Background It is important to recognize the coronavirus disease 2019 (COVID-19) patients in severe conditions from moderate ones, thus more effective predictors should be developed. Methods Clinical 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. Results SPCA 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. Conclusions Prediction models produced by SPCA showed robust disease severity prediction efficiency for COVID-19 patients and have the potential for clinical application. Supplementary Information The online version contains supplementary material available at 10.1186/s12985-021-01561-9.
【저자키워드】 COVID-19, SARS-CoV-2, severity, prediction, principal component analysis, 【초록키워드】 coronavirus disease, Coronavirus disease 2019, disease severity, neutrophil, Neutrophil-to-lymphocyte ratio, lymphocyte, Cohort, validation, clinical, Patient, albumin, predictor, moderate, patients, COVID-19 patients, Analysis, principal components, Odds ratio, COVID-19 patient, Efficiency, AUC, best, Principal component, training, supplementary material, cohorts, validation cohort, component, decision curve analysis, training data, total variance, effective, independent, Receiver operator characteristic, robust, Result, produced, classical, significantly, evaluated, was performed, condition, were used, recognize, comparable, clinical marker, PC1, 【제목키워드】 Patient, produced,