Dataset Information

Chief Complaints, Underlying Diagnoses, and Mortality in Adult, Non-trauma Emergency Department Visits: A Population-based, Multicenter Cohort Study.

ABSTRACT:

Introduction

Knowledge about the relationship between symptoms, diagnoses, and mortality in emergency department (ED) patients is essential for the emergency physician to optimize treatment, monitoring, and flow. In this study, we investigated the association between symptoms and discharge diagnoses; symptoms and mortality; and we then analyzed whether the association between symptoms and mortality was influenced by other risk factors.

Methods

This was a population-based, multicenter cohort study of all non-trauma ED patients ≥18 years who presented at a hospital in the Region of Southern Denmark between January 1, 2016-March 20, 2018. We used multivariable logistic regression to examine the association between symptoms and mortality adjusted for other risk factors.

Results

We included 223,612 ED visits with a median patient age of 63 and even distribution of females and males. The frequency of the chief complaints at presentation were as follows: non-specific symptoms (19%); abdominal pain (16%); dyspnea (12%); fever (8%); chest pain (8%); and neurologic complaints (7%). Discharge diagnoses were symptom-based (24%), observational (hospital visit for observation or examination, 17%), circulatory (12%), or respiratory (12%). The overall 30-day mortality was 3.5%, with 1.7% dead within 0-7 days and 1.8% within 8-30 days. The presenting symptom was associated with mortality at 0-7 days but not with mortality at 8-30 days. Patients whose charts were missing documentation of symptoms (adjusted odds ratio [aOR] 3.5) and dyspneic patients (aOR 2.4) had the highest mortality at 0-7 days across patients with different primary symptoms. Patients ≥80 years and patients with a higher degree of comorbidity had increased mortality from 0-7 days to 8-30 days (aOR from 24.0 to 42.7 and 1.9 to 2.8, respectively).

Conclusion

Short-term mortality was more strongly associated with patient-related factors than with the primary presenting symptom at arrival to the hospital.

SUBMITTER: Arvig MD

PROVIDER: S-EPMC9683768 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Chief Complaints, Underlying Diagnoses, and Mortality in Adult, Non-trauma Emergency Department Visits: A Population-based, Multicenter Cohort Study.

Arvig Michael Dan MD Mogensen Christian Backer CB Skjøt-Arkil Helene H Johansen Isik Somuncu IS Rosenvinge Flemming Schønning FS Lassen Annmarie Touborg AT

The western journal of emergency medicine 20221031 6

<h4>Introduction</h4>Knowledge about the relationship between symptoms, diagnoses, and mortality in emergency department (ED) patients is essential for the emergency physician to optimize treatment, monitoring, and flow. In this study, we investigated the association between symptoms and discharge diagnoses; symptoms and mortality; and we then analyzed whether the association between symptoms and mortality was influenced by other risk factors.<h4>Methods</h4>This was a population-based, multicen ...[more]

PMID: 36409936

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Project description:ObjectiveWe learn contextual embeddings for emergency department (ED) chief complaints using Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art language model, to derive a compact and computationally useful representation for free-text chief complaints.Materials and methodsRetrospective data on 2.1 million adult and pediatric ED visits was obtained from a large healthcare system covering the period of March 2013 to July 2019. A total of 355 497 (16.4%) visits from 65 737 (8.9%) patients were removed for absence of either a structured or unstructured chief complaint. To ensure adequate training set size, chief complaint labels that comprised less than 0.01%, or 1 in 10 000, of all visits were excluded. The cutoff threshold was incremented on a log scale to create seven datasets of decreasing sparsity. The classification task was to predict the provider-assigned label from the free-text chief complaint using BERT, with Long Short-Term Memory (LSTM) and Embeddings from Language Models (ELMo) as baselines. Performance was measured as the Top-k accuracy from k = 1:5 on a hold-out test set comprising 5% of the samples. The embedding for each free-text chief complaint was extracted as the final 768-dimensional layer of the BERT model and visualized using t-distributed stochastic neighbor embedding (t-SNE).ResultsThe models achieved increasing performance with datasets of decreasing sparsity, with BERT outperforming both LSTM and ELMo. The BERT model yielded Top-1 accuracies of 0.65 and 0.69, Top-3 accuracies of 0.87 and 0.90, and Top-5 accuracies of 0.92 and 0.94 on datasets comprised of 434 and 188 labels, respectively. Visualization using t-SNE mapped the learned embeddings in a clinically meaningful way, with related concepts embedded close to each other and broader types of chief complaints clustered together.DiscussionDespite the inherent noise in the chief complaint label space, the model was able to learn a rich representation of chief complaints and generate reasonable predictions of their labels. The learned embeddings accurately predict provider-assigned chief complaint labels and map semantically similar chief complaints to nearby points in vector space.ConclusionSuch a model may be used to automatically map free-text chief complaints to structured fields and to assist the development of a standardized, data-driven ontology of chief complaints for healthcare institutions.

