Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-71611/v2

Download PDF

Research

Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-71611/v2

This work is licensed under a CC BY 4.0 License

Journal Publication

published 16 Nov, 2020

Read the published version in Critical Care →

You are reading this latest preprint version

Background：Coronavirus disease 2019 (COVID-19), a highly infectious disease, has been rapidly spreading all over the world and remains a great threat to global public health. Patients diagnosed with severe or critical cases have a poor prognosis. Hence, it is crucial for us to identify potentially severe or critical cases early, and give timely treatments for targeted patients. In the clinical practice of treating patients with COVID-19, we have observed that the neutrophil-to-lymphocyte ratio (NLR) of severe patients is higher than that in mild patients. We performed this systematic review and meta-analysis to evaluate the predictive values of NLR on disease severity and mortality in patients with COVID-19.

Methods: We searched PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases to identify eligible studies (up to August 11, 2020). Two authors independently screened studies and extracted data. The methodological quality of the included studies was assessed by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

Results: Thirteen studies involving 1579 patients reported the predictive value of NLR on disease severity. The pooled sensitivity (SEN), specificity (SPE) and area under curve (AUC) were 0.78 (95% CI 0.70-0.84), 0.78 (95% CI 0.73-0.83) and 0.85 (95% CI 0.81-0.88), respectively. Ten studies involving 2967 patients reported the predictive value of NLR on mortality. The pooled SEN, SPE and AUC were 0.83 (95% CI 0.75-0.89), 0.83 (95% CI 0.74-0.89) and 0.90 (95% CI 0.87-0.92), respectively.

Conclusions: NLR has good predictive values on disease severity and mortality in patients with COVID-19 infection. Evaluating NLR can help clinicians identify potentially severe cases early, conduct early triage and initiate effective management in time, which may reduce the overall mortality of COVID-19.

Trial registration: This meta-analysis was prospectively registered on PROSPERO database (Registration number: CRD42020203612).

Critical Care & Emergency Medicine

Neutrophil-to-lymphocyte ratio

Disease severity

Mortality

Predictive

Systematic review

Meta-analysis

Coronavirus disease 2019 (COVID-19), a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been rapidly spreading all over the world and remains a great threat to global public health [1]. The clinical symptoms of patients with COVID-19 vary widely. A significant proportion of patients with COVID-19 have mild symptoms, such as fever, muscle ache, cough, shortness of breath and fatigue, and about half of patients don't show any obvious symptoms [2, 3]. However, some severe cases with severe pneumonia can develop into acute respiratory distress syndrome (ARDS), pulmonary edema, or multiple organ dysfunction syndrome(MODS), hence leading to a high mortality [4-6]. Although many patients have mild symptoms, they may suddenly progress to ARDS, septic shock, or even MODS [7]. Patients diagnosed with severe or critical illness have a poor prognosis. Hence, it is crucial for us to identify potentially severe or critical cases early, and give timely treatments for targeted patients. Therefore, we can prevent the progression of COVID-19, save medical resources and reduce mortality.

Similar to patients with Middle East respiratory syndrome (MERS) and severe acute respiratory syndrome (SARS), dysregulated inflammation leading to cytokine storms is associated with worsening clinical outcomes in patients with COVID-19 [8-10]. Emerging evidences suggested that peripheral blood neutrophil-to-lymphocyte ratio (NLR) can be used as a marker of systemic inflammation. Furthermore, NLR has shown good predictive values on progression and clinical outcomes in various disease, such as solid tumors, chronic obstructive pulmonary disease (COPD), cardiovascular disease and pancreatitis [11-14]. Recently, several studies have reported that NLR may differentiate between mild/moderate and severe/critical groups; and probability of death in patients with COVID-19 infection. In addition, a series of studies have suggested NLR is a reliable predictor of COVID-19 progression and elevated NLR is associated with high mortality [15-20].

NLR is a readily available biomarker that can be calculated from components of the differential white cell count (dividing neutrophil by lymphocyte count). We performed this systematic review and meta-analysis to evaluate the predictive values of NLR on disease severity and mortality in patients with COVID-19, and to provide a reliable marker for early identification of potentially severe or critically ill cases.

We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA statement) guidelines to perform this meta-analysis [21]. It was prospectively registered on PROSPERO database (Registration number: CRD42020203612).

