Next Article in Journal
Drug Retention Rates and the Safety of Janus Kinase Inhibitors in Elderly Patients with Rheumatoid Arthritis
Previous Article in Journal
Minimally Invasive Hepatectomy for Liver Tumors: Where Are We Now?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 12
Issue 14
10.3390/jcm12144584
jcm-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:
Systematic Review

Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis

by
Angelo Zinellu
1,
Panagiotis Paliogiannis
2,3 and
Arduino A. Mangoni
4,5,*
1
Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
2
Anatomical Pathology and Histology, University Hospital (AOU) of Sassari, 07100 Sassari, Italy
3
Department of Medicine, Surgery and Pharmacy, University of Sassari, 07100 Sassari, Italy
4
Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia
5
Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Bedford Park, SA 5042, Australia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(14), 4584; https://doi.org/10.3390/jcm12144584
Submission received: 20 June 2023 / Revised: 4 July 2023 / Accepted: 7 July 2023 / Published: 10 July 2023
(This article belongs to the Section Hematology)
Browse Figures
Versions Notes

Abstract

:
Combined indices of different haematological cell types appear to be particularly promising for investigating the link between systemic inflammation and coronavirus disease 2019 (COVID-19). We conducted a systematic review and meta-analysis to assess the aggregate index of systemic inflammation (AISI), an emerging index derived from neutrophil, monocyte, platelet, and lymphocyte counts, in hospitalized COVID-19 patients with different disease severity and survival status. We searched electronic databases between the 1st of December 2019 and the 10th of June 2023 and assessed the risk of bias and the certainty of evidence. In 13 studies, severe disease/death was associated with significantly higher AISI values on admission vs. non-severe disease/survival (standard mean difference (SMD) = 0.68, 95% CI 0.38 to 0.97, p < 0.001). The AISI was also significantly associated with severe disease/death in five studies reporting odds ratios (4.39, 95% CI 2.12 to 9.06, p ˂ 0.001), but not in three studies reporting hazard ratios (HR = 1.000, 95% CI 0.999 to 1.002, p = 0.39). The pooled sensitivity, specificity, and area under the curve values for severe disease/death were 0.66 (95% CI 0.58 to 0.73), 0.78 (95% CI 0.73 to 0.83), and 0.79 (95% CI 0.76 to 0.83), respectively. Our study has shown that the AISI on admission can effectively discriminate between patients with different disease severity and survival outcome (PROSPERO registration number: CRD42023438025).

Graphical Abstract

1. Introduction

There is very good evidence that immune dysregulation and a state of excessive systemic inflammation predispose patients with coronavirus disease 2019 (COVID-19) to homeostatic alterations in various organs and tissues and an increased risk of severe clinical manifestations, prolonged hospitalization, and in-hospital mortality [1,2,3,4,5,6,7,8,9,10]. Although these observations have been instrumental in the identification of safe and effective immunomodulatory and anti-inflammatory treatment strategies [11,12], they have also stimulated the search for robust biomarkers of excessive inflammatory response for early risk stratification and appropriate management in this patient group [13,14]. Concomitantly, abnormalities in specific blood cell types, particularly, neutrophilia, lymphopenia, and thrombocytopenia, have led to studies reporting significant associations between derived indices, particularly the neutrophil-to-lymphocyte ratio (NLR), and severe disease and mortality [15,16,17,18]. The potential clinical use of another haematological index, the aggregate index of systemic inflammation (AISI, calculated by multiplying the neutrophil, monocyte, and platelet count, and dividing the product by the lymphocyte count), originally developed by Paliogiannis et al. in surgical patients [19], has been increasingly investigated in other disease states as well as COVID-19 [20,21,22,23,24,25].
Given the significant temporal changes in viral variants, pharmacological treatments, and vaccines during the COVID-19 pandemic, we critically appraised, by means of systematic review and meta-analysis, the potential utility of the AISI in discriminating between hospitalized COVID-19 patients with different severity and survival outcomes. We hypothesised that severe disease or in-hospital mortality were associated with higher AISI values at the time of admission. We also investigated possible associations between the effect size of the AISI values and various study and patient characteristics.

2. Materials and Methods

We searched Scopus, Web of Science, and PubMed for articles published from the 1st of December 2019 to the 10th of June 2023 using the following terms: (1) “AISI”, (2) “aggregate index of systemic inflammation”, (3) “COVID-19”, (4) “2019-nCoV”, (5) “SARS-CoV-2”, and (6) “coronavirus disease 2019”. We also hand searched the reference lists of each article for additional studies. The inclusion criteria were as follows: (a) investigating hospitalized patients with COVID-19 with different severity and survival outcomes; (b) reporting continuous data on AISI values in COVID-19 patients; (c) reporting multivariate adjusted odds ratio (OR) or hazard ratio (HR) with 95% confidence intervals (CI) for disease severity and/or mortality; (d) reporting prognostic accuracy (area under the receiver operating characteristic curve, AUROC, with 95% CIs); (e) full-text available; (f) age of participants ≥18 years; and (g) English language used. Two investigators independently reviewed the abstracts and full-text articles, with a third involved in case of disagreement.
The following information was extracted from each article: participant age and sex, publication year, sample size, study design, study country and continent, clinical endpoint studied (measures of disease severity and/or survival status), AISI, AUROC, sensitivity, specificity, cut-off, true positive (TP), false positive (FP), false negative (FN), and true negative (TN) values.
The Joanna Briggs Institute Critical Appraisal Checklist for case-control studies was used to assess the risk of bias (studies addressing <50%, ≥50% and <75%, and ≥75% of checklist items were adjudicated as having a high, moderate, and low risk, respectively [26]). The certainty of evidence was evaluated with the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) Working Group system [27]. We complied with the PRISMA 2020 statement (Supplementary Tables S1 and S2) [28], and registered our study in the International Prospective Register of Systematic Reviews (PROSPERO registration number: CRD42023438025).

