Prolonged Length of Stay in the Emergency Department and Increased Risk of In-Hospital Cardiac Arrest: A nationwide Population-Based Study in South Korea, 2016–2017

Kim, June-Sung; Seo, Dong Woo; Jeong, Jin Woo; Kang, Hyunggoo; Han, Kap Su; Kim, Su Jin; Lee, Sung Woo; Kim, Won Young; Kim, Youn-Jung

doi:10.3390/jcm9072284

Cited by 14 publications

(10 citation statements)

References 24 publications

(25 reference statements)

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“…Additionally, input errors from each hospital could occur. However, the NEDIS dataset is quality-controlled by the National Emergency Medical Center of Korea and regularly undergoes a quality assessment process [ 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of critical illness in children visiting the emergency department

Hwang

Lee

2022

PLoS ONE

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Objectives Triage is an essential emergency department (ED) process designed to provide timely management depending on acuity and severity; however, the process may be inconsistent with clinical and hospitalization outcomes. Therefore, studies have attempted to augment this process with machine learning models, showing advantages in predicting critical conditions and hospitalization outcomes. The aim of this study was to utilize nationwide registry data to develop a machine learning-based classification model to predict the clinical course of pediatric ED visits. Methods This cross-sectional observational study used data from the National Emergency Department Information System on emergency visits of children under 15 years of age from January 1, 2016, to December 31, 2017. The primary and secondary outcomes were to identify critically ill children and predict hospitalization from triage data, respectively. We developed and tested a random forest model with the under sampled dataset and validated the model using the entire dataset. We compared the model’s performance with that of the conventional triage system. Results A total of 2,621,710 children were eligible for the analysis and included 12,951 (0.5%) critical outcomes and 303,808 (11.6%) hospitalizations. After validation, the area under the receiver operating characteristic curve was 0.991 (95% confidence interval [CI] 0.991–0.992) for critical outcomes and 0.943 (95% CI 0.943–0.944) for hospitalization, which were higher than those of the conventional triage system. Conclusions The machine learning-based model using structured triage data from a nationwide database can effectively predict critical illness and hospitalizations among children visiting the ED.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based prediction of critical illness in children visiting the emergency department

Hwang

Lee

2022

PLoS ONE

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“…Several population-based OHCA studies have been conducted using administrative databases in the US 20–24 and outside the US 7 , 25 , 26 based on the above-mentioned validated OHCA-related diagnostic codes. However, because coding practices and reimbursement policies may differ across healthcare systems, it should not be taken for granted that the validity of case definitions established in one database can be extrapolated to another database.…”

Section: Discussionmentioning

confidence: 99%

Validation of ICD-9-CM and ICD-10-CM Diagnostic Codes for Identifying Patients with Out-of-Hospital Cardiac Arrest in a National Health Insurance Claims Database

Tsai¹,

Tsai²,

Pan³

et al. 2022

CLEP

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Purpose Taiwan’s national health insurance (NHI) database is a valuable resource for large-scale epidemiological and long-term survival research for out-of-hospital cardiac arrest (OHCA). We developed and validated case definition algorithms for OHCA based on the International Classification of Diseases (ICD) diagnostic codes and billing codes for NHI reimbursement. Patients and Methods Claims data and medical records of all emergency department visits from 2010 to 2020 were retrieved from the hospital’s research-based database. Death-related diagnostic codes and keywords were used to identify potential OHCA cases, which were ascertained by chart reviews. We tested the performance of the developed OHCA algorithms and validated them on an external dataset. Results The algorithm defining OHCA as any cardiac arrest (CA)-related ICD code in the first three diagnosis fields performed the best with a sensitivity of 89.5% (95% confidence interval [CI], 88.2–90.7%), a positive predictive value (PPV) of 90.6% (95% CI, 89.4–91.8%), and a kappa value of 0.900 (95% CI, 0.891–0.909). The second-best algorithm consists of any CA-related ICD code in any diagnosis field with a billing code for triage acuity level 1, achieving a sensitivity of 85.6% (95% CI, 84.1–87.0%), a PPV of 93.6% (95% CI, 92.5–94.5), and a kappa value of 0.894 (95% CI, 0.884–0.903). Both algorithms performed well in external validation. In subgroup analyses, the former algorithm performed the best in adult patients, outpatient claims, and during the ICD-9 era. The latter algorithm performed the best in the inpatient claims and during the ICD-10 era. The best algorithm for identifying pediatric OHCAs was any CA-related ICD code in the first three diagnosis fields with a billing code for triage acuity level 1. Conclusion Our results may serve as a reference for future OHCA studies using the Taiwan NHI database.

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“…The high need for hospitalization may also have resulted in longer LOS because patients had to wait for inpatient beds to be allocated. Prolonged ED LOS has been associated with poor outcomes of various diseases [ 25 ], and LOS is suggested as a quality indicator in EDs recently [ 26 ]. Therefore, it is important to monitor the LOS of referred patients to maintain the quality of care in EDs.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Patients Who Visited Emergency Department: A Nationwide Population-Based Study in South Korea (2016–2018)

Hong

Kim

Seo

et al. 2022

IJERPH

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The utilization of the emergency department (ED) has been continuously increasing and has become a burden for ED resources. The aim of this study was to describe the characteristics, outcomes, common diagnoses, and disease classifications of patients who were referred to the ED. This nationwide epidemiologic study examined the data from adult patients (>18 years) who visited EDs from 1 January 2016 to 31 December 2018. Most EDs in Korea provide data from ED patients to the National Emergency Medical Center (NEMC). The disposition of ED patients was classified as discharge, admission, death, and re-transfer. From 2016 to 2018, the proportion of referred patients out of the total ED visits increased from 7.3% to 7.8%. The referred patients were older (61.1 vs. 50.5 years), had worse vital signs, longer ED lengths of stay (409.1 vs. 153.3 min), and higher admission (62.3 vs. 16.9%) and re-transfer rates (4.4 vs. 1.9%) than the direct-visit patients. Among the referred patients in the 3 years, 62.3% were hospitalized, and the most common disease classification was “disease of the digestive system” (19.8%). The most common diagnosis was pneumonia (6.0%), followed by urinary tract infection, gastrointestinal bleeding, and hepatobiliary infection. The number of patients referred to EDs is increasing, and more than 60% of referred patients are hospitalized. Detailed characteristics of these patients will be helpful for improving ED management and the distribution of medical resources.

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Prolonged Length of Stay in the Emergency Department and Increased Risk of In-Hospital Cardiac Arrest: A nationwide Population-Based Study in South Korea, 2016–2017

Cited by 14 publications

References 24 publications

Machine learning-based prediction of critical illness in children visiting the emergency department

Machine learning-based prediction of critical illness in children visiting the emergency department

Validation of ICD-9-CM and ICD-10-CM Diagnostic Codes for Identifying Patients with Out-of-Hospital Cardiac Arrest in a National Health Insurance Claims Database

Characteristics of Patients Who Visited Emergency Department: A Nationwide Population-Based Study in South Korea (2016–2018)

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