United States

Background: Revision total joint arthroplasty (TJA) becomes necessary when a primary TJA fails, leading to worse patient outcomes, higher complication rates, and increased economic burdens on the healthcare system. With the global aging population, the demand for revision TJA’s are projected to rise significantly by 2030 which highlights the need for effective machine learning (ML) models to improve outcomes in revision TJA. Although prior systematic reviews have evaluated the utility of ML in predicting outcomes for primary TJA, there is a notable gap in assessing ML’s potential in the context of revision TJA . This study aims to systematically review the application of ML for predicting outcomes following revision total hip arthroplasty (THA) and revision total knee arthroplasty (TKA).

Methods: A comprehensive literature search was conducted to May 2024 in Ovid MEDLINE, Ovid Embase, and Web of Science. Studies were imported into Covidence for screening. The exclusion criteria were: (1) other systematic reviews; (2) animal models; (3) studies not utilizing artificial intelligence; (4) studies not involving revision arthroplasty; (5) case reports; (6) technical notes; (7) editorial notes; (8) studies on joints other than the hip and knee; and (9) studies without full-text availability. The search strategy yielded 1289 articles, of which 19 were assessed as eligible for this review.

Results: Search criteria yielded 19 relevant studies. Topics of study included patient complications (n = 12), readmissions (n = 2), discharge dispositions (n = 2), patient-reported outcome measures (n = 0), inpatient status and length of stay (LOS) (n = 4), and patient function (n = 1). Studies involved revision TKA (n = 8), revision THA (n = 7), or a combination (n = 5). Studies were of solely revision arthroplasty (n = 16) or included primary and revision arthroplasty (n = 4). Of reported AUC, greater than 71% of predictive outcomes for at least one model had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range (AUC &gt; 0.8). Additionally, 14 studies were internally validated and only 3 studies were externally validated.

Discussion/Conclusion: Machine learning algorithms are being increasingly used to predict patient outcomes after revision THA and TKA. Machine learning has shown utility in clinical settings in the context of risk stratification and prediction of complications, patient reported outcome scores, and readmissions. While machine learning algorithms have been received with considerable interest, they should be critically assessed and externally validated prior to clinical adoption.


AMA Research Challenge 2024 

Using Machine Learning to Enhance Predictive Outcomes in Revision Total Joint Arthroplasty: A Systematic Review

2025 National Inter-University Health Data & AI Inquiry Program - this program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. Presented research projects explore how generative AI is being applied to medication management, mental health screening, and chronic disease monitoring to align with the future of pharmacy practice.

Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity

2025 National Inter-University Health Data & AI Inquiry Program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. 

Background: Revision total joint arthroplasty (TJA) becomes necessary when a primary TJA fails, leading to worse patient outcomes, higher complication rates, and increased economic burdens on the healthcare system. With the global aging population, the demand for revision TJA’s are projected to rise significantly by 2030 which highlights the need for effective machine learning (ML) models to improve outcomes in revision TJA. Although prior systematic reviews have evaluated the utility of ML in predicting outcomes for primary TJA, there is a notable gap in assessing ML’s potential in the context of revision TJA . This study aims to systematically review the application of ML for predicting outcomes following revision total hip arthroplasty (THA) and revision total knee arthroplasty (TKA).

Methods: A comprehensive literature search was conducted to May 2024 in Ovid MEDLINE, Ovid Embase, and Web of Science. Studies were imported into Covidence for screening. The exclusion criteria were: (1) other systematic reviews; (2) animal models; (3) studies not utilizing artificial intelligence; (4) studies not involving revision arthroplasty; (5) case reports; (6) technical notes; (7) editorial notes; (8) studies on joints other than the hip and knee; and (9) studies without full-text availability. The search strategy yielded 1289 articles, of which 19 were assessed as eligible for this review.

Results: Search criteria yielded 19 relevant studies. Topics of study included patient complications (n = 12), readmissions (n = 2), discharge dispositions (n = 2), patient-reported outcome measures (n = 0), inpatient status and length of stay (LOS) (n = 4), and patient function (n = 1). Studies involved revision TKA (n = 8), revision THA (n = 7), or a combination (n = 5). Studies were of solely revision arthroplasty (n = 16) or included primary and revision arthroplasty (n = 4). Of reported AUC, greater than 71% of predictive outcomes for at least one model had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range (AUC > 0.8). Additionally, 14 studies were internally validated and only 3 studies were externally validated.

