Abstract
Background
To determine the diagnostic value of hematologic markers for coronavirus disease 2019 (COVID‐19) and explore their relationship with disease severity.
Methods
Subjects included 190 COVID‐19 patients, 190 healthy subjects, and 105 influenza pneumonia (IP) patients. COVID‐19 patients were divided into the ARDS and non‐ARDS groups. Routine blood examination, biochemistry indicator, days in hospital, body temperature, pneumonia severity index (PSI), CURB‐65, and MuLBSTA were recorded. Correlations between variables were assessed using Spearman's correlation analysis. Receiver operating characteristic (ROC) curves were used to study the accuracy of the various diagnostic tests.
Results
Compared with healthy subjects, COVID‐19 patients had lower white blood cell (WBC), lymphocyte, platelet, and hemoglobin levels; higher percentages of neutrophils and monocytes; lower percentages of lymphocytes and higher neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR) values (P < .05). COVID‐19 patients had higher WBC and neutrophil levels and lower percentages of lymphocytes compared to IP (P < .05). ROC curve analysis revealed that MLR had a high diagnostic value in differentiating COVID‐19 patients from healthy subjects, but not from IP patients. NLR showed significant positive correlations with PSI, CURB‐65, and MuLBSTA. Lymphocyte count was lower in the ARDS group and yielded a higher diagnostic value than the other variables.
Conclusions
Monocyte‐to‐lymphocyte ratio showed an acceptable efficiency to separate COVID‐19 patients from healthy subjects, but failed to rule out IP patients. NLR may be a reliable marker to evaluate the disease severity of COVID‐19. Lymphocyte count may be useful to establish the early diagnosis of ARDS in the COVID‐19 patients.
Keywords: coronavirus disease 2019, diagnostic value, lymphocyte, monocyte‐to‐lymphocyte ratio, neutrophil‐to‐lymphocyte ratio
Hematologic markers, including neutrophils, lymphocytes, monocytes, platelets, neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR), have been proposed as indicators to assist in the diagnosis, early warning, and risk stratification of infectious diseases. We thus performed this study to determine the diagnostic value of hematological markers for coronavirus disease 2019 (COVID‐19) and explore their relationship with disease severity.
Abbreviations
- ALT
alanine aminotransferase;
- ARDS
acute respiratory distress syndrome;
- AST
aspartate aminotransferase;
- AUC
area under the curve;
- CI
confidence interval;
- CKMB
creatine kinase isoenzyme;
- COVID‐19
coronavirus disease 2019;
- CREA
creatinine;
- CRP
C‐reactive protein;
- CT
computerized tomography;
- ESR
erythrocyte sedimentation rate;
- HGB
hemoglobin;
- IP
influenza pneumonia;
- LHS
length of hospital stay;
- MERS‐CoV
middle east respiratory syndrome coronavirus;
- MLR
monocyte‐to‐lymphocyte ratio;
- NLR
neutrophil‐to‐lymphocyte ratio;
- PCT
procalcitonin;
- PLR
platelet‐to‐lymphocyte ratio;
- PSI
pneumonia severity index;
- ROC
receiver operating characteristic;
- RT‐PCT
real‐time reverse transcription polymerase chain reaction;
- SARS‐CoV‐2
severe acute respiratory syndrome coronavirus 2;
- SE
standard error;
- WBC
white blood cell;
- WHO
World Health Organization
1. INTRODUCTION
Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the disease it causes, coronavirus disease 2019 (COVID‐19), were first reported in Wuhan, the capital of Hubei Province, and have quickly spread to different regions of China and other countries, including the United States, Japan, South Korea, and Thailand. 1 , 2 The World Health Organization (WHO) has declared that the outbreak of SARS‐CoV‐2 can be characterized as a global pandemic, and as of May 5, 2020, the number of confirmed COVID‐19 cases surged above 3 570 000, with nearly 250 000 deaths. 3 Several published reports of early clinical descriptions of COVID‐19 found that 26%‐33% of patients required intensive care and 4%‐15% died. 1 , 4 , 5 , 6 Subsequently, a report of 72 314 cases estimated that approximately 19% of people with COVID‐19 have severe or critical disease, with a case fatality rate of 2.3%. 7 Early identification and management are the most effective ways to improve the curative efficacy, which is a challenging task for physicians in clinical practice.
Currently, a positive result on high‐throughput sequencing or real‐time reverse transcription polymerase chain reaction (RT‐PCR) of nasal and pharyngeal swab specimens is considered an optimal standard of COVID‐19. Chest computerized tomography (CT) is widely used as an important diagnostic tool of COVID‐19, and some CT manifestations may be associated with the progression and prognosis of COVID‐19. 8 However, their clinical application is restricted by many factors, such as limited medical resources and high examination costs. Thus, convenient and cost‐effective indicators are urgently required to simplify the diagnostic process and evaluate the disease severity.
Hematologic markers, including neutrophils, lymphocytes, monocytes, platelets, neutrophil‐to‐lymphocyte ratio (NLR), monocyte‐to‐lymphocyte ratio (MLR), and platelet‐to‐lymphocyte ratio (PLR), have been proposed as indicators to assist in the diagnosis, early warning, and risk stratification of infectious diseases. 9 NLR has been demonstrated as an informative biomarker of diagnosis and disease severity in community‐acquired pneumonia and bacteremia. 10 , 11 MLR and PLR have also been recognized as a surrogate biological marker for the diagnosis of influenza virus infection in patients with respiratory tract infection. 12 , 13 , 14 However, few studies have focused on the diagnostic value of hematologic markers for COVID‐19.
Therefore, this study aimed to examine neutrophils, lymphocytes, monocytes, platelets, NLR, MLR, and PLR in COVID‐19 patients, determine their diagnostic value for COVID‐19, and explore their relationship with disease severity.
