Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

Abstract

Background Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/ . Conclusion The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules

“…Fifty-five studies aimed to exclusively predict in-hospital mortality [ 2 , 11 , 14 , 15 , 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 29 , 30 , 31 , 32 , 34 , 35 , 36 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 47 , 50 , 51 , 52 , 54 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 79 ], while thirteen reported on combined outcome...…”

“…Study setting included general population from database sources [ 19 , 23 , 49 , 58 , 68 , 71 ], clinical records from series of patients admitted to hospital [ 2 , 11 , 12 , 14 , 16 , 17 , 18 , 20 , 21 , 22 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 59 , 60 , 61 , 62 , 63 , 6...…”

“…Thirty-four nomograms were derived and validated (in different patients from the same institution) in the same study; random training test splits [ 14 , 17 , 18 , 19 , 23 , 24 , 30 , 32 , 33 , 41 , 45 , 50 , 51 , 55 , 59 , 60 , 61 , 62 , 63 , 65 , 66 , 67 , 71 , 75 , 76 , 77 ] and temporal splits [ 15 , 43 , 44 , 46 , 53 , 70 ] were performed in fourteen and five studies, respectively. One study performed validation by leave-one-hospital-out cross validation [ 72 ].…”

Sex-Based Differences in Outcomes of Coronavirus Disease 2019 (COVID-19) in Korea

“…In a meta-analysis study, it was shown that comorbid diseases such as hypertension, diabetes mellitus, cardiovascular disease, and respiratory diseases can have a significant effect on the prognosis of COVID-19 [15]. Moon et al [25] reported that elderly age can influence the clinical course of COVID-19 and COVID-19-related mortality through immune aging or high prevalence of comorbid diseases. A study of adult patients with COVID-19 in England and Wales [26] showed that hypertension, dementia, chronic lung disease, and diabetes mellitus were associated with death.…”

Effectiveness and Safety of Regdanvimab in Patients With Mild-To-Moderate COVID-19: A Retrospective Cohort Study

“…21 Co-medications were included, and sex was added based on recent findings. 32 33 34 To confirm the consistency of the results, we performed sensitivity analyses by changing the covariates included in the PS estimation model, changing the matching method, and adding covariates in the multivariable logistic regression model of analyzing matching data.…”