Discussion/Conclusion: Machine learning algorithms are being increasingly used to predict patient outcomes after revision THA and TKA. Machine learning has shown utility in clinical settings in the context of risk stratification and prediction of complications, patient reported outcome scores, and readmissions. While machine learning algorithms have been received with considerable interest, they should be critically assessed and externally validated prior to clinical adoption.


poster

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Background
Otolaryngologists utilize drug-induced sleep endoscopy (DISE) to evaluate the anatomic location and pattern of upper airway collapse in patients with obstructive sleep apnea (OSA). The cardiovascular consequences of OSA are due to intermittent hypoxia and negative intrathoracic pressure swings, leading to sympathetic activation. It is challenging, however, to assess these physiologic pressure swings during natural sleep; therefore, we applied objective measures of airflow and respiratory effort during DISE to measure pharyngeal pressure changes. We hypothesized greater retroepiglottic pressure changes during respiratory events from DISE to correlate with reduced nocturnal blood pressure “dipping” during 24-hour ambulatory blood pressure monitoring (ABPM), thereby enhancing surgical management of OSA by linking intraoperative findings to cardiovascular outcomes.

Methods
Patients with untreated OSA prospectively underwent ABPM and DISE with pharyngeal manometry. Retroepiglottic pressure (cm H2O) was calculated as the mean pressure differential for each obstructed breath between the start of inspiration and the nadir of negative pressure during inspiration. ABPM recorded blood pressure every 30 minutes while awake and hourly while asleep. Pearson’s correlation evaluated associations between retroepiglottic pressure changes and ABPM values. 

Results
Twenty patients were included for analysis. Patients, on average, were middle-aged (48.7±16.3 years), overweight (body-mass index [BMI] 29.5±3.46 kg/m2), male (65%), White (60%), with moderate-severe OSA at baseline (AHI 40.0±21.2 events/hour), normotensive (24-hour systolic & diastolic pressure 121±13.5 and 72.3±8.24 mmHg), and taking anti-hypertensive medications (45%). Correlational analysis showed greater retroepiglottic pressure swings were correlated with higher nocturnal, diurnal, and 24-hour ABPM values: 24-hour mean arterial pressure (MAP) (r=0.48, p=0.03), 24-hour systolic blood pressure (SBP) (r=0.46, p=0.04), 
higher 24-hour diastolic blood pressure (DBP) (r=0.39, p=0.09).

Conclusion
In this study, a relationship between nocturnal blood pressure and retroepiglottic pressure swings during DISE was identified. These findings underscore the importance of intraoperative physiologic assessments in predicting cardiovascular risk in patients with OSA and highlight Otolaryngologists’ role in managing these risks. Future directions involve expanding the sample size and comparing these pharyngeal pressures against conventional polysomnographic OSA measures of cardiovascular risk.

Association Between Retroepiglottic Pressure During Drug-Induced Sleep Endoscopy & Nocturnal Blood Pressure: A Pilot Study

Subglottic stenosis (SGS) is a narrowing of the airway distal to the vocal cords. It is a common post-intubation complication treated by otolaryngologists. The narrowing is typically due to a band of fibrotic scar tissue, with few cases reporting calcification of this band in the post-cricoid region. To date, there have been fewer than 5 reports of calcified SGS, each treated with a unique surgical approach. In this case report, we describe our technique for the removal of a calcified subglottic stenotic band at the level of the cricoid in a 46 year-old female. We also review the current literature to compare outcomes and complications of the various surgical techniques implemented for this unique pathology. The technique utilized in this report adds to the previous literature and offers a viable treatment alternative for calcified SGS.

Diagnosis and Surgical Management of a Patient with Calcified Subglottic Stenosis

Introduction: The objective is to develop a proof-of-concept smartphone-based eye-tracking algorithm to assess non-pathologic optokinetic (OKN) nystagmus in healthy participants. Current videonystagmography (VNG) is typically restricted to in-office use, and advances in ambulatory or portable vestibular diagnostic devices would yield immense public health benefits.

Methods: Healthy participants (n=39) without a history of dizziness or vertigo were recruited for this prospective comparison study. A smartphone attached to a custom head stabilization device illuminated by a white LED circuit was used to record nystagmus induced with a 30 frames/sec OKN stimulus over a 30-second period. A centroid tracking algorithm ("VertiScope") was created and optimized to detect slow-phase velocity (SPV) of horizontal nystagmus in a diverse subject cohort in a variety of lighting conditions. A cohort of subjects measured in ideal lighting conditions (n=16) underwent standard VNG for comparison of nystagmus recordings with our novel VertiScope algorithm. 

