Literature DB >> 34191766

Factors associated with a high-risk return visit to the emergency department: a case-crossover study.

Chih-Wei Sung1, Tsung-Chien Lu2,3, Cheng-Chung Fang2,3, Jia-You Lin2, Huang-Fu Yeh2, Chien-Hua Huang2,3, Chu-Lin Tsai2,3.   

Abstract

BACKGROUND AND IMPORTANCE: Although factors related to a return emergency department (ED) visit have been reported, few studies have examined 'high-risk' return ED visits with serious adverse outcomes. Understanding factors associated with high-risk return ED visits may help with early recognition and prevention of these catastrophic events.
OBJECTIVES: We aimed to (1) estimate the incidence of high-risk return ED visits, and (2) to investigate time-varying factors associated with these revisits.
DESIGN: Case-crossover study. SETTINGS AND PARTICIPANTS: We used electronic clinical warehouse data from a tertiary medical center. We retrieved data from 651 815 ED visits over a 6-year period. Patient demographics and computerized triage information were extracted. OUTCOME MEASURE AND ANALYSIS: A high-risk return ED visit was defined as a revisit within 72 h of the index visit with ICU admission, receiving emergency surgery, or with in-hospital cardiac arrest during the return ED visit. Time-varying factors associated with a return visit were identified. MAIN
RESULTS: There were 440 281 adult index visits, of which 19 675 (4.5%) return visits occurred within 72 h. Of them, 417 (0.1%) were high-risk revisits. Multivariable analysis showed that time-varying factors associated with an increased risk of high-risk revisits included the following: arrival by ambulance, dyspnea, or chest pain on ED presentation, triage level 1 or 2, acute change in levels of consciousness, tachycardia (>90/min), and high fever (>39°C).
CONCLUSIONS: We found a relatively small fraction of discharges (0.1%) developed serious adverse events during the return ED visits. We identified symptom-based and vital sign-based warning signs that may be used for patient self-monitoring at home, as well as new-onset signs during the return visit to alert healthcare providers for timely management of these high-risk revisits.
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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Year:  2021        PMID: 34191766     DOI: 10.1097/MEJ.0000000000000851

Source DB:  PubMed          Journal:  Eur J Emerg Med        ISSN: 0969-9546            Impact factor:   2.799


  1 in total

1.  A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain.

Authors:  Chun-Chuan Hsu; Cheng-C J Chu; Ching-Heng Lin; Chien-Hsiung Huang; Chip-Jin Ng; Guan-Yu Lin; Meng-Jiun Chiou; Hsiang-Yun Lo; Shou-Yen Chen
Journal:  Diagnostics (Basel)       Date:  2021-12-30
  1 in total

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