Statistical Analysis

Forest plots of continuous AISI values were generated using standardized mean differences (SMDs) and 95% CIs to assess differences between patients with non-severe disease/survivors (NSDS) and those with severe disease/non-survivors (SDNS) (p < 0.05 for statistical significance). Additional forest plots were generated using the ORs or HRs and 95% CIs of the multivariate associations between the AISI and disease severity and survival status. Heterogeneity was assessed using the Q statistic (p < 0.10 for statistical significance) and random-effect models were used accordingly [29]. Sensitivity analyses assessed the stability of the effect size [30]. Publication bias was assessed using (1) Begg’s adjusted rank correlation test, (2) Egger’s regression asymmetry test (p < 0.05 for statistical significance) [31,32], and (3) the “trim-and-fill” method [33]. Univariate meta-regression and subgroup analyses investigated associations between the effect size and age, sex, number of study participants, study continent, publication year, and study endpoint.
We generated a summary receiver operating characteristic (SROC) curve [34], used empirical Bayes estimates, and calculated the pooled sensitivity and specificity. The HSROC model was also used to account for heterogeneity [34,35,36,37]. Publication bias was assessed using the Deeks method [38]. The relationship between prior probability, likelihood ratio, and posterior test probability was assessed using the Fagan’s nomogram plot [39]. Analyses were performed using Stata 14 (StataCorp LLC, College Station, TX, USA).

3. Results

3.1. Study Selection

After initially identifying 52 articles, 36 were removed (either duplicates or irrelevant). After full-text review, a further three were excluded (missing data: two studies; participants aged <18 years: one study), leaving 13 articles, all retrospective studies, for final analysis (Figure 1) [25,40,41,42,43,44,45,46,47,48,49,50,51]. Clinical endpoints included mortality (11 study groups) [25,40,43,44,47,48,49,50,51], and the following measures of disease severity: transfer to the intensive care unit (two study groups) [42,46], invasive mechanical ventilation (two study groups) [44,45], prolonged hospital stay (one study group) [41], acute limb ischemia (one study group) [42], deep vein thrombosis (one study group) [47], and acute pulmonary embolism (one study group) [47]. The AISI was measured on admission in all studies. The risk of bias and the initial certainty of evidence (case-control design; rating 2, ⊕⊕⊖⊖) were low in all studies (Supplementary Table S3) [25,40,41,42,43,44,45,46,47,48,49,50,51].

3.2. Standardized Mean Differences

Eleven studies (14 groups) reported the AISI in 1600 non-severe disease/survivor (NSDS, mean age 68 years, 57% males) and 4521 severe disease/non-survivor patients (SDNS, mean age 62 years, 56% males) [25,40,41,42,43,44,46,47,48,50,51]. Five studies were conducted in Europe [25,40,41,42,47], four in Asia [43,48,50,51], one in America [44], and one in Africa [46] (Table 1).
Random effects models were used because of the high heterogeneity observed (I2 = 95.2%, p < 0.001). SDNS patients had significantly higher AISI values vs. NSDS (SMD = 0.68, 95% CI 0.38 to 0.97, p < 0.001; Figure 2). The pooled SMD values were stable in sensitivity analysis (range 0.53–0.72; Figure 3).
No publication bias was detected with either the Begg’s test (p = 0.23), the Egger’s test (p = 0.46), or the “trim-and-fill” method (Figure 4). However, the funnel plot revealed the distortive effect of one study [42]. Its removal did not tangibly influence the effect size (SMD = 0.53, 95% CI 0.32 to 0.74, p < 0.001; I2 = 89.9%, p < 0.001).
In meta-regression, there were non-significant correlations between the effect size and age (t = 0.25, p = 0.81), proportion of males (t = 0.63, p = 0.55), publication year (t = −0.76, p = 0.46), and sample size (t = 0.70, p = 0.50). In sub-group analysis, there were non-significant differences (p = 0.15) in SMD between studies reporting mortality (SMD = 0.52, 95% CI 0.24 to 0.80, p < 0.001; I2 = 92.1%, p < 0.001) and those reporting measures of disease severity (SMD = 1.09, 95% CI 0.22 to 1.95, p = 0.014; I2 = 97.8%, p < 0.001; Figure 5). Similarly, non-significant differences (p = 0.19) were observed in the pooled SMD between European (SMD = 1.04, 95% CI 0.35 to 1.72, p = 0.003; I2 = 96.0%, p < 0.001), Asian (SMD = 0.42, 95% CI 0.28 to 0.55, p < 0.001; I2 = 26.2%, p < 0.001), and American studies (SMD = 0.28, 95% CI 0.04 to 0.53, p = 0.024; I2 = 78.5%, p < 0.001; Figure 6). However, the variance was substantially reduced in the Asian subgroup (I2 = 26.5%).
The level of certainty remained low (rating 2, ⊕⊕⊖⊖) after considering the low risk of bias in all studies, the high but partly explainable heterogeneity, the lack of indirectness, the relatively low imprecision, the moderate effect size, and the absence of publication bias.

3.3. Odds Ratios

Five studies (including nine groups) reported multivariate logistic associations between the AISI and disease severity/survival as ORs in 5794 COVID-19 patients (59% males, mean age 66 years) [42,44,45,46,47]. Endpoints included mortality (two studies) [44,47], acute limb ischemia [42], invasive mechanical ventilation (two studies) [44,45], deep vein thrombosis (one study) [47], and acute pulmonary embolism (one study) [47]. Three studies were conducted in Europe [42,45,47], one in America [44], and one in Africa (Table 2) [46].
Using random-effects models (I2 = 98.0%, p < 0.001), higher AISI values were significantly associated with SDNS (OR = 4.39, 95% CI 2.12 to 9.06, p < 0.001; Figure 7). The effect size was stable in sensitivity analysis (range 3.79–5.23; Figure 8).
Assessment of publication bias and meta-regression analysis were prevented by the relatively small number of studies. In sub-group analysis, the effect size was significantly different in studies conducted in Europe (OR = 7.06, 95 % CI 5.14 to 9.68, p < 0.001; I2 = 59.2%, p = 0.03), but not in other geographical areas (OR = 1.43, 95 % CI 0.86 to 2.38, p = 0.17; I2 = 92.0%, p < 0.001; Figure 9), with a lower between-study variance in the former subgroup.
The level of certainty remained low (rating 2, ⊕⊕⊖⊖) after considering the low risk of bias in all studies, the high but partly explainable heterogeneity, the lack of indirectness, the relatively low imprecision, the relatively large effect size (OR = 4.39; upgrade one level), and the lack of assessment of publication bias (downgrade one level).