Results: Non-pathologic nystagmus was measured across multiple lighting conditions, with high signal-to-noise ratios (SNR) and mean SPV of 22.53+/-5.16 deg/sec. Nystagmus SPV was not significantly different between the device and standard VNG system (t= -0.5, p=0.6). Lighting conditions produced SNRs of 49.28 (dark), 48.90 (backlit), 51.33 (side-lit), 55.65 (outdoor lighting), and 58.42 (ideal). VertiScope SPV was not significantly different when compared against quantitative facial metrics for all participants. 

Conclusions: Here, we demonstrate the feasibility of a novel oculography device and in the detection of non-pathologic nystagmus in healthy subjects. Future applications of this device include: obtaining real-time measurements of nystagmus during an acute vertigo attack in patients with vestibular disorders; testing patients unable or unwilling to present to a specialized vestibular laboratory; efficiently repeating testing overtime; and improving accessibility of vestibular testing.

Novel Ambulatory Event Capture Device: Characterization of Non-Pathologic Nystagmus

Abstract Title:
Predicting 30-Day Outcomes in Head and Neck Cancer Surgery Using Machine Learning

Background:
Readmission following surgical resection of head and neck squamous cell carcinoma (HNSCC) has been reported to range from 7.7% to 26.5%, with worse outcomes and cost increase of $15,123 for care of patients with unplanned readmission. While previous studies have shown correlations between various clinical factors and hospital readmission risk, they have not provided actionable clinical predictions leveraging these factors to impact clinical decisions. This study aims to fill this gap by developing and validating a machine learning model to predict 30-day mortality and hospital readmission likelihood following definitive surgical resection for HNSCC using a large-scale, well-curated dataset. This model may serve as an actionable, transparent, and highly accurate clinical decision support tool to identify high-risk patients and redirect preventative resources effectively.

Methods:
This prognostic predictive modeling study utilized retrospective data from the 2006 to 2018 National Cancer Database (NCDB), including 103,891 patients with HNSCC undergoing surgical resection. Predictors included comprehensive pre-treatment demographic and clinical variables available in the NCDB. A combination of five machine learning models was trained on 80% of the data and tested on the remaining 20% to predict the primary outcomes of 30-day mortality and 30-day readmission.

Results:
Among the 103,891 patients satisfying the inclusion criteria, 5,838 (5.6%) were readmitted, and 829 (0.8%) died within 30 days. The models achieved an AUC of 0.80 (95% CI: 0.77-0.83) for mortality prediction and an AUC of 0.66 (95% CI: 0.65-0.68) for readmission prediction. SHAP value analysis identified sex and urban-rural index as the most predictive variables for mortality and readmission, respectively.

Conclusion:
The machine learning model demonstrated high sensitivity and specificity in predicting 30-day mortality and readmission risks in a large-scale dataset. This model highlights critical features influencing predictions, providing key clinical insights supporting clinical decision-making. With further validation in prospective datasets, this model can serve as an effective clinical decision support tool, guiding resource allocation and follow-up care for patients undergoing surgical resection of HNSCC. This study presents a significant advancement in large-scale validation of a potential clinical decision support tool that is poised to enhance patient outcomes by providing transparent and actionable predictions on patient outcomes.

Predicting 30-Day Outcomes in Head and Neck Cancer Surgery Using Machine Learning

Authors: Lucy Nguyen, Michelle Alder, Natoshia Cunningham 

Michigan State University, College of Human Medicine, Department of Family Medicine 

Abstract Title: Assessing the Influence of ADAPT on Comorbid Anxiety and Depressive Disorders. 

Background: Functional abdominal pain disorders (FAPD) are common chronic pain conditions impacting more than 10% of youth and are associated with adverse outcomes. A tailored approach to care addressing both pain and comorbid anxiety called the Aim to Decrease Anxiety and Pain Treatment (ADAPT) has been shown to improve pain-related impairment in youth with FAPD. In this secondary analysis of the ADAPT RCT, we explored how specific mental health diagnoses MDD, GAD, social anxiety, separation anxiety, panic disorder, and OCD responded following ADAPT. 

