Next Article in Journal
Optimization of Nazarov Cyclization of 2,4-Dimethyl-1,5-diphenylpenta-1,4-dien-3-one in Deep Eutectic Solvents by a Design of Experiments Approach
Next Article in Special Issue
Suppressive Effect of Two Cucurbitane-Type Triterpenoids from Momordica charantia on Cutibacterium acnes-Induced Inflammatory Responses in Human THP-1 Monocytic Cell and Mouse Models
Previous Article in Journal
Temporin-Like Peptides Show Antimicrobial and Anti-Biofilm Activities against Streptococcus mutans with Reduced Hemolysis
Previous Article in Special Issue
Oxidative Stress Biomarkers and Peripheral Endothelial Dysfunction in Rheumatoid Arthritis: A Monocentric Cross-Sectional Case-Control Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Molecules
Volume 25
Issue 23
10.3390/molecules25235725
molecules-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients

by
Alessandro G. Fois
1,2,
Panagiotis Paliogiannis
1,*,
Valentina Scano
1,
Stefania Cau
1,
Sergio Babudieri
1,
Roberto Perra
3,
Giulia Ruzzittu
4,
Elisabetta Zinellu
2,
Pietro Pirina
1,2,
Ciriaco Carru
5,
Luigi B. Arru
6,
Alessandro Fancellu
1,
Michele Mondoni
7,
Arduino A. Mangoni
8 and
Angelo Zinellu
5
1
Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
2
Unit of Respiratory Diseases, University Hospital Sassari (AOU), 07100 Sassari, Italy
3
Pneumology Unit, Santissima Trinità Hospital, 09121 Cagliari, Italy
4
Mater Olbia Hospital, 07026 Olbia, Italy
5
Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
6
Operative Unit of Hematology, Center for Stem Cell Transplantation, San Francesco Hospital, 08100 Nuoro, Italy
7
Respiratory Unit, ASST Santi Paolo e Carlo, San Paolo Hospital, Department of Health Sciences, University of Milan, 20122 Milan, Italy
8
Department of Clinical Pharmacology, College of Medicine and Public Health, Flinders University and Flinders Medical Centre, Adelaide 5042, Australia
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(23), 5725; https://doi.org/10.3390/molecules25235725
Submission received: 18 November 2020 / Revised: 30 November 2020 / Accepted: 1 December 2020 / Published: 4 December 2020
(This article belongs to the Special Issue Anti-inflammatory and Anti-allergy Agents in Medicinal Chemistry-III)
Browse Figure
Review Reports Versions Notes

Abstract

:
Background. The rapid onset of a systemic pro-inflammatory state followed by acute respiratory distress syndrome is the leading cause of mortality in patients with COVID-19. We performed a retrospective observational study to explore the capacity of different complete blood cell count (CBC)-derived inflammation indexes to predict in-hospital mortality in this group. Methods. The neutrophil to lymphocyte ratio (NLR), derived NLR (dNLR), platelet to lymphocyte ratio (PLR), mean platelet volume to platelet ratio (MPR), neutrophil to lymphocyte × platelet ratio (NLPR), monocyte to lymphocyte ratio (MLR), systemic inflammation response index (SIRI), systemic inflammation index (SII), and the aggregate index of systemic inflammation (AISI) were calculated on hospital admission in 119 patients with laboratory confirmed COVID-19. Results. Non-survivors had significantly higher AISI, dNLR, NLPR, NLR, SII, and SIRI values when compared to survivors. Similarly, Kaplan–Meier survival curves showed significantly lower survival in patients with higher AISI, dNLR, MLR, NLPR, NLR, SII, and SIRI. However, after adjusting for confounders, only the SII remained significantly associated with survival (HR = 1.0001; 95% CI, 1.0000–1.0001, p = 0.029) in multivariate Cox regression analysis. Conclusions. The SII on admission independently predicts in-hospital mortality in COVID-19 patients and may assist with early risk stratification in this group.

1. Introduction

In December 2019, an ongoing outbreak of unexplained pneumonia in China gained global attention [1]. Gene sequencing allowed the identification of a novel β-coronavirus as the responsible agent, which the International Committee on Taxonomy of Viruses identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 30 January 2020, the associated pneumonia was referred to as coronavirus disease 2019 (COVID-19) by the World Health Organization [2]. As of 28 October 2020, more than forty-four million confirmed cases and over 1,175,000 deaths were reported worldwide [3]. Severity of the disease in patients is generally categorized as mild, moderate, severe, and critical based on clinical symptoms and laboratory test results [4,5]. In the majority of cases (about 81%), patients experience mild/moderate symptoms such as low-grade fever and cough [6]. Patients with severe symptoms and critical cases comprise only 14% and 5%, respectively, of all infected subjects, but they can develop severe pneumonia, acute respiratory distress syndrome (ARDS), and multiple organ failure that requires hospitalization and may lead to death [7]. Indeed, almost 20% of hospitalized patients need transfer to the intensive care unit (ICU), where the mortality rate is 61.5% [8,9]. Therefore, the identification of early biomarkers of disease severity might facilitate early aggressive treatment, reducing mortality and improving hospital resource allocation.
Routine blood tests for the assessment of inflammatory processes are often useful in the early diagnosis of several diseases [10]. In particular, the complete blood count is easy to perform, inexpensive, and provides information regarding various cell types and morphological parameters, i.e., white blood count, lymphocytes, neutrophils, monocytes, platelet count (PLT), and mean platelet volume. In addition, combined ratios of these parameters are also used as inflammation indexes and have been proposed as biomarkers to assist with the diagnosis, progress, and risk stratification of inflammatory diseases [11,12,13,14,15]. Recently, the neutrophil to lymphocyte ratio (NLR), derived NLR (dNLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), and systemic inflammation response index (SII) have been shown to be useful for the diagnosis and severity assessment of COVID-19 patients [10,16,17]. However, few studies have assessed the prognostic capacity of these indexes, and they were limited to the NLR, dNLR, and PLR [17,18]. We sought to address this issue by comparing the capacity of the NLR, dNRL, PLR, MLR, SII, NLPR, MPR, systemic inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI) to predict mortality in COVID-19 patients admitted to hospital.