3.4. Hazard Ratios

Three studies reported multivariate logistic associations between the AISI and mortality as HRs in 504 COVID-19 patients (57% males, mean age 70 years) [40,48,50]. Two studies were conducted in Asia [48,50], and one in Europe (Table 2) [40]. The risk of bias was low in all studies (Supplementary Table S3) [40,48,50].
Using random-effects models (I2 = 89.8%, p < 0.001), the AISI was not associated with mortality (HR = 1.000, 95% CI 0.999 to 1.002, p = 0.39; Figure 10). Assessment of publication bias, meta-regression, and subgroup analyses was not possible because of the relatively small number of studies.
The level of certainty was downgraded to extremely low (rating 0, ⊖⊖⊖⊖) after considering the high heterogeneity, the relatively high imprecision, and the lack of assessment of publication bias.

3.5. Diagnostic Accuracy for Prediction of Severe Disease or Death

Eleven studies (including 15 patient groups) reported the diagnostic accuracy of the AISI towards severe disease or death in 7427 COVID-19 patients (56% males, mean age 58 years) (Table 3) [25,40,41,42,43,45,46,47,48,49,50]. Six studies were conducted in Europe [25,40,41,42,45,47], four in Asia [43,48,49,50], and one in Africa [46]. Endpoints included mortality (eight study groups) [25,40,43,47,48,49,50], admission to the intensive care unit (two study groups) [42,46], prolonged hospital stay (one study group) [41], acute limb ischemia (one study group) [42], invasive mechanical ventilation (one study group) [45], deep vein thrombosis (one study group) [47], and acute pulmonary embolism (one study group) [47]. The risk of bias was low in all studies (Supplementary Table S3) [25,40,41,42,43,45,46,47,48,49,50].
The pooled sensitivity and specificity of the AISI for severe disease/death were 0.66 (95% CI 0.58 to 0.73) and 0.78 (95% CI 0.73 to 0.83), respectively (Figure 11). The AUC was 0.79 (95% CI 0.76 to 0.83), with sensitivity of 0.66 and specificity of 0.78 (Figure 12). The empirical Bayes estimates in HSROC analysis are shown in Figure 13. The midas command was used to evaluate the quantile plot of residual-based goodness-of-fit, the Chi-squared probability plot of squared Mahalanobis distances for assessment of the bivariate normality assumption, the spikeplot for checking for particularly influential observations using Cook’s distance, and a scatter plot for checking for outliers using standardized predicted random effects (Figure 14). The analysis identified one outlier [49]. Its removal resulted in an AUC value of 0.80, a sensitivity of 0.69, and a specificity of 0.79.
The Fagan’s nomogram, generated to assess the potential clinical utility of the AISI, showed that, assuming a pre-test probability of 25% for adverse outcomes, the post-test probability was 50% with relatively high AISI values and 13% with relatively low AISI values (Figure 15).
There was no significant publication bias (p = 0.38) according to the Deeks funnel plot asymmetry test (Figure 16).
The HSROC curve (Figure 12) was symmetric, given the negative correlation coefficient between logit transformed sensitivity and specificity (HSROC model; −0.634, 95% CI −0.897 to −0.432), and the non-significant (p = 0.50) symmetry parameter β (0.209, 95% CI −0.394 to 0.812; data not reported in tables or graphs). This suggests the absence of between-study heterogeneity [34,36]. However, the visual representation of SROC (Figure 12) suggests moderate heterogeneity (95% CI 0.76 to 0.83). Using midas, the pooled sensitivity and specificity showed an inconsistency (I2) of 90.48 and 92.07%, respectively (Figure 11). Using the bivariate boxplot with logit_Se and logit_Sp (Figure 17), four studies fell outside the circles [41,45,49,50], which indicates the presence of heterogeneity across studies. None of the investigated parameters exhibited significant associations with the effect size for sensitivity or specificity (Figure 18).

4. Discussion

In our study, the AISI values on admission were significantly higher in hospitalized SDNS vs. NSDS COVID-19 patients. The AISI between-group differences were significant when expressed either as SMDs or as ORs, whereas the lack of significant differences in studies reporting HRs is likely to be secondary to their limited number (n = 3). Importantly, the AISI exhibited good diagnostic performance towards severe disease or mortality, with an AUC of 0.79. Sensitivity analysis showed that the results of the meta-analysis were stable. There were non-significant associations in meta-regression between the effect size and either age, sex, publication year, or sample size. In particular, the absence of significant associations with the year of publication suggests that the capacity of the AISI to discriminate between COVID-19 patients with different disease severity and survival status persisted during different phases of the COVID-19 pandemic, regardless of study participants with different vaccines, viral variants, and treatments. In subgroup analysis, there were differences in effect size with study continent, particularly for ORs, indicating possible ethnicity-related effects on the association between the AISI and COVID-19.
Compared to other indexes of inflammation derived from haematological parameters, e.g., NLR and the platelet-to-lymphocyte ratio (PLR), the AISI is calculated using information from four types of blood cell that are involved in inflammation, i.e., neutrophils, monocytes, platelets, and lymphocytes. The AISI was initially investigated in 2018 to predict outcomes in surgical patients [19]. Since then, studies have assessed the potential clinical utility of the AISI in patients with other disease states characterized by a systemic pro-inflammatory state, such as macular degeneration [20], idiopathic pulmonary fibrosis [21,23], and cancer [24]. Notably, in patients with idiopathic pulmonary fibrosis, the capacity of the AISI to predict for four-year survival was superior to white blood cell, neutrophil, monocyte, lymphocyte, and platelet count taken singly, and the NLR and the PLR, further supporting its potential clinical utility [23]. Over the last three years, an increasing number of studies have also investigated the AISI in patients with COVID-19, given the critical pathophysiological and prognostic role of excess systemic inflammation in terms of severe clinical manifestations, multiorgan involvement, requirement for intensive care, prolonged hospitalization, and in-hospital mortality [1,2,3,4,5,6,7,8,9,10].
The comprehensive appraisal of the available evidence strongly supports the concept that assessing the AISI at the time of admission can provide useful information to rapidly direct subgroups of patients with COVID-19 to different treatment and monitoring pathways. This proposition is further supported by the observed pooled AUC value and the Fagan’s nomogram, which indicated a wide separation of the post-test probability of severe disease/death compared to the pre-test probability, according to whether the admission AISI values were relatively low or relatively high [52]. However, it should be emphasised that further studies are required before routinely using the AISI to assess COVID-19 patients. Specifically, prospective studies should investigate whether the AISI can predict, with or without other clinical parameters, ethnicity, and specific comorbidities, disease severity and mortality in this group, in a similar way to other conditions [53].
One limitation of our study is the moderate-to-high between-study heterogeneity. However, we identified specific sources of heterogeneity when investigating the SMD (study continent) and the OR (study continent). Furthermore, publication bias could not be assessed with the OR and the HR because of the limited number of relevant studies. In contrast, significant strengths are the comprehensive assessment of the significance of the AISI with meta-regression and subgroup analysis, SROC, and Fagan’s nomogram.