Methods: Participants, between the ages of 9-14 diagnosed with FAPD, underwent a diagnostic interview (ADIS for DSM-IV, Child Version [ADIS-IV]). Participants were then randomized to continue treatment as usual with additional tailored cognitive behavioral therapy (ADAPT +TAU), or TAU alone. A post-assessment to assess the outcomes was done 8 weeks later. This secondary analysis is focused on participants who completed ADAPT (n=39). SPSS was utilized for data analysis. Chi-squares using Fischer’s exact tests were used to assess significant differences between the incidence of anxiety diagnoses before and after receiving ADAPT. 

Results: The number of people with GAD diagnoses significantly decreased from 20 to 10 individuals after ADAPT treatment (p < 0.001). The number of people with social anxiety diagnosis prior to ADAPT treatment was 20, which decreased significantly to 10 at the post-assessment (p<0.001). The number of people with a SAD diagnosis pretreatment was 13 which then dropped to 4 (p = 0.009). The number of people with a panic disorder diagnosis dropped from 4 to 2 (not significant). The number of OCD diagnosis from pre- to post-assessment significantly decreased from 7 to 3 (p = 0.004). 

For each depressive disorder, dysthymia and MDD, the number of depressive diagnoses in the treatment as usual group increased at the end of assessment. For those diagnosed with dysthymia in the ADAPT group, the number did not change (not significant). The number of people with MDD diagnoses dropped from 5 to 2 after ADAPT treatment (p = 0.013). 

Conclusion: Youths with FAPD and who received ADAPT demonstrated statistically significant improvement in mental health diagnoses including, major depression, dysthymia, generalized anxiety, social anxiety, separation anxiety, and OCD. CBT is often underutilized in the treatment of youth with chronic medical conditions. When addressing pain, co-occurring anxiety and depression is often overlooked although it is common and associated with poor outcomes. CBT should continue to be researched and promoted for symptom management for chronic medical issues with co-occurring psychological disorders.

Assessing the Influence of ADAPT, a Tailored Cognitive Behavioral Treatment for Youth with Functional Abdominal Pain Disorders, on Comorbid Anxiety and Depressive Disorders.​

Background
Recurring lab studies in the form of ABG, VBG, CBC, electrolytes, renal function panels and coagulation measurements are commonplace in PCICU. This practice pattern provides an element of safety by providing surveillance of patient’s volatile physiologies. This practice has unintended negative consequences including expense, laboratory “alarm fatigue” for providers, and increased risk of CLABSI. These consequences are intensified when recurring lab studies occur indefinitely and when lab draws are poorly synchronized so that the vascular catheter is accessed more frequently than necessary. 

Methods
The investigators worked with EPIC software professionals to construct a lab order set that speeds order entry, provides a comprehensive, individualized menu of lab tests most commonly ordered in the PCICU, enforces synchronization of blood draws, caps the number of days the selected labs will be drawn, and notifies the care team when slates of recurring lab tests are expiring. We wish to examine the effect of this intervention on the total number of labs drawn per patient per day, the daily cost, the number of times vascular catheters are accessed, and the number of patient resuscitations observed. Our lab order set is currently under construction by EPIC.

Results
We are currently receiving via automated EPIC electronic database a daily report of the number of each type of lab test performed per patient during each 24-hour period for one month. From this data we will tabulate a) the total number of each type of lab test performed per patient per day, b) the estimated total cost and charge for all lab testing per patient per day, and c) the number of times per day that blood is drawn from a vascular catheter per patient per day. 

As surrogates of potential negative consequences of lab reduction, we will monitor number of patient resuscitations that occur during the pre-intervention and post-intervention phases and number of times per patient per day that vascular catheters are accessed. We will compare these outcomes before and after implementation. Finally, to assess compliance, we will express the percentage of the average daily census who have an active laboratory order set in use with a specified expiration date. 

Conclusion
It is our hope that the convenience and simplicity of the PCICU order set will result in a) regular utilization by our providers, b) reduced overall lab testing, c) reduced cost and patient charges, d) fewer accessions of the indwelling vascular catheters, and e) no increase in resuscitation events in our intensive care unit.


Design and evaluation of an individualized lab order set in EPIC to decrease recurrent lab draws in the Pediatric Cardiac Intensive Care Unit (PCICU)

Does Height of Fever Influence the Probability of Serious Bacterial Infections in Febrile Children? 