Selection of studies

We reviewed PubMed, EMBASE, China National Knowledge Infrastructure (CNKI) and Wanfang databases through August 11, 2020. The search terms were as follows: (“Neutrophil to lymphocyte ratio” or “neutrophil lymphocyte ratio” or “neutrophil-to-lymphocyte ratio” or “neutrophil/lymphocyte ratio” or “NLR”) and (“Coronavirus disease 2019” or “2019 Novel Coronavirus” or “SARS-CoV-2” or “2019-nCoV” or “COVID-19”). The detail of search strategy of PubMed was shown in the Additional File 1. No language restrictions were imposed. To find additional citations, the reference lists of the included studies and recent review articles were screened when necessary.

Two authors (X.L and C.L) independently screened all identified citations to find studies for the final analysis. Any disagreement was resolved through discussion. In case of persistent disagreement, we consulted the third reviewer (F.Z) for arbitration. Studies were selected if they met the following criteria: 1) the predictive value of NLR on disease severity or mortality in patients with COVID-19 was evaluated; 2) a 2 × 2 table of results could be constructed [sufficient information to calculate true positive (TP), false positive (FP), false negative (FN), and true negative (TN)]. The exclusion criteria were as follows: 1) case report, review, editorial, conference abstract, comment, letter, animal study; 2) unable to extract a 2 × 2 table of results.

Data extraction and quality assessment

Two authors (X.L and C.L) independently extracted relevant information from individual studies, including first author, publication year, country, publication language, number of patients (male/female), mean age, cut-off value, area under curve (AUC), TP, TN, FP, FN, sensitivity (SEN) and specificity (SPE). The extracted informations were checked by a third author (Z.M). We used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria to evaluate each of the included studies in 4 domains: patient selection; index test; reference standard; and flow and test timing [22].

Statistical analysis

The statistical analyses were conducted by STATA (version 14.0) using MIDAS module [23]. A bivariate random-effects regression model was performed to calculate SEN, SPE, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR) and corresponding 95% credible interval (CI). A summary receiver operating characteristic (SROC) curve was drawn to assess the overall diagnostic accuracy. The higher the AUC value, the better the diagnostic power [24]. We used Deeks funnel plot to detect publication bias. If the P value is less than 0.1, publication bias may exist. I² index was calculated to assess heterogeneity between studies and I² values above 50% were regarded as the indicative of substantial heterogeneity [25]. We conducted Fagan nomograph to explore the relationship between the pretest probability, likelihood ratio, and the posttest probability. To investigate potential sources of heterogeneity among included studies, sensitivity and subgroup analyses were conducted. In sensitivity analyses, we only included studies published in English. We did subgroup analyses based on cut-off value.

Selection and characteristics of studies

As a result of the literature search, a total of 298 studies were identified, including 97 from PubMed, 91 from Embase, 62 from CNKI, and 48 from Wanfang debase. Figure 1 showed the study selection process. 111 duplicate publications were excluded. According to the inclusion and exclusion criteria, we excluded 145 studies by evaluating the titles and abstracts. The remaining 42 studies were further scrutinized by reading the full text. Finally, only 19 studies were included in this meta-analysis, of which 9 reported the predictive value on disease severity [26-34], 6 reported the predictive value on mortality [35-40], and 4 reported the predictive value on both disease severity and mortality [41-44].

The characteristics of the included studies and the predictive value of NLR on disease severity or mortality in each study are presented in Table 1. Most studies were conducted in China. Twelve studies were published in English, six in Chinese and one in Spanish. Except one prospective study [27] , all others were retrospective studies. The number of participants across studies ranged from 45 to 1004. Notably, the SEN, SPE, AUC and cut-off value of NLR predicting mortality and disease severity ranged greatly among the included studies. Except two studies [41, 42], all other studies defined severe patients as meeting at least one of the following criterions: Shortness of breath, respiratory rate (RR)≥30 times/min, or oxygen saturation (resting state) ≤ 93%, or PaO2/FiO2 ≤300mmHg.

Study quality and publication bias

The methodological quality of the included studies is presented in Additional File 2. One study only included patients classified as moderate [36], one included only severe patients [35], and another included only elderly patients [40]. Therefore, these three studies were considered to have a high risk of patient selection bias. One study included 32 moderate cases, and another 31 severe cases were included as a control group [32]. One study included 48 moderate cases, and another 37 severe cases were included as a control group [34]. One study included 50 moderate cases, and another 43 severe cases were included as a control group [43]. One study included 42 dead patients, and another 42 discharged patients were included as a control group [37]. These four studies were also assessed to show high risk of patient selection bias, because they did not avoid a case-control design. One study did not provide sufficient information about patients enrolled and leaded to a high risk of patient selection in our opinion [33].Most studies were considered to have unclear risk of bias regarding index tests, because they did not report the blindness between index and reference tests. Deek funnel plot is shown in Additional File 3, publication bias may exist among studies reporting the predictive value of NLR on disease severity (P=0.04).