2. MATERIALS AND METHODS
2.1. Study design and participant characteristics
This multicenter cross‐sectional study was conducted in three hospitals (the Second Affiliated Hospital of Chongqing Medical University, Chongqing Public Health Medical Center and Qianjiang Central Hospital of Chongqing) in Chongqing Province municipality from January 19 to March 25, 2020. According to the World Health Organization interim guidance, 15 the diagnostic criteria of COVID‐19 were based on virus RNA detection, clinical characteristics, chest imaging, and ruling out of common pathogens. Patients who had been treated with antibiotics or glucocorticoids before admission and had other kinds of disease, such as renal or liver failure, malignant tumors, active infection, hypertension, diabetes mellitus, or coronary heart disease, were excluded from the study. A same number of healthy controls were recruited from the clinical database of health examine center after matching for age and gender. In addition, with the same exclusion criteria, patients who had positive influenza virus RT‐PCR results or influenza virus‐specific IgM antibody and CT manifestations of viral pneumonia were included from January 2018 to March 2020. Finally, a total of 190 confirmed cases with COVID‐19, 190 healthy subjects, and 105 influenza pneumonia (IP) patients were enrolled in the study.
This study was approved by the institutional ethics board of the Second Affiliated Hospital of Chongqing Medical University (No. 2020‐09), Chongqing Public Health Medical Center (No. 2020‐015‐01‐KY), and Qianjiang Central Hospital of Chongqing (No. 2020‐07) and was in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study design, informed consent was waived.
2.2. Data collection
Data including demographic information, medical history, clinical manifestations, laboratory findings, radiologic images, and days in hospital were collected from the patients' medical and nursing records. Laboratory results included WBCs, neutrophils, lymphocytes, monocytes, platelets, hemoglobin (HGB), C‐reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine (CREA), and creatine kinase isoenzyme (CKMB). NLR, MLR, and PLR were calculated by dividing the absolute neutrophil, monocyte, and platelet counts by the absolute lymphocyte counts. The pneumonia severity index (PSI), CURB‐65, and MuLBSTA 16 were determined within 24 hours after admission by standardized forms. The diagnosis of acute respiratory distress syndrome (ARDS) was based on the Berlin definition. 17 Two senior physicians independently reviewed the data.
2.3. Statistical analysis
Continuous variables are presented as the mean ± standard deviation (SD), and categorical variables are indicated as numbers (n) and percentages (%). The normal distribution test was conducted in the variables by Kolmogorov‐Smirnov test. Groups were compared using Student's t tests for parametric continuous variables or Mann‐Whitney U tests for non‐parametric continuous variables. Proportions for categorical variables were compared using the chi‐square test. Correlations between variables were assessed using Spearman's correlation analysis. Receiver operating characteristic (ROC) curves were used to study the accuracy of the various diagnostic tests. All statistical analyses were performed using SPSS 22.0 software, and pictures were drawn by GraphPad Prism 8.0 software. A two‐sided P‐value of <.05 was considered significant.
3. RESULTS
3.1. Demographics and baseline characteristics
A total of 190 COVID‐19 patients, 190 healthy subjects, and 105 IP patients were enrolled in the study. Age and gender distribution were similar among the three groups. Compared with healthy subjects, COVID‐19 patients had lower WBC counts (5.45 ± 2.16 vs 5.89 ± 1.07 × 109/L, P < .001), lymphocyte counts (1.30 ± 0.57 vs 2.00 ± 0.41 × 109/L, P < .001), platelet counts (193.71 ± 84.28 vs 205.03 ± 47.12 × 109/L, P < .001), and hemoglobin levels (135.18 ± 16.66 vs 146.98 ± 13.55 g/dL, P < .001); higher percentages of neutrophils (3.53 ± 1.97 vs 3.41 ± 0.85%, P < .001) and monocytes (7.71 ± 2.61 vs 5.79 ± 1.26%, P < .001); lower percentages of lymphocytes (1.30 ± 0.57 vs 2.00 ± 0.41%, P < .001); and higher NLR (3.29 ± 2.76 vs 1.77 ± 0.58, P < .001), MLR (0.34 ± 0.17 vs 0.17 ± 0.05, P < .001), and PLR (169.51 ± 97.51 vs 105.87 ± 30.54, P < .001). The ESR was 32.76 ± 20.41 mm/h (normal range: <20.00 mm/h), CRP was 29.12 ± 31.21 mg/L (normal range < 10.00 mg/L), and PCT was 0.057 ± 0.055 ng/mL (normal range < 0.050 ng/mL) in the COVID‐19 patients. In addition, the length of hospital stay was 13.91 ± 1.84 days, the body temperature was 37.38 ± 0.87°C, ALT was 28.77 ± 24.87 U/L (normal range: 7.00‐40.00 U/L), AST was 27.82 ± 18.28 U/L (normal range: 13.00‐35.00 U/L), CREA was 68.93 ± 19.10 µmol/L (normal range: 41.00‐81.00 µmol/L), CKMB was 11.59 ± 16.74 ng/mL (normal range: <5.00 ng/mL), PSI was 61.77 ± 27.03, CURB‐65 was 0.51 ± 0.62, and MuLBSTA was 7.09 ± 2.50 in the COVID‐19 group. There were no statistical differences in laboratory findings between COVID‐19 and IP groups except for the WBC counts (5.45 ± 2.16 vs 6.04 ± 2.31 × 109/L, P = .013), neutrophil counts (3.53 ± 1.97 vs 3.97 ± 2.16 × 109/L, P = .043), and the percentages of lymphocytes (25.52 ± 10.31 vs 23.07 ± 11.58%, P = .032; Table 1).
Table 1.
