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[Expressions of MPV, P-LCR and NLR in patients with novel coronavirus disease 2019]
1Department of Laboratory Medicine, the Third Central Hospital of Tianjin, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases,Artificial Cell Engineering Technology Research Center, Tianjin Institute of Hepatobiliary Disease, Tianjin 300170, China.
Insights
Platelet parameters like mean platelet volume (MPV) and platelet-large cell ratio (P-LCR), along with neutrophil-lymphocyte ratio (NLR), effectively predict severe COVID-19. These markers, especially when combined, show high accuracy in assessing patient prognosis.
Area of Science:
- Hematology
- Infectious Diseases
- Clinical Diagnostics
Background:
- Coronavirus disease 2019 (COVID-19) poses significant diagnostic and therapeutic challenges.
- Understanding prognostic markers is crucial for managing COVID-19 severity.
- Platelet parameters and white blood cell counts are potential indicators of inflammatory responses in infections.
Purpose of the Study:
- To investigate the expression levels of various platelet parameters and their prognostic value in different COVID-19 severity groups.
- To identify key hematological indicators for predicting severe COVID-19 outcomes.
- To establish a predictive model for severe COVID-19 using platelet indices and NLR.
Main Methods:
- Retrospective analysis of 69 COVID-19 patients (mild, moderate, severe) and 70 controls.
- Analysis of white blood cell count (WBC), neutrophil count (NEU#), lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large cell ratio (P-LCR).
- Binary logistic regression and Receiver Operating Characteristic (ROC) curve analysis were used to assess predictive and prognostic values.
Main Results:
- Severe COVID-19 cases showed increased WBC, NEU#, and NLR, with decreased LY#, PLT, PDW, MPV, and P-LCR compared to controls.
- MPV, P-LCR, and NLR demonstrated significant predictive value for severe COVID-19, with MPV and P-LCR negatively correlated and NLR positively correlated.
- A combined ROC analysis of MPV, P-LCR, and NLR achieved an AUC of 0.931, with high sensitivity (91.5%) and specificity (94.1%) for predicting severe outcomes.
Conclusions:
- Platelet parameters (MPV, P-LCR) and NLR are significantly altered in COVID-19 patients and correlate with disease severity.
- MPV, P-LCR, and NLR serve as valuable predictors for identifying severe COVID-19 cases.
- The combined assessment of MPV, P-LCR, and NLR offers a robust tool for predicting COVID-19 prognosis.
Abstract:
To provide new ideas for clinical diagnosis and treatment of coronavirus disease 2019 (COVID-19), this study explore the expression level and prognostic value of platelet parameters in mild, moderate and severe COVID-19. This is a retrospective analysis. From January to May 2020, a total of 69 patients who were diagnosed with COVID-19 in the Third Central Hospital and the Jinnan Hospital (both situated in Tianjin) were enrolled in the disease group. According to the severity, these patients were divided into mild group (15 cases), moderate group (46 cases), and severe group (8 cases). In the same period, 70 non-infected patients were enrolled in control group. The level of white blood cell count (WBC), absolute neutrophil count (NEU#), absolute lymphocyte count (LY#), neutrophil-lymphocyte ratio (NLR), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet-large contrast ratio (P-LCR) before and after treatment were analyzed. Binary logistic regression analysis is used to establish a mathematical model of the relationship between these indexes and the outcome of severe COVID-19 patients. The receiver operating characteristic(ROC) curve is used to further explore the prognosis value of MPV, P-LCR, NLR separately and jointly in COVID-19 patients. Compare to the control group, WBC and NE# increase (Z=-5.63, P<0.01;Z=-9.19,P<0.01) and LY# decrease (Z=-9.34, P<0.01) in the severe group; NLR increase with the aggravation of the disease, there is significant difference between groups (Z=17.61, P<0.01); PLT, PDW, MPV and P-LCR decrease with the aggravation of the disease, there is significant difference between groups (Z=9.47, P<0.01; Z=11.41, P<0.01; Z =16.76, P<0.01; Z=13.97, P<0.01). Binary logistic regression analysis shows MPV, P-LCR and NLR have predictive value for severe COVID-19 patients. There is a negative correlation between MPV, P-LCR and severe COVID-19 patients (OR=1.004, P=0.034; OR=1.097, P=0.046). There is a positive correlation between NLR and severe COVID-19 patients (OR=1.052, P=0.016). MPV and P-LCR of patients with good prognosis after treatment were significantly higher than those before treatment (Z=-6.47, P<0.01; Z=-5.36, P<0.01). NLR was significantly lower than that before treatment (Z=-8.13, P<0.01). MPV and P-LCR in poor prognosis group were significantly lower than those before treatment (Z=-9.46, P<0.01; Z=-6.81, P<0.01). NLR was significantly higher than that before treatment (Z=-3.24, P<0.01). There were significant differences between good and poor prognosis groups before and after treatment in MPV, P-LCR and NLR (P<0.01). Combination of these three indexes, ROC shows the AUC is 0.931, the sensitivity is 91.5%, the specificity is 94.1%, the positive predictive value is 88.9%, and the negative predictive value is 87.4%, which is better than any of these indexes separately. Changes in these parameters are closely related to clinical stage of COVID-19 patients. MPV, P-LCR and NLR are of great value in the prediction and prognosis of severe COVID-19 patients.
