Predicting 30-day mortality of patients with pneumonia in an emergency department setting using machine-learning models - Kang, Soo Yeon; Cha, Won Chul; Yoo, Junsang; Kim, Taerim; Park, Joo Hyun; Yoon, Hee; Hwang, Sung Yeon; Sim, Min Seob; Jo, Ik Joon; Shin, Tae Gun (Clinical and experimental emergency medicine, 2020)
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). 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. 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. 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.