%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 2
%P e26257
%T Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study
%A Cho,Sung-Yeon
%A Park,Sung-Soo
%A Song,Min-Kyu
%A Bae,Young Yi
%A Lee,Dong-Gun
%A Kim,Dong-Wook
%+ Catholic Hematology Hospital, College of Medicine, The Catholic University of Korea, Seoul St. Mary's Hospital, 222 Banpo-daero, Seocho-Gu, Seoul, 06591, Republic of Korea, 82 222586003, symonlee@catholic.ac.kr
%K COVID-19
%K length of stay
%K mortality
%K prognosis
%K triage
%K digital health
%K prediction
%K cohort
%K risk
%K allocation
%K disease management
%K intensive care
%K decision making
%D 2021
%7 22.2.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: As the COVID-19 pandemic continues, an initial risk-adapted allocation is crucial for managing medical resources and providing intensive care. Objective: In this study, we aimed to identify factors that predict the overall survival rate for COVID-19 cases and develop a COVID-19 prognosis score (COPS) system based on these factors. In addition, disease severity and the length of hospital stay for patients with COVID-19 were analyzed. Methods: We retrospectively analyzed a nationwide cohort of laboratory-confirmed COVID-19 cases between January and April 2020 in Korea. The cohort was split randomly into a development cohort and a validation cohort with a 2:1 ratio. In the development cohort (n=3729), we tried to identify factors associated with overall survival and develop a scoring system to predict the overall survival rate by using parameters identified by the Cox proportional hazard regression model with bootstrapping methods. In the validation cohort (n=1865), we evaluated the prediction accuracy using the area under the receiver operating characteristic curve. The score of each variable in the COPS system was rounded off following the log-scaled conversion of the adjusted hazard ratio. Results: Among the 5594 patients included in this analysis, 234 (4.2%) died after receiving a COVID-19 diagnosis. In the development cohort, six parameters were significantly related to poor overall survival: older age, dementia, chronic renal failure, dyspnea, mental disturbance, and absolute lymphocyte count <1000/mm3. The following risk groups were formed: low-risk (score 0-2), intermediate-risk (score 3), high-risk (score 4), and very high-risk (score 5-7) groups. The COPS system yielded an area under the curve value of 0.918 for predicting the 14-day survival rate and 0.896 for predicting the 28-day survival rate in the validation cohort. Using the COPS system, 28-day survival rates were discriminatively estimated at 99.8%, 95.4%, 82.3%, and 55.1% in the low-risk, intermediate-risk, high-risk, and very high-risk groups, respectively, of the total cohort (P<.001). The length of hospital stay and disease severity were directly associated with overall survival (P<.001), and the hospital stay duration was significantly longer among survivors (mean 26.1, SD 10.7 days) than among nonsurvivors (mean 15.6, SD 13.3 days). Conclusions: The newly developed predictive COPS system may assist in making risk-adapted decisions for the allocation of medical resources, including intensive care, during the COVID-19 pandemic. 
%M 33539312
%R 10.2196/26257
%U https://www.jmir.org/2021/2/e26257
%U https://doi.org/10.2196/26257
%U http://www.ncbi.nlm.nih.gov/pubmed/33539312