Predictive value of NLR on disease severity

Thirteen studies involving 1579 patients reported the predictive value of NLR on disease severity. The pooled SEN and SPE were 0.78 (95% CI 0.70-0.84, I²=71.83) and 0.78 (95% CI 0.73-0.83, I²=77.80), respectively (Figure 2a). The positive likelihood ratio was 3.6 (95% CI 2.9-4.4), and the negative likelihood ratio was 0.28(95% CI 0.21-0.38). The DOR was 13(95% CI 9-18). The SROC curve is shown in Figure 3a. The AUC of NLR for predicting disease severity was 0.85 (95% CI 0.81-0.88), indicating high diagnostic value. We can learn from Fagan nomogram (Figure 4a) that if the pre-test probability was set to 50%, the post-test probability of NLR for the detection of severe cases was 78% when the NLR was above the cut-off value. On the contrary, when the NLR was below the cut-off value, the post-test probability was 26%.

Predictive value of NLR on mortality

Ten studies involving 2967 patients reported the predictive value of NLR on mortality. The pooled SEN and SPE were 0.83 (95% CI 0.75-0.89, I²=66.13) and 0.83 (95% CI 0.74-0.89, I²=90.34), respectively (Figure 2b). The positive likelihood ratio was 4.8 (95% CI 3.3-7.0), and the negative likelihood ratio was 0.21(95% CI 0.15-0.30). The DOR was 23(95% CI 15-36). The SROC with pooled diagnostic accuracy was 0.90 (95% CI 0.87-0.92), presented in Figure 3b. The Fagan nomogram showed that the post-test probability of NLR for the detection of mortality was 83% when the NLR was above the cut-off value and the post-test probability was 17% when the NLR was below the cut-off value. (Figure 4b).

Subgroup analyses and sensitivity analyses

We conducted the subgroup analyses based on the cut-off value. In terms of predicting disease severity, the cut-off value in six studies was higher than 4.5, and were termed the “high cut-off value” subgroup. Seven others used a lower cut-off value, which were included in the “low cut-off value” subgroup. The AUC were 0.86 (95% CI 0.83-0.89) and 0.82 (95% CI 0.78-0.85), respectively. Similarly, ten studies reporting the predictive value of NLR on mortality were divided into “high cut-off value” (cut-off≥6.5) and “low cut-off value”(<6.5) subgroups, and the AUC were 0.92 (95% CI 0.89-0.94) and 0.84 (95% CI 0.80-0.87), respectively. In the sensitivity analyses, we only included studies published in English. The pooled AUC for predicting disease severity and mortality were 0.83 (95% CI 0.80-0.86) and 0.90 (95% CI 0.87-0.92), respectively. Detailed results about subgroup analyses and sensitivity analyses are presented in Table 2.

Although in the clinical practice of treating patients with COVID-19, we have observed that the NLR of severe patients is higher than that in mild patients, there is no systematic review and meta-analysis to evaluate the predictive values of NLR on disease severity and mortality in patients with COVID-19. Studies have reported various thresholds to NLR. Clinicians are therefore unclear regarding the thresholds of NLR that should be applied in order to categorize severity of disease and predict prognosis. Our study suggested that NLR can not only be a good biomarker predicting disease severity in patients with COVID-19 (AUC=0.85, SEN=0.78, and SPE=0.78), but also have value in predicting mortality (AUC=0.90, SEN=0.83, and SPE=0.83).

COVID-19 spread rapidly and is an ongoing global pandemic. Medical workers from different countries make efforts to explore the best diagnostic method and the most effective treatment for COVID-19. More and more studies have focused on COVID-19 and published in different languages. To find enough studies that reported the predictive values of NLR on disease severity and mortality in patients with COVID-19, we did not impose any language restrictions. In our final analyses, twelve studies were published in English, six in Chinese and one in Spanish. To our knowledge, English is the most widely used language in the world. Studies published in English may have a wider readership and receive peer review from different countries, while studies published in other languages may be available only to native speakers. Therefore, we performed sensitivity analyses by omitting studies not published in English. The results of the sensitivity analyses were accordance with the main analyses, indicating that the publish language was not a confounding factor.