2. Results

A total of 119 COVID-19 patients (77 men and 42 women) were included in the study (Table 1). The median age was 72 (IQR: 60–81) years. Ninety patients (75.6%) were discharged alive, whereas the remaining 29 (24.4%) died. Of the 119 patients, 81 (68.1%) had one or more pre-existing diseases, of which cardiovascular disease, respiratory disease, and diabetes were the most frequent (between 58 and 21%). The median hospitalization duration was 15 (IQR: 10–26) days. As reported in Table 1, non-survivors were significantly older (80 years, IQR 74–85 years vs. 68 years, IQR 57–78 years; p < 0.001), suffered more frequently from cardiovascular disease (79% vs. 51%, p = 0.008), and had higher values of the Charlson comorbidity index (median: 6.0, IQR 5.0–8.0 vs. 3.5, IQR 2.0–6.0, p < 0.001). Survivors had significantly higher P/F ratio values (278 ± 90 vs. 208 ± 88), longer time interval between disease onset and admission (median: 7.0 days, IQR 4.0–9.0 days vs. 4.0 days, IQR 1.5–6.0 days, p = 0.006), and longer hospital stay (median: 20 days, IQR: 13–34 days vs. 6 days, IQR 4–12 days). A significant difference between survivors and non-survivors was also observed in intensity of care (p = 0.03). In addition, non-survivors had significantly higher values of white blood cells (WBCs) (median: 9.16 × 109 L; IQR: 5.65–13.52 × 109 L vs. 6.47 × 109 L IQR: 5.00–8.66 × 109 L, p = 0.006), neutrophils (median: 7.30 × 109 L; IQR: 3.75–11.15 × 109 L vs. 5.00 × 109 L IQR: 3.56–6.30 × 109 L, p = 0.012), and lower values of lymphocytes (median: 0.70 × 109 L; IQR: 0.60–1.03 × 109 L vs. 1.00 × 109 L IQR: 0.70–1.20 × 109 L, p = 0.012). By contrast, there were no significant differences between survivors and non-survivors in gender, body mass index (BMI), smoking status, chest CT severity score, kidney disease, respiratory disease, diabetes, autoimmunity disease, malignancies, use of angiotensin converting enzyme (ACE) inhibitors or angiotensin II receptor blockers (ARBs), monocytes, platelets, red cell distribution width (RDW), and mean platelet volume (MPV).
Combined blood cell count indexes of inflammation are reported in Table 2. Non-survivors had higher values of AISI (median; 952; IQR: 201–1943 vs. 382; IQR 3139–894, p = 0.025), dNLR (median; 6.33; IQR: 2.87–10.19 vs. 3.06; IQR 2.16–5.07, p = 0.008), NLPR (median; 0.0215; IQR: 0.0142–0.0508 vs. 0.0121; IQR 0.0075–0.0194, p = 0.0009), NLR (median; 9.17; IQR: 5.35–18.55 vs. 5.00; IQR 3.27–8.44, p = 0.0015), SII (median; 1854; IQR: 706–4566 vs. 1022; IQR 616–1896, p = 0.039), and SIRI (median; 3.11; IQR: 1.19–5.56 vs. 1.64; IQR 1.07–2.94, p = 0.010). By contrast, there were no significant between-group differences in MLR, MPR, and PLR.
With respect to survival, the optimal cutoff values identified by ROC analysis were as follows: AISI, 798; dNLR, 6.2; MLR, 0.364; MPR, 0.040; NLPR, 0.019; NLR15.2; PLR, 240; SII, 1835; SIRI, 2.93 (Table 3). The values of area under the curve (AUC) were 0.640 (0.546 to 0.726) for AISI, 0.664 (0.571 to 0.748) for dNLR, 0.617 (0.523 to 0.705) for MLR, 0.558 (0.464 to 0.649) for MPR, 0.706 (0.615 to 0.787) for NLPR, 0.697 (0.605 to 0.778) for NLR, 0.572 (0.478 to 0.663) for PLR, 0.628 (0.534 to 0.715) for SII, and 0.659 (0.567 to 0.744) for SIRI. The Kaplan–Meier survival curves, after classifying the patients on the basis of Youden cut-offs obtained by ROC curves (Figure 1), showed significant lower survival with higher values of AISI (HR = 3.53; 95% CI 1.61–7.78, p = 0.002), dNLR (HR = 7.33; 95% CI 2.94–8.30, p < 0.001), MLR (HR = 2.84; 95% CI 1.35–5.95, p = 0.006), NLPR (HR = 4.21; 95% CI 1.94–9.13, p < 0.001), NLR (HR = 108; 95% CI 26–450, p < 0.001), SII (HR = 3.29; 95% CI 1.48–7.32, p = 0.034), and SIRI (HR = 3.63; 95% CI 1.64–8.00, p < 0.001). The multivariate Cox regression models reported in Table 4 showed that only the SII was significantly associated with survival (HR = 1.0001; 95% CI, 1.0000–1.0001, p = 0.029) after correction for age, P/F ratio, intensity of care, and Charlson comorbidity index. A trend towards statistical significance was also observed with the dNLR (HR = 1.0926; 95% CI, 0.9943–1.2006, p = 0.066) and the PLR (HR = 1.0006; 95% CI, 1.0000–1.0013, p = 0.058).
Table 5 shows demographic, clinical, and hematological characteristics of COVID-19 patients stratified according to SII values. Patients with SII > 1835 had lower P/F ratios when compared with patients with SII ≤ 1835 (226 ± 79 vs. 301 ± 85, p < 0.0001); higher chest CT severity score (median: 21.0, IQR 13.3–23.0 vs. 9.0, IQR 5.0–14.0, p < 0.001); increased values of WBC (median: 9.90 × 109 L; IQR: 7.49–12.60 × 109L vs. 5.90 × 109 L IQR: 4.8–7.87 × 109 L, p < 0.001), neutrophils (median: 8.65 × 109 L; IQR: 6.00–11.60 × 109 L vs. 4.40 × 109 L IQR: 3.40–5.55 × 109 L, p < 0.001), and platelets (267 × 109 L; IQR: 220–361 × 109 L vs. 183 × 109 L IQR: 147–233 × 109 L, p < 0.001); and reduced values of lymphocytes (median: 80.64 × 109 L; IQR: 0.50–0.80 × 109 L vs. 1.00 × 109 L IQR: 0.80–1.35 × 109 L, p < 0.001). Furthermore, patients with higher SII values had increased mortality rate (42% vs. 16%, p = 0.002). No significant differences were observed in other variables.

