A Retrospective Review of Opioid Prescribing Practices for At-Risk Pediatric Populations Undergoing Ambulatory Surgery

OBJECTIVE Pediatric patients with sleep-disordered breathing (SDB) and obesity are at risk for opioid-induced respiratory depression. Although monitoring in the inpatient setting allows for early recognition of opioid-related adverse events, there is far less vigilance after ambulatory surgery as patients are discharged home. Guidelines for proper opioid dosing in these pediatric subsets have not been established. We sought to determine if at-risk children were more likely to receive doses of opioids outside the recommended range. METHODS Baseline opioid prescribing data for all outpatient surgery patients receiving an opioid prescription between January 2019 and June 2020 were retrospectively reviewed. Patients with SDB or obesity were identified. To obtain more information about prescribing practices, we analyzed patient demographics, size descriptors used for calculations, and prescription characteristics (dose, duration, and prescribing surgical service). RESULTS A total of 4674 patients received an opioid prescription after outpatient surgery. Of those, 173 patients had SDB and 128 were obese. Surgical subspecialties rendering most of the opioid prescriptions included otolaryngology and orthopedics. Obese patients were more likely (64%) to be prescribed opioids using ideal weight at higher mg/kg doses (>0.05 mg/kg; 83.3%; p < 0.0001). When providers used actual body weight, lower mg/kg doses were more likely to be used (53.7%; p < 0.0001). No prescriptions used lean body mass. CONCLUSIONS Overweight/obese children were more likely to receive opioid doses outside the recommended range. Variability in prescribing patterns demonstrates the need for more detailed guidelines to minimize the risk of opioid-induced respiratory complications in vulnerable pediatric populations.

Rates of Operative Management of Midshaft Clavicle Fracture in Adolescents Have Increased in Florida between 2005 and 2014

Background: It is not known if the adult literature on midshaft clavicle fracture treatment with open reduction internal fixation (ORIF) has influenced injury management in adolescents. Purpose: We sought to longitudinally evaluate the rates of operative management of adolescent midshaft clavicle fractures in the state of Florida. Methods: We conducted a retrospective review of data from the following Healthcare Cost and Utilization Project databases: the State Inpatient Database, the State Ambulatory Surgery and Services Database, and the State Emergency Department Database. Patients in Florida ages 10 to 18 years with midshaft clavicle fractures between 2005 and 2014 were identified along with data on age, sex, race/ethnicity, insurance type, treatment, and income percentile. We reviewed the data to identify trends in the rates of operative management of midshaft clavicle fractures. We then compared the rates of operative management between the first 3 years and the most recent 3 years (2005–2007 vs 2012–2014). Various demographic and socioeconomic factors were compared between patients treated with and without surgery. Descriptive statistics as well as univariate and multivariate analyses were performed. Results: There were 4297 midshaft clavicle fractures in adolescents identified between 2005 and 2014, and 338 (7.8%) of these fractures underwent operative management. There was a significant increase in the rate of operative management; it increased from 4.3% (n = 59) of the 1373 clavicle fractures that occurred between 2005 and 2007 to 11.2% (n = 130) of the 1164 clavicle fractures that occurred between 2012 and 2014. Patients with commercial insurance and patients who were older were more likely to undergo ORIF. Patients with Medicaid were more likely to undergo ORIF between 2012 and 2014 compared with patients with Medicaid between 2005 and 2007. Conclusions: Operative management rates of adolescent midshaft clavicle fractures have significantly increased in Florida over a decade; additional research is needed to understand these findings.

Detecting early signs of Alzheimer’s disease and related dementia onset from the EHR

As dementia is widely under-detected, a predictive model using electronic health records (EHR) could provide a method for early screening to implement preventive strategies. There is limited research on using EHR to identify persons with Alzheimer’s disease (AD) and related dementias (RD). In a data-driven approach, we used all ICD-9 diagnosis and CPT procedure codes from statewide inpatient, ambulatory surgery, and Medicare records, in addition to age at baseline and gender, to detect AD/RD from the Cache County Study on Memory in Aging (1995–2009). After removing participants diagnosed with dementia at baseline (n=335), 3882 (82%) Cache County Study participants could be linked to inpatient, ambulatory surgery, and/or Medicare EHR records; 484 (12.5%) of these 3882 had incident all-cause dementia, with 308 (7.9%) having AD/AD comorbid with RD; and 176 (4.5%) having RD without AD. We removed participant’s ICD-9 codes occurring after first AD/RD diagnoses. EHR features (~2000) along with gold-standard diagnoses as class labels were then used to train and detect AD and/or RD using a Gradient Boosting Trees machine learning algorithm. Models evaluated with nested cross-validation yielded AUCs of 0.70 for all-cause dementia, 0.69 for AD/AD comorbid with RD, and 0.67 for RD without AD. Key factors detecting AD/RD included age at enrollment, cardiovascular, metabolic, and kidney disease, and sleep disturbances, with feature importance varying by record type and time frame prior to dementia onset. Our findings suggest that a patient’s health status up to 12 years prior may be useful in identifying individuals at-risk for dementia development.