Demographic features and laboratory findings of the participants
| Healthy subject (n = 190) | COVID‐19 (n = 190) | IP (n = 105) | P 1 | P 2 | |
|---|---|---|---|---|---|
| Age (years) | 46.44 ± 14.48 | 46.35 ± 15.07 | 47.52 ± 17.13 | .956 | .569 |
| Male (%) | 90 (47.3%) | 90 (47.3%) | 56 (53.33%) | 1.000 | .327 |
| WBCs (×109/L) | 5.89 ± 1.07 | 5.45 ± 2.16 | 6.04 ± 2.31 | <.001 | .013 |
| Neutrophils (×109/L) | 3.41 ± 0.85 | 3.53 ± 1.97 | 3.97 ± 2.16 | .118 | .043 |
| Neutrophil percentage, % | 57.57 ± 6.96 | 62.31 ± 12.43 | 62.60 ± 15.89 | <.001 | .298 |
| Lymphocytes (×109/L) | 2.00 ± 0.41 | 1.30 ± 0.57 | 1.26 ± 0.56 | <.001 | .595 |
| Lymphocyte percentage, % | 34.41 ± 6.40 | 25.52 ± 10.31 | 23.07 ± 11.58 | <.001 | .032 |
| Monocytes (×109/L) | 0.39 ± 0.08 | 0.40 ± 0.15 | 0.46 ± 0.40 | .659 | .534 |
| Monocyte percentage, % | 5.79 ± 1.26 | 7.71 ± 2.61 | 7.55 ± 4.01 | <.001 | .210 |
| Platelets (×109/L) | 205.03 ± 47.12 | 193.71 ± 84.28 | 203.19 ± 108.22 | <.001 | .796 |
| Hemoglobin (g/dL) | 146.98 ± 13.55 | 135.18 ± 16.66 | 135.52 ± 22.40 | <.001 | .526 |
| NLR | 1.77 ± 0.58 | 3.29 ± 2.76 | 4.08 ± 3.76 | <.001 | .090 |
| MLR | 0.17 ± 0.05 | 0.34 ± 0.17 | 0.42 ± 0.34 | <.001 | .256 |
| PLR | 105.87 ± 30.54 | 169.51 ± 97.51 | 180.54 ± 105.40 | <.001 | .745 |
| ESR (mm/h) | — | 32.76 ± 20.41 | 35.75 ± 22.07 | — | .221 |
| CRP (mg/L) | — | 29.12 ± 31.21 | 25.73 ± 25.20 | — | .712 |
| PCT (ng/mL) | — | 0.057 ± 0.055 | 0.057 ± 0.046 | — | .429 |
| Body temperature (℃) | — | 37.38 ± 0.87 | 37.46 ± 0.95 | — | .495 |
| Hospital stay (days） | — | 13.91 ± 1.84 | 13.83 ± 2.13 | — | .214 |
| ALT (U/L) | — | 28.77 ± 24.87 | 28.17 ± 19.32 | — | .784 |
| AST (U/L) | — | 27.82 ± 18.28 | 28.14 ± 15.62 | — | .780 |
| CREA (ng/mL) | — | 68.93 ± 19.10 | 69.87 ± 20.70 | — | .638 |
| CKMB (μmol/L) | — | 11.59 ± 16.74 | 10.95 ± 11.02 | — | .452 |
| PSI | — | 61.77 ± 27.03 | 63.15 ± 17.86 | — | .372 |
| CURB‐65 | — | 0.51 ± 0.62 | 0.53 ± 0.76 | — | .701 |
| MuLBSTA | — | 7.09 ± 2.50 | 7.00 ± 2.72 | — | .460 |
Data are presented as mean ± standard deviation (SD) and n (%). P1: Compared between COVID‐19 and healthy subjects; P2: Compared between COVID‐19 and IP.
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CKMB, creatine kinase isoenzyme; COVID‐19, coronavirus disease 2019; CREA, creatinine; CRP, C‐reactive protein; ESR, erythrocyte sedimentation rate; IP, influenza pneumonia; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PCT, procalcitonin; PLR, platelet‐to‐lymphocyte ratio; PSI, pneumonia severity index; WBC, white blood cell.
3.2. Monocyte‐to‐lymphocyte ratio has a high diagnostic value in differentiating COVID‐19 patients from healthy subjects
Receiver operating characteristic curves for the concentrations of hematologic markers were computed for the prediction of COVID‐19. When comparing COVID‐19 patients with healthy subjects, the area under the curve (AUC) for neutrophils was 0.546 (95% CI: 0.495‐0.597), for lymphocytes was 0.847 (95% CI: 0.806‐0.881), for monocytes was 0.513 (95% CI: 0.462‐0.564), for platelets was 0.605 (95% CI: 0.554‐0.655), for NLR was 0.722 (95% CI: 0.674‐0.766), for MLR was 0.892 (95% CI: 0.856‐0.921), and for PLR was 0.748 (95% CI: 0.702‐0.791). ROC curve analysis revealed that MLR had a higher diagnostic value than neutrophils, lymphocytes, monocytes, platelets, NLR, and PLR. In addition, the results showed that an MLR level with a threshold of 0.23 could discriminate COVID‐19 patients from healthy subjects with 90.00% specificity and 75.79% sensitivity. However, when comparing patients with COVID‐19 to those with IP, the AUC was lower than 0.600 in all the hematologic markers examined (Figure 1 and Table 2).
Figure 1.
Receiver operating characteristic (ROC) curve was carried out to evaluate the diagnostic value of hematologic markers for COVID‐19. A, Diagnostic value of neutrophil, lymphocyte, monocyte, and platelet in differentiating COVID‐19 patients from healthy subjects; B, diagnostic value of NLR, MLR, and PLR in differentiating COVID‐19 patients from healthy subjects; C, diagnostic value of neutrophil, lymphocyte, monocyte, and platelet in differentiating COVID‐19 patients from influenza pneumonia patients; D, diagnostic value of NLR, MLR, and PLR in differentiating COVID‐19 patients from influenza pneumonia patients
Table 2.
ROC curve analysis was carried out to evaluate the diagnostic value of hematologic markers for COVID‐19
| AUC | 95% CI | P | Optimal cutoff value | Specificity (%) | Sensitivity (%) | |
|---|---|---|---|---|---|---|
| COVID‐19 vs healthy subjects | ||||||
| Neutrophils | 0.546 | 0.495‐0.597 | .1236 | 2.13 | 95.79 | 21.05 |
| Lymphocytes | 0.847 | 0.806‐0.881 | <.0001 | 1.50 | 88.90 | 68.90 |
| Monocytes | 0.513 | 0.462‐0.564 | .6690 | 0.50 | 9.47 | 74.21 |
| Platelets | 0.605 | 0.554‐0.655 | .0003 | 152.00 | 85.30 | 36.30 |
| NLR | 0.722 | 0.674‐0.766 | <.0001 | 2.72 | 95.26 | 46.32 |
| MLR | 0.892 | 0.856‐0.921 | <.0001 | 0.23 | 90.00 | 75.79 |
| PLR | 0.748 | 0.702‐0.791 | <.0001 | 144.39 | 91.05 | 49.47 |
| COVID‐19 vs IP | ||||||
| Neutrophils | 0.571 | 0.513‐0.629 | .0494 | 3.55 | 63.68 | 53.33 |
| Lymphocytes | 0.519 | 0.460‐0.577 | .6017 | 0.85 | 79.47 | 30.48 |
| Monocytes | 0.522 | 0.463‐0.580 | .5571 | 0.53 | 83.68 | 29.52 |
| Platelets | 0.509 | 0.450‐0.567 | .8042 | 256.00 | 85.26 | 26.67 |
| NLR | 0.560 | 0.501‐0.617 | .1030 | 3.94 | 75.79 | 42.86 |
| MLR | 0.513 | 0.454‐0.571 | .7383 | 0.1378 | 94.74 | 19.05 |
| PLR | 0.540 | 0.481‐0.598 | .2645 | 172.27 | 68.42 | 44.76 |
Abbreviations: AUC, area under the curve; CI, confidence interval; COVID‐19, coronavirus disease 2019; IP, influenza pneumonia; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PLR, platelet‐to‐lymphocyte ratio.
3.3. Correlation between the variables
Neutrophils were positively correlated with CRP (r = .345, P < .001), hospital stay (r = .148, P‐0.042), ALT (r = .216, P = .003), and PSI (r = .228, P = .002). Lymphocytes were negatively correlated with the ESR (r = −.050, P = .023), PCT (r = −.263, P < .001), body temperature (r = −.243, P = .001), AST (r = −.226, P = .002), PSI (r = −.255, P < .001), and MuLBSTA (r = −.175, P = .016). Platelets were negatively correlated with PCT (r = −.213, P = .003), body temperature (r = −.189, P = .009), AST (r = −.233, P = .001), and PSI (r = −.147, P = .043). Monocytes were negatively correlated with AST (r = −.275, P < .001) but positively correlated with CREA (r = .147, P = .042). NLR was positively correlated with CRP (r = .272, P < .001), PCT (r = .152, P = .036), hospital stay (r = .216, P = .003), ALT (r = .213, P = .003), PSI (r = .332, P < .001), CURB‐65 (r = .144, P = .047), and MuLBSTA (r = .203, P = .005). MLR was positively correlated with body temperature (r = .193, P = .008). PLR was positively correlated with PCT (r = .285, P < .001). These results suggested that there were significant positive correlations between NLR and disease severity of COVID‐19 (Table 3).
Table 3.
Correlation between the variables
| Neutrophils | Lymphocytes | Platelets | Monocytes | NLR | MLR | PLR | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| r | P | r | P | r | P | r | P | r | P | r | P | r | P | |
| ESR | .098 | .177 | −.050 | .023 | .023 | .752 | .036 | .624 | .127 | .082 | .099 | .175 | .086 | .240 |
| CRP | .345 | <.001 | −.027 | .709 | −.057 | .437 | .082 | .261 | .272 | <.001 | .072 | .325 | −.007 | .919 |
| PCT | .021 | .777 | −.263 | <.001 | −.213 | .003 | −.102 | .160 | .152 | .036 | .069 | .346 | .285 | <.001 |
| Body temp | −.068 | .349 | −.243 | .001 | −.189 | .009 | −.019 | .792 | .114 | .118 | .193 | .008 | .052 | .476 |
| LHS | .148 | .042 | −.132 | .069 | −.034 | .643 | −.030 | .677 | .216 | .003 | .073 | .316 | .093 | .203 |
| ALT | .216 | .003 | −.098 | .181 | .034 | .640 | −.091 | .213 | .213 | .003 | −.005 | .944 | .089 | .220 |
| AST | −.028 | .702 | −.226 | .002 | −.233 | .001 | −.275 | <.001 | .135 | .064 | −.043 | .558 | −.002 | .983 |
| CREA | .130 | .073 | −.006 | .930 | −.111 | .129 | .147 | .042 | .120 | .098 | .117 | .109 | −.089 | .223 |
| CKMB | .019 | .799 | −.040 | .582 | −.129 | .076 | .017 | .818 | .058 | .427 | .026 | .720 | −.022 | .764 |
| PSI | .228 | .002 | −.255 | <.001 | −.147 | .043 | −.111 | .129 | .332 | <.001 | .107 | .144 | .090 | .218 |
| CURB‐65 | .059 | .419 | −.128 | .078 | −.119 | .103 | −.037 | .613 | .144 | .047 | .105 | .833 | .015 | .833 |
| MuLBSTA | .129 | .076 | −.175 | .016 | −.098 | .180 | −.055 | .454 | .203 | .005 | .114 | .117 | .094 | .203 |
Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; CKMB, creatine kinase isoenzyme; CREA, creatinine; CRP, C‐reactive protein; ESR, erythrocyte sedimentation rate; LHS, length of hospital stay; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PCT, procalcitonin; PLR, platelet‐to‐lymphocyte ratio; PSI, pneumonia severity index; WBC, white blood cell.
Bold values are significantly p< .05.
3.4. Comparison between the non‐ARDS and ARDS groups
According to the Berlin definition, 17 COVID‐19 patients were divided into the non‐ARDS (n = 159) and ARDS (n = 31) groups, and the differences between the two groups were analyzed. There were no significant differences of age and gender between the two groups. The results showed that when compared with those of the non‐ARDS group, the lymphocyte and platelet counts were markedly decreased (0.83 ± 0.35 vs 1.40 ± 0.55 × 109/L, P < .001; 147.23 ± 51.96 vs 202.77 ± 86.47, P < .001), while NLR, MLR, CRP, PCT, PSI, CURB‐65, and MuLBSTA were markedly elevated in the ARDS patients (NLR: 5.08 ± 4.48 vs 2.95 ± 2.13, P < .001; MLR: 0.49 ± 0.22 vs 0.32 ± 0.14, P < .001; CRP: 48.14 ± 34.56 vs 25.41 ± 29.22 mg/L, P < .001; PCT: 0.09 ± 0.06 vs 0.05 ± 0.05 ng/mL, P < .001; PSI: 88.65 ± 29.13 vs 56.53 ± 23.32, P < .001; CURB‐65: 0.97 ± 0.87 vs 0.42 ± 0.52, P = .001; MuLBSTA: 8.94 ± 2.69 vs 6.73 ± 2.29, P < .001; Table 4).
Table 4.
Comparison of variables between the non‐ARDS and ARDS groups
| non‐ARDS group (n = 159） | ARDS group (n = 31) | P | |
|---|---|---|---|
| Age (years) | 45.56 ± 14.89 | 50.26 ± 15.65 | .166 |
| Male (%) | 72 (45.28%) | 17 (54.84%) | .329 |
| Neutrophils (×109/L) | 3.53 ± 1.95 | 3.53 ± 2.12 | .422 |
| Lymphocytes (×109/L) | 1.40 ± 0.55 | 0.83 ± 0.35 | <.001 |
| Monocytes (×109/L) | 0.40 ± 0.15 | 0.38 ± 0.15 | .637 |
| Platelets (×109/L) | 202.77 ± 86.47 | 147.23 ± 51.96 | <.001 |
| NLR | 2.95 ± 2.13 | 5.08 ± 4.48 | <.001 |
| MLR | 0.32 ± 0.14 | 0.49 ± 0.22 | <.001 |
| PLR | 163.37 ± 95.38 | 201.00 ± 103.79 | .013 |
| ESR (mm/h) | 31.80 ± 19.90 | 37.71 ± 22.52 | .129 |
| CRP (mg/L) | 25.41 ± 29.22 | 48.14 ± 34.56 | <.001 |
| PCT (ng/mL) | 0.05 ± 0.05 | 0.09 ± 0.06 | <.001 |
| PSI | 56.53 ± 23.32 | 88.65 ± 29.13 | <.001 |
| CURB‐65 | 0.42 ± 0.52 | 0.97 ± 0.87 | .001 |
| MuLBSTA | 6.73 ± 2.29 | 8.94 ± 2.69 | <.001 |
Data are presented as mean ± standard deviation (SD) and n (%).
Abbreviations: ARDS: acute respiratory distress syndrome; COVID‐19, coronavirus disease 2019; CRP, C‐reactive protein; ESR, erythrocyte sedimentation rate; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PCT, procalcitonin; PLR, platelet‐to‐lymphocyte ratio; PSI, pneumonia severity index.
3.5. Lymphocytes have a high diagnostic value for ARDS
Receiver operating characteristic curve analysis was carried out to evaluate the accuracy, specificity, and sensitivity of the hematologic markers, PSI, CURB‐65, and MuLBSTA for the diagnosis of ARDS in the COVID‐19 patients. The results showed that a lymphocyte level <0.87 × 109/L was an optimal cutoff for predicting ARDS in COVID‐19 patients with 88.05% specificity and 70.97% sensitivity, which was higher than that of other variables (Table 5 and Figure 2).
Table 5.
Diagnostic value of hematological markers, ESR, CRP, PCT, PSI, CURB‐65, and MuLBSTA for ARDS
| AUC | 95% CI | P | Optimal cutoff value | Specificity (%) | Sensitivity (%) | |
|---|---|---|---|---|---|---|
| Neutrophils | 0.546 | 0.472‐0.618 | .4234 | 3.20 | 53.46 | 67.74 |
| Lymphocytes | 0.836 | 0.776‐0.886 | <.0001 | 0.87 | 88.05 | 70.97 |
| Monocytes | 0.527 | 0.453‐0.599 | .6716 | 0.22 | 91.82 | 22.58 |
| Platelets | 0.706 | 0.635‐0.769 | <.0001 | 164.00 | 65.41 | 70.97 |
| NLR | 0.722 | 0.652‐0.784 | <.0001 | 2.23 | 49.69 | 87.10 |
| MLR | 0.785 | 0.720‐0.841 | <.0001 | 0.30 | 57.86 | 87.10 |
| PLR | 0.642 | 0.569‐0.710 | .0107 | 138.30 | 51.57 | 77.42 |
| ESR | 0.586 | 0.513‐0.657 | .1078 | 35.00 | 67.30 | 51.61 |
| CRP | 0.705 | 0.634‐0.769 | .0005 | 25.87 | 71.70 | 70.97 |
| PCT | 0.736 | 0.668‐0.797 | <.0001 | 0.04 | 66.67 | 70.97 |
| PSI | 0.806 | 0.742‐0.860 | <.0001 | 78.62 | 67.74 | 78.62 |
| CURB‐65 | 0.674 | 0.602‐0.740 | .0017 | 1.00 | 98.74 | 29.03 |
| MuLBSTA | 0.730 | 0.661‐0.792 | .0001 | 8.00 | 88.68 | 64.52 |
Abbreviations: ARDS: acute respiratory distress syndrome; AUC, area under the curve; CI, confidence interval; CRP, C‐reactive protein; ESR, erythrocyte sedimentation rate; MLR, monocyte‐to‐lymphocyte ratio; NLR, neutrophil‐to‐lymphocyte ratio; PCT, procalcitonin; PLR, platelet‐to‐lymphocyte ratio; PSI, pneumonia severity index.
