Abstract
BackgroundIt is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed.MethodsClinical indicators of COVID-19 patients from two independent cohorts (Training data: Hefei Cohort, 82 patients; Validation data: Nanchang Cohort, 169 patients) were retrospected. Sparse principal component analysis (SPCA) using Hefei Cohort was performed and prediction models were deduced. Prediction results were evaluated by receiver operator characteristic curve and decision curve analysis (DCA) in above two cohorts.ResultsSPCA using Hefei Cohort revealed that the first 13 principal components (PCs) account for 80.8% of the total variance of original data. The PC1 and PC12 were significantly associated with disease severity with odds ratio of 4.049 and 3.318, respectively. They were used to construct prediction model, named Model-A. In disease severity prediction, Model-A gave the best prediction efficiency with area under curve (AUC) of 0.867 and 0.835 in Hefei and Nanchang Cohort, respectively. Model-A’s simplified version, named as LMN index, gave comparable prediction efficiency as classical clinical markers with AUC of 0.837 and 0.800 in training and validation cohort, respectively. According to DCA, Model-A gave slightly better performance than others and LMN index showed similar performance as albumin or neutrophil-to-lymphocyte ratio.ConclusionsPrediction models produced by SPCA showed robust disease severity prediction efficiency for COVID-19 patients and have the potential for clinical application.
Highlights
It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, more effective predictors should be developed
In such Sparse principal component analysis (SPCA) models, cumulative variance of the first 13 principal components (PCs) were greater than 80% of the total variance
Because PC1 depending on blood routine test markers accounted 17.8% of the total variance, Model-A was further simplified to LMN index, which predicted disease severity just using PC1
Summary
It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, more effective predictors should be developed. Since December 2019, the novel Coronavirus Disease 2019 (COVID-19) outbreak, which occurred in Wuhan, Hubei province, China, has infected over 5.7 million people globally by May 29th, 2020 [1]. As this severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spreads globally, great strains are put on health care system of every country. Possible risk factors for progressing to severe illness may include, but are not limited to, older age, and pre-existing chronic medical conditions such as lung disease, heart failure, cerebrovascular disease, and so on [2]. Plenty of clinical laboratory markers could be used to predict the severity of COVID19 patients and it is challenging to utilize such rich laboratory indicators for clinical diagnosis and treatment
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