5. Conclusions

In our study, higher AISI values on admission were significantly associated with severe disease/death in COVID-19. Further studies in patients with a wide range of comorbidities and ethnic backgrounds are warranted to determine whether the AISI can routinely assist with discriminating between COVID-19 patients with different severity and outcomes in order to optimize management and outcomes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm12144584/s1, Table S1: PRISMA 2020 for abstracts checklist; Table S2: PRISMA 2020 checklist; Table S3: The Joanna Briggs Institute Critical Appraisal Checklist. References [25,40,41,42,43,44,45,46,47,48,49,50,51] are cited in the Supplementary Materials.

Author Contributions

A.Z., P.P. and A.A.M. conceived the study, conducted the literature search, and analysed the data. A.A.M. wrote the first draft. A.Z., P.P. and A.A.M. reviewed further drafts and the final version. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The relevant data are available from A.Z. upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Merad, M.; Blish, C.A.; Sallusto, F.; Iwasaki, A. The immunology and immunopathology of COVID-19. Science 2022, 375, 1122–1127. [Google Scholar] [CrossRef] [PubMed]
  2. Li, Q.; Wang, Y.; Sun, Q.; Knopf, J.; Herrmann, M.; Lin, L.; Jiang, J.; Shao, C.; Li, P.; He, X.; et al. Immune response in COVID-19: What is next? Cell Death Differ. 2022, 29, 1107–1122. [Google Scholar] [CrossRef] [PubMed]
  3. Silberberg, E.; Filep, J.G.; Ariel, A. Weathering the Storm: Harnessing the Resolution of Inflammation to Limit COVID-19 Pathogenesis. Front. Immunol. 2022, 13, 863449. [Google Scholar] [CrossRef] [PubMed]
  4. de Andrade, S.A.; de Souza, D.A.; Torres, A.L.; de Lima, C.F.G.; Ebram, M.C.; Celano, R.M.G.; Schattner, M.; Chudzinski-Tavassi, A.M. Pathophysiology of COVID-19: Critical Role of Hemostasis. Front. Cell Infect. Microbiol. 2022, 12, 896972. [Google Scholar] [CrossRef]
  5. Ghofrani Nezhad, M.; Jami, G.; Kooshkaki, O.; Chamani, S.; Naghizadeh, A. The Role of Inflammatory Cytokines (Interleukin-1 and Interleukin-6) as a Potential Biomarker in the Different Stages of COVID-19 (Mild, Severe, and Critical). J. Interferon Cytokine Res. 2023, 43, 147–163. [Google Scholar] [CrossRef]
  6. Silva, M.J.A.; Ribeiro, L.R.; Gouveia, M.I.M.; Marcelino, B.D.R.; Santos, C.S.D.; Lima, K.V.B.; Lima, L. Hyperinflammatory Response in COVID-19: A Systematic Review. Viruses 2023, 15, 553. [Google Scholar] [CrossRef]
  7. Nasrollahi, H.; Talepoor, A.G.; Saleh, Z.; Eshkevar Vakili, M.; Heydarinezhad, P.; Karami, N.; Noroozi, M.; Meri, S.; Kalantar, K. Immune responses in mildly versus critically ill COVID-19 patients. Front. Immunol. 2023, 14, 1077236. [Google Scholar] [CrossRef]
  8. Arish, M.; Qian, W.; Narasimhan, H.; Sun, J. COVID-19 immunopathology: From acute diseases to chronic sequelae. J. Med. Virol. 2023, 95, e28122. [Google Scholar] [CrossRef]
  9. Montazersaheb, S.; Hosseiniyan Khatibi, S.M.; Hejazi, M.S.; Tarhriz, V.; Farjami, A.; Ghasemian Sorbeni, F.; Farahzadi, R.; Ghasemnejad, T. COVID-19 infection: An overview on cytokine storm and related interventions. Virol. J. 2022, 19, 92. [Google Scholar] [CrossRef]
  10. Tan, L.Y.; Komarasamy, T.V.; Rmt Balasubramaniam, V. Hyperinflammatory Immune Response and COVID-19: A Double Edged Sword. Front. Immunol. 2021, 12, 742941. [Google Scholar] [CrossRef]
  11. Selvarajan, S.; Anandaradje, A.; Shivabasappa, S.; Melepurakkal Sadanandan, D.; Nair, N.S.; George, M. Efficacy of pharmacological interventions in COVID-19: A network meta-analysis. Br. J. Clin. Pharmacol. 2022, 88, 4080–4091. [Google Scholar] [CrossRef] [PubMed]
  12. Yuan, Y.; Jiao, B.; Qu, L.; Yang, D.; Liu, R. The development of COVID-19 treatment. Front. Immunol. 2023, 14, 1125246. [Google Scholar] [CrossRef] [PubMed]
  13. Qin, R.; He, L.; Yang, Z.; Jia, N.; Chen, R.; Xie, J.; Fu, W.; Chen, H.; Lin, X.; Huang, R.; et al. Identification of Parameters Representative of Immune Dysfunction in Patients with Severe and Fatal COVID-19 Infection: A Systematic Review and Meta-analysis. Clin. Rev. Allergy Immunol. 2023, 64, 33–65. [Google Scholar] [CrossRef] [PubMed]
  14. Zinellu, A.; Mangoni, A.A. Serum Prealbumin Concentrations, COVID-19 Severity, and Mortality: A Systematic Review and Meta-Analysis. Front. Med. 2021, 8, 638529. [Google Scholar] [CrossRef]
  15. Terpos, E.; Ntanasis-Stathopoulos, I.; Elalamy, I.; Kastritis, E.; Sergentanis, T.N.; Politou, M.; Psaltopoulou, T.; Gerotziafas, G.; Dimopoulos, M.A. Hematological findings and complications of COVID-19. Am. J. Hematol. 2020, 95, 834–847. [Google Scholar] [CrossRef]
  16. Rahman, A.; Niloofa, R.; Jayarajah, U.; De Mel, S.; Abeysuriya, V.; Seneviratne, S.L. Hematological Abnormalities in COVID-19: A Narrative Review. Am. J. Trop. Med. Hyg. 2021, 104, 1188–1201. [Google Scholar] [CrossRef]
