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Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models

Clinical and Experimental Emergency Medicine 2020년 7권 3호 p.197 ~ 205
정태오, 차원철, 유준상, 김태림, 박주현, 윤희, 황승연, 심민섭, 조익준, 신태건,
소속 상세정보
정태오 ( Jeong Tae-Oh ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
차원철 ( Cha Won-Chul ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
유준상 ( Yoo Jun-Sang ) - Sungkyunkwan University Samsung Advanced Institute for Health Sciences and Technology Department of Digital Health
김태림 ( Kim Tae-Rim ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
박주현 ( Park Joo-Hyun ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
윤희 ( Yoon Hee ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
황승연 ( Hwang Sung-Yeon ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
심민섭 ( Sim Min-Seob ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
조익준 ( Jo Ik-Joon ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine
신태건 ( Shin Tae-Gun ) - Sungkyunkwan University School of Medicine Samsung Medical Center Department of Emergency Medicine

Abstract


Objective: This study aimed to confirm the accuracy of a machine-learning-based model in predicting the 30-day mortality of patients with pneumonia and evaluating whether they were required to be admitted to the intensive care unit (ICU).

Methods: The study conducted a retrospective analysis of pneumonia patients at an emergency department (ED) in Seoul, Korea, from January 1, 2016 to December 31, 2017. Patients aged 18 years or older with a pneumonia registry designation on their electronic medical record were enrolled. We collected their demographic information, mental status, and laboratory findings. Three models were used: the pre-existing CURB-65 model, and the CURB-RF and Extensive CURB-RF models, which were machine-learning models that used a random forest algorithm. The primary outcomes were ICU admission from the ED or 30-day mortality. Receiver operating characteristic curves were constructed for the models, and the areas under these curves were compared.

Results: Out of the 1,974 pneumonia patients, 1,732 patients were eligible to be included in the study; from these, 473 patients died within 30 days or were initially admitted to the ICU from the ED. The area under receiver operating characteristic curves of CURB-65, CURB-RF, and extensive-CURB-RF were 0.615 (0.614?0.616), 0.701 (0.700?0.702), and 0.844 (0.843?0.845), respectively.

Conclusion: The proposed machine-learning models could predict the mortality of patients with pneumonia more accurately than the pre-existing CURB-65 model and can help decide whether the patient should be admitted to the ICU.

키워드

Pneumonia; Machine-learning; Mortality; Emergency service, hospital

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