To our knowledge, the treatments for mild cases and severe cases are greatly different. For mild cases, there is no need to intervene too much. Some patients can even recover without any treatments. However, for severe cases, even is we take many kinds of measures, such as mechanical ventilation, extracorporeal membrane oxygenation (ECMO) and continuous renal replacement therapy (CRRT), the mortality is still high [45, 46]. Therefore, if the potentially severe cases were identified early and effective treatments were taken to prevent the progression of those patients, more patients’ lives may be saved.

The current criteria for classifying mild cases and severe cases are mainly based on RR, oxygen saturation and PaO2/FiO2. These indicators are important but lack specificity for COVID-19. In laboratory examination of patients with COVID-19, the absolute value of peripheral white blood cells is usually normal or low, and lymphopenia is common [47]. However, in severe or non-survival patients with COVID-19, the lymphocytes count decreases progressively while the neutrophils count gradually increases. This may be due to excessive inflammation and immune suppression caused by SARS-CoV-2 infection. On the one hand, neutrophils are generally regarded as pro-inflammatory cells with a range of antimicrobial activities, which can be triggered by virus-related inflammatory factors, such as interleukin-6 and interleukin-8[9]. On the other hand, systematic inflammation triggered by SARS-CoV-2 significantly depresses cellular immunity, leading to a decrease in CD3+ T cells, CD4+ T cells and CD8+ T cells. In addition, SARS-CoV-2 infected T cells may also cause cytopathic effects on T cells [10, 48-50]. Therefore, NLR, a cost‐effective marker can be easily calculated from peripheral blood routine tests, and may be associated with the progression and prognosis of COVID-19. To date, four meta-analyses have reported that patients with severe COVID-19 infection had a higher NLR than those with non-severe COVID-19 infection [51-54]. However, none of them evaluated the predictive values of NLR on disease severity and mortality.

There are several limitations in this meta-analysis. First, all but one of the studies were retrospective, meaning the data were prone to confounding factors. Second, the progression and prognosis of disease were influenced by many factors, such as age, sex, and comorbidities, while we did not evaluate other factors. Finally, there was considerable heterogeneity among the included studies. Although we conducted sensitivity and subgroup analyses, the heterogeneity was not significantly decreased. That may be caused by different cut-off values, different conditions of patients, or different comorbidities among the included studies. Additional high-quality studies are required to shed light on the role of NLR in the progression and prognosis of COVID-19 and find the optimal cut-off value.

NLR has good predictive values on disease severity and mortality in patients with COVID-19 infection. Evaluating NLR can help clinicians identify potentially severe cases early, conduct early triage and initiate effective management in time, which may reduce the overall mortality of COVID-19.

COVID-19: Coronavirus disease 2019; NLR: neutrophil-to-lymphocyte ratio; CNKI: China National Knowledge Infrastructure; QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies 2; SEN: sensitivity; SPE: specificity; AUC: area under curve; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; ARDS: acute respiratory distress syndrome; MODS: multiple organ dysfunction syndrome; MERS: Middle East respiratory syndrome; SARS: severe acute respiratory syndrome; COPD: chronic obstructive pulmonary disease; PRISMA statement: Preferred Reporting Items for Systematic Reviews and Meta-Analyses; TP: true positive; FP: false positive; FN: false negative; TN: true negative; DOR: diagnostic odds ratio; CI: credible interval; SROC: summary receiver operating characteristic; RR: respiratory rate; ECMO: extracorporeal membrane oxygenation; CRRT: continuous renal replacement therapy.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Competing interests

The authors declare that they have no competing interests.

Funding

WU JIEPING MEDICAL FOUNDATION (No. 320.6750.18383).

Authors' contributions

XML and CL contributed equally to this work. XML and CL conceived the study, participated in the design, collected the data, performed statistical analyses and drafted the manuscript. ZM and LW collected the data and performed statistical analyses. MLX and SQ collected the data and revised the manuscript critically for important intellectual content. FHZ performed statistical analyses and helped to revise the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

Acknowledgements

We thank all researchers and clinicians involved in the individual trials.