3. Discussion

This study described 119 COVID-19 patients hospitalized at four referral centers in Sardinia (Italy) from 15 March to 15 May 2020, with clinical and demographic characteristics similar to those recently described in other COVID-19 cohorts [1,6,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]. The lag time between the onset of symptoms and admission to hospital, a crucial factor in the community transmission rate, was 6.5 days (IQR 3–8 days), within the range of previous reports, between 4 and 12 days [17,19,20,21,22,24,25,28,30,31]. The median length of hospital stay in our cohort (15 days, IQR 10–26 days) was also similar to that of recent reports (between 12 and 22 days) [25,29,31,32]. As previously reported, we found that disease severity was significantly related to age [6,18,25,27,28,29,32,33], cardiovascular disease [6,18,29,33,34], interval between disease onset and admission [29,32], and P/F ratio [35].
Several laboratory abnormalities were reported in COVID-19 patients, particularly in severe and critically ill patients [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. In line with other studies, among hematological parameters, leukocytosis, lymphopenia, and increased neutrophil count were related to disease severity in our cohort [1,6,18,19,25,27,28,29,36,37,38,39,40,41]. Neutrophils, the most abundant circulating white blood cells, are an important component of the immune system. They represent the first line of the innate immune defense, since they play a fundamental protective role during infection from bacteria and fungi, by killing these microorganisms by phagocytosis as well as neutrophil extracellular trap (NET) formation. However, their role in viral infections remains unclear. In mice infected by SARS-CoV, neutrophils seem not necessary for virus clearance from pulmonary cells and host survival [42]. Lungs from autopsy of patients affected by COVID-19 showed extensive neutrophil infiltration in pulmonary capillaries with extravasation into the alveolar space. The presence of both acute capillaritis as well as trachea neutrophilic mucositis demonstrates widespread inflammation across the airways [42].
In addition, inflammatory cell accumulation associated with endothelial cells infection during COVID-19 disease may induce endothelitis in different organs, thus contributing to systemic damage of microcirculatory function and leading to the phenomenon known as “happy hypoxia” [42]. On the other hand, the lymphopenia observed in COVID-19 seems to be linked to the ability of the virus to infect T cells through the angiotensin-converting enzyme 2 (ACE2) receptors and CD147-spike protein [43]. Therefore, a decreased level of CD3+, CD4+, and CD8+ T lymphocytes and an increased number of regulatory T cells are frequently observed in COVID-19 disease.
The increase of proinflammatory cytokines during T cell lymphopenia predisposes severe COVID-19 patients to a cytokine storm, thus resulting in multi-organ failure and death. In general, T lymphocytes CD4+ and CD8+ decrease is associated with disease severity and leads to increased NLR values, which have been reported to be a more sensitive biomarker of inflammation than the individual levels of neutrophils and lymphocytes [43]. In agreement with previous reports, we found increased values of NLR and dNLR in severe COVID-19 disease patients [18,19]. However, we also report for the first time that other blood cell count-derived inflammation indexes such as NLPR, SII, SIRI, and AISI are also significantly associated with disease severity. In particular, in our study, the AUC values of both NLR and NLPR were the highest, among the combined indexes evaluated, in predicting disease severity (about 0.70). The data of NLR AUC agreed well with those of previous reports, ranging between 0.65 and 0.73 [44,45,46]. In addition, we found that the AUC was significant with the dNLR, AISI, and SIRI, and had borderline significant with the SII and MLR (p = 0.060 and p = 0.054, respectively).
Kaplan–Meier survival curves using cut-off values obtained from ROC curves showed that survival was significantly associated with AISI, dNLR, MLR, NLPR, NLR, SII, and SIRI (Figure 1). However, after correction for age, P/F ratio, intensity of care, and Charlson comorbidity index, confounders identified in univariate analysis, only the SII remained associated with survival after multivariate Cox regression analysis. The SII includes three peripheral blood parameters, namely neutrophil, platelet, and lymphocyte count, which comprehensively summarizes the balance of host immune and inflammatory status. It has been already suggested as a prognostic biomarker in sepsis patients [47]. In addition, the SII has also been shown to be associated with worse survival in small cell lung cancer, hepatocellular carcinoma, colorectal cancer, and gastric cancer [48,49,50,51]. Recently, it has also been reported that the SII was significantly altered in COVID-19 patients when compared to healthy controls, suggesting a diagnostic role in SARS-CoV2-infected patients [10]. In addition, in multivariate Cox regression analysis, we found a borderline significance between worse survival and PLR (p = 0.058) and dNLR (p = 0.066). The data on PLR agree with previous findings by Qu R. et al. that reported that the PLR may predict mortality in COVID-19 patients [52]. Interestingly, patients with higher SII values had a significantly worst P/F ratio and chest CT severity score, with no differences regarding the Charlson comorbidity index; this suggests that SII might reflect specifically the pulmonary and respiratory damage occurring in COVID-19 patients rather than a general impairment of their clinical conditions due to comorbidities.
Some limitations of this study should be underlined, including its retrospective nature and the relatively small sample size, which may have impacted the statistical analysis, even if patients were recruited from four different centers, thus improving the generalization of the results. On the other hand, this is the first study to investigate and compare the prognostic roles of a wide range of blood cell count systemic inflammation indexes in COVID-19.

4. Materials and Methods

We retrospectively studied 119 COVID-19 patients admitted to the Respiratory Disease and Infectious Disease Units of the University Hospital of Sassari, the Pneumology Unit of the Santissima Trinità Hospital of Cagliari, and the Pneumology Unit of the Mater Olbia Hospital of Olbia, Sardinia, Italy, between 15 March and 15 May 2020. COVID-19 disease was confirmed by reverse transcription polymerase chain reaction (RT-PCR) in all cases. Demographic, clinical, and laboratory data were entered in a dedicated electronic database. In particular, we assessed the following blood cell count inflammation parameters: white blood cell count (WBC), monocytes, lymphocytes, neutrophils, platelets, red blood cell distribution width (RDW), and mean platelet volume (MPV). We then extrapolated combined blood cell indexes of systemic inflammation: the NLR (neutrophil/lymphocyte ratio), dNLR (neutrophils/(white blood cells − neutrophils)), PLR (platelet/lymphocyte ratio), MPR (mean platelet volume/platelet ratio), NLPR (neutrophil/(lymphocyte × platelet ratio)), MLR (monocyte/lymphocyte ratio), SIRI ((neutrophils × monocytes)/lymphocytes), SII ((neutrophils × platelets)/lymphocytes) and AISI ((neutrophils × monocytes × platelets)/lymphocytes). We also collected information regarding the intensity of care received during hospitalization in terms of respiratory support (oxygen supplementation, non-invasive or invasive respiratory support), established parameters of comorbidity (Charlson comorbidity index), hypoxia (PaO2/FiO2), and lung inflammation severity (chest CT severity score).
The patients were monitored until in-hospital death (non-survivors) or discharge (survivors). The criteria for discharge were (i) fever absence for at least 3 days; (ii) signs of improvement on chest CT scan or X-ray; (iii) two consecutive negative nucleic acid tests performed at least 24 h from each other. The study was conducted in accordance with the declaration of Helsinki and was approved by the ethics committee of the University Hospital (AOU) of Cagliari (PG/2020/10915).
Data are expressed as mean values (mean ± SD) or median values (median and IQR). The Kolmogorov–Smirnov test was performed to evaluate variable distribution. Between-group differences of continuous variables were compared using unpaired Student’s t-test or Mann–Whitney rank sum test, as appropriate. Differences between categorical variables were evaluated by Fisher test or chi-squared test, as appropriate. Receiver operating characteristics (ROC) curve analysis was performed to estimate optimal cut-off values, maximizing sensitivity and specificity according to the Youden index. For survival analysis, time zero was defined as the time of hospital admission.
Survival probability for CBC-derived inflammation indexes was estimated using the means of the Kaplan–Meier curves with the end point being death. Cox proportional hazards regression was performed for both univariate and multivariate analyses. To avoid collinearity bias, the independent prognostic power of the combined blood cell count-derived indexes was separately assessed for each parameter, by correcting for confounders that have a p < 0.2 in univariate analysis (age, P/F ratio, intensity of care, and Charlson comorbidity index). Statistical analyses were performed using MedCalc for Windows, version 19.4.1 64 bit (MedCalc Software, Ostend, Belgium).