  17. Zinellu, A.; Mangoni, A.A. A systematic review and meta-analysis of the association between the neutrophil, lymphocyte, and platelet count, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio and COVID-19 progression and mortality. Expert Rev. Clin. Immunol. 2022, 18, 1187–1202. [Google Scholar] [CrossRef]
  18. Wei, T.; Li, J.; Cheng, Z.; Jiang, L.; Zhang, J.; Wang, H.; Zhou, L. Hematological characteristics of COVID-19 patients with fever infected by the Omicron variant in Shanghai: A retrospective cohort study in China. J. Clin. Lab. Anal. 2023, 37, e24808. [Google Scholar] [CrossRef]
  19. Paliogiannis, P.; Ginesu, G.C.; Tanda, C.; Feo, C.F.; Fancellu, A.; Fois, A.G.; Mangoni, A.A.; Sotgia, S.; Carru, C.; Porcu, A.; et al. Inflammatory cell indexes as preoperative predictors of hospital stay in open elective thoracic surgery. ANZ J. Surg. 2018, 88, 616–620. [Google Scholar] [CrossRef]
  20. Pinna, A.; Porcu, T.; D’Amico-Ricci, G.; Dore, S.; Boscia, F.; Paliogiannis, P.; Carru, C.; Zinellu, A. Complete Blood Cell Count-Derived Inflammation Biomarkers in Men with Age-Related Macular Degeneration. Ocul. Immunol. Inflamm. 2019, 27, 932–936. [Google Scholar] [CrossRef]
  21. 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] [PubMed]
  22. Campesi, I.; Montella, A.; Sotgiu, G.; Dore, S.; Carru, C.; Zinellu, A.; Palermo, M.; Franconi, F. Combined oral contraceptives modify the effect of smoking on inflammatory cellular indexes and endothelial function in healthy subjects. Eur. J. Pharmacol. 2021, 891, 173762. [Google Scholar] [CrossRef] [PubMed]
  23. Zinellu, A.; Collu, C.; Nasser, M.; Paliogiannis, P.; Mellino, S.; Zinellu, E.; Traclet, J.; Ahmad, K.; Mangoni, A.A.; Carru, C.; et al. The Aggregate Index of Systemic Inflammation (AISI): A Novel Prognostic Biomarker in Idiopathic Pulmonary Fibrosis. J. Clin. Med. 2021, 10, 4134. [Google Scholar] [CrossRef] [PubMed]
  24. Ginesu, G.C.; Paliogiannis, P.; Feo, C.F.; Cossu, M.L.; Scanu, A.M.; Fancellu, A.; Fois, A.G.; Zinellu, A.; Perra, T.; Veneroni, S.; et al. Inflammatory Indexes as Predictive Biomarkers of Postoperative Complications in Oncological Thoracic Surgery. Curr. Oncol. 2022, 29, 3425–3432. [Google Scholar] [CrossRef]
  25. Fois, S.S.; Zinellu, E.; Zinellu, A.; Merella, M.; Pau, M.C.; Carru, C.; Fois, A.G.; Pirina, P. Comparison of Clinical Features, Complete Blood Count Parameters, and Outcomes between Two Distinct Waves of COVID-19: A Monocentric Report from Italy. Healthcare 2022, 10, 2427. [Google Scholar] [CrossRef]
  26. Moola, S.; Munn, Z.; Tufanaru, C.; Aromataris, E.; Sears, K.; Sfetcu, R.; Currie, M.; Qureshi, R.; Mattis, P.; Lisy, K.; et al. Systematic reviews of etiology and risk. Joanna Briggs Inst. Rev. Man. 2017, 1–7. [Google Scholar]
  27. Balshem, H.; Helfand, M.; Schunemann, H.J.; Oxman, A.D.; Kunz, R.; Brozek, J.; Vist, G.E.; Falck-Ytter, Y.; Meerpohl, J.; Norris, S.; et al. GRADE guidelines: 3. Rating the quality of evidence. J. Clin. Epidemiol. 2011, 64, 401–406. [Google Scholar] [CrossRef] [PubMed]
  28. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef]
  29. Deeks, J.J.; Higgins, J.P.T.; Altman, D.G. Analysing data and undertaking meta-analyses. Cochrane Handb. Syst. Rev. Interv. 2022, 241–284. [Google Scholar]
  30. Tobias, A. Assessing the influence of a single study in the meta-analysis estimate. Stata Tech. Bull. 1999, 47, 15–17. [Google Scholar]
  31. Begg, C.B.; Mazumdar, M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994, 50, 1088–1101. [Google Scholar] [CrossRef] [PubMed]
  32. Sterne, J.A.; Egger, M. Funnel plots for detecting bias in meta-analysis: Guidelines on choice of axis. J. Clin. Epidemiol. 2001, 54, 1046–1055. [Google Scholar] [CrossRef] [PubMed]
  33. Duval, S.; Tweedie, R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics 2000, 56, 455–463. [Google Scholar] [CrossRef] [PubMed]
  34. Rutter, C.M.; Gatsonis, C.A. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat. Med. 2001, 20, 2865–2884. [Google Scholar] [CrossRef]
  35. Reitsma, J.B.; Glas, A.S.; Rutjes, A.W.; Scholten, R.J.; Bossuyt, P.M.; Zwinderman, A.H. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 2005, 58, 982–990. [Google Scholar] [CrossRef]
  36. Harbord, R.M.; Whiting, P. Metandi: Meta-analysis of diagnostic accuracy using hierarchical logistic regression. Stata J. 2009, 9, 211–229. [Google Scholar] [CrossRef] [Green Version]
  37. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 2009, 36, 1–48. [Google Scholar]
  38. Deeks, J.J.; Macaskill, P.; Irwig, L. The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J. Clin. Epidemiol. 2005, 58, 882–893. [Google Scholar] [CrossRef]
  39. Fagan, T.J. Letter: Nomogram for Bayes theorem. N. Engl. J. Med. 1975, 293, 257. [Google Scholar] [CrossRef]
