Dataset Information

Clinical Features and Outcomes Associated with Angioedema in the Emergency Department.

ABSTRACT:

Introduction

Angioedema represents self-limited, localized swelling of submucosal or subcutaneous tissues. While the underlying etiology may be undeterminable in the emergent setting, nonhistaminergic and histaminergic angioedema respond differently to therapeutic interventions, with implications for empiric treatment. Clinical features and outcome differences among nonhistaminergic vs histaminergic angioedema patients in the emergency department (ED) are poorly characterized. We aim to describe the clinical characteristics and outcomes among ED patients with angioedema by suspected etiology.

Methods

This was a 10-year retrospective study of adult ED patients with angioedema, using data abstracted from the electronic health record. We evaluated univariable associations of select clinical features with etiology and used them to develop a multivariable logistic regression model for nonhistaminergic vs histaminergic angioedema.

Results

Among 450 adult angioedema patients, the mean +/- standard deviation age was 57 +/- 18 years, and 264 (59%) were female. Among patients, 30% had suspected nonhistaminergic angioedema, 30% had suspected histaminergic angioedema, and 40% were of unknown etiology. As compared to histaminergic angioedema, nonhistaminergic angioedema was associated with angiotensin-converting enzyme inhibitors (ACEI) or use of angiotensin II receptor blockers (ARB) (odds ratio [OR] [60.9]; 95% confidence interval [CI], 23.16-160.14) and time of onset one hour or more prior to ED arrival (OR [5.91]; 95% CI,1.87-18.70) and was inversely associated with urticaria (OR [0.05]; 95% CI, 0.02-0.15), dyspnea (OR [0.23]; 95% CI, 0.08-0.67), and periorbital or lip edema (OR [0.25]; 95% CI, 0.08-0.79 and OR [0.32]; 95% CI, 0.13-0.79, respectively).

Conclusion

As compared to histaminergic angioedema, patients with nonhistaminergic angioedema were more likely to present one hour or more after symptom onset and take ACEI or ARB medications, and were less likely to have urticaria, dyspnea, or periorbital or lip angioedema. Identification of characteristics associated with the etiology of angioedema may assist providers in more rapidly initiating targeted therapies.

SUBMITTER: Sandefur BJ

PROVIDER: S-EPMC6754201 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Publications

Clinical Features and Outcomes Associated with Angioedema in the Emergency Department.

Sandefur Benjamin J BJ E Silva Lucas Oliveira J LOJ Lohse Christine M CM Goyal Kiran A KA Barbara David W DW Castaneda-Guarderas Ana A Liu Xiao-Wei XW Campbell Ronna L RL

The western journal of emergency medicine 20190806 5

<h4>Introduction</h4>Angioedema represents self-limited, localized swelling of submucosal or subcutaneous tissues. While the underlying etiology may be undeterminable in the emergent setting, nonhistaminergic and histaminergic angioedema respond differently to therapeutic interventions, with implications for empiric treatment. Clinical features and outcome differences among nonhistaminergic vs histaminergic angioedema patients in the emergency department (ED) are poorly characterized. We aim to ...[more]

PMID: 31539333

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Project description:BackgroundDevelopment of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).MethodsUsing National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.ResultsOf 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85-0.87] in the deep neural network vs 0.74 [95%CI 0.72-0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82-0.83] in the deep neural network vs 0.69 [95%CI 0.68-0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.ConclusionsCompared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.

Project description:ImportanceNoninvasive testing and coronary angiography are used to evaluate patients who present to the emergency department (ED) with chest pain, but their effects on outcomes are uncertain.ObjectiveTo determine whether cardiovascular testing-noninvasive imaging or coronary angiography-is associated with changes in the rates of coronary revascularization or acute myocardial infarction (AMI) admission in patients who present to the ED with chest pain without initial findings of ischemia.Design, setting, and participantsThis retrospective cohort analysis used weekday (Monday-Thursday) vs weekend (Friday-Sunday) presentation as an instrument to adjust for unobserved case-mix variation (selection bias) between 2011 and 2012. National claims data (Truven MarketScan) was used. The data included a total of 926 633 privately insured patients ages 18 to 64 years who presented to the ED with chest pain without initial diagnosis consistent with acute ischemia.ExposuresNoninvasive testing or coronary angiography within 2 days or 30 days of presentation.Main outcomes and measuresThe primary end points were coronary revascularization (percutaneous coronary intervention or coronary artery bypass graft surgery) and AMI admission at 7, 30, 180, and 365 days. The secondary end points were coronary angiography and coronary artery bypass grafting in those who underwent angiography.ResultsThe patients were ages 18 to 64 years with an average age of 44.4 years. A total of 536 197 patients (57.9%) were women. Patients who received testing (224 973) had increased risk at baseline and had greater risk of AMI admission than those who did not receive testing (701 660) (0.35% vs 0.14% at 30 days). Weekday patients (571 988) had similar baseline comorbidities to weekend patients (354 645) but were more likely to receive testing. After risk factor adjustment, testing within 30 days was associated with a significant increase in coronary angiography (36.5 per 1000 patients tested; 95% CI, 21.0-52.0) and revascularization (22.8 per 1000 patients tested; 95% CI, 10.6-35.0) at 1 year but no significant change in AMI admissions (7.8 per 1000 patients tested; 95% CI, -1.4 to 17.0). Testing within 2 days was also associated with a significant increase in coronary revascularization but no difference in AMI admissions.Conclusions and relevanceCardiac testing in patients with chest pain was associated with increased downstream testing and treatment without a reduction in AMI admissions, suggesting that routine testing may not be warranted. Further research into whether specific high-risk subgroups benefit from testing is needed.

Project description:ImportanceWhile machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.ObjectivesTo examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.Design, setting, and participantsPrognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.Main outcomes and measuresThe outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.ResultsOf 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.Conclusions and relevanceMachine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.

Dataset Information

Clinical Features and Outcomes Associated with Angioedema in the Emergency Department.

Introduction

Methods

Results

Conclusion

Publications

Clinical Features and Outcomes Associated with Angioedema in the Emergency Department.

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