Prognosis Score System to Predict Survival for COVID-19 Cases: a Korean Nationwide Cohort Study

J Med Internet Res. 2021 Feb 22;23(2):e26257. doi: 10.2196/26257.


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.

Keywords: COVID-19; allocation; cohort; decision making; digital health; disease management; intensive care; length of stay; mortality; prediction; prognosis; risk; triage.

MeSH terms

  • Age Factors
  • Aged
  • COVID-19 / diagnosis*
  • COVID-19 / mortality*
  • Critical Care / statistics & numerical data
  • Dementia / epidemiology
  • Female
  • Humans
  • Kidney Failure, Chronic / epidemiology
  • Length of Stay / statistics & numerical data
  • Male
  • Middle Aged
  • Pandemics
  • Prognosis
  • Proportional Hazards Models
  • ROC Curve
  • Republic of Korea / epidemiology
  • Retrospective Studies
  • Risk Factors
  • Severity of Illness Index
  • Survival Rate