  40. 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. [Google Scholar] [CrossRef]
  41. Zinellu, A.; Scano, V.; Masotto, E.; De Riu, G.; Vaira, L.A.; Carru, C.; Pirina, P.; Babudieri, S.; Mangoni, A.A.; Fois, A.G. The Systemic Inflammation Index on admission is independently associated with length of stay in hospitalized COVID-19 patients. Minerva Respir. Med. 2021, 60, 65–72. [Google Scholar] [CrossRef]
  42. Arbanasi, E.M.; Halmaciu, I.; Kaller, R.; Muresan, A.V.; Arbanasi, E.M.; Suciu, B.A.; Cosarca, C.M.; Cojocaru, I.I.; Melinte, R.M.; Russu, E. Systemic Inflammatory Biomarkers and Chest CT Findings as Predictors of Acute Limb Ischemia Risk, Intensive Care Unit Admission, and Mortality in COVID-19 Patients. Diagnostics 2022, 12, 2379. [Google Scholar] [CrossRef] [PubMed]
  43. Ghobadi, H.; Mohammadshahi, J.; Javaheri, N.; Fouladi, N.; Mirzazadeh, Y.; Aslani, M.R. Role of leukocytes and systemic inflammation indexes (NLR, PLR, MLP, dNLR, NLPR, AISI, SIR-I, and SII) on admission predicts in-hospital mortality in non-elderly and elderly COVID-19 patients. Front. Med. 2022, 9, 916453. [Google Scholar] [CrossRef] [PubMed]
  44. Gutierrez-Perez, I.A.; Buendia-Roldan, I.; Perez-Rubio, G.; Chavez-Galan, L.; Hernandez-Zenteno, R.J.; Aguilar-Duran, H.; Fricke-Galindo, I.; Zaragoza-Garcia, O.; Falfan-Valencia, R.; Guzman-Guzman, I.P. Outcome predictors in COVID-19: An analysis of emergent systemic inflammation indices in Mexican population. Front. Med. 2022, 9, 1000147. [Google Scholar] [CrossRef]
  45. Halmaciu, I.; Arbanasi, E.M.; Kaller, R.; Muresan, A.V.; Arbanasi, E.M.; Bacalbasa, N.; Suciu, B.A.; Cojocaru, I.I.; Runcan, A.I.; Grosu, F.; et al. Chest CT Severity Score and Systemic Inflammatory Biomarkers as Predictors of the Need for Invasive Mechanical Ventilation and of COVID-19 Patients’ Mortality. Diagnostics 2022, 12, 2089. [Google Scholar] [CrossRef] [PubMed]
  46. Hamad, D.A.; Aly, M.M.; Abdelhameid, M.A.; Ahmed, S.A.; Shaltout, A.S.; Abdel-Moniem, A.E.; Ragheb, A.M.R.; Attia, M.N.; Meshref, T.S. Combined Blood Indexes of Systemic Inflammation as a Mirror to Admission to Intensive Care Unit in COVID-19 Patients: A Multicentric Study. J. Epidemiol. Glob. Health 2022, 12, 64–73. [Google Scholar] [CrossRef]
  47. Muresan, A.V.; Halmaciu, I.; Arbanasi, E.M.; Kaller, R.; Arbanasi, E.M.; Budisca, O.A.; Melinte, R.M.; Vunvulea, V.; Filep, R.C.; Marginean, L.; et al. Prognostic Nutritional Index, Controlling Nutritional Status (CONUT) Score, and Inflammatory Biomarkers as Predictors of Deep Vein Thrombosis, Acute Pulmonary Embolism, and Mortality in COVID-19 Patients. Diagnostics 2022, 12, 2757. [Google Scholar] [CrossRef]
  48. Ercan, Z.; Evren Oztop, K.; Pinar, M.; Varim, C.; Dheir, H.; Karacaer, C.; Yaylaci, S.; Bilal Genc, A.; Cekic, D.; Nalbant, A.; et al. The aggregate index of systemic inflammation may predict mortality in COVID-19 patients with chronic renal failure. Eur. Rev. Med. Pharmacol. Sci. 2023, 27, 3747–3752. [Google Scholar] [CrossRef]
  49. Haryati, H.; Wicaksono, B.; Syahadatina, M. Complete blood count derived inflammation indexes predict outcome in COVID-19 patients: A study in Indonesia. J. Infect. Dev. Ctries 2023, 17, 319–326. [Google Scholar] [CrossRef]
  50. Hosseninia, S.; Ghobadi, H.; Garjani, K.; Hosseini, S.A.H.; Aslani, M.R. Aggregate index of systemic inflammation (AISI) in admission as a reliable predictor of mortality in COPD patients with COVID-19. BMC Pulm. Med. 2023, 23, 107. [Google Scholar] [CrossRef]
  51. Khadzhieva, M.B.; Gracheva, A.S.; Belopolskaya, O.B.; Chursinova, Y.V.; Redkin, I.V.; Pisarev, M.V.; Kuzovlev, A.N. Serial Changes in Blood-Cell-Count-Derived and CRP-Derived Inflammatory Indices of COVID-19 Patients. Diagnostics 2023, 13, 746. [Google Scholar] [CrossRef] [PubMed]
  52. Caraguel, C.G.; Vanderstichel, R. The two-step Fagan’s nomogram: Ad hoc interpretation of a diagnostic test result without calculation. Evid. Based Med. 2013, 18, 125–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Xiu, J.; Lin, X.; Chen, Q.; Yu, P.; Lu, J.; Yang, Y.; Chen, W.; Bao, K.; Wang, J.; Zhu, J.; et al. The aggregate index of systemic inflammation (AISI): A novel predictor for hypertension. Front. Cardiovasc. Med. 2023, 10, 1163900. [Google Scholar] [CrossRef] [PubMed]
Jcm 12 04584 g001 550
Figure 1. PRISMA 2020 flow chart of study selection.
Figure 1. PRISMA 2020 flow chart of study selection.
Jcm 12 04584 g001
Jcm 12 04584 g002 550
Figure 2. Forest plot of AISI values in NSDS and SDNS patients [25,40,41,42,43,44,46,47,48,50,51].
Figure 2. Forest plot of AISI values in NSDS and SDNS patients [25,40,41,42,43,44,46,47,48,50,51].
Jcm 12 04584 g002
Jcm 12 04584 g003 550
Figure 3. Sensitivity analysis of studies reporting the AISI in COVID-19 [25,40,41,42,43,44,46,47,48,50,51].