5. Conclusions

The results of this retrospective study showed that the SII is the most significant prognostic biomarker for survival in patients with SARS-CoV2-infected patients when compared to other widely employed blood cell count-derived inflammation indexes such as the NLR, dNLR, PLR, MLR, NLPR, MPR, AISI, and SIRI. Properly designed prospective studies should be performed to confirm the prognostic ability of SII in COVID-19 patients and its utility in the early identification of high-risk patients and the implementation of optimal individualized treatment strategies.

Author Contributions

A.G.F., P.P. (Panagiotis Paliogiannis), and A.Z. conceived, organized, and supervised the study; V.S., S.C., S.B., R.P., G.R., E.Z., P.P. (Pietro Pirina), C.C., L.B.A., A.F., and M.M. collected data, critically interpreted statistical results, and drafted or critically revised parts of the manuscript; A.A.M., M.M., and A.Z. performed the statistical analyses and English editing; A.G.F. and P.P. (Panagiotis Paliogiannis) drafted the initial manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “Fondazione di Sardegna” (Sassari, Italy) Prat. 2016.0917.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Huang, C.; Wang, Y.; Li, X.; Ren, L.; Zhao, J.; Hu, Y.; Zhang, L.; Fan, G.; Xu, J.; Gu, X.; et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020, 395, 497–506. [Google Scholar] [CrossRef] [Green Version]
  2. Kong, M.; Zhang, H.; Cao, X.; Mao, X.; Lu, Z. Higher level of neutrophil-to-lymphocyte is associated with severe COVID-19. Epidemiol. Infect. 2020, 148, e139. [Google Scholar] [CrossRef]
  3. World Health Organization. Available online: https://covid19.who.int/ (accessed on 28 October 2020).
  4. Wu, Z.; McGoogan, J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020, 323, 1239–1242. [Google Scholar] [CrossRef]
  5. He, F.; Deng, Y.; Li, W. Coronavirus disease 2019: What we know? J. Med Virol. 2020, 92, 719–725. [Google Scholar] [CrossRef] [Green Version]
  6. Zhou, F.; Yu, T.; Du, R.; Fan, G.; Liu, Y.; Liu, Z.; Xiang, J.; Wang, Y.; Song, B.; Gu, X.; et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 2020, 395, 1054–1062. [Google Scholar] [CrossRef]
  7. Sun, P.; Qie, S.; Liu, Z.; Ren, J.; Li, K.; Xi, J. Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis. J. Med. Virol. 2020, 92, 612–617. [Google Scholar] [CrossRef] [Green Version]
  8. Rodriguez-Morales, A.J.; Cardona-Ospina, J.A.; Gutiérrez-Ocampo, E.; Villamizar-Peña, R.; Holguin-Rivera, Y.; Escalera-Antezana, J.P.; Alvarado-Arnez, L.E.; Bonilla-Aldana, D.K.; Franco-Paredes, C.; Henao-Martinez, A.F.; et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med. Infect. Dis. 2020, 34, 101623. [Google Scholar] [CrossRef]
  9. Yang, X.; Yu, Y.; Xu, J.; Shu, H.; Xia, J.; Liu, H.; Wu, Y.; Zhang, L.; Yu, Z.; Fang, M.; et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Resp. Med. 2020, 8, 475–481. [Google Scholar] [CrossRef] [Green Version]
  10. Usul, E.; Şan, I.; Bekgöz, B.; Şahin, A. Role of hematological parameters in COVID-19 patients in the emergency room. Biomark. Med. 2020, 14, 1207–1215. [Google Scholar] [CrossRef]
  11. Zinellu, A.; Paliogiannis, P.; Sotgiu, E.; Mellino, S.; Mangoni, A.A.; Zinellu, E.; Negri, S.; Collu, C.; Pintus, G.; Serra, A.; et al. Blood Cell Count Derived Inflammation Indexes in Patients with Idiopathic Pulmonary Fibrosis. Lung 2020, 198, 821–827. [Google Scholar] [CrossRef]
  12. Putzu, C.; Cortinovis, D.; Colonese, F.; Canova, S.; Carru, C.; Zinellu, A.; Paliogiannis, P. Blood cell count indexes as predictors of outcomes in advanced non-small-cell lung cancer patients treated with Nivolumab. Cancer Immunol. Immunother. 2018, 67, 1349–1353. [Google Scholar] [CrossRef]
  13. Paliogiannis, P.; Satta, R.; Deligia, G.; Farina, G.; Bassu, S.; Mangoni, A.A.; Carru, C.; Zinellu, A. Associations between the neutrophil-to-lymphocyte and the platelet-to-lymphocyte ratios and the presence and severity of psoriasis: A systematic review and meta-analysis. Clin. Exp. Med. 2018, 19, 37–45. [Google Scholar] [CrossRef]
  14. Paliogiannis, P.; Fois, A.G.; Sotgia, S.; Mangoni, A.A.; Zinellu, E.; Pirina, P.; Carru, C.; Zinellu, A. The neutrophil-to-lymphocyte ratio as a marker of chronic obstructive pulmonary disease and its exacerbations: A systematic review and meta-analysis. Eur. J. Clin. Investig. 2018, 48, e12984. [Google Scholar] [CrossRef] [Green Version]
  15. Paliogiannis, P.; Scognamillo, F.; Bellomo, M.; Pittalis, M.L.; Pisano, I.P.; Karligkiotis, A.; Bozzo, C.; Sotgiu, G.; Attene, F. Neutrophil to lymphocyte ratio as a predictor of thyroid papillary carcinoma. Acta Med. Mediterr. 2015, 31, 371–375. [Google Scholar]
  16. Peng, J.; Qi, D.; Yuan, G.; Deng, X.; Mei, Y.; Feng, L.; Wang, D. Diagnostic value of peripheral hematologic markers for coronavirus disease 2019 (COVID-19): A multicenter, cross-sectional study. J. Clin. Lab. Anal. 2020, 34, e23475. [Google Scholar] [CrossRef]