Project description:ObjectivesVeteran persons living with dementia (PLWDs) have high acute care utilization. We aim to understand why PLWDs seek care in the emergency department (ED) and how their utilization differs from older Veterans with no dementia diagnosis. We demonstrate the use of a novel national chief complaint data set in the Veteran Affairs Health Care System.MethodsThis was a retrospective observational study of ED users 65 years or older as of FY2017. The primary outcome is presence of one or more ED visits in FYs 2017-2018 using a logistic regression model controlling for dementia and other variables. Secondary outcomes include counts of ED visits by disposition, Emergency Severity Index, chief complaints defined by a natural language processing program, and ED encounter diagnoses defined by primary International Statistical Classification of Diseases, Tenth Revision (ICD-10-CM) code.ResultsOur cohort of Veterans comprised 3,115,263 patients. Of those, 255,372 (8.2%) had a diagnosis of dementia. Logistic regression modeling demonstrated that dementia is a significant predictor of ED use (p < 0.0001), with PLWDs more likely to have an ED visit (odds ratio 1.96, 95% confidence interval 1.94-1.98). PLWDs were admitted at higher rates when accounting for age and acuity. Chief complaints that were more common among PLWDs included falls (6.7% dementia vs. 3.3% without dementia), weakness (3.6% vs. 2.2%), and abnormal mental state (2.2% vs. 0.4%). ICD-10-CM codes were largely similar between the two groups.ConclusionsOur results reinforce that the ED is a common access point for Veterans with dementia. These patients require special consideration as they are more likely to visit the ED and be admitted. Our use of a novel national chief complaint data set suggests that they more commonly present with certain geriatric syndromes and nonspecific complaints. Further work is needed to determine whether these would warrant targeted interventions to improve quality of acute care.

Project description:Background and objectivesMany older adults adopt equipment to address physical limitations and reduce dependence on others to complete basic activities of daily living. Although a few prior studies have considered injuries associated with assistive devices for older adults, those studies focused on older adults' health and functional risks for injury. There is limited analysis of older adult injuries involving defective or malfunctioning assistive devices.Research design and methodsData from this study are from the National Electronic Surveillance System All Injury Program which collected data on consumer product-related injuries from a probability sample of 66 hospital Emergency Departments across the United States. Data from 30 776 older adult Emergency Department (ED) injury narratives from 2016 to 2020 were coded according to the assistive device involved and whether malfunctioning led to the injury. The study team manually examined all narratives in which the assistive device was coded to have malfunctioned.ResultsA total of 10 974 older adult ED cases were treated for 12 488 injuries involving a defective device. Injuries included 4 212 head and neck injuries (eg, concussion), 4 317 trunk injuries (eg, hip fractures), and 3 959 arm or leg injuries (eg, leg fracture). Of these patients, 4 586 were admitted to a hospital ward for further evaluation and treatment. Seventy percent of these patients were injured while using a walker; in contrast, wheelchairs were implicated in only 4% of the above cases. Design flaws were identified in 8 158 cases and part breakage/decoupling incidents in 2 816 cases.Discussion and implicationsOur findings provide evidence that assistive devices are actively involved in older adult injuries. Further research is needed to reduce injuries associated with assistive devices by educating patients and their careproviders about device use and assembly and developing effective methods for informing manufacturers about malfunctioning devices.

Dataset Information

Chief Complaints, Underlying Diagnoses, and Mortality in Adult, Non-trauma Emergency Department Visits: A Population-based, Multicenter Cohort Study.

Introduction

Methods

Results

Conclusion

Publications

Chief Complaints, Underlying Diagnoses, and Mortality in Adult, Non-trauma Emergency Department Visits: A Population-based, Multicenter Cohort Study.

Similar Datasets

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