Dhama K, Khan S, Tiwari R, Sircar S, Bhat S, Malik YS, Singh KP, Chaicumpa W, Bonilla-Aldana DK, Rodriguez-Morales AJ: Coronavirus Disease 2019-COVID-19. Clin Microbiol Rev 2020, 33(4).
Feng W, Zong W, Wang F, Ju S: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): a review. Mol Cancer 2020, 19(1):100.
Tabata S, Imai K, Kawano S, Ikeda M, Kodama T, Miyoshi K, Obinata H, Mimura S, Kodera T, Kitagaki M et al: Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: a retrospective analysis. Lancet Infect Dis 2020.
Qin H, Zhao A: Mesenchymal stem cell therapy for acute respiratory distress syndrome: from basic to clinics. Protein Cell 2020.
Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, Huang H, Zhang L, Zhou X, Du C 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.
Ferrando C, Suarez-Sipmann F, Mellado-Artigas R, Hernandez M, Gea A, Arruti E, Aldecoa C, Martinez-Palli G, Martinez-Gonzalez MA, Slutsky AS et al: Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS. Intensive Care Med 2020.
Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y 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.
Manjili RH, Zarei M, Habibi M, Manjili MH: COVID-19 as an Acute Inflammatory Disease. J Immunol 2020, 205(1):12-19.
Mangalmurti N, Hunter CA: Cytokine Storms: Understanding COVID-19. Immunity 2020, 53(1):19-25.
Azkur AK, Akdis M, Azkur D, Sokolowska M, van de Veen W, Bruggen MC, O'Mahony L, Gao Y, Nadeau K, Akdis CA: Immune response to SARS-CoV-2 and mechanisms of immunopathological changes in COVID-19. Allergy 2020, 75(7):1564-1581.
Templeton AJ, McNamara MG, Seruga B, Vera-Badillo FE, Aneja P, Ocana A, Leibowitz-Amit R, Sonpavde G, Knox JJ, Tran B et al: Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst 2014, 106(6):dju124.
Kim S, Eliot M, Koestler DC, Wu WC, Kelsey KT: Association of Neutrophil-to-Lymphocyte Ratio With Mortality and Cardiovascular Disease in the Jackson Heart Study and Modification by the Duffy Antigen Variant. JAMA Cardiol 2018, 3(6):455-462.
Paliogiannis P, Fois AG, Sotgia S, Mangoni AA, Zinellu E, Pirina P, Negri S, Carru C, Zinellu A: Neutrophil to lymphocyte ratio and clinical outcomes in COPD: recent evidence and future perspectives. Eur Respir Rev 2018, 27(147).
Kong W, He Y, Bao H, Zhang W, Wang X: Diagnostic Value of Neutrophil-Lymphocyte Ratio for Predicting the Severity of Acute Pancreatitis: A Meta-Analysis. Dis Markers 2020, 2020:9731854.
Lian J, Jin C, Hao S, Zhang X, Yang M, Jin X, Lu Y, Hu J, Zhang S, Zheng L et al: High neutrophil-to-lymphocyte ratio associated with progression to critical illness in older patients with COVID-19: a multicenter retrospective study. Aging (Albany NY) 2020, 12(14):13849-13859.
Zhang JJ, Cao YY, Tan G, Dong X, Wang BC, Lin J, Yan YQ, Liu GH, Akdis M, Akdis CA et al: Clinical, radiological and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy 2020.
Fu J, Kong J, Wang W, Wu M, Yao L, Wang Z, Jin J, Wu D, Yu X: The clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: A retrospective study in Suzhou China. Thromb Res 2020, 192:3-8.
Liao D, Zhou F, Luo L, Xu M, Wang H, Xia J, Gao Y, Cai L, Wang Z, Yin P et al: Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study. Lancet Haematol 2020.
Nalbant A, Kaya T, Varim C, Yaylaci S, Tamer A, Cinemre H: Can the neutrophil/lymphocyte ratio (NLR) have a role in the diagnosis of coronavirus 2019 disease (COVID-19)? Rev Assoc Med Bras (1992) 2020, 66(6):746-751.
Ma A, Cheng J, Yang J, Dong M, Liao X, Kang Y: Neutrophil-to-lymphocyte ratio as a predictive biomarker for moderate-severe ARDS in severe COVID-19 patients. Crit Care 2020, 24(1):288.
Moher D, Liberati A, Tetzlaff J, Altman DG, Group P: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 2010, 8(5):336-341.
Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, Group Q-: QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011, 155(8):529-536.
Dwamena BJSSC: MIDAS: Stata module for meta-analytical integration of diagnostic test accuracy studies. 2007.
Jones CM, Athanasiou T: Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. Ann Thorac Surg 2005, 79(1):16-20.
Higgins JP, Thompson SG, Deeks JJ, Altman DG: Measuring inconsistency in meta-analyses. BMJ 2003, 327(7414):557-560.
Fei M, Tong F, Tao X, Wang J: Value of neutrophil-to-lymphocyte ratio in the classification diagnosis of coronavirus disease 2019. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue 2020, 32(5):554-558.
Liu J, Liu Y, Xiang P, Pu L, Xiong H, Li C, Zhang M, Tan J, Xu Y, Song R et al: Neutrophil-to-lymphocyte ratio predicts critical illness patients with 2019 coronavirus disease in the early stage. J Transl Med 2020, 18(1):206.
Liu YP, Li GM, He J, Liu Y, Li M, Zhang R, Li YL, Wu YZ, Diao B: Combined use of the neutrophil-to-lymphocyte ratio and CRP to predict 7-day disease severity in 84 hospitalized patients with COVID-19 pneumonia: a retrospective cohort study. Ann Transl Med 2020, 8(10):635.