  17. Yang, A.-P.; Liu, J.-P.; Tao, W.-Q.; Li, H.-M. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int. Immunopharmacol. 2020, 84, 106504. [Google Scholar] [CrossRef]
  18. Chen, L.; Yu, J.; He, W.; Yuan, G.; Dong, F.; Chen, W.; Cao, Y.; Yang, J.; Cai, L.; Wu, D.; et al. Risk factors for death in 1859 subjects with COVID-19. Leukemia 2020, 34, 2173–2183. [Google Scholar] [CrossRef]
  19. Paliogiannis, P.; Zinellu, A.; Scano, V.; Mulas, G.; De Riu, G.; Pascale, R.M.; Arru, L.B.; Carru, C.; Pirina, P.; Mangoni, A.A.; et al. Laboratory test alterations in patients with COVID-19 and non COVID-19 interstitial pneumonia: A preliminary report. J. Infect. Dev. Ctries. 2020, 14, 685–690. [Google Scholar] [CrossRef]
  20. Labenz, C.; Toenges, G.; Wörns, M.A.; Sprinzl, M.F.; Galle, P.R.; Schattenberg, J.M. Liver injury in patients with severe acute respiratory syndrome coronavirus-2 infection: A systematic review and meta-analysis. Eur. J. Gastroenterol. Hepatol. 2020. [Google Scholar] [CrossRef]
  21. 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, 507–513. [Google Scholar] [CrossRef] [Green Version]
  22. Wang, D.; Hu, B.; Hu, C.; Zhu, F.; Liu, X.; Zhang, J.; Wang, B.; Xiang, H.; Cheng, Z.; Xiong, Y.; et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China. JAMA 2020, 323, 1061. [Google Scholar] [CrossRef] [PubMed]
  23. Lu, R.; Zhao, X.; Li, J.; Niu, P.; Yang, B.; Wu, H.; Wang, W.; Song, H.; Huang, B.; Zhu, N.; et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet 2020, 395, 565–574. [Google Scholar] [CrossRef] [Green Version]
  24. Li, Q.; Guan, X.; Wu, P.; Wang, X.; Zhou, L.; Tong, Y.; Ren, R.; Leung, K.S.M.; Lau, E.H.Y.; Wong, J.Y.; et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia. N. Engl. J. Med. 2020, 382, 1199–1207. [Google Scholar] [CrossRef] [PubMed]
  25. Guan, W.-J.; Ni, Z.-Y.; Hu, Y.; Liang, W.-H.; Ou, C.-Q.; He, J.-X.; Liu, L.; Shan, H.; Lei, C.-L.; Hui, D.S.C.; et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N. Engl. J. Med. 2020, 382, 1708–1720. [Google Scholar] [CrossRef] [PubMed]
  26. Liu, X.; Zhou, H.; Zhou, Y.; Wu, X.; Zhao, Y.; Lu, Y.; Tan, W.; Yuan, M.; Ding, X.; Zou, J.; et al. Risk factors associated with disease severity and length of hospital stay in COVID-19 patients. J. Infect. 2020, 81, e95–e97. [Google Scholar] [CrossRef]
  27. Gong, J.; Ou, J.; Qiu, X.; Jie, Y.; Chen, Y.; Yuan, L.; Cao, J.; Tan, M.; Xu, W.; Zheng, F.; et al. A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China. Clin. Infect. Dis. 2020, 71, 833–840. [Google Scholar] [CrossRef] [Green Version]
  28. Itelman, E.; Wasserstrum, Y.; Segev, A.; Avaky, C.; Negru, L.; Cohen, D.; Turpashvili, N.; Anani, S.; Zilber, E.; Lasman, N.; et al. Clinical Characterization of 162 COVID-19 patients in Israel: Preliminary Report from a Large Tertiary Center. Isr Med. Assoc. J. 2020, 22, 271–274. [Google Scholar]
  29. Li, X.; Xu, S.; Yu, M.; Wang, K.; Tao, Y.; Zhou, Y.; Shi, J.; Zhou, M.; Wu, B.; Yang, Z.; et al. Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan. J. Allergy Clin. Immunol. 2020, 146, 110–118. [Google Scholar] [CrossRef]
  30. Hua, J.; Chen, R.; Zhao, L.; Wu, X.; Guo, Q.; He, C.; Li, T.; Ren, X.; Liu, Z.; Li, Q.; et al. Epidemiological features and medical care-seeking process of patients with COVID-19 in Wuhan, China. Erj Open Res. 2020, 6, 00142–02020. [Google Scholar] [CrossRef]
  31. Shi, X.; Lu, Y.; Li, R.; Tang, Y.; Shi, N.; Song, F.; Shan, F.; Chen, G.; Song, P.; Shi, Y. Evaluation of antiviral therapies for coronavirus disease 2019 pneumonia in Shanghai, China. J. Med. Virol. 2020, 92, 1922–1931. [Google Scholar] [CrossRef]
  32. Wang, F.; Qu, M.; Zhou, X.; Zhao, K.; Lai, C.; Tang, Q.; Xian, W.; Chen, R.; Li, X.; Li, Z.; et al. The timeline and risk factors of clinical progression of COVID-19 in Shenzhen, China. J. Transl. Med. 2020, 18, 1–11. [Google Scholar] [CrossRef] [PubMed]
  33. 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, 26, 1400–1405. [Google Scholar] [CrossRef] [PubMed]
  34. Chen, F.-F.; Zhong, M.; Liu, Y.; Zhang, Y.; Zhang, K.; Su, D.-Z.; Meng, X.; Zhang, Y. The characteristics and outcomes of 681 severe cases with COVID-19 in China. J. Crit. Care 2020, 60, 32–37. [Google Scholar] [CrossRef]
  35. Colaneri, M.; Sacchi, P.; Zuccaro, V.; Biscarini, S.; Sachs, M.; Roda, S.; Pieri, T.C.; Valsecchi, P.; Piralla, A.; Seminari, E.; et al. Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020. Eurosurveillance 2020, 25, 2000460. [Google Scholar] [CrossRef] [Green Version]
  36. Gerotziafas, G.T.; Sergentanis, T.N.; Voiriot, G.; Lassel, L.; Papageorgiou, C.; Elabbadi, A.; Turpin, M.; Vandreden, P.; Papageorgiou, L.; Psaltopoulou, T.; et al. Derivation and Validation of a Predictive Score for Disease Worsening in Patients with COVID-19. Thromb. Haemost. 2020. [Google Scholar] [CrossRef]
  37. COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators. Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: A prospective cohort study. Intensive Care Med. 2020. [Google Scholar] [CrossRef]