Figure 2.
Receiver operating characteristic (ROC) curve was carried out to evaluate the diagnostic value of hematologic markers, PSI, CURB‐65, and MuLBSTA for the diagnosis of ARDS in the COVID‐19 patients. A, Diagnostic value of neutrophil, lymphocyte, monocyte, platelet, NLR; B, diagnostic value of MLR, PLR, ESR level, CRP, and PCT; C, diagnostic value of PSI, CURB‐65, and MuLBSTA
4. DISCUSSION
Despite extensive implementation of control measures, the global pandemic of COVID‐19 is still devastating, with a high rate of complications and mortality. There is an urgent requirement for convenient and cost‐effective indicators to simplify the diagnostic process and evaluate the disease severity. In our study, we found that MLR had a high diagnostic value in differentiating COVID‐19 patients from healthy subjects, but not from IP patients. The NLR showed positive correlations with PSI, CURB‐65, and MuLBSTA, indicating the disease severity of COVID‐19. Lymphocyte count had a higher diagnostic value for ARDS in patients with COVID‐19 than the other variables.
The complete blood count (CBC) is a quick, easy, and inexpensive measurement in clinical practice, providing rich information about hematologic contents, including WBCs, neutrophils, lymphocytes, monocytes, platelets, NLR, MLR, and PLR. In recent years, hematologic markers have drawn attention as potential indicators to assist in the diagnosis, early warning, and risk stratification in many infectious diseases, such as sepsis, bacteremia, and urinary tract infection, as well as various non‐infectious diseases, including hepatic cirrhosis, coronary artery disease, and solid tumors. 9 , 18 , 19 , 20 As reported by Huang et al, 21 patients with CAP presented with higher NLR and MLR, and NLR was correlated with PSI, suggesting that some hematologic markers may act as potential predictors for CAP and indicate disease severity. However, their study was focused on CAP patients without specific pathogens, and the sample size was relatively small. In our study, 190 COVID‐19 patients, 190 healthy subjects, and 105 IP patients were enrolled, and our results suggested that the percentages of neutrophils and monocytes, NLR, MLR, and PLR in the COVID‐19 group were significantly higher than those of the healthy control group, whereas the numbers of WBCs, lymphocytes, platelets, and hemoglobin were lower. The ROC curve revealed that MLR yielded a higher AUC value than the other variables, and the optimal cutoff value of MLR for COVID‐19 was 0.23, with 90.00% specificity and 75.79% sensitivity. MLR showed a significant positive correlation with body temperature. Several studies have suggested that a lymphocyte‐to‐monocyte ratio value <2 is a surrogate indicator for influenza A. 12 , 13 In addition, increased monocyte and decreased lymphocyte counts were found in patients with Middle East respiratory syndrome coronavirus (MERS‐CoV), indicating the potential predictive value of MLR for MERS. 22 It has been reported that some serum cytokines (IL‐6 and IFN‐α) and chemokines (IL‐8, CXCL‐10, and CCL‐5) are correlated with increased peripheral blood monocytes in patients with MERS, suggesting a possible effect on increasing the MLR. 23 Notably, the differences in monocyte count between the two groups were not statistically significant in our study, although the COVID‐19 group indeed had a higher mean value and wider range than the healthy control group. One possible reason may be that some COVID‐19 patients presented with a decreased WBC count, which may produce a lower monocyte count despite the increased monocyte percentage. Meanwhile, it should also be noticed that these hematologic markers had relatively low diagnostic value in differentiating between COVID‐19 and IP. This is consistent with previous studies showing that no single hematologic marker carried sufficient weight to confirm or refute specific respiratory virus. 24 , 25 In contrast, Han et al 14 reported that hematologic markers provided an adjunct diagnostic approach in the differential diagnosis of respiratory infections due to influenza virus and other respiratory viruses, but their study did not include patients infected with SARS‐CoV‐2. Thus, the diagnostic value of hematologic markers for different respiratory virus infection deserves more studies.
It has been widely demonstrated that with serious infection or systemic inflammation, NLR increases as a result of the severity of clinical status and outcome. 10 , 11 In the present study, we found that NLR was significantly and positively correlated with PSI, CURB‐65, and MuLBSTA, indicating the disease severity of COVID‐19. Elevated NLR was defined as increased neutrophil and decreased lymphocyte counts. Neutrophil proliferation and lymphocyte apoptosis are physiological responses of the innate immune system to systemic inflammation. 26 Although it is well documented that neutrophil levels are elevated during bacterial invasion, accumulating evidence indicates that viral infection stimulates neutrophils and triggers neutrophil‐mediated innate immune responses. 27 Interaction with other immune cell populations, virus internalization, and inactivation, the release of leukocyte cytokines and antimicrobial components are the major mechanisms by which neutrophils can contribute to the clearance of viruses. 27 Qin et al 28 found that in patients with COVID‐19, severe cases tended to have a higher NLR, and surveillance of NLR is helpful in the early screening of critical illness.