Shang W, Dong J, Ren Y, Tian M, Li W, Hu J, Li Y: The value of clinical parameters in predicting the severity of COVID-19. J Med Virol 2020.
Sun S, Cai X, Wang H, He G, Lin Y, Lu B, Chen C, Pan Y, Hu X: Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clin Chim Acta 2020, 507:174-180.
Wang C, Deng R, Gou L, Fu Z, Zhang X, Shao F, Wang G, Fu W, Xiao J, Ding X et al: Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters. Ann Transl Med 2020, 8(9):593.
Xia X, Wen M, Zhan S, He J, Chen W: An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19. Nan Fang Yi Ke Da Xue Xue Bao 2020, 40(3):333-336.
Yang AP, Liu JP, Tao WQ, Li HM: The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol 2020, 84:106504.
Zha Q, Feng B, Li X, Zhou D, Kang Y, Qin H: Study on early laboratory warning of severe COVID-19. Laboratory Medicine 2020, 35(06):557-560.
Chen FF, Zhong M, Liu Y, Zhang Y, Zhang K, Su DZ, Meng X, Zhang Y: The characteristics and outcomes of 681 severe cases with COVID-19 in China. J Crit Care 2020, 60:32-37.
Cheng B, Hu J, Zuo X, Chen J, Li X, Chen Y, Yang G, Shi X, Deng A: Predictors of progression from moderate to severe coronavirus disease 2019: a retrospective cohort. Clin Microbiol Infect 2020.
Song H, Bai T, Shi J, Yang J: Predictive Value of Multiple Inflammatory Indexes on the Prognosis of Patients with Corona Virus Disease 2019. Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease 2020, 28(06):13-16.
Tatum D, Taghavi S, Houghton A, Stover J, Toraih E, Duchesne J: Neutrophil-to-Lymphocyte Ratio and Outcomes in Louisiana Covid-19 Patients. Shock 2020.
Yan X, Li F, Wang X, Yan J, Zhu F, Tang S, Deng Y, Wang H, Chen R, Yu Z et al: Neutrophil to lymphocyte ratio as prognostic and predictive factor in patients with coronavirus disease 2019: A retrospective cross-sectional study. J Med Virol 2020.
Zhang W, Li S, Xie X, Wang J, Guo W, Lei Y, Wang X, Xu C, Li Z, Chen Y et al: Clinical characteristics and risk factors of mortality in elderly patients with novel coronavirus pneumonia. Pract Geriatr 2020, 34(7):745-749.
Asghar MS, Haider Kazmi SJ, Ahmed Khan N, Akram M, Ahmed Khan S, Rasheed U, Hassan M, Memon GM: Clinical Profiles, Characteristics, and Outcomes of the First 100 Admitted COVID-19 Patients in Pakistan: A Single-Center Retrospective Study in a Tertiary Care Hospital of Karachi. Cureus 2020, 12(6):e8712.
Basbus L, Lapidus MI, Martingano I, Puga MC, Pollan J: Neutrophil to lymphocyte ratio as a prognostic marker in COVID-19. Medicina (B Aires) 2020, 80 Suppl 3:31-36.
Li H, Zhao M, Xu Y: Biochemical analysis between common type and critical type of COVID-19 and clinical value of neutrophil/lymphocyte ratio. Zhejiang Da Xue Xue Bao Yi Xue Ban 2020, 40(7):965-971.
Ok F, Erdogan O, Durmus E, Carkci S, Canik A: Predictive values of blood urea nitrogen/creatinine ratio and other routine blood parameters on disease severity and survival of COVID-19 patients. J Med Virol 2020.
Schmidt M, Hajage D, Lebreton G, Monsel A, Voiriot G, Levy D, Baron E, Beurton A, Chommeloux J, Meng P et al: Extracorporeal membrane oxygenation for severe acute respiratory distress syndrome associated with COVID-19: a retrospective cohort study. Lancet Respir Med 2020.
Xie J, Wu W, Li S, Hu Y, Hu M, Li J, Yang Y, Huang T, Zheng K, Wang Y et al: Clinical characteristics and outcomes of critically ill patients with novel coronavirus infectious disease (COVID-19) in China: a retrospective multicenter study. Intensive Care Med 2020.
Singhal T: A Review of Coronavirus Disease-2019 (COVID-19). Indian J Pediatr 2020, 87(4):281-286.
Wang X, Xu W, Hu G, Xia S, Sun Z, Liu Z, Xie Y, Zhang R, Jiang S, Lu L: RETRACTED ARTICLE: SARS-CoV-2 infects T lymphocytes through its spike protein-mediated membrane fusion. Cell Mol Immunol 2020.
van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG: The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol 2017, 17(7):407-420.
Zhang X, Tan Y, Ling Y, Lu G, Liu F, Yi Z, Jia X, Wu M, Shi B, Xu S et al: Viral and host factors related to the clinical outcome of COVID-19. Nature 2020, 583(7816):437-440.
Chan AS, Rout A: Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVID-19. J Clin Med Res 2020, 12(7):448-453.
Zeng F, Li L, Zeng J, Deng Y, Huang H, Chen B, Deng G: Can we predict the severity of coronavirus disease 2019 with a routine blood test? Pol Arch Intern Med 2020, 130(5):400-406.
Ghahramani S, Tabrizi R, Lankarani KB, Kashani SMA, Rezaei S, Zeidi N, Akbari M, Heydari ST, Akbari H, Nowrouzi-Sohrabi P et al: Laboratory features of severe vs. non-severe COVID-19 patients in Asian populations: a systematic review and meta-analysis. Eur J Med Res 2020, 25(1):30.
Lagunas-Rangel FA: Neutrophil-to-lymphocyte ratio and lymphocyte-to-C-reactive protein ratio in patients with severe coronavirus disease 2019 (COVID-19): A meta-analysis. J Med Virol 2020.