  38. Terpos, E.; Ntanasis-Stathopoulos, I.; Elalamy, I.; Kastritis, E.; Sergentanis, T.N.; Politou, M.; Psaltopoulou, T.; Gerotziafas, G.; A Dimopoulos, M. Hematological findings and complications of COVID-19. Am. J. Hematol. 2020, 95, 834–847. [Google Scholar] [CrossRef] [Green Version]
  39. Gavriatopoulou, M.; Korompoki, E.; Fotiou, D.; Ntanasis-Stathopoulos, I.; Psaltopoulou, T.; Kastritis, E.; Terpos, E.; Dimopoulos, M.A. Organ-specific manifestations of COVID-19 infection. Clin. Exp. Med. 2020, 20, 493–506. [Google Scholar] [CrossRef]
  40. Rokni, M.; Ahmadikia, K.; Asghari, S.; Mashaei, S.; Hassanali, F. Comparison of clinical, para-clinical and laboratory findings in survived and deceased patients with COVID-19: Diagnostic role of inflammatory indications in determining the severity of illness. BMC Infect. Dis. 2020, 20, 869. [Google Scholar] [CrossRef]
  41. Tjendra, Y.; Al Mana, A.F.; Espejo, A.P.; Akgun, Y.; Millan, N.C.; Gomez-Fernandez, C.; Cray, C. Predicting Disease Severity and Outcome in COVID-19 Patients: A Review of Multiple Biomarkers. Arch. Pathol. Lab. Med. 2020, 144, 1465–1474. [Google Scholar] [CrossRef]
  42. Tomar, B.; Anders, H.-J.; Desai, J.; Mulay, S.R. Neutrophils and Neutrophil Extracellular Traps Drive Necroinflammation in COVID-19. Cells 2020, 9, 1383. [Google Scholar] [CrossRef] [PubMed]
  43. Chan, A.S.; Rout, A. Use of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in COVID-19. J. Clin. Med. Res. 2020, 12, 448–453. [Google Scholar] [CrossRef] [PubMed]
  44. 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, 54, 652–658. [Google Scholar] [CrossRef] [PubMed]
  45. Zhang, H.; Cao, X.; Kong, M.; Mao, X.; Huang, L.; He, P.; Pan, S.; Li, J.; Lu, Z. Clinical and hematological characteristics of 88 patients with COVID-19. Int. J. Lab. Hematol. 2020, 42, 780–787. [Google Scholar] [CrossRef]
  46. 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, 92, 2188–2192. [Google Scholar] [CrossRef]
  47. Lagunas-Alvarado, M.; Mijangos-Huesca, F.J.; Terán-González, J.O.; Lagunas-Alvarado, M.G.; Martínez-Zavala, N.; Reyes-Franco, I.; Hernández-Mendiola, R.; Santillán-Fragoso, W.J.; Copca-Nieto, D.V.; López y López, L.R.; et al. Systemic immune inflammatory index in sepsis. Med. Int. Méx 2017, 33, 303–309. [Google Scholar]
  48. Hong, X.; Cui, B.; Wang, M.; Yang, Z.; Wang, L.; Xu, Q. Systemic Immune-inflammation Index, Based on Platelet Counts and Neutrophil-Lymphocyte Ratio, Is Useful for Predicting Prognosis in Small Cell Lung Cancer. Tohoku J. Exp. Med. 2015, 236, 297–304. [Google Scholar] [CrossRef] [Green Version]
  49. Hu, B.; Yang, X.-R.; Xu, Y.; Sun, Y.-F.; Sun, C.; Guo, W.; Zhang, X.; Wang, W.-M.; Qiu, S.-J.; Zhou, J.; et al. Systemic Immune-Inflammation Index Predicts Prognosis of Patients after Curative Resection for Hepatocellular Carcinoma. Clin. Cancer Res. 2014, 20, 6212–6222. [Google Scholar] [CrossRef] [Green Version]
  50. Dong, M.; Shi, Y.; Yang, J.; Zhou, Q.; Lian, Y.; Wang, D.; Ma, T.; Zhang, Y.; Mi, Y.; Gu, X.; et al. Prognostic and clinicopathological significance of systemic immune-inflammation index in colorectal cancer: A meta-analysis. Ther. Adv. Med Oncol. 2020, 12. [Google Scholar] [CrossRef]
  51. Hirahara, N.; Matsubara, T.; Fujii, Y.; Kaji, S.; Kawabata, Y.; Hyakudomi, R.; Yamamoto, T.; Taniura, T.; Tajima, Y. Comparison of the prognostic value of immunoinflammation-based biomarkers in patients with gastric cancer. Oncotarget 2020, 11, 2625–2635. [Google Scholar] [CrossRef]
  52. Qu, R.; Ling, Y.; Zhang, Y.H.; Wei, L.Y.; Chen, X.; Li, X.M.; Liu, X.Y.; Liu, H.M.; Guo, Z.; Ren, H.; et al. Platelet-to-lymphocyte ratio is associated with prognosis in patients with coronavirus disease-19. J. Med. Virol. 2020. [Google Scholar] [CrossRef] [PubMed]
Molecules 25 05725 g001 550
Figure 1. Kaplan–Meier survival curves during hospitalization of COVID-19 patients with different cut-off values of the systemic inflammation indexes investigated. (A): AISI; (B): dNLR; (C): MLR; (D): MPR; (E): NLPR; (F): NLR; (G): PLR; (H): SII; (I): SIRI.
Figure 1. Kaplan–Meier survival curves during hospitalization of COVID-19 patients with different cut-off values of the systemic inflammation indexes investigated. (A): AISI; (B): dNLR; (C): MLR; (D): MPR; (E): NLPR; (F): NLR; (G): PLR; (H): SII; (I): SIRI.
Molecules 25 05725 g001
Table
Table 1. Demographic, clinical, and hematological features of COVID-19 survivors and non-survivors.
Table 1. Demographic, clinical, and hematological features of COVID-19 survivors and non-survivors.
Global Cohort
(n = 119)		Survivors
(n = 90)		Non-Survivors
(n = 29)		p-Value
Age (Years)72 (60–81)68 (57–78)80 (74–85)<0.001
Gender (F/M)42/7734/568/210.32
Smoking Status (No/Yes/Former)67/36/1651/27/1216/9/40.99
BMI (Non-Obese/Obese)92/2768/2224/50.42
P/F Ratio278 ± 90301 ± 79208 ± 88<0.001
Chest CT Severity Score11.0 (6.0–21.0)10.0 (6.0–19.5)15.0 (10.5–21.5)0.12
