Lymphocyte–monocyte–neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse principal component analysis

Background: It is important to recognize severe ill coronavirus disease 2019 (COVID-19) patients from moderate ones in order to save more lives. We attempted to present an predictor for disease severity from clinical laboratory markers using sparse principal component analysis method. Methods: Forty-four clinical characteristics and laboratory markers of 82 COVID-19 patients (Hefei cohort) from the First Affiliated Hospital of University of Science and Technology of China (USTC) were analyzed retrospectively and sparse principal component analysis (SPCA) was performed to examine the correlation between the markers and extract relevant features. The controlling parameter alpha of SPCA was adjusted for better variable selection. Then the produced principal components (PCs) by SPCA were subjected to multivariate logistic regression for disease severity prediction, and the significant PCs were selected. Then, a Lymphocyte-Monocyte-Neutrophil index (LMN index) was deduced from the significant PCs and was used for disease severity prediction. Furthermore, an independent cohort including 169 COVID-19 patients (Nanchang Cohort) from the First Affiliated Hospital of NanChang University was used as a validation dataset and prediction efficiency of LMN index and classical clinical markers were also evaluated. Findings: Using SPCA, the first to thirteenth PCs accounted for 81·7% of the cumulative proportion variance of the original 44 clinical characteristics and laboratory markers. Multivariate logistic regression revealed the PC1 was significantly associated with disease severity with odds ratio of 74272·28 (623·83 - 178483250). When the controlling parameter alpha was adjusted to 0·001, the PC1 is only dependent on five laboratory markers: lymphocyte count (LYM), lymphocyte percentage (LYM%), neutrophil count (NEU), monocyte count (MONO) ,and serum phosphorus. LMN index determined by LYM, LYM%, NEU ,and MONO was deduced from the PC1 and significant relationships were investigated between LMN indices with age, comorbidity status and CD4+ ,and CD8 T lymphocyte counts. More important, during hospitalization, LMN indices decreased obviously as treatment takes effect, and they declined more sharply for mild ill COVID-19 patients compared with those of severe ill ones. When used to predict disease progression, the LMN index gave the accuracy of 0·780 and 0·760 in the training data (Hefei cohort) and the independent validation data (Nanchang Cohort) respectively, which was more efficient than classical clinical markers. Interpretation: Using SPCA method, the LMN index determined by four blood routine test markers was deduced. It showed robust disease severity prediction efficiency of COVID-19 patients and have the potential for clinical applications. Funding Statement: Fundamental Research Funds for the Central Universities of China(No. YD9110002001). Declaration of Interests: XLM reports grants from Fundamental Research Funds for the Central Universities of China. All other authors declare no competing interests. Ethics Approval Statement: This study was approved by the Ethics Committee of the First Affiliated Hospital of USTC and the Ethics Committee of the First Affiliated Hospital of NanChang University.