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Machine Learning-Based Mortality Prediction Model for Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU)

Authors
 Ryoung-Eun Ko  ;  Jaehyeong Cho  ;  Min-Kyue Shin  ;  Sung Woo Oh  ;  Yeonchan Seong  ;  Jeongseok Jeon  ;  Kyeongman Jeon  ;  Soonmyung Paik  ;  Joon Seok Lim  ;  Sang Joon Shin  ;  Joong Bae Ahn  ;  Jong Hyuck Park  ;  Seng Chan You  ;  Han Sang Kim 
Citation
 CANCERS, Vol.15(3) : 569, 2023-01 
Journal Title
CANCERS
Issue Date
2023-01
Keywords
artificial intelligence ; clinical decision support system ; critically ill cancer patients ; prognosis prediction
Abstract
Background: Although cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. This study describes a new ML-based mortality prediction model for critically ill cancer patients admitted to ICU.

Patients and methods: We developed CanICU, a machine learning-based 28-day mortality prediction model for adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (n = 766), Yonsei Cancer Center (YCC, n = 3571), and Samsung Medical Center in Korea (SMC, n = 2563) from 2 January 2008 to 31 December 2017. The accuracy of CanICU was measured using sensitivity, specificity, and area under the receiver operating curve (AUROC).

Results: A total of 6900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and a 1-year mortality of 30.0%/36.6%/58.5% in the YCC, SMC, and MIMIC-III cohort. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had 96% sensitivity/73% specificity with the area under the receiver operating characteristic (AUROC) of 0.94 for 28-day, showing better performance than current prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE) or Sequential Organ Failure Assessment (SOFA) score. Application of CanICU in two external data sets across the countries yielded 79-89% sensitivity, 58-59% specificity, and 0.75-0.78 AUROC for 28-day mortality. The CanICU score was also correlated with one-year mortality with 88-93% specificity.

Conclusion: CanICU offers improved performance for predicting mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification to allocate ICU care for cancer patients.
Files in This Item:
T202301572.pdf Download
DOI
10.3390/cancers15030569
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Han Sang(김한상) ORCID logo https://orcid.org/0000-0002-6504-9927
Shin, Sang Joon(신상준) ORCID logo https://orcid.org/0000-0001-5350-7241
Ahn, Joong Bae(안중배) ORCID logo https://orcid.org/0000-0001-6787-1503
You, Seng Chan(유승찬) ORCID logo https://orcid.org/0000-0002-5052-6399
Lim, Joon Seok(임준석) ORCID logo https://orcid.org/0000-0002-0334-5042
Cho, Jaehyeong(조재형)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/193670
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