The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis
Open accessAbstract
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 registry: This meta-analysis was prospectively registered on PROSPERO database (Registration number: CRD42020203612).
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1 retracted
Wang, X., Xu, W., Hu, G., Xia, S., Sun, Z., Liu, Z., Xie, Y., Zhang, R., Jiang, S., & Lu, L. (2020) · Cellular & Molecular Immunology · doi:10.1038/s41423-020-0498-4 · Cited by 175
RetractedMeasuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2010) · International Journal of Surgery · doi:10.1016/j.ijsu.2010.02.007 · Cited by 12,156
Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., Huang, H., Zhang, L., Zhou, X., Du, C., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.0994 · Cited by 8,249
Self-citationA Review of Coronavirus Disease-2019 (COVID-19)
Singhal, T. (2020) · The Indian Journal of Pediatrics · doi:10.1007/s12098-020-03263-6 · Cited by 3,537
Templeton, A. J., McNamara, M. G., Šeruga, B., Vera-Badillo, F. E., Aneja, P., Ocaña, A., Leibowitz-Amit, R., Sonpavde, G., Knox, J. J., Tran, B., et al. (2014) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/dju124 · Cited by 2,829
The immunopathology of sepsis and potential therapeutic targets
van der Poll, T., van de Veerdonk, F. L., Scicluna, B. P., & Netea, M. G. (2017) · Nature Reviews Immunology · doi:10.1038/nri.2017.36 · Cited by 1,957
Coronavirus Disease 2019–COVID-19
Dhama, K., Khan, S., Tiwari, R., Sircar, S., Bhat, S., Malik, Y. S., Singh, K. P., Chaicumpa, W., Bonilla-Aldana, D. K., & Rodriguez-Morales, A. J. (2020) · Clinical Microbiology Reviews · doi:10.1128/cmr.00028-20 · Cited by 1,876
Cited by
Buonacera, A., Stancanelli, B., Colaci, M., & Malatino, L. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23073636 · Cited by 1,083
Novel Systemic Inflammation Markers to Predict COVID-19 Prognosis
Karimi, A., Shobeiri, P., Kulasinghe, A., & Rezaei, N. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.741061 · Cited by 171
Metadata
- Authors
- Li, X., Liu, C., Mao, Z., Xiao, M., Wang, L., Qi, S., & Zhou, F.
- Venue
- Critical Care
- Published
- 2020-11-16
- Volume
- 24
- Issue
- 1
- Pages
- 647-647
- ISSN
- 1364-8535 (ISSN-L)
- OpenAlex
- W3102695835
- Cited by
- 380
- References
- 51
- FWCI
- 4.03
- Type
- journal article
- Language
- en
- Open access
- gold
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- Li, X., Liu, C., Mao, Z., Xiao, M., Wang, L., Qi, S., & Zhou, F. (2020). Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: a systematic review and meta-analysis. Critical Care. https://doi.org/10.1186/s13054-020-03374-8
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