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. 2020 Jun 5;24:288. doi: 10.1186/s13054-020-03007-0

Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients

Aijia Ma 1,#, Jiangli Cheng 1,#, Jing Yang 1,#, Meiling Dong 1, Xuelian Liao 1, Yan Kang 1,
PMCID: PMC7273815  PMID: 32503668

Trial registration

ChiCTR, ChiCTR2000029758. Registered 12 February 2020 - Retrospectively registered


Dear editors:

The COVID-19 pandemic has spread rapidly around the world and overwhelmed the supply of intensive care beds and ventilators; judicious ICU resource allocation is still one of the major challenges for clinicians and management [1]. The higher incidence of ARDS is the main reason for the burden of ventilator equipment. Early prediction of the occurrence and aggravation of ARDS in the ICU helps clinicians prepare for respiratory support equipment given the absence of effective treatment strategies. Moreover, early selected patients with severe ARDS who do not benefit from conventional treatment might be successfully supported with V-V ECMO [2], which is a relatively scarce critical care resource. Therefore, early prediction of moderate-severe ARDS can help clinicians better allocate scarce ICU resources for COVID-19 crisis.

Neutrophil-to-lymphocyte ratio (NLR) is a simple biomarker of inflammation that can be measured during routine hematology. Previous studies have exhibited that higher NLR was associated with clinical deterioration and mortality for COVID-19 patients [3]. However, it remains unclear to what extent the significance of NLR would predict the occurrence of ARDS and ICU ventilator requirements for the COVID-19 crisis.

Patients diagnosed with severe COVID-19 from 21 hospitals in Sichuan Province between January 16 and March 15 were included in the analysis (ChiCTR2000029758). The maximum value of NLR, PLR, PCT, and CRP during the first 3 days after being diagnosed as severe COVID-19 was included in the analysis. Severe COVID-19 and ARDS were defined according to previous study [4] and Berlin definition [5], respectively. Multivariate logistic regression analysis and the area under the receiver operating characteristic (ROC) curve were used to analyze the ability of NLR in predicting ARDS.

Of totally 81 patients defined as severe COVID-19, 44 were diagnosed as ARDS. The baseline characteristics of the non-ARDS group and ARDS group are listed in Table 1. The area under the ROC curve for ARDS was 0.71, 0.591, 0.494, and 0.625 for NLR, PLR, PCT, and CRP, respectively. We used the median as the cutoff value to divide the patients into two groups. The high NLR group (NLR > 9.8) showed a higher incidence of ARDS (P = 0.005) and higher rate of noninvasive (P = 0.002) and invasive (P = 0.048) mechanical ventilation. Further, we defined moderate-severe ARDS as ARDS patients with oxygenation index less than 150. The area under the ROC curve for moderate-severe ARDS was 0.749, 0.660, 0.531, and 0.635 for NLR, PLR, PCT, and CRP, respectively (Fig. 1); the cutoff value of NLR for moderate-severe ARDS is 11.

Table 1.

Baseline characteristics and clinical outcomes stratified by median NLR value

Baseline characteristics Non-ARDSN = 37 ARDSN = 44 Pvalues
 Age 49 (36.5–62.5) 53.5(43–70.5) 0.110
 Gender/case (%) 0.891
  Male 23 (62.3%) 28 (63.6%)
  Female 14 (37.8%) 16 (36.4%)
 BMI (kg/m2) 23.05 (22.00–27.25) 24.78(21.29–27.41) 0.816
 Smoking/case (%) 1 (2.7%) 2 (4.5%) 1.000
 Comorbidities/case (%)
  Diabetes 3 (8.1%) 15 (34.15) 0.007
  Hypertension 7 (18.9%) 8 (18.2%) 0.932
  Chronic pulmonary disease 2 (5.4%) 9 (20.5%) 0.049
  Cardiovascular disease 2 (5.4%) 2 (4.5%) 1.000
  Cerebrovascular disease 0 (0%) 3 (6.8%) 0.246
  Renal disease 1 (2.7%) 2 (4.5%) 1.000
  Liver disease 2 (5.4%) 2 (4.5%) 1.000
 Vital signs
  MAP/mmHg 94.67 (89.17–100.50) 97.83(91.75,108.84) 0.162
  Heart rate (beats/min) 88 (77.5–99) 92.5 (85.25–104) 0.175
  Respiratory rate (breaths/min) 20 (20–22.5) 21 (20–23) 0.107
  Pulse oxygen saturation/% 96 (93.75–97.25) 95 (90.25–97) 0.486
 Laboratory findings
  WBC/109/L 5.43 (4.05–6.59) 6.47 (3.94–9.62) 0.122
  Hemoglobin/g/L 141 (127–153.5) 132 (117.25–146.5) 0.107
  Total bilirubin (μmol/L) 9 (5.93–15.6) 9.3 (6.65–14.3) 0.927
  AST (IU/L) 30.5 (19–39.75) 29.15 (15.75–57.68) 0.764
  ALT (IU/L) 30 (25–39.8) 35 (25.75–51.6) 0.221
  Creatinine (μmol/L) 71.75 (54.35–79.75) 69.2 (54.63–80.53) 0.980
  PT/s 12.7 (12.5–13.98) 13.1 (12.6–13.8) 0.787
  APTT/s 32.75 (29.1–40.13) 31.3 (28.8–35.5) 0.246
  NLR/% 6.4 (3.75–13.1) 13.55 (6.05–24.13) 0.002
Clinical outcomes Low NLRN = 41 High NLRN = 40 Pvalue
 Respiratory support
  High-flow nasal cannula 15 (36.6%) 16 (40%) 0.752
  Noninvasive ventilation 5 (12.2%) 17 (42.5%) 0.002
  Invasive ventilation 2 (4.9%) 8 (20%) 0.048
 ARDS
  Mild-moderate ARDS 11 (26.8%) 11 (27.5%) 0.946
  Moderate-severe ARDS 5 (12.2%) 11 (42.5%) 0.002

