Activity

Previous research has suggested caution about opioid analgesic usage in the emergency department (ED) setting and raised concerns about variations in prescription opioid analgesic usage, both across institutions and for whom they are prescribed. We examined opioid analgesic usage in ED patients with suspected urolithiasis across fifteen participating hospitals.

This is a secondary analysis of a clinical trial including adult ED patients with suspected urolithiasis. In multilevel models accounting for clustering by hospital, we assessed demographic, clinical, state-level, and hospital-level factors associated with opioid analgesic administration during the ED visit and prescription at discharge.

Of 2352 participants, 67% received an opioid analgesic during the ED visit and 61% were prescribed one at discharge. Opioid analgesic usage varied greatly across hospitals, ranging from 46% to 88% (during visit) and 34% to 85% (at discharge). Hispanic patients were less likely than non-Hispanic white patients to ce.

Acute chloroquine and hydroxychloroquine toxicity is characterized by a combination of direct cardiovascular effects and electrolyte derangements with resultant dysrhythmias and is associated with significant morbidity and mortality.

This review describes acute chloroquine and hydroxychloroquine toxicity, outlines the complex pathophysiologic derangements, and addresses the emergency department (ED) management of this patient population.

Chloroquine and hydroxychloroquine are aminoquinoline derivatives widely used in the treatment of rheumatologic diseases including systemic lupus erythematosus and rheumatoid arthritis as well as for malaria prophylaxis. In early 2020, anecdotal reports and preliminary data suggested utility of hydroxychloroquine in attenuating viral loads and symptoms in patients with SARS-CoV-2 infection. Aminoquinoline drugs pose unique and significant toxicological risks, both during their intended use as well as in unsupervised settings by laypersons. The therapeutic range for chloroquine is narrow. Acute severe toxicity is associated with 10-30% mortality owing to a combination of direct cardiovascular effects and electrolyte derangements with resultant dysrhythmias. Treatment in the ED is focused on decontamination, stabilization of cardiac dysrhythmias, hemodynamic support, electrolyte correction, and seizure prevention.

An understanding of the pathophysiology of acute chloroquine and hydroxychloroquine toxicity and available emergency treatments can assist emergency clinicians in reducing the immediate morbidity and mortality associated with this disease.

An understanding of the pathophysiology of acute chloroquine and hydroxychloroquine toxicity and available emergency treatments can assist emergency clinicians in reducing the immediate morbidity and mortality associated with this disease.

To identify predictors of 30-day emergency department (ED) return visits in patients age 65-79years and age≥80years.

This was a cohort study of older adults who presented to the ED over a 1-year period. A mixed-effects logistic regression model was used to identify predictors for returning to the ED within 30days. We stratified the cohort into those aged 65-79years and aged ≥80years. Adjusted odds ratios (aORs) with 95% confidence intervals (CI) were reported. This study adhered to the STROBE reporting guidelines.

A total of 21,460 ED visits representing 14,528 unique patients were included. The overall return rate was 15% (1998/13,300 visits) for age 65-79years, and 16% (1306/8160 visits) for age≥80years. A history of congestive heart failure (CHF), dementia, or prior hospitalization within 2years were associated with increased odds of returning in both age groups (for age 65-79 CHF aOR 1.36 [CI 1.16-1.59], dementia aOR 1.27 [CI 1.07-1.49], prior hospitalization aOR 1.36 [CI 1.19-1.56]; for age≥80 CHF aOR 1.32 [CI 1.13-1.55], dementia aOR 1.22 [CI 1.04-1.42], and prior hospitalization aOR 1.27 [CI 1.09-1.47]). Being admitted from the ED was associated with decreased odds of returning to the ED within 30days (aOR 0.72 [CI 0.64-0.80] for age 65-79years and 0.72 [CI 0.63-0.82] for age≥80).

Age alone was not an independent predictor of return visits. Prior hospitalization, dementia and CHF were predictors of 30-day ED return. PF-03084014 cost The identification of predictors of return visits may help to optimize care transition in the ED.

Age alone was not an independent predictor of return visits. Prior hospitalization, dementia and CHF were predictors of 30-day ED return. The identification of predictors of return visits may help to optimize care transition in the ED.

The opioid epidemic has altered normative clinical perceptions on addressing both acute and chronic pain, particularly within the Emergency Department (ED) setting, where providers are now confronted with balancing pain management and potential abuse. This study aims to examine patient sociodemographic and ED clinical characteristics to comprehensively determine predictors of opioid administration during an ED visit (ED-RX) and prescribing upon discharge (DC-RX).

ED visit data of patients ≥18years old from the National Hospital Ambulatory Medical Care Survey (NHAMCS) from 2014 to 2017 were used. Opioid prescriptions were determined utilizing Lexicon narcotic drug classifications. Visit characteristics studied included sociodemographic variables, and ED clinical variables, such as chief complaint, and discharge diagnosis. Machine learning methods were used to determine predictors of ED-RX and DC-RX and weighted logistic regressions were performed using selected predictors.

Of the 44,227 ED visits identified, patients tended to be female (57.4%), and White (74.2%) with an average age of 46.4years (SE=0.3). Weighted proportions of ED-RX and DC-RX were 23.2% and 18.9%, respectively. The strongest predictors of ED-RX were CT scan ordered (OR=2.18, 95% CI=1.84-2.58), abdominal pain (OR=1.93, 95% CI1.59-2.34) and back pain (OR=1.81, 95% CI1.45-2.27). Tooth pain (OR=6.94, 95% CI=4.40-10.94) and fracture injury diagnoses (OR=3.76, 95% CI=2.72-5.19) were the strongest predictors of DC-RX.

These findings demonstrate the utility of machine learning for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines.

These findings demonstrate the utility of machine learning for understanding clinical predictors of opioid administration and prescribing in the ED, and its potential in informing standardized prescribing recommendations and guidelines.