Figure 3. Sensitivity analysis of studies reporting the AISI in COVID-19 [25,40,41,42,43,44,46,47,48,50,51].
Jcm 12 04584 g003
Jcm 12 04584 g004 550
Figure 4. Funnel plot of studies investigating AISI values in COVID-19 patients after “trimming-and-filling”.
Figure 4. Funnel plot of studies investigating AISI values in COVID-19 patients after “trimming-and-filling”.
Jcm 12 04584 g004
Jcm 12 04584 g005 550
Figure 5. Forest plot of studies of AISI in COVID-19 according to clinical endpoint [25,40,41,42,43,44,46,47,48,50,51].
Figure 5. Forest plot of studies of AISI in COVID-19 according to clinical endpoint [25,40,41,42,43,44,46,47,48,50,51].
Jcm 12 04584 g005
Jcm 12 04584 g006 550
Figure 6. Forest plot of studies examining AISI in COVID-19 according to study continent [25,40,41,42,43,44,46,47,48,50,51].
Figure 6. Forest plot of studies examining AISI in COVID-19 according to study continent [25,40,41,42,43,44,46,47,48,50,51].
Jcm 12 04584 g006
Jcm 12 04584 g007 550
Figure 7. Forest plot of studies examining the association between AISI and disease severity/survival in COVID-19 with odds ratio [42,44,45,46,47].
Figure 7. Forest plot of studies examining the association between AISI and disease severity/survival in COVID-19 with odds ratio [42,44,45,46,47].
Jcm 12 04584 g007
Jcm 12 04584 g008 550
Figure 8. Sensitivity analysis of the association between the AISI and COVID-19 using odds ratios [42,44,45,46,47].
Figure 8. Sensitivity analysis of the association between the AISI and COVID-19 using odds ratios [42,44,45,46,47].
Jcm 12 04584 g008
Jcm 12 04584 g009 550
Figure 9. Forest plot of studies examining the odds ratio of AISI and COVID-19 severity/survival according to study continent [42,44,45,46,47].
Figure 9. Forest plot of studies examining the odds ratio of AISI and COVID-19 severity/survival according to study continent [42,44,45,46,47].
Jcm 12 04584 g009
Jcm 12 04584 g010 550
Figure 10. Forest plot of studies examining the association between AISI and COVID-19 severity/survival using hazard ratio [40,48,50].
Figure 10. Forest plot of studies examining the association between AISI and COVID-19 severity/survival using hazard ratio [40,48,50].
Jcm 12 04584 g010
Jcm 12 04584 g011 550
Figure 11. Forest plot for the pooled estimates of sensitivity and specificity of AISI values for adverse outcome prediction [25,40,41,42,43,45,46,47,48,49,50].
Figure 11. Forest plot for the pooled estimates of sensitivity and specificity of AISI values for adverse outcome prediction [25,40,41,42,43,45,46,47,48,49,50].
Jcm 12 04584 g011
Jcm 12 04584 g012 550
Figure 12. SROC curve of AISI values for adverse outcome prediction.
Figure 12. SROC curve of AISI values for adverse outcome prediction.
Jcm 12 04584 g012
Jcm 12 04584 g013 550
Figure 13. Empirical Bayes estimates of HSROC curve for adverse outcome prediction.
Figure 13. Empirical Bayes estimates of HSROC curve for adverse outcome prediction.
Jcm 12 04584 g013
Jcm 12 04584 g014 550
Figure 14. Residual-based goodness-of-fit, bivariate normality, influence, and outlier detection.
Figure 14. Residual-based goodness-of-fit, bivariate normality, influence, and outlier detection.
Jcm 12 04584 g014
Jcm 12 04584 g015 550
Figure 15. Fagan’s nomogram of AISI for adverse outcome prediction.
Figure 15. Fagan’s nomogram of AISI for adverse outcome prediction.
Jcm 12 04584 g015
Jcm 12 04584 g016 550
Figure 16. Deeks funnel plot asymmetry test.
Figure 16. Deeks funnel plot asymmetry test.
Jcm 12 04584 g016
Jcm 12 04584 g017 550
Figure 17. Bivariate boxplot exploring heterogeneity.
Figure 17. Bivariate boxplot exploring heterogeneity.
Jcm 12 04584 g017
Jcm 12 04584 g018 550
Figure 18. Forest plots of sensitivity and specificity for the study level covariates included in the univariate meta-regression model.
Figure 18. Forest plots of sensitivity and specificity for the study level covariates included in the univariate meta-regression model.
Jcm 12 04584 g018
Table
Table 1. Studies reporting AISI in COVID-19 patients with NSDS and SDNS.
Table 1. Studies reporting AISI in COVID-19 patients with NSDS and SDNS.
Non-Severe Disease or Survivor (NSDS)Severe Disease or Non-Survivor (SDNS) Outcome
StudynAge
(Years)		M/FAISI
(Mean ± SD)		nAge
(Years)		M/FAISI
(Mean ± SD)
Fois AG et al., 2020, Italy [40]906856/34472 ± 559298021/81032 ± 1290Mortality
Zinellu A et al., 2021, Italy [41]436627/16413 ± 444226916/6937 ± 1367Length of stay
Arbanasi EM et al. (a) 2022, Romania [42]46170284/177610 ± 573497421/283027 ± 2361ALI
Fois SS et al. (a), 2022, Italy [25]1406891/49694 ± 782428232/101401 ± 1509Mortality
Fois SS et al. (b), 2022, Italy [25]1207366/54644 ± 727408220/20751 ± 817Mortality
Ghobadi H et al. (a) 2022, Iran [43]94748548/399192 ± 1771355488/47311 ± 344Mortality
