Platelet-to-Lymphocyte Ratio (PLR), Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), and Eosinophil-to-Lymphocyte Ratio (ELR) as Biomarkers in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD)

Abstract

Purpose: The study comprehensively evaluated the prognostic roles of the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), basophil-to-lymphocyte ratio (BLR), and eosinophil-to-lymphocyte ratio (ELR) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).

Patients and methods: Six hundred and nineteen patients with AECOPD and 300 healthy volunteers were retrospectively included into the study. The clinical characteristics of the patients with AECOPD and the complete blood counts (CBCs) of the healthy volunteers were collected. The associations of PLR, NLR, MLR, BLR, and ELR with airflow limitation, hospital length of stay (LOS), C-reactive protein (CRP), and in-hospital mortality in patients with AECOPD were analyzed.

Results: Compared with the healthy volunteers, PLR, NLR, MLR, BLR, and ELR were all elevated in COPD patients under stable condition. PLR, NLR, MLR, and BLR were further elevated while ELR was lowered during exacerbation. In the patients with AECOPD, PLR, NLR, and MLR were positively correlated with hospital LOS as well as CRP. In contrast, ELR was negatively correlated with hospital LOS as well as CRP. Elevated PLR, NLR, and MLR were all associated with more severe airflow limitation in AECOPD. Elevated PLR, NLR, and MLR were all associated with increased in-hospital mortality while elevated ELR was associated with decreased in-hospital mortality. Binary logistic regression analysis showed that smoking history, FEV1% predicted, pneumonia, pulmonary heart disease (PHD), uric acid (UA), albumin, and MLR were significant independent predictors ofin-hospital mortality. These predictors along with ELR were used to construct a nomogram for predicting in-hospital mortality in AECOPD. The nomogram had a C-index of 0.850 (95% CI: 0.799-0.901), and the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) further demonstrated its good predictive value and clinical applicability.

Conclusion: In summary, PLR, NLR, MLR, and ELR served as useful biomarkers in patients with AECOPD.

Keywords: healthy volunteers; in-hospital mortality; length of stay; nomogram; pneumonia; pulmonary heart disease.

Figure 1

Associations of PLR, NLR, MLR, BLR, and ELR with GOLD stages in patients with AECOPD. (A) PLR, platelet-to-lymphocyte ratio; (B) NLR, neutrophil-to-lymphocyte; (C) MLR, monocyte-to-lymphocyte ratio; (D) ELR, eosinophil-to-lymphocyte ratio. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Figure 2

Correlations of PLR, NLR, MLR, BLR, and ELR with FEV1% predicted in patients with AECOPD. (A) PLR, platelet-to-lymphocyte ratio; (B) NLR, neutrophil-to-lymphocyte; (C) MLR, monocyte-to-lymphocyte ratio; (D) BLR, basophil-to-lymphocyte ratio; (E) ELR, eosinophil-to-lymphocyte ratio.

Figure 3

Correlations of PLR, NLR, MLR, BLR, and ELR with FEV1/FVC in patients with AECOPD. (A) PLR, platelet-to-lymphocyte ratio; (B) NLR, neutrophil-to-lymphocyte; (C) MLR, monocyte-to-lymphocyte ratio; (D) BLR, basophil-to-lymphocyte ratio; (E) ELR, eosinophil-to-lymphocyte ratio.

Figure 4

Correlations of PLR, NLR, MLR, BLR, and ELR with hospital length of stay in patients with AECOPD. (A) PLR, platelet-to-lymphocyte ratio; (B) NLR, neutrophil-to-lymphocyte; (C) MLR, monocyte-to-lymphocyte ratio; (D) ELR, eosinophil-to-lymphocyte ratio.

Figure 5

Correlations of PLR, NLR, MLR, BLR, and ELR with C-reaction protein in patients with acute exacerbation of chronic obstructive pulmonary disease. (A) PLR, platelet-to-lymphocyte ratio; (B) NLR, neutrophil-to-lymphocyte; (C) MLR, monocyte-to-lymphocyte ratio; (D) ELR, eosinophil-to-lymphocyte ratio.

Figure 6

Receiver operating characteristic (ROC) curves for evaluating the predictive ability of clinical characteristics in patients with AECOPD.

Figure 7

The nomogram for in-hospital mortality in patients with AECOPD.

Figure 8

Validation of nomograms in AECOPD. (A) Receiver operating characteristic (ROC) curves for nomograms; (B) Decision curve analysis (DCA) of nomograms; the simple model referred to the nomogram without MLR or ELR; (C) The calibration curve for the nomogram with MLR and ELR; (D) The calibration curve for the nomogram without MLR or ELR; In panels (C and D), perfect prediction corresponded to the “ideal” line, the “Apparent” line represented the entire cohort (n = 619), and the “Bias-corrected” line was plotted through bootstrapping (B = 2000 repetitions), indicating observed nomogram performance.

Figure 9

Clinical impact curves of nomograms in AECOPD. (A) Nomogram without MLR or ELR; (B) Nomogram with MLR and ELR. The red curve and blue curve indicated the numbers of patients who were classified as high risk and true high risk by the model at threshold probabilities, respectively. True high risk denoted the high risk with events. The clinical impact curves indicated that both the nomograms had high net benefits, and suggested the high clinical predictive value of the nomograms.