Furthermore, our present study showed that elevated NLR, MLR, PLR, CRP, PCT, PSI, and MuLBSTA but decreased lymphocyte and platelet levels were detected in patients with ARDS compared with those without ARDS. The ROC curve results revealed that the AUC value of lymphocytes was 0.836 (95% CI 0.776‐0.886), which was higher than that of other variables. The best cutoff value for lymphocytes was 0.87, with 88.05% specificity and 70.97% sensitivity. The relationship between decreased lymphocyte count in the peripheral blood and the progression of ARDS resulting from SARS‐CoV‐2 is attracting increased attention, as many COVID‐19 patients with ARDS present with a dysregulated immune state. 24 , 28 , 29 Recently, an autopsy report on a 50‐year‐old COVID‐19 patient with ARDS provided further information about this pathological process, showing that although the peripheral blood lymphocyte count was substantially reduced in the patient, there was an inflammatory infiltration of lymphocytes in both lungs and immune hyperactivation, suggesting an exaggerated immunopathological injury. 30 Additionally, activated immune cells stimulate further infiltration and induce the production of reactive oxygen species and nitric oxide, which will damage the epithelial‐endothelial barrier and cause an imbalance in the ventilation/blood flow ratio, leading to the progression of ARDS 23 , 31
Previous studies have demonstrated that clinical risk scores and inflammatory biomarkers provide useful diagnostic and prognostic information about infectious diseases. The PSI and CURB‐65 scores are the most commonly used tools for the evaluation of disease severity in patients with pneumonia. 32 More recently, an early warning model of viral pneumonia, the MuLBSTA score (multilobular infiltrates, lymphopenia, bacterial coinfection, smoking history, hypertension, age ≥ 60 years), showed an excellent risk stratification capacity in patients with COVID‐19. 6 , 16 In addition, some studies have suggested that CRP and PCT are effective indicators to assist in risk stratification of patients with pneumonia. 33 In our study, we found that NLR showed significant positive correlations with PSI, CURB‐65, MuLBSTA, CRP, and PCT, indicating its potential value for the evaluation of disease severity in COVID‐19. However, MLR had no correlation with these clinical risk scores and inflammatory biomarkers but possessed a higher diagnostic value for COVID‐19. In addition, lymphocytes had a higher diagnostic value for ARDS in the COVID‐19 patients than CRP, PCT, and ESR. Therefore, it seems that MLR, NLR, and lymphocyte count play different roles in COVID‐19, and a combination with clinical risk scores and inflammatory biomarkers may be beneficial for the assessment and management of COVID‐19.
This study has several limitations. First, it was of retrospective nature. All included patients were obtained from hospital medical systems after satisfying a series of criteria, which could introduce a selection bias. Second, we only used one measure in time rather than a longitudinal measure because of insufficient data. Third, although some of the conditions were adjusted, we could not have completely removed all the confounding factors that might influence the level of hematologic markers. Forth, all our patients were Asian descent, and as these hematologic markers have racial differences, the study findings may not be applicable to the white and black populations. 34 , 35 Therefore, an additional large‐scale multicenter, randomized control study will be required to further confirm our findings.
In conclusion, MLR had a high diagnostic value in differentiating COVID‐19 patients from healthy subjects, but failed to rule out IP patients. NLR showed a positive and significant correlation with PSI, CURB‐65, and MuLBSTA, indicating the disease severity of COVID‐19. Lymphocyte count may be useful to establish the early diagnosis of ARDS in the COVID‐19 patients. MLR, NLR, and lymphocyte count may be rapid, cost‐effective, and promising potential markers to assist in the diagnosis, early warning, and risk stratification of COVID‐19.
AUTHOR CONTRIBUTIONS
Daoxin Wang: Conceptualization, Methodology, Supervision. Junnan Peng: Data curation, Writing‐Original draft preparation, Software. Di Qi: Data curation, Writing‐Original draft preparation, Validation. Guodan Yuan: Visualization, Investigation. Xinyu Deng: Investigation, Formal analysis. Ying Mei: Resources Data Curation. Longhua Feng: Writing‐Reviewing and Editing.
ETHICAL APPROVAL
This study was approved by the institutional ethics board of the Second Affiliated Hospital of Chongqing Medical University (No. 2020‐09), Chongqing Public Health Medical Center (No. 2020‐015‐01‐KY), and Qianjiang Central Hospital of Chongqing (No. 2020‐07) and was in accordance with the Declaration of Helsinki.
CONSENT FOR PUBLICATION
The manuscript is approved by all authors for publication.
Peng J, Qi D, Yuan G, et al. Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID‐19): A multicenter, cross‐sectional study. J Clin Lab Anal. 2020;34:e23475 10.1002/jcla.23475
Funding information
This study was supported by the National Natural Science Foundation of China (Grant NO.81670071), the Emergency Foundation for Novel Coronavirus Pneumonia of Chongqing Health Committee (Grant NO.2020NCPZX19), the Natural Science Foundation of Chongqing (Grant No. cstc2020jscx‐fyzx0230), and the Major Emergency Clinical Research Project for 2019‐nCoV of ChongQing Medical University (Grant NO.6).
DATA AVAILABILITY STATEMENT
The datasets used during the current study are available from the corresponding author on reasonable request.
REFERENCES
- 1. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497‐506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Bogoch II, Watts A, Thomas‐Bachli A, et al. Potential for global spread of a novel coronavirus from China. Travel Med. 2020;27(2):taaa011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. World Health Organization . Coronavirus Disease (COVID‐19) Outbreak. https://www.who.int. Accessed May 5, 2020.