Ghobadi H et al. (b) 2022, Iran [43]49276238/254252 ± 22521878114/104328 ± 324Mortality
Gutiérrez-Pérez IA et al. (a) 2022, Mexico [44]352NRNR1957 ± 3232196NRNR4002 ± 7085IMV
Gutiérrez-Pérez IA et al. (b) 2022, Mexico [44]491NRNR3367 ± 5964316NRNR4489 ± 8119Mortality
Hamad DA et al., 2022, Egypt [46]1853391/94256 ± 50531058181/1291319 ± 1949ICU admission
Muresan AV et al. (a), 2022 Romania [47]74670397/3491087 ± 12991437277/663476 ± 3237Mortality
Ercan Z et al., 2023, Turkey [48]427423/19986 ± 1646437316/271675 ± 1639Mortality
Hosseninia S et al., 2023, Romania [50]2746870/53316 ± 299267328/18436 ± 424Mortality
Khadzhieva MB et al., 2023, Russia [51]1385773/65416 ± 513316218/13652 ± 921Mortality
Legend: AISI, aggregate index of systemic inflammation; ALI, acute limb ischemia; F, female; ICU, intensive care unit; IMV, invasive mechanical ventilation; M, male; NR, not reported.
Table
Table 2. Studies reporting associations between AISI and disease severity/survival in COVID-19 with odds ratio or hazard ratio.
Table 2. Studies reporting associations between AISI and disease severity/survival in COVID-19 with odds ratio or hazard ratio.
StudyDesignnAge
(Years)		M/FOR95% CIOutcome
Arbanasi EM et al. (a) 2022, Romania [42]R51070305/20514.827.14–30.77Acute limb ischemia
Arbanasi EM et al. (b) 2022, Romania [42]R51070305/2054.653.16–6.85Admission to intensive care unit
Gutiérrez-Pérez IA et al. (a) 2022, Mexico [44]R548NR352/1962.641.79–3.89Invasive mechanical ventilation
Gutiérrez-Pérez IA et al. (b) 2022, Mexico [44]R807NR491/3161.20.87–1.65Mortality
Halmaciu I et al., 2022, Romania [45]R26771159/10815.826.86–36.65Invasive mechanical ventilation
Hamad DA et al., 2022, Egypt [46]R48549272/2131.0031.001–1.005Admission to intensive care unit
Muresan AV et al. (a) 2022, Romania [47]R88971474/4156.274.21–9.34Mortality
Muresan AV et al. (b) 2022, Romania [47]R88971474/4156.244.38–8.89Deep vein thrombosis
Muresan AV et al. (c) 2022, Romania [47]R88971474/4155.983.49–10.24Acute pulmonary embolism
StudyDesignnAge
(Years)		M/FHR95% CIOutcome
Fois AG et al., 2020, Italy [40]R1197277/4211–1.0001Mortality
Ercan Z et al., 2023, Turkey [48]R857239/461.0011–1.001Mortality
Hosseninia S et al., 2023, Iran [50]R3006998/712.011.048–3.855Mortality
Legend: CI, confidence interval; F, female; HR, hazard ratio; M, male; NR, not reported; OR, odds ratio; retrospective.
Table
Table 3. Studies reporting the diagnostic accuracy of AISI for predicting severe disease/death in COVID-19.
Table 3. Studies reporting the diagnostic accuracy of AISI for predicting severe disease/death in COVID-19.
StudyDesignnAUC
(95% CI)		Cut-OffSensitivity (%)Specificity (%)Outcome
Fois AG et al., 2020, Italy [40]R1190.6407980.590.72Mortality
0.546–0.726
Zinellu A et al., 2021, Italy [41]R650.67411530.40.93Length of stay
0.547–0.786
Arbanasi EM et al. (a) 2022, Romania [42]R5100.8511296.620.80.79Acute limb ischemia
0.789–0.913
Arbanasi EM et al. (b) 2022, Romania [42]R5100.738650.580.6790.687Admission to intensive care unit
0.692–0.783
Fois SS et al. (a) 2022, Italy [25]R1820.64512820.510.77Mortality
0.570–0.715
Ghobadi H et al. (a) 2022, Iran [43]R10820.8714920.6930.865Mortality
0.849–0.890
Ghobadi H et al. (b) 2022, Iran [43]R7100.8265180.7110.89Mortality
0.796–0.853
Halmaciu I et al., 2022, Romania [45]R2670.813994.760.8830.676Invasive mechanical ventilation
0.754–0.871
Hamad DA et al., 2022, Egypt [46]R4850.8077290.5170.919Admission to intensive care unit
0.767–0.846
Muresan AV et al. (a) 2022, Romania [47]R8890.7801696.180.720.709Mortality
0.740–0.821
Muresan AV et al. (b) 2022, Romania [47]R8890.7841605.40.7120.716Deep vein thrombosis
0.745–0.823
Muresan AV et al. (c) 2022, Romania [47]R8890.7502769.850.6130.791Acute pulmonary oedema
0.684–0.816
Ercan Z et al., 2023, Turkey [48]R850.820621.10.810.691Mortality
0.733–0.903
Haryati H et al., 2023, Indonesia [49]R4450.55814220.320.788Mortality
0.510–0.614
Hosseninia S et al., 2023, Iran [50]R3000.6302600.6960.61Mortality
0.552–0.703
Legend: AUC, area under the curve; R, retrospective.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zinellu, A.; Paliogiannis, P.; Mangoni, A.A. Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis. J. Clin. Med. 2023, 12, 4584. https://doi.org/10.3390/jcm12144584

AMA Style

Zinellu A, Paliogiannis P, Mangoni AA. Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine. 2023; 12(14):4584. https://doi.org/10.3390/jcm12144584

Chicago/Turabian Style

Zinellu, Angelo, Panagiotis Paliogiannis, and Arduino A. Mangoni. 2023. "Aggregate Index of Systemic Inflammation (AISI), Disease Severity, and Mortality in COVID-19: A Systematic Review and Meta-Analysis" Journal of Clinical Medicine 12, no. 14: 4584. https://doi.org/10.3390/jcm12144584

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop