<p><strong>OBJECTIVES: </strong>Avascular necrosis (AVN) is a rare, but serious, complication after sepsis in adults. We sought to determine if sepsis is associated with postillness diagnosis of AVN, as well as potential-associated risk factors for AVN in children with sepsis.</p>

<p><strong>DESIGN: </strong>Retrospective observational study.</p>

<p><strong>SETTING: </strong>Single academic children's hospital.</p>

<p><strong>PATIENTS: </strong>Patients less than 18 years treated for sepsis or suspected bacterial infection from 2011 to 2017. Patients who developed AVN within 3 years after sepsis were compared with patients who developed AVN after suspected bacterial infection and with patients with sepsis who did not develop AVN.</p>

<p><strong>INTERVENTION: </strong>None.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>AVN was determined using International Classification of Diseases, 9th Edition/10th Edition codes and confirmed by chart review. The prevalence of AVN after sepsis was 0.73% (21/2,883) and after suspected bacterial infection was 0.43% (53/12,276; risk difference, 0.30; 95% CI, 0.0-0.63; p = 0.05). Compared with 43 sepsis controls without AVN, AVN in the 21 sepsis cases was associated with being older, having sickle cell disease and malignancy, higher body mass index, unknown source of infection, and low platelet count in the first 7 days of sepsis. Half of sepsis patients were treated with corticosteroids, and higher median cumulative dose of steroids was associated with AVN (23.2 vs 5.4 mg/kg; p &lt; 0.01). Older age at infection (odds ratio [OR], 1.3; 95% CI, 1.1-1.4), malignancy (OR, 8.8; 95% CI, 2.6-32.9), unknown site of infection (OR, 12.7; 95% CI, 3.3-48.6), and minimal platelet count less than 100,000/µL in first 7 days of sepsis (OR, 5.0; 95% CI, 1.6-15.4) were identified as potential risk factors for AVN after sepsis following adjustment for multiple comparisons.</p>

<p><strong>CONCLUSIONS: </strong>Although rare, sepsis was associated with a higher risk of subsequent AVN than suspected bacterial infection in children. Older age, malignancy, unknown site of infection, and minimum platelet count were potential risk factors for AVN after sepsis.</p>

<p>Commercially available wearable biosensors have the potential to enhance psychophysiology research and digital health technologies for autism by enabling stress or arousal monitoring in naturalistic settings. However, such monitors may not be comfortable for children with autism due to sensory sensitivities. To determine the feasibility of wearable technology in children with autism age 8-12 years, we first selected six consumer-grade wireless cardiovascular monitors and tested them during rest and movement conditions in 23 typically developing adults. Subsequently, the best performing monitors (based on data quality robustness statistics), Polar and Mio Fuse, were evaluated in 32 children with autism and 23 typically developing children during a 2-h session, including rest and mild stress-inducing tasks. Cardiovascular data were recorded simultaneously across monitors using custom software. We administered the Comfort Rating Scales to children. Although the Polar monitor was less comfortable for children with autism than typically developing children, absolute scores demonstrated that, on average, all children found each monitor comfortable. For most children, data from the Mio Fuse (96%-100%) and Polar (83%-96%) passed quality thresholds of data robustness. Moreover, in the stress relative to rest condition, heart rate increased for the Polar, F(1,53)&nbsp;=&nbsp;135.70, p &lt; 0.001, ηp &nbsp;=&nbsp;0.78, and Mio Fuse, F(1,53)&nbsp;=&nbsp;71.98, p &lt; 0.001, ηp &nbsp;=&nbsp;0.61, respectively, and heart rate variability decreased for the Polar, F(1,53)&nbsp;=&nbsp;13.41, p&nbsp;=&nbsp;0.001, ηp &nbsp;=&nbsp;0.26, and Mio Fuse, F(1,53)&nbsp;=&nbsp;8.89, p&nbsp;=&nbsp;0.005, ηp &nbsp;=&nbsp;0.16, respectively. This feasibility study suggests that select consumer-grade wearable cardiovascular monitors can be used with children with autism and may be a promising means for tracking physiological stress or arousal responses in community settings. LAY SUMMARY: Commercially available heart rate trackers have the potential to advance stress research with individuals with autism. Due to sensory sensitivities common in autism, their comfort wearing such trackers is vital to gathering robust and valid data. After assessing six trackers with typically developing adults, we tested the best trackers (based on data quality) in typically developing children and children with autism and found that two of them met criteria for comfort, robustness, and validity.</p>