- 4. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020;382(8):727‐733. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus‐infected pneumonia in Wuhan, China. JAMA. 2020;323(11):1061‐1069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507‐513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID‐19) outbreak in China. JAMA. 2020;323(13):1239. [DOI] [PubMed] [Google Scholar]
- 8. Shi H, Han X, Jiang N, et al. Radiological findings from 81 patients with COVID‐19 pneumonia in Wuhan, China: a descriptive study. Lancet Infect Dis. 2020;20(4):425‐434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Russell CD, Parajuli A, Gale HJ, et al. The utility of peripheral blood leucocyte ratios as biomarkers in infectious diseases: a systematic review and meta‐analysis. J Infect. 2019;78:339‐348. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Curbelo J, Rajas O, Arnalich B, et al. Neutrophil count percentage and neutrophil‐lymphocyte ratio as prognostic markers in patients hospitalized for community‐acquired pneumonia. Arch Bronconeumol. 2019;55:472‐477. [DOI] [PubMed] [Google Scholar]
- 11. de Jager CPC, van Wijk PTL, Mathoera RB, et al. Lymphocytopenia and neutrophil‐lymphocyte count ratio predict bacteremia better than conventional infection markers in an emergency care unit. Crit Care. 2010;14(5):R192. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. McClain MT, Park LP, Nicholson B, et al. Longitudinal analysis of leukocyte differentials in peripheral blood of patients with acute respiratory viral infections. J Clin Virol. 2013;58:689‐695. [DOI] [PubMed] [Google Scholar]
- 13. Merekoulias G, Alexopoulos EC, Belezos T, et al. Lymphocyte to monocyte ratio as a screening tool for influenza. PLoS Curr. 2010;2:RRN1154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Han Q, Wen X, Wang L, et al. Role of hematological parameters in the diagnosis of influenza virus infection in patients with respiratory tract infection symptoms. J Clin Lab Anal. 2020;34(5):e23191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. World Health Organization . Clinical Management Of Severe Acute Respiratory Infection When Novel Coronavirus (2019‐ncov) Infection Is Suspected: Interim Guidance. https://apps.who.int/iris/handle/10665/330854. Accessed May 5, 2020.
- 16. Guo L, Wei D, Zhang X, et al. Clinical features predicting mortality risk in patients with viral pneumonia: the MuLBSTA Score. Front Microbiol. 2019;10:2752. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Ranieri VM, Rubenfeld GD, Thompson BT, et al. Acute respiratory distress syndrome: the Berlin definition. JAMA. 2012;307(23):2526‐2533. [DOI] [PubMed] [Google Scholar]
- 18. Alempijevic TM, Stojkovic Lalosevic M, Dumic I, et al. Diagnostic accuracy of platelet count and platelet indices in noninvasive assessment of fibrosis in nonalcoholic fatty liver disease patients. Can J Gastroenterol Hepatol. 2017;2017:e6070135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Serrano CV, De Mattos FR, Pitta FG, et al. Association between neutrophil‐lymphocyte and platelet‐lymphocyte ratios and coronary artery calcification score among asymptomatic patients: data from a cross‐sectional study. Mediators Inflamm. 2019;2019:e6513847. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Bilen MA, Martini DJ, Liu Y, et al. The prognostic and predictive impact of inflammatory biomarkers in patients who have advanced‐stage cancer treated with immunotherapy. Cancer. 2019;125(1):127‐134. [DOI] [PubMed] [Google Scholar]
- 21. Huang Y, Liu A, Liang L, et al. Diagnostic value of blood parameters for community‐acquired pneumonia. Int Immunopharmacol. 2018;64:10‐15. [DOI] [PubMed] [Google Scholar]
- 22. Park GE, Kang CI, Ko JH, et al. Differential cell count and CRP level in blood as predictors for middle east respiratory syndrome coronavirus infection in acute febrile patients during nosocomial outbreak. J Korean Med Sci. 2017;32(1):151‐154. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Channappanavar R, Perlman S. Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology. S Semin Immunopathol. 2017;39(5):529‐539. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Tang X, Du R, Wang R, et al. Comparison of hospitalized patients with acute respiratory distress syndrome caused by COVID‐19 and H1N1. Chest. 2020;158(1):195‐205. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Li Y, Wang J, Wang C, et al. Characteristics of respiratory virus infection during the outbreak of 2019 novel coronavirus in Beijing. Int J Infect Dis. 2020;96:266‐269. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Cataudella E, Giraffa CM, Di Marca S, et al. Neutrophil‐to‐lymphocyte ratio: an emerging marker predicting prognosis in elderly adults with community‐acquired pneumonia. J Am Geriatr Soc. 2017;65:1796‐1801. [DOI] [PubMed] [Google Scholar]
- 27. Naumenko V, Turk M, Jenne CN, et al. Neutrophils in viral infection. Cell Tissue Res. 2018;371:505‐516. [DOI] [PubMed] [Google Scholar]
- 28. Qin C, Zhou L, Hu Z, et al. Dysregulation of immune response in patients with COVID‐19 in Wuhan, China. Clin Infect Dis. 2020;ciaa248 https://academic.oup.com/cid/article/doi/10.1093/cid/ciaa248/5803306 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Wu C, Chen X, Cai Y, et al. Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Xu Z, Shi L, Wang Y, et al. Pathological findings of COVID‐19 associated with acute respiratory distress syndrome. Lancet Respir. 2020;2600:19‐21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Short KR, Kroeze EJBV, Fouchier RAM, et al. Pathogenesis of influenza‐induced acute respiratory distress syndrome. Lancet Infect Dis. 2014;14(1):57‐69. [DOI] [PubMed] [Google Scholar]
- 32. Spindler C, Örtqvist Å. Prognostic score systems and community‐acquired bacteraemic pneumococcal pneumonia. Eur Respir J. 2006;28(4):816‐823. [DOI] [PubMed] [Google Scholar]
- 33. Wang Y, Zhang S, Li L, et al. The usefulness of serum procalcitonin, C‐reactive protein, soluble triggering receptor expressed on myeloid cells 1 and clinical pulmonary infection score for evaluation of severity and prognosis of community‐acquired pneumonia in elderly patients. Arch Gerontol Geriatr. 2019;80:53‐57. [DOI] [PubMed] [Google Scholar]
- 34. Azab B, Camacho‐Rivera M, Taioli E. Average values and racial differences of neutrophil lymphocyte ratio among a nationally representative sample of United States subjects. PLoS One. 2014;9(11):e112361. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Bain BJ. Ethnic and sex differences in the total and differential white cell count and platelet count. J Clin Pathol. 1996;49(8):664‐666. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used during the current study are available from the corresponding author on reasonable request.