Objective 



Fever represents 10% to 25% of pediatric emergency department (ED) visits in young children. Most children presenting to ED with febrile illness are self-limited viral infections. On the other hand, Serious Bacterial Infection (SBI) rates are low. They are estimated to occur between 6% of infants younger than 3 months and 5% to 7% of children between 3 and 36 months of age. Although it is challenging to differentiate between viral and bacterial infections based on the degree of fever, historically, high fever has been associated with increased incidence of SBI. Fever may be the only presenting finding, as symptoms may be subtle, and early infections may not be clinically apparent. Therefore, there is considerable variability in evaluation, treatment, and overall resource utilization in managing febrile children presenting to ED. This study aims to determine whether the height or duration of fever can predict the likelihood of SBI in children. 



Methods 

We performed a retrospective review of febrile children (3-36 months old) who presented to the Pediatric ED between January 2017 and December 2021. Children 3-36 months of age presenting to ED with a documented temperature of 102.2 0F (39 0C) or more were included. Older age groups and/or critically ill children requiring resuscitation were excluded. Clinical characteristics, such as temperature at admission, fever duration, admission diagnosis, and associated symptoms, were analyzed. Serious Bacterial Infection (SBI) was defined as Pneumonia, Urinary Tract Infection (UTI), Meningitis, or Sepsis/Bacteremia. Data was tabulated as number, frequency, median, and range. 

Result 

We reviewed 444 febrile children, including 36 (8%) with SBI and 408 (92%) without SBI. The mean temperature at admission of children with an SBI was significantly higher than those without SBI (mean Temp 103.0°F±1.6 vs. 102.1°F±1.3, p<0.001) and 64% did have an admitting temperature ≥102.20F (p=0.022). There was a significant difference in SBI versus non-SBI group in fever duration (2.5±1.6 vs. 1.6±1.1 days, p<0.001), WBC count (14.2±7.7 vs. 10.4±3.9, p<0.001), Procalcitonin level (2.2±3.1 vs 0.8±1.7, p=0.020) and immunization status (58.3% vs 70.3%, p<0.018). The SBI was 64% pneumonia and 36% UTI. Both groups had associated symptoms (cough, rhinorrhea, vomiting/diarrhea) at presentation to the ED (SBI, 86.1% vs. non-SBI, 85.8%, p=0.95). 

Conclusion 

The rate of SBI was 8% in our patient population, and it was associated with a longer fever duration. Although the mean temperature was slightly higher in the SBI group than in the non-SBI group, it was not strongly associated with an SBI. Thus, a single parameter, such as the degree of fever alone, cannot be used to stratify the risk of febrile children. Height and duration of fever, along with increased WBC count, could indicate the severity of illness and call for further investigation.

Does Height of Fever Influence the Probability of Serious Bacterial Infections in Febrile Children?

Effect of Environmental PFAS Exposure on Pediatric Neuromorphometry: The HOME Study

BACKGROUND
Per- and poly-fluoroalkyl substances (PFAS), also known as “forever chemicals”, is a class of poorly biodegradable chemicals widely used in commercial and industrial applications. The neurodevelopmental effects of PFAS have not been previously studied, warranting investigation of this prevalent and persistent chemical class.

The Health Outcomes and Measures of the Environment (HOME) Study is an ongoing pregnancy and birth cohort study (enrollment from 2003-2006) designed to evaluate known and potentially adverse environmental exposures affecting infant and child health. Thus, we examined the impact of PFAS exposure on pediatric structural MRI (sMRI) imaging acquired in HOME Study participants. 

METHODS
468 pregnant individuals were enrolled in the HOME Study. Offspring serum concentrations (ng/mL) of five PFAS (PFOA, PFOS, PFHxS, PFNA, and PFDEA) were measured at multiple time points. High-resolution T1-weighted MRI was performed at 12 years. Post-processing of data was performed in CAT12 software, allowing for voxel-based and surface-based neuromorphometrical analysis. 215 children had complete serum PFAS, sMRI, and covariate information.

We created a general linear model to test for changes in neuromorphometry associated with exposure to log2-transformed PFAS compounds at various time points, while controlling for gender, race, birth weight, and maternal IQ.

RESULTS
We found multiple significant (p-value < 0.05) associations with a small Cohen’s effect size (0.2 < d < 0.5) between PFAS exposure and neuromorphometry (at 12 years old). PFOA had a significant effect on GM volume at P3 and P4, while PFNA had a significant effect on GM volume at 11-14 years. PFDEA had a significant effect on cortical surface area at 7-9 years. PFNA had the most significant (p = 0.009) and largest effect size (d = 0.44) on both voxel-based and surface-based morphometry at the 11-14 years.