Intensity of Care (No, OT, RSni, RSi)25/50/22/2224/37/13/161/13/9/60.03
Interval between Disease Onset and Admission (Days)6.5 (3.0–8.0)7.0 (4.0–9.0)4.0 (1.5–6.0)0.006
Hospital Stay (Days)15 (10–26)20 (13–34)6 (4–12)<0.001
Charlson Comorbidity Index4.0 (2.0–7.0)3.5 (2.0–6.0)6.0 (5.0–8.0)<0.001
Cardiovascular Disease (No/Yes)50/6944/466/230.008
Respiratory Disease (No/Yes)93/2672/1821/80.39
Kidney Disease (No/Yes)102/1777/1325/40.93
Diabetes (No/Yes)94/2572/1822/70.64
Cancer (No/Yes)101/1876/1425/40.82
Autoimmunity (No/Yes)113/685/528/10.65
ACE inhibitors (No/Yes)96/2375/1521/80.20
ARBs (No/Yes)96/2373/1723/60.83
WBC (×109 L)6.74 (5.00–9.32)6.47 (5.00–8.66)9.16 (5.65–13.52)0.006
Monocytes (×109 L)0.35 (0.20–0.50)0.40 (0.20–0.50)0.30 (0.26–0.53)0.85
Lymphocytes (×109 L)0.90 (0.67–1.14)1.00 (0.70–1.20)0.70 (0.60–1.03)0.012
Neutrophils (×109 L)5.24 (6.30–7.58)5.00 (3.56–6.30)7.30 (3.75–11.15)0.012
Platelets (×109 L)204 (162–265)211 (166–265)184 (159–259)0.43
RDW (%)15.1 (14.1–16.3)14.9 (13.7–16.4)15.5 (14.8–16.1)0.41
MPV (fL)8.45 (8.10–9.00)8.40 (8.08–8.92)8.60 (8.18–9.320.37)0.37
ACE: angiotensin converting enzyme; ARBs: angiotensin II receptor blockers; BMI: body mass index; COVID-19: coronavirus disease 2019; F: female; M: male; MPV, mean platelet volume; OT: oxygen therapy; RDW, red blood cell distribution width; RSi: invasive respiratory support; RSni: non-invasive respiratory support; WBC: white blood cells. All continuous variables are reported as medians and interquartile ranges. Statistical significance set at 0.05. All statistically significant values are reported in bold.
Table
Table 2. Blood cell count-derived inflammation indexes of COVID-19 survivors and non-survivors.
Table 2. Blood cell count-derived inflammation indexes of COVID-19 survivors and non-survivors.
Global Cohort
(n = 119)		Survivors
(n = 90)		Non-Survivors
(n = 29)		p-Value
AISI404 (170–1324)382 (139–894)952 (201–1943)0.025
dNLR3.45 (2.22–5.77)3.06 (2.16–5.07)6.33 (2.87–10.19)0.008
MLR0.364 (0.250–0.566)0.333 (0.250–0.514)0.429 (0.297–0.686)0.058
MPR0.042 (0.036–0.056)0.040 (0.030–0.056)0.047 (0.031–0.057)0.35
NLPR0.0145 (0.0080–0.02490.0121 (0.0075–0.0194)0.0215 (0.0142–0.0508)0.0009
NLR5.67 (3.52–10.37)5.00 (3.27–8.44)9.17 (5.35–18.55)0.0015
PLR221 (145–349)214 (145–339)265 (144–428)0.24
SII1137 (645–2393)1022 (616–1896)1854 (706–4566)0.039
SIRI1.95 (1.13–4.11)1.64 (1.07–2.94)3.11 (1.19–5.56)0.010
AISI, aggregate index of systemic inflammation (neutrophil*platelet* monocyte to lymphocyte ratio); COVID-19: coronavirus disease 2019; dNLR, derived neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; MPR, mean platelet volume to platelet ratio; NLR, neutrophil to lymphocyte ratio; NLPR, neutrophil to lymphocyte*platelet ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune-inflammation index (neutrophil*platelet to lymphocyte ratio); SIRI, systemic inflammation response index (neutrophil* monocyte to lymphocyte ratio). All variables are reported as medians and interquartile ranges. Statistical significance set at 0.05. All statistically significant values are reported in bold.
Table
Table 3. Receiver operating characteristics (ROC) curves and prognostic accuracy of blood cell count-derived inflammation indexes.
Table 3. Receiver operating characteristics (ROC) curves and prognostic accuracy of blood cell count-derived inflammation indexes.
AUC95% CIp-ValueCut-offSensitivity (%)Specificity (%)
AISI0.6400.546 to 0.7260.034>7985972
dNLR0.6640.571 to 0.7480.014>6.25285
MLR0.6170.523 to 0.7050.054>0.3646957
MPR0.5580.464 to 0.6490.37>0.0406652
NLPR0.7060.615 to 0.7870.0005>0.0196675
NLR0.6970.605 to 0.7780.002>15.23897
PLR0.5720.478 to 0.6630.26>2405958
SII0.6280.534 to 0.7150.06>18355575
SIRI0.6590.567 to 0.7440.013>2.935974
AISI, aggregate index of systemic inflammation (neutrophil*platelet* monocyte to lymphocyte ratio); AUC: area under the curve; CI: confidence interval; COVID-19: coronavirus disease 2019; dNLR, derived neutrophil to lymphocyte ratio; MLR, monocyte to lymphocyte ratio; MPR, mean platelet volume to platelet ratio; NLR, neutrophil to lymphocyte ratio; NLPR, neutrophil to lymphocyte*platelet ratio; PLR, platelet to lymphocyte ratio; SII, systemic immune-inflammation index (neutrophil*platelet to lymphocyte ratio); SIRI, systemic inflammation response index (neutrophil* monocyte to lymphocyte ratio). Statistical significance set at 0.05.
Table
Table 4. Hazard ratios of the indexes under investigation obtained by Cox regression analysis.
Table 4. Hazard ratios of the indexes under investigation obtained by Cox regression analysis.
HR95% CIp-Value
Age1.01340.9701 to 1.05880.549
P/F Ratio0.99090.9851 to 0.99670.002
Intensity of Care1.00390.6568 to 1.53450.985
Charlson Comorbidity Index1.17531.0154 to 1.36040.030
AISI1.00001.0000 to 1.00010.144
Age1.02160.9757 to 1.06970.362
P/F Ratio0.99180.9857 to 0.99780.008
Intensity of Care0.94290.6239 to 1.42490.780
Charlson Comorbidity Index1.14750.9862 to 1.33520.075
dNLR1.09260.9943 to 1.20060.066
