Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease

Abstract

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96–5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91–7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68–5.96), and age ≥60 years (HR 2.31, 95% CI 1.43–3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83–0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81–0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

Improving and Externally Validating Mortality Prediction Models for COVID-19 Using Publicly Available Data

“…This was confirmed in this study, where the median age was significantly higher for the mortality group across all datasets. Other studies have shown that elevated CRP levels are associated with a risk of dying of COVID-19 [23,24]. This held true for most cohorts, with the median CRP level being significantly higher for the mortality group in all except the French dataset, which has a bias due to only containing ICU patients.…”

Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases

“…In the literature, there are studies proposing nomograms and/or different models to predict serious illness or death in COVID-19 patients [ 19 , 21 , 22 , 24 , 25 , 35 – 37 ]. Age was reported as a predictor for severe COVID-19 or mortality in previous studies and is included in most of the prediction models [ 16 – 21 , 24 , 25 , 32 , 35 , 38 , 39 ].…”

“…The association between comorbid diseases and the development of severe infection was indicated in previous studies [ 14 , 16 , 17 , 31 , 41 , 42 ]. Some were severity and mortality risk scores [ 16 , 19 , 22 , 31 , 32 , 37 ]. Although we found that comorbid diseases were more frequent in severe cases requiring ICU follow up than the patients without ICU need, comorbidities were not identified as optimal predictors during the development process of the nomogram.…”

The Quest for System-Theoretical Medicine in the COVID-19 Era

“…Genome-wide-association studies (GWAS) of COVID-19 are rather at the beginning but are highly promising to reveal critical illness cases (22,23). For data gathering, diagnostics and prediction models machine and deep learning techniques and applications of artificial intelligence are of utmost importance but also need to be critically reviewed (24)(25)(26)(27)(28)(29). Altogether, there is a risk that classical medical knowledge, especially qualitative, and intuitive knowledge of organismic pathology, will be lost before the transition to MSM can be implemented clinically.…”