Formula Otorrhea in Pediatric Patients With Cleft Palate.

The case series details 2 unusual cases of male newborns with cleft lip and palate (CLP) that later developed formula otorrhea. Both patients underwent bilateral myringotomies with the insertion of pressure equalizing (PE) tubes for chronic otitis media with effusion (OME). Chronic otorrhea associated with feeding occurred post-PE tube insertion and the otorrhea was later confirmed to be due to reflux of formula. Patients were treated with antibiotic ear drops, routine ear cleaning, anti-reflux medication, and reflux precautions. After definite cleft palate repair, formula otorrhea completely resolved. When patients with CLP develop chronic OME or otorrhea following PE tube placement, reflux of formula into the middle ear should be considered and treated accordingly.

Skull base osteomyelitis with extensive bony erosion complicated by venous sinus thrombosis.

Skull base osteomyelitis (SBO) mimics the presentation of various conditions, including solid tumors. Computed tomography-guided core biopsy for culture informs antibiotic selection, and with intravenous corticosteroids, may minimize chronic neurologic dysfunction. Although SBO predominantly affects individuals who are diabetic or immunocompromised, it is important to be able to recognize SBO presenting in an otherwise healthy individual.

Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status.

OBJECTIVE: Hearing loss (HL) is highly prevalent, yet underrecognized and underdiagnosed. Lack of standardized screening, awareness, cost, and access to hearing testing present barriers to HL identification. To facilitate prescreening and selection of patients who warrant audiometric evaluation, we developed a machine learning (ML) model to predict speech-frequency pure-tone average (PTA). STUDY DESIGN: Cross-sectional study. SETTING: National Health and Nutrition Examination Survey (NHANES). METHODS: The cohort included 8918 adults (≥20 years) who completed audiometric testing with NHANES (2012-2018). The primary outcome measure was the prediction of better hearing ear speech-frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree-based architecture was used. Regression performance was determined by the mean absolute error (MAE) with binary classification assessed with area under the receiver operating characteristic curve (AUC). RESULTS: Using the full set of predictors, the test set MAE between the ML-predicted and actual PTA was 5.29 dB HL (95% confidence interval [CI]: 4.97-5.61). The 5 most influential predictors of higher PTA were increased age, worse subjective hearing, male gender, increased body mass index, and history of smoking. The 5-factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03-5.69) and AUC for PTA > 25 dB HL of 0.92 (95% CI: 0.90-0.94). CONCLUSION: The ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML-based prediction may be used to identify individuals who could benefit most from audiometric evaluation.

Joint engineering-medical school programs prepare physicians for clinical challenges: An observational study.

Background and Aims: Modern health care faces a plethora of challenges including the delivery of quality and cost-efficient care. Physicians are first-hand observers of clinical problems but may lack the requisite training and education to develop innovations that improve patient care. Few medical education programs address innovation, leadership, and transdisciplinary collaboration despite being highlighted by national medical and education organizations including the American Medical Association. The University of Minnesota has implemented the Augustine program over the last 10-years to produce physicians that are leaders in medical innovation. Methods: As a novel joint engineering-medical school curriculum to educate medical students, residents, and fellows, the Augustine program incorporates engineering coursework, biomedical research, and a multidisciplinary design and business development experience to produce physicians capable of designing and marketing "disruptive technologies." The Augustine program takes 1-year to complete in addition to the 4-year medical education and provides a Master of Biomedical Engineering upon completion. Results: Augustine program graduates (n = 6) have reported significant contributions related to the joint engineering-medical education including peer-reviewed publications (Median: 13), deployable assets (Median: 2), and intellectual property (Median: 1). Most surveyed graduates (n = 5, 83%) continue to be active contributors to medical innovation and all (n = 6, 100%) utilize their transdisciplinary education to improve patient care. Conclusion: Augustine program graduates impact the entire spectrum of innovation and continue to improve patient care. The program will seek to emphasize the inclusion of physician residents and fellows with position expansion. The addition of a multi-week medical innovation clerkship will provide a more focused experience for students unable to dedicate an entire year to a transdisciplinary experience.

Demographic Risk Factors for Malnutrition in Patients With Cleft Lip and Palate.

OBJECTIVES/HYPOTHESIS: Patients with cleft lip and/or palate (CLP) are at increased risk of malnutrition. Acute and chronic malnutrition have been associated with elevated risk of postsurgical wound complications, adding morbidity and cost to patients and their families. To study the association between demographic factors, including insurance type, race, and median neighborhood income (MNI), and malnutrition in patients with CLP. STUDY DESIGN: Retrospective cohort study. METHODS: Retrospective review was performed in patients undergoing their first cleft-related surgery at a large tertiary pediatric hospital from 2006 to 2018. Demographic data, weight and height at surgery, type of insurance, race, and primary residential address were collected. Geocoded information on MNI was generated using patient address. World Health Organization Z-scores for weight-for-age (WFA) and height-for-age (HFA) were used as proxies for acute and chronic malnutrition, respectively. Linear regression models were generated to analyze the relationship of insurance type, race, and MNI on WFA and HFA Z-scores. RESULTS: About 313 patients met inclusion criteria. Increasing MNI predicted increasing WFA Z-score (0.05 increase in WFA per $1,000 increase, P = .047) as well as HFA Z-score (0.09 increase in HFA per $1,000 increase, P = .011). The effect of MNI was not independently modified by race for either WFA (P = .841) nor HFA (P = .404). Race and insurance type did not predict WFA or HFA. CONCLUSIONS: Lower MNI is a significant independent risk factor for acute and chronic malnutrition in children with CLP. Combined with previous investigation linking malnutrition to surgical outcomes in this population, this offers a target area for intervention to improve patient outcomes. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:1482-1486, 2022.

Performance of Smartphone Automated Audiogram Image Recognition for Personalized Sound Amplification.

This quality improvement study examines the accuracy of the automated audiogram image recognition on the Apple iPhone using a large sample of audiogram reports representing various degrees of hearing loss.