CONCLUSION
PFOA, PFNA, and PFDEA play a significant role in brain-wide neurodevelopment, although there was not sufficient evidence to state that PFOS or PFHxS were influential in our model. There were varied effects depending on the particular exposure time point. Ultimately, this study provides novel evidence regarding the neurodevelopmental impacts of a highly prevalent and persistent chemical class, justifying extensive further investigation into its safety. Limitations of this study included somewhat small sample size and only one imaging time point. 

Future analysis will focus on the relationship between PFAS, imaging, and behavioral outcomes. New imaging data at 18 years is also being gathered.

Effect of Environmental PFAS Exposure on Pediatric Neuromorphometry: The HOME Study

Background: 
Pediatric primary care is an ideal setting to prevent mental and behavioral health disorders given the high frequency of contacts through well-child visits birth to age 5. Bjelland et al have detailed that childhood mental and behavioral health have strong associations with adult well-being1. As such, early detection and intervention is key to addressing the secondary effects of behavioral health issue; however, many barriers, such as family circumstances and lack of awareness of behavioral health programs, hinder successful access to behavioral healthcare resources in pediatric practice2. The goal of this study was to examine the perspectives of families with young children on barriers to accessing behavioral health care and how to improve access. 
Methods:
Semi-structured one-on-one virtual interviews were used to qualitatively explore the perspectives of families with young children regarding access to behavioral healthcare. Participants were recruited from the CHP-Primary Care Center (PCC) in Oakland, a clinic serving a high proportion of Medicaid-eligible families (>85%). Interviews focused on thoughts and experiences related to accessing behavioral healthcare for children, with demographic information collected at the end of each interview. Deductive and inductive methods were used to develop a codebook followed by content and thematic analysis techniques to identify recurring themes and patterns, utilizing the program Nvivo.
Results: 
Participants included 27 caregivers (M age=30.7, SD=6.3) of at least one child under the age of 5. Eleven percent of caregivers identified as Hispanic. Seventy percent identified as African American and 30% as white. Forty-four percent reported an income below 40k and 48% were employed full-time. The analysis of the interviews revealed several interconnected barriers that families face when accessing behavioral healthcare for their young children, categorized into logistical, financial, and psychosocial challenges. Logistical barriers included 1) childcare, 2) time, 3) transportation, and 4) employment-related issues. Financial barriers were evident in the form of 1) inadequate insurance coverage, and 2) broader financial constraints that made affording care difficult. Psychosocial barriers included 1) education and understanding of behavioral health issues and 2) pride and stigma, which deterred some parents from seeking care due to concerns about judgment.
Conclusions: 
These findings underscore the need for a multifaceted approach addressing the diverse challenges families face in accessing behavioral healthcare services for their children. Comprehensive solutions must consider both systemic and individual factors, such as improving insurance coverage and financial support, enhancing public transportation options, and providing flexible appointment scheduling, while also advising initiatives aimed at increasing education about behavioral health and reducing stigma. Addressing these barriers in a coordinated manner could significantly enhance access to behavioral healthcare services for young children.


Exploring Caregiver’s Perspectives on the Barriers to Accessing Behavioral Healthcare for their Children

Selective mutism (SM), categorized under anxiety disorders in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), presents challenges in timely identification and treatment, which is especially crucial for pediatricians as early intervention can significantly improve a child’s social and emotional development. SM is characterized by a child’s inability to speak in certain situations but speaks adequately in other settings. While recorded SM diagnoses may not be largely prevalent within the US, affecting approximately 0.2-1.6% of people. SM's significance lies in its potential impact on a child's social, academic, and emotional well-being. This retrospective observational study explores pediatrician referrals to a specialized program for adolescents with SM, aiming to address awareness gaps and improve early intervention. Findings reveal a notable 11.5% contribution from pediatricians, compared to the other referring parties-- searching online (33.7%), other (32.6%), psychologist (8.6%), friend (8.5%), other therapist (4.6%), and recruitment event (0.5%)--emphasizing the need for targeted clinician awareness initiatives. Recognizing the potential benefits of early intervention, this research underscores the importance of collaborative efforts, education, and specialized programs in enhancing referral patterns by pediatricians for improved outcomes in selective mutism, as children are more likely to see their pediatrician before any of the other previously listed referral parties. The study acknowledges limitations in its design and suggests avenues for future research to explore factors influencing clinician awareness and the long-term outcomes of early SM treatment.

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Association Between Retroepiglottic Pressure During Drug-Induced Sleep Endoscopy & Nocturnal Blood Pressure: A Pilot Study

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