Identification and Validation of a Novel Clinical Signature to Predict the Prognosis in Confirmed Coronavirus Disease 2019 Patients

Abstract

Background This study aims to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of COVID-19, which has caused a worldwide pandemic. Methods COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. Furthermore, the disease prognosis signature of COVID-19 was validated in the test group. Results The signature of COVID-19 was combined with five indicators, namely neutrophil count, lymphocyte count, procalcitonin, older age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P<0.001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test P<0.001). The area under ROC curve (AUC) showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the five indicators alone. Conclusion The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill.

Early prediction of level-of-care requirements in patients with COVID-19

“…Much of the modeling work related to the pandemic has focused on spread dynamics ( Kucharski et al, 2020 ). Others have described patients who were hospitalized ( Richardson et al, 2020 ) (n = 5700) and ( Buckner et al, 2020 ) (n = 105), became critically ill ( Gong et al, 2020 ) (n = 372), or succumbed to the disease (n = 1625 ( Onder et al, 2020 ), n = 270 [ Wu et al, 2020 ]). In data from the New York City, 14.2% required ICU treatment and 12.2% mechanical ventilation ( Richardson et al, 2020 ).…”

A Novel Severity Score to Predict Inpatient Mortality in COVID-19 patients

“…2Further reports have shown other predictors of poor outcome such as acute kidney injury, acute hepatic injury, the need for mechanical ventilation, elevated c-reactive protein (CRP), interleukin-6 (IL-6), lymphocyte count, and Procalcitonin levels. (3)(4)(5)(6) COVID-19 is unique in its ability to not only cause sepsis, and multi-system organ failure, but also to cause a severe in ammatory response that can lead to systemic multi-vascular thrombosis. (7,8) While the SOFA score is also predictive of mortality for COVID-19, it does not address the additional thrombotic mitigators of severe illness.…”

Dynamic Prognosis Model for Predicting Survival in Severe and Critically Ill COVID-19 Patients Using Machine Learning

“…Thus, to test if the COVID-GRAM model can be generalized to predict survival, we validated COVID-GRAM in our discovery set, which obtained 77% AUC to predict the survival of COVID-19 patients. Additional prognostic models proposed by Wu et al [22] and Xie et al [23] obtained 93% and 96% AUC, respectively, but these models resulted in unsatisfactory values of AUC and C-indices of 0.64. Our prediction forest model appears to be superior in accuracy compared to the existing models for predicting COVID-19 patient survival (Table S4).…”

“…We identi ed 11 laboratory measures at admission which appear to associate with the poor COVID-19 outcomes. Among the regularly measured laboratory indicators at admission, the high neutrophil count, low lymphocyte count, high neutrophil-to-lymphocyte ratio, high direct bilirubin, and elevated lactate dehydrogenase have previously been identi ed as prognostic predictors for an unfavorable outcome [12,13,22,23]. However, in our discovery set, these predictors were not ranked at the top of VIS list for all candidate factors possibly because all patients in our study had progressed to severe or critical disease and the previous predictors may be less prognostic for severity.…”