Age1.01460.9714 to 1.05970.515
P/F Ratio0.99080.9850 to 0.99670.002
Intensity of Care1.00440.6565 to 1.53650.984
Charlson Comorbidity Index1.17521.0172 to 1.35780.028
MLR1.60280.6277 to 4.09240.324
Age1.01120.9691 to 1.05500.608
P/F Ratio0.99030.9847 to 0.99590.001
Intensity of Care0.97850.6435 to 1.48800.919
Charlson Comorbidity Index1.17981.0219 to 1.36210.024
MPR0.39960.0004 to 363.960.792
Age1.01060.9686 to 1.05440.627
P/F Ratio0.99020.9846 to 0.99580.001
Intensity of Care0.96980.6384 to 1.47320.886
Charlson Comorbidity Index1.17841.0200 to 1.36140.026
NLPR0.96690.0035 to 269.420.991
Age1.01730.9727 to 1.06390.454
P/F Ratio0.99140.9854 to 0.99740.005
Intensity of Care0.96580.6374 to 1.46330.870
Charlson Comorbidity Index1.15820.9971 to 1.34540.055
NLR1.02750.9948 to 1.06120.100
Age1.01910.9740 to 1.06630.412
P/F Ratio0.99050.9847 to 0.99630.001
Intensity of Care0.99280.6474 to 1.52250.974
Charlson Comorbidity Index1.13670.9708 to 1.33090.111
PLR1.00061.0000 to 1.00130.058
Age1.02130.9754 to 1.06940.369
P/F Ratio0.99130.9854 to 0.99720.004
Intensity of Care1.00690.6553 to 1.54690.975
Charlson Comorbidity Index1.12990.9651 to 1.32290.129
SII1.00011.0000 to 1.00010.029
Age1.01460.9710 to 1.06010.517
P/F Ratio0.99120.9853 to 0.99710.004
Intensity of Care1.01270.6621 to 1.54890.954
Charlson Comorbidity Index1.17671.0166 to 1.36210.029
SIRI1.02790.9885 to 1.06880.167
AISI, aggregate index of systemic inflammation; CI: confidence interval; COVID-19: coronavirus disease 2019; dNLR, derived neutrophil to lymphocyte ratio; HR: hazard ratio; MLR, monocyte to lymphocyte ratio; MPR, mean platelet volume to platelet ratio; NLR, neutrophil to lymphocyte ratio; NLPR, neutrophil to lymphocyte x platelet ratio; P/F: PaO2/FiO2; PLR, platelet to lymphocyte ratio; SII, systemic immune-inflammation index; SIRI, systemic inflammation response index. All statistically significant values are reported in bold.
Table
Table 5. Demographic, clinical, and laboratory characteristics of COVID-19 patients stratified by the SII.
Table 5. Demographic, clinical, and laboratory characteristics of COVID-19 patients stratified by the SII.
SII
≤1835
(n = 81)		SII
>1835
(n = 38)		p-Value
Age (Years)71 (58–80)74 (62–80)0.57
Gender (F/M)32/4910/280.16
Smoking status (No/Yes/Former)48/25/819/11/80.24
BMI (Non-Obese/Obese)62/1930/80.77
P/F Ratio301 ± 85226 ± 79<0.001
Chest CT Severity Score9.0 (5.0–14.0)21.0 (13.3–23.0)<0.001
Intensity of Care (No, OT, RSni, RSi)16/39/12/149/11/10/80.21
Interval between Disease Onset and Admission (Days)6.0 (3.0–8.0)6.0 (3.0–8.8)0.99
Hospital Stay (Days)17 (11–26)14 (6–27)0.29
Survivors (No/Yes)13/6816/220.002
Charlson Comorbidity Index4.0 (2.0–6.3)4.5 (2.0–7.0)0.23
Cardiovascular Disease (No/Yes)35/4615/230.7
Respiratory Disease (No/Yes)67/1426/120.08
Kidney Disease (No/Yes)69/1233/50.81
Diabetes (No/Yes)62/1932/60.34
Cancer (No/Yes)70/1131/70.49
Autoimmunity (No/Yes)77/436/20.94
ACE inhibitors (No/Yes)64/1732/60.51
ARBs (No/Yes)65/1631/70.86
WBC (×109 L)5.90 (4.81–7.87)9.90 (7.49–12.60)<0.001
Monocytes (×109 L)0.39 (0.20–0.50)0.30 (0.28–0.50)0.77
Lymphocytes(×109 L)1.00 (0.80–1.35)0.64 (0.50–0.80)<0.001
Neutrophils(×109 L)4.40 (3.40–5.55)8.65 (6.00–11.60)<0.001
Platelets (×109 L)183 (147–233)267 (220–361)<0.001
RDW (%)15.4 (14.2–16.6)14.9 (13.7–16.0)0.14
MPV (fL)8.45 (8.20–9.00)8.45 (7.9–8.9)0.38
ACE: angiotensin converting enzyme; ARBs: angiotensin II receptor blockers; BMI: body mass index; COVID-19: coronavirus disease 2019; F: female; M: male; MPV, mean platelet volume; OT: oxygen therapy; P/F: PaO2/FiO2; RDW, red blood cell distribution width; RSi: invasive respiratory support; RSni: non-invasive respiratory support; WBC: white blood cells. All continuous variables are reported as medians and interquartile ranges. Statistical significance set at 0.05. All statistically significant values are reported in bold.
Sample Availability: Data are available from the corresponding author upon reasonable request.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Fois, A.G.; Paliogiannis, P.; Scano, V.; Cau, S.; Babudieri, S.; Perra, R.; Ruzzittu, G.; Zinellu, E.; Pirina, P.; Carru, C.; et al. The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients. Molecules 2020, 25, 5725. https://doi.org/10.3390/molecules25235725

AMA Style

Fois AG, Paliogiannis P, Scano V, Cau S, Babudieri S, Perra R, Ruzzittu G, Zinellu E, Pirina P, Carru C, et al. The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients. Molecules. 2020; 25(23):5725. https://doi.org/10.3390/molecules25235725

Chicago/Turabian Style

Fois, Alessandro G., Panagiotis Paliogiannis, Valentina Scano, Stefania Cau, Sergio Babudieri, Roberto Perra, Giulia Ruzzittu, Elisabetta Zinellu, Pietro Pirina, Ciriaco Carru, and et al. 2020. "The Systemic Inflammation Index on Admission Predicts In-Hospital Mortality in COVID-19 Patients" Molecules 25, no. 23: 5725. https://doi.org/10.3390/molecules25235725

Article Metrics

Back to TopTop