests the difference could be nearly 10 times greater.

Conclusions and Relevance

The risk for sPTH is not increased with use of postoperative ibuprofen but is increased in patients 12 years or older and patients undergoing tonsillectomy with a history of recurrent tonsillitis. Hemorrhage severity is significantly increased with ibuprofen use when using transfusion rate as a surrogate marker for severity.

Visual analytical tool for evaluation of 10-year perioperative transfusion practice at a children's hospital

Children are a vulnerable population in the operating room, and are particularly at risk of complications from unanticipated hemorrhage. The decision to prepare blood products prior to surgery varies depending on the personal experience of the clinician caring for the patient. We present the first application of a data visualization technique to study large datasets in the context of blood product transfusions at a tertiary pediatric hospital. The visual analytical interface allows real-time interaction with datasets from 230 000 procedure records. Clinicians can use the visual analytical interface to analyze blood product usage based on procedure- and patient-specific factors, and then use that information to guide policies for ordering blood products.

[Scale of organizational learning in schools. Organizational learning is a key element for the development of organizations]

Organizational learning is a key element for the development of organizations. School organizations are not exempt from this challenge and they currently face a highly dynamic and demanding context of education policies that emphasize the school's ability to learn. Thus, research on organizational learning in educational contexts requires valid instruments that are sensitive to the specifics of schools as organizations. In this study, we adapted and validated a scale of organizational learning in a sample of 119 Chilean municipal schools (N= 1,545). The results suggest a structural model made up of three factors: culture of learning, strategic clarity, and group learning. These factors predicted dimensions of educational achievement, as measured through the National Assessment System of Educational Achievement (SNED). Results are discussed in view of the literature on school improvement.

Psychology over the Internet: On-Line Experiences

The "International Symposium on Psychology over the Internet: On-Line Experiences" was held in Lima, Peru, July 2003, at the 29th InterAmerican Congress of Psychology. The main topic was the advantages and disadvantages of using this technology in the applied field of psychology. The Internet has been considered a new alternative for teaching-learning processes (virtual classroom); vocational assessment; counseling and orientation (virtual psychological consultation); and intervention focused on specific health-related problems. These experiences of on-line psychological services and their conclusions are briefly described herein.