<p>The School District of Philadelphia reopened for in-school instruction the week of March 21, 2021, and required weekly testing for SARS-CoV-2, the virus that causes COVID-19, for all employees returning to in-school responsibilities. The resumption of in-school instruction followed a mass vaccination program using the Pfizer-BioNTech 2-dose vaccine offered under a partnership between the Philadelphia Department of Public Health and Children's Hospital of Philadelphia to all 22,808 School District of Philadelphia employees during February 23-April 3, 2021.* The subsequent mandatory testing program provided an opportunity to assess the percentage of positive BinaxNow point-of-care antigen tests (Abbott Laboratories) identified among school staff members based on their self-reported vaccination status (i.e., received zero, 1, or 2 vaccine doses) at the time of testing. During the initial 5 weeks after schools reopened, 34,048 screening tests were performed. Overall, 0.70% of tests returned a positive result. The percentage of positive test results was lower among persons who reported receipt of 2 vaccine doses (0.09%) compared with those who reported receipt of 1 dose (1.21%) or zero doses (1.76%) (p&lt;0.001) representing a 95% reduction in percentage of positive SARS-CoV-2 test results among persons reporting receipt of 2 compared with zero doses of Pfizer-BioNTech vaccine. Vaccination of school staff members has been highlighted as an important strategy to maximize the safety of in-person education of K-12 students this fall (1). These findings reinforce the importance of promoting COVID-19 vaccination among school staff members before commencement of the 2021-22 school year.</p>

<p>The use of "big data" for pediatric hearing research requires new approaches to both data collection and research methods. The widespread deployment of electronic health record systems creates new opportunities and corresponding challenges in the secondary use of large volumes of audiological and medical data. Opportunities include cost-effective hypothesis generation, rapid cohort expansion for rare conditions, and observational studies based on sample sizes in the thousands to tens of thousands. Challenges include finding and forming appropriately skilled teams, access to data, data quality assessment, and engagement with a research community new to big data. The authors share their experience and perspective on the work required to build and validate a pediatric hearing research database that integrates clinical data for over 185,000 patients from the electronic health record systems of three major academic medical centers.</p>

<p><strong>OBJECTIVES: </strong>A method to identify pediatric sepsis episodes that is not affected by changing diagnosis and claims-based coding practices does not exist. We derived and validated a surveillance algorithm to identify pediatric sepsis using routine clinical data and applied the algorithm to study longitudinal trends in sepsis epidemiology.</p>

<p><strong>DESIGN: </strong>Retrospective observational study.</p>

<p><strong>SETTING: </strong>Single academic children's hospital.</p>

<p><strong>PATIENTS: </strong>All emergency and hospital encounters from January 2011 to January 2019, excluding neonatal ICU and cardiac center.</p>

<p><strong>EXPOSURE: </strong>Sepsis episodes identified by a surveillance algorithm using clinical data to identify infection and concurrent organ dysfunction.</p>

