Developing an emergency department order set to treat acute pain in sickle cell disease.
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ABSTRACT: Study objective
Patients with sickle cell disease (SCD) have many emergency department visits because of painful vaso-occlusive episodes (VOE). Guidelines recommend treatment within 30 minutes of triage, but this is rarely achieved in clinical practice. Our goal was to develop an order set that is being implemented in the ED to facilitate and standardize emergency care for SCD patients in acute pain from VOEs presenting to the emergency department (ED) in New York City (NYC).Methods
Using a RAND/University of California, Los Angeles modified Delphi panel, we convened a multidisciplinary panel and reviewed evidence on how to best manage SCD pain in the ED. Panelists collaboratively developed then rated 202 items that could be included in an ED order set.Results
A consensus order set, a practical how-to guide for managing SCD pain in the ED, was developed based on items that received high median ratings.Conclusions
The management of acute pain experienced during VOEs is critical to patients with SCD; ED order sets, such as this one, can help standardize pain management, including at triage, evaluation, discharge, and follow-up care. After implementation in NYC EDs, studies to examine changes in quality care metrics (eg, wait times, readmissions) are planned.
SUBMITTER: Duroseau Y
PROVIDER: S-EPMC8349222 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sickle cell disease (SCD) can lead to potentially devastating complications that occur secondary to vaso-occlusion. Current national clinical guidelines are largely based on expert opinion, resulting in significant variation of management. Provider awareness regarding emergency department (ED) management of vaso-occlusive crises (VOC) remains unknown. A 23-question assessment of VOC management was administered to all eligible ED providers at Riley Hospital for Children between September and November 2018. Univariate analyses were performed to evaluate responses between groups. Of 52 respondents comprised of ED staff attendings (27%), resident trainees (58%), and ED nurses (15%), the majority were not aware of SCD management guidelines being available. Approximately 54% of providers endorsed a high comfort level in managing VOC, with staff and nurses more likely to report this than trainees (P=0.02). Less than 10% of all providers knew the recommended timeframe from triage to initial medication administration. Prolonged time between pain assessments was reported by 25% of providers with a high comfort level in managing VOC, which was similar to providers with a lower comfort level (13%, P=0.217). Only one fourth of all respondents appropriately did not use vital signs as an indication of a patient's pain level, and >10% reported not utilizing patient-reported pain scores. This was not significantly different between provider comfort levels (P=0.285 and 0.412, relatively). Our results suggest education regarding recommended practices was inadequate regardless of reported provider comfort. Further provider education and/or standardized ED VOC management guidelines may serve as areas for improvement in SCD care.
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Project description:BackgroundDue to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain.Methods and resultsA chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71-0.79]; troponin model, 0.73 [0.69-0.77]; logistic regression model, 0.73 [0.70-0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model.ConclusionWe developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.
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