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KMID : 1100620200070030197
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Clinical and Experimental Emergency Medicine
2020 Volume.7 No. 3 p.197 ~ p.205
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Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models
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Kang Soo-Yeon
Cha Won-Chul
Yoo Jun-Sang
Kim Tae-Rim
Park Joo-Hyun
Yoon Hee
Hwang Sung-Yeon
Sim Min-Seob
Jo Ik-Joon
Shin Tae-Gun
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Abstract
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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.
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KEYWORD
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Pneumonia, Machine-learning, Mortality, Emergency service, hospital
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