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Lymphocyte-monocyte-neutrophil index: a predictor of severity of coronavirus disease 2019 patients produced by sparse
Yingjie Qi1, Jian-An Jia2, Huiming Li3
1The First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital Infection Hospital), Susong Road 218#, Hefei, 230022, Anhui Province, China.
Insights
Developing effective predictors for coronavirus disease 2019 (COVID-19) severity is crucial. Sparse Principal Component Analysis (SPCA) identified key predictors, creating models with robust disease prediction efficiency for clinical use.
Area of Science:
- Medical Informatics
- Biostatistics
- Epidemiology
Background:
- Distinguishing severe coronavirus disease 2019 (COVID-19) from moderate cases requires improved predictive tools.
- Current clinical indicators may not sufficiently predict disease severity.
- Effective prediction models are essential for timely and appropriate patient management.
Purpose of the Study:
- To develop and validate effective prediction models for COVID-19 disease severity.
- To identify key clinical indicators associated with severe COVID-19 outcomes.
- To assess the clinical utility of novel prediction models.
Main Methods:
- Retrospective analysis of clinical indicators from two independent COVID-19 patient cohorts (Hefei and Nanchang).
- Application of Sparse Principal Component Analysis (SPCA) on the training cohort to identify significant principal components (PCs).
- Construction and evaluation of prediction models (Model-A and LMN index) using receiver operator characteristic curve and decision curve analysis (DCA).
Main Results:
- SPCA identified PC1 and PC12 as significantly associated with COVID-19 severity (OR 4.049 and 3.318).
- Model-A demonstrated high prediction efficiency with Area Under Curve (AUC) of 0.867 (Hefei) and 0.835 (Nanchang).
- A simplified LMN index showed comparable performance to established markers like albumin and neutrophil-to-lymphocyte ratio (AUC 0.837 and 0.800).
Conclusions:
- SPCA-derived prediction models exhibit robust efficiency in predicting COVID-19 disease severity.
- The developed models, including the LMN index, show potential for practical clinical application.
- These findings support the use of advanced statistical methods for identifying critical clinical predictors in infectious diseases.
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.
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