Data are presented as interquartile range or number (percentage)

BMI body mass index, MAP mean arterial pressure, WBC white blood cell, AST aspartate aminotransferase, ALT alanine aminotransferase, PT prothrombin time, APTT activated partial thromboplastin time, NLR neutrophil-to-lymphocyte ratio, ARDS acute respiratory distress syndrome

Fig. 1.

Fig. 1

Moderate-severe ARDS prediction biomarkers in severe COVID-19 patients: NLR (0.749, 95% CI 0.624–0.850), PLR (0.660, 95% CI 0.530–0.775), PCT (0.531, 95% CI 0.401–0.658), and CRP (0.635, 95% CI 0.504–0.752)

Our data revealed that NLR could be a valuable biomarker to recognize severe COVID-19 patients with moderate-severe ARDS, which facilitated clinicians to give effective respiratory supporting strategies and quickly find out moderate-severe ARDS patients who are at high indication for V-V ECMO.

Because of the mismatch of the oxygenation and lung function [6], a comprehensive consideration of immune indicators would improve early prediction for COVID-19 patients with “atypical” ARDS [6]. NLR is an extremely common laboratory test wherein the initial NLR value can be used to identify high-risk patients with moderate-severe ARDS, with the optimal threshold value of 11. This biomarker may be helpful in assessing the allocation of respiratory equipment in ICU patients and early assessment of ECMO. However, further clinical studies are needed to evaluate the benefits of NLR in ARDS.

Acknowledgements

We would like to thank all the medical workers involved in the rescue and the staff for collection of the data in Sichuan. We would like to thank all the investigators of the study of 2019 novel coronavirus pneumonia-infected critically ill patients in Sichuan province (SUNRISE).

Abbreviations

COVID-19

Coronavirus disease 2019

ARDS

Acute respiratory distress syndrome

ICU

Intensive care unit

WHO

World Health Organization

ECMO

Extracorporeal membrane oxygenation

V-V ECMO

Veno-venous extracorporeal membrane oxygenation

NLR

Neutrophil-to-lymphocyte ratio

PLR

Platelet-to-lymphocyte ratio

PCT

Procalcitonin

CRP

C-reactive protein

ROC

Receiver operating characteristic

Authors’ contributions

AJM, JLC, MLD, and JY designed the study. MLD and YK participated in the rescue work on the clinical frontline. XLL and YK organized and managed the data and its quality. JLC and AJM collected the data, performed the statistical analysis, and drafted the manuscript with JY. All authors participated in the data interpretation. All authors read the manuscript carefully and approved the final version.

Funding

This project was supported by Project of Novel Coronavirus Pneumonia in West China Hospital (HX2019nCoV027).

Availability of data and materials

The datasets used for the analysis in the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the West China Hospital of Sichuan University.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Aijia Ma, Jiangli Chen, and Jing Yang contributed to the work equally and should be regarded as co-first authors.

Contributor Information

Aijia Ma, Email: maaaijiaicu@gmail.com.

Jiangli Cheng, Email: 2581715118@qq.com.

Jing Yang, Email: yjingscu@163.com.

Meiling Dong, Email: dml332639860@163.com.

Xuelian Liao, Email: xuelianliao@hotmail.com.

Yan Kang, Email: Kangyan@scu.edu.cn.

References

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The datasets used for the analysis in the current study are available from the corresponding author on reasonable request.


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