Predicting mid-term survival of patients during emergency department triage for resuscitation decision
1Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, 06355 Seoul, Republic of Korea
2Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06355 Seoul, Republic of Korea
3Smart Health Lab, Research institute of Future Medicine, Samsung Medical Center, 06351 Seoul, Republic of Korea
4Digital Innovation Center, Samsung Medical Center, 06351 Seoul, Republic of Korea
DOI: 10.22514/sv.2022.018 Vol.19,Issue 2,March 2023 pp.28-38
Submitted: 11 December 2021 Accepted: 18 January 2022
Published: 08 March 2023
*Corresponding Author(s): Won Chul Cha E-mail: email@example.com
† These authors contributed equally.
In patients with non-small cell lung cancer (NSCLC) visiting the emergency department (ED), clinical decisions must be made based on their disease prognosis. This study aims to predict the disease outcome of patients visiting the ED for the first time after NSCLC diagnosis. This study included patients who visited the ED in 2016–2020 after being diagnosed with NSCLC in study site or within 30 days before the first outpatient clinic visit after diagnosis. Primary outcome of prediction model was 3-month mortality from the initial ED visit. We analyzed the association between outcome and each variable as a risk factor and built a prediction model using these variables. Both oncologic factors and ED-associated factors were associated with the 3-month mortality of NSCLC from the first ED visit. We also visualized the treatment trace as a sequence and utilized it in prediction model building. The areas under the receiver operating curve (AUROCs) of the prediction model of 3-month mortality from the first ED visit ranged from 0.677 (95% Confidence Interval (CI), 0.640–0.708) to 0.729 (95% CI, 0.697–0.761). This study provides the prediction model about 3-month survival in first ED visit point and identified patient and disease-related factors to predict the prognosis of patients.
Non-small cell lung cancer; Emergency department; Prognosis; Machine learning
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