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Cited 2 time in webofscience Cited 2 time in scopus
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Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Studyopen access

Authors
Ko, Byuk SungJeon, SanghoonSon, DongheeChoi, Sung-HyukShin, Tae GunJo, You HwanRyoo, Seung MokKim, Youn-JungPark, Yoo SeokKwon, Woon YongSuh, Gil JoonLim, Tae HoKim, Won YoungKorean Shock Society InvestigatorsHan, Kap Su (KoSS)
Issue Date
Dec-2022
Publisher
MDPI AG
Keywords
cancer patient; septic shock; machine learning; prognosis
Citation
Journal of Clinical Medicine, v.11, no.23
Indexed
SCIE
SCOPUS
Journal Title
Journal of Clinical Medicine
Volume
11
Number
23
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/62117
DOI
10.3390/jcm11237231
ISSN
2077-0383
Abstract
A reliable prognostic score for minimizing futile treatments in advanced cancer patients with septic shock is rare. A machine learning (ML) model to classify the risk of advanced cancer patients with septic shock is proposed and compared with the existing scoring systems. A multi-center, retrospective, observational study of the septic shock registry in patients with stage 4 cancer was divided into a training set and a test set in a 7:3 ratio. The primary outcome was 28-day mortality. The best ML model was determined using a stratified 10-fold cross-validation in the training set. A total of 897 patients were included, and the 28-day mortality was 26.4%. The best ML model in the training set was balanced random forest (BRF), with an area under the curve (AUC) of 0.821 to predict 28-day mortality. The AUC of the BRF to predict the 28-day mortality in the test set was 0.859. The AUC of the BRF was significantly higher than those of the Sequential Organ Failure Assessment score and the Acute Physiology and Chronic Health Evaluation II score (both p < 0.001). The ML model outperformed the existing scores for predicting 28-day mortality in stage 4 cancer patients with septic shock. However, further studies are needed to improve the prediction algorithm and to validate it in various countries. This model might support clinicians in real-time to adopt appropriate levels of care.
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Han, Kap Su
Anam Hospital (Department of Emergency Medicine, Anam Hospital)
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