<p><strong>INTERVENTIONS: </strong>None.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>A surveillance algorithm was derived and validated in separate cohorts with suspected sepsis after clinician-adjudication of final sepsis diagnosis. We then applied the surveillance algorithm to determine longitudinal trends in incidence and mortality of pediatric sepsis over 8 years. Among 93,987 hospital encounters and 1,065 episodes of suspected sepsis in the derivation period, the surveillance algorithm yielded sensitivity 78% (95% CI, 72-84%), specificity 76% (95% CI, 74-79%), positive predictive value 41% (95% CI, 36-46%), and negative predictive value 94% (95% CI, 92-96%). In the validation period, the surveillance algorithm yielded sensitivity 84% (95% CI, 77-92%), specificity of 65% (95% CI, 59-70%), positive predictive value 43% (95% CI, 35-50%), and negative predictive value 93% (95% CI, 90-97%). Notably, most "false-positives" were deemed clinically relevant sepsis cases after manual review. The hospital-wide incidence of sepsis was 0.69% (95% CI, 0.67-0.71%), and the inpatient incidence was 2.8% (95% CI, 2.7-2.9%). Risk-adjusted sepsis incidence, without bias from changing diagnosis or coding practices, increased over time (adjusted incidence rate ratio per year 1.07; 95% CI, 1.06-1.08; p &lt; 0.001). Mortality was 6.7% and did not change over time (adjusted odds ratio per year 0.98; 95% CI, 0.93-1.03; p = 0.38).</p>

<p><strong>CONCLUSIONS: </strong>An algorithm using routine clinical data provided an objective, efficient, and reliable method for pediatric sepsis surveillance. An increased sepsis incidence and stable mortality, free from influence of changes in diagnosis or billing practices, were evident.</p>

<p><strong>BACKGROUND: </strong>Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB) is a public, de-identified research database that contains over 16,000 radiology reports. Because the reports are unlabeled, it is difficult to select those with specific abnormalities. We implemented a classification pipeline using a human-in-the-loop machine learning approach and open source libraries to label the reports with one or more of four abnormality region labels: inner, middle, outer, and mastoid, indicating the presence of an abnormality in the specified ear region.</p>

<p><strong>METHODS: </strong>Trained abstractors labeled radiology reports taken from AudGenDB to form a gold standard. These were split into training (80&nbsp;%) and test (20&nbsp;%) sets. We applied open source libraries to normalize and convert every report to an n-gram feature vector. We trained logistic regression, support vector machine (linear and Gaussian), decision tree, random forest, and naïve Bayes models for each ear region. The models were evaluated on the hold-out test set.</p>

<p><strong>RESULTS: </strong>Our gold-standard data set contained 726 reports. The best classifiers were linear support vector machine for inner and outer ear, logistic regression for middle ear, and decision tree for mastoid. Classifier test set accuracy was 90&nbsp;%, 90&nbsp;%, 93&nbsp;%, and 82&nbsp;% for the inner, middle, outer and mastoid regions, respectively. The logistic regression method was very consistent, achieving accuracy scores within 2.75&nbsp;% of the best classifier across regions and a receiver operator characteristic area under the curve of 0.92 or greater across all regions.</p>

<p><strong>CONCLUSIONS: </strong>Our results indicate that the applied methods achieve accuracy scores sufficient to support our objective of extracting discrete features from radiology reports to enhance cohort identification in AudGenDB. The models described here are available in several free, open source libraries that make them more accessible and simplify their utilization as demonstrated in this work. We additionally implemented the models as a web service that accepts radiology report text in an HTTP request and provides the predicted region labels. This service has been used to label the reports in AudGenDB and is freely available.</p>

<p>Clinical genome and exome sequencing can diagnose pediatric patients with complex conditions that often require follow-up care with multiple specialties. The American Academy of Pediatrics emphasizes the role of the medical home and the primary care pediatrician in coordinating care for patients who need multidisciplinary support. In addition, the electronic health record (EHR) with embedded clinical decision support is recognized as an important component in providing care in this setting. We interviewed 6 clinicians to assess their experience caring for patients with complex and rare genetic findings and hear their opinions about how the EHR currently supports this role. Using these results, we designed a candidate EHR clinical decision support application mock-up and conducted formative exploratory user testing with 26 pediatric primary care providers to capture opinions on its utility in practice with respect to a specific clinical scenario. Our results indicate agreement that the functionality represented by the mock-up would effectively assist with care and warrants further development.</p>