Table 1 Characteristics of the included studies and diagnostic test performance of NLR for disease severity and mortality
	Study	Country	Publication language	No. of patients	Male/ female	Mean age	Cut-off	AUC	TP	FP	FN	TN	SEN (%)	SPE (%)
Severity	Yang2020[33]	China	English	93	56/37	46.4 ± 17.6	3.3	0.84	21	25	3	44	88.0	63.6
	Wang2020[31]	China	English	45	23/22	39.0 ± 11.5	13.4	0.89	8	6	2	29	83.3	82.4
	Fesih2020[44]	Turkey	English	139	62/77	55.5 ± 18.5	3.3	0.87	43	25	11	60	79.0	71.0
	Jingyuan Liu2020[27]	China	English	115	64/51	NA	3.1	NA	28	13	9	65	75.7	83.3
	Asghar2020	Pakistan	English	100	69/31	52.6 ± 15.7	3.7	0.80	29	25	4	42	87.9	62.1
	Sun2020[30]	China	English	116	60/56	50.0 ± 4.0	4.5	0.89	20	9	7	80	74.1	89.9
	Shang2020[29]	China	English	443	220/223	56.0 ± 17.4	4.3	0.74	78	50	61	254	56.3	83.7
	Yueping Liu2020[28]	China	English	84	47/37	53.0 ± 17.8	4.9	0.76	13	8	10	53	56.5	86.9
	Basbus2020[42]	Spain	Spanish	131	71/60	52.0 ± 30.4	3.0	NA	17	36	4	74	80.9	67.3
	Li2020[43]	China	Chinese	93	55/38	62.1 ± 16.8	11.3	NA	34	4	9	46	79.1	92.0
	Zha2020[34]	China	Chinese	85	57/28	54.2 ± 16.0	5.6	0.77	25	10	12	38	68.8	78.4
	Fei2020[26]	China	Chinese	72	32/40	58.0 ± 13.8	3.0	0.89	20	14	0	38	100	73.1
	Xia2020[32]	China	Chinese	63	33/30	63.4 ± 14.9	4.8	0.83	26	8	5	24	83.9	75.0
Mortality
	Cheng2020[36]	China	English	456	211/245	55.0 ± 18.6	3.2	0.81	26	107	10	303	78.3	73.9
	Tatum2020[38]	America	English	125	57/68	58.7 ± 14.8	10.0	0.71	12	4	11	98	52.4	96.7
	Chen2020[35]	China	English	681	362/219	65.0 ± 13.3	6.7	0.86	87	130	17	447	83.7	77.5
	Fesih2020[44]	Turkey	English	139	62/77	55.5 ± 18.5	5.7	0.85	11	13	2	113	83.0	90.0
	Asghar2020[41]	Pakistan	English	100	69/31	52.6 ± 15.7	4.2	0.81	20	29	2	49	90.9	62.6
	Yan2020[39]	China	English	1004	493/511	NA	11.8	0.95	39	211	1	753	97.5	78.1
	Basbus2020[42]	Spain	Spanish	131	71/60	52.0 ± 30.4	3	NA	7	46	2	76	77.8	62.3
	Li2020[43]	China	Chinese	93	55/38	62.1 ± 16.8	11.3	0.92	28	10	3	52	90.3	83.9
	Song2020[37]	China	Chinese	84	56/28	66.5 ± 12.2	6.1	0.87	32	5	10	37	76.2	88.1
	Zhang2020[40]	China	Chinese	154	81/73	69.2 ± 7.5	9.4	0.86	21	10	6	117	76.2	92.0
AUC, area under curve; TP, true positive; FP, false positive; FN, false negative; TN, true negative; SEN, sensitivity; SPE, specificity; NLR, neutrophil-to-lymphocyte ratio; NA, not available.

Table 2 Results of sensitivity analysis and subgroup analysis
Categories	No. of studies	Sensitivity (95% CI)/I²	Specificity (95% CI) /I²	AUC (95% CI)	DOR (95% CI)	PLR/NLR
Disease severity
Cut-of ≥ 4.5	6	0.74(0.66,0.80)/25.56	0.86(0.81,0.89)/36.40	0.86(0.83,0.89)	17(10,28)	5.1/0.31
Cut-of < 4.5	7	0.82(0.71,0.89)/82.74	0.72(0.66,0.78)/79.70	0.82(0.78,0.85)	12(7,19)	3.0/0.25
Published in English	8	0.74(0.63,0.83)/73.82	0.78(0.71,0.84)/81.99	0.83(0.80,0.86)	10(7,16)	3.4/0.33
Mortality
Cut-off ≥ 6.5	5	0.83(0.66,0.92)/84.97	0.87(0.77,0.93)/92.60	0.92(0.89,0.94)	32(17,61)	6.3/0.20
Cut-off < 6.5	5	0.81(0.72,0.87)/0	0.77(0.64,0.86)/89.07	0.84(0.80,0.87)	14(7,27)	3.5/0.25
Published in English	6	0.83(0.69,0.91)/79.98	0.82(0.71,0.90)/89.87	0.90(0.87,0.92)	23(12,41)	4.7/0.21
AUC, area under curve; PLR, positive likelihood ratio; NLR, negative likelihood ratio; DOR, diagnostic odds ratio; CI, credible interval.

AdditionalFile1.docx
Additional file 1: Search strategy terms and results of Pubmed.
AdditionalFile2.tif
Additional file 2: Summary of the methodological quality of the studies according to the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) criteria.
AdditionalFile3.tif
Additional file 3: Deek funnel plot asymmetry test for publication bias，with P < 0.1 indicating publication bias.

Download PDF

Journal Publication

published 16 Nov, 2020

Read the published version in Critical Care →

Editorial decision: Accept
03 Nov, 2020
Editor assigned by journal
26 Oct, 2020
Submission checks completed at journal
25 Oct, 2020
Editor invited by journal
25 Oct, 2020

You are reading this latest preprint version

Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis

Status:

Journal Publication

Version 2

Abstract

Figures

Introduction

Methods

Results

Discussion

Conclusions

List Of Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 2