Previous Article in Journal
Effect of Human Immunodeficiency Virus (HIV) Infection on Mortality Among Hospitalised COVID-19 Patients at Levy Mwanawasa University Teaching Hospital, Lusaka, Zambia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
COVID
Volume 5
Issue 6
10.3390/covid5060089
covid-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Endothelial Activation and Stress Index (EASIX) to Predict the Outcome of Patients with COVID-19

by
Derya Gokcinar
1,*,
Ayse Lafci
2 and
Seval Izdes
3
1
Department of Anesthesiology, University of Health Science, Ankara Bilkent City Hospital, Ankara 06800, Turkey
2
Department of Anesthesiology, Ankara Bilkent City Hospital, Ankara 06800, Turkey
3
Department of Anesthesiology, Ankara Yildirim Beyazit University, Ankara Bilkent City Hospital, Ankara 06800, Turkey
*
Author to whom correspondence should be addressed.
COVID 2025, 5(6), 89; https://doi.org/10.3390/covid5060089 (registering DOI)
Submission received: 4 May 2025 / Revised: 28 May 2025 / Accepted: 5 June 2025 / Published: 9 June 2025
(This article belongs to the Section COVID Clinical Manifestations and Management)
Browse Figures
Versions Notes

Abstract

:
Endotheliopathy plays an essential role in the pathophysiology of COVID-19. The endothelial activation and stress index (EASIX) indicates endothelial dysfunction. We aimed to investigate the relationship between a high EASIX score and mortality in patients with COVID-19. We retrospectively reviewed COVID-19 patients admitted to the ICU (intensive care unit) of the Ankara Bilkent City Hospital. We recorded hematological and biochemical parameters at the ICU admission and further calculated EASIX with the following equation: EASIX = Lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelet count (109/L). Statistical comparisons were made between the surviving and non-surviving groups in terms of EASIX. The median EASIX score was 1.2 (0.7–2.0) in the survivor group and a median of 2.5 (1.6–4.2) in the non-survivor group (p < 0.001). The mean log2-EASIX was 0.2 ± 0.9 in the survivor group and 1.3 ± 1.2 in the non-survivor group (p < 0.001). Lactate dehydrogenase, creatinine, Troponin I, D-dimer, procalcitonin, ferritin, and IL-6 were statistically significantly higher in the non-survivor group compared to the survivor group. The receiver operating characteristic (ROC) curve analysis showed that the cut-off value of the EASIX score was 2.05 (The area under the curve [AUC] = 0.764, p = 0.001, 95% CI: 0.662–0.847). Our study showed an association between high EASIX scores and poor prognosis in COVID-19 patients. Lactate dehydrogenase, creatinine, Troponin I, D-dimer, procalcitonin, ferritin, IL-6, EASIX, and log2-EASIX were statistically significantly higher in the non-survivor group compared to the survivor group. Being old and having chronic kidney disease increases the risk of death. Eventually, EASIX can be used to predict mortality in COVID-19 patients.

1. Introduction

The endothelial activation and stress index (EASIX) indicates endothelial dysfunction. The EASIX score can be calculated by measuring lactate dehydrogenase levels (LDH), creatinine, and platelets. EASIX was first reported by Luft et al. to predict survival in patients who developed acute graft-versus-host disease secondary to allogeneic stem cell transplantation [1]. In a large-scale cohort analysis, an increase in EASIX before allogeneic stem cell transplantation was found to be associated with an increased risk of individual post-interventional mortality [2]. A high EASIX score has been suggested to be associated with transplant-associated microangiopathy, endothelial dysfunction, and sinusoidal obstruction syndrome [3]. Another study with multiple myeloma patients reported that higher EASIX scores indicated advanced disease and poor prognosis [4].
Viral infections affecting endothelial cells activate coagulation and cause vascular complications [5]. The SARS-CoV-2 virus, the causative agent of COVID-19, infects vascular endothelial cells, leading to damage and apoptosis in endothelial cells [6]. Autopsy studies of deceased patients with COVID-19 reported vascular effects such as thrombosis, microangiopathy, and hemorrhage in small pulmonary vessels [7,8]. In fact, pulmonary embolism might be a direct cause of death in these patients [9].
Endothelial cells are essential in ARDS pathophysiology, multi-organ failure, and related mortality. Demonstration of endothelial activation in hospitalized SARS-CoV-2-infected patients can be achieved with EASIX. The measurement of EASIX is a matter of curiosity in critically ill COVID-19 patients. It has been suggested that a high EASIX score during admission in COVID-19 patients predicts severe disease progression and may predict the need for intensive care. A few recently published studies reported increased mortality in hospitalized SARS-CoV-2 patients among those with a high EASIX score [10,11,12].
Identifying patients with high EASIX scores might provide important information about endothelial activation in critically ill patients infected with SARS-CoV-2. Easy calculability of EASIX through LDH, creatinine, and platelet counts could help to identify those at high risk for endothelial activation and contribute to the early provision of targeted and appropriate treatment. In this study, we aimed to investigate the relationship between EASIX and mortality in COVID-19 patients admitted to the ICU.

2. Methods

2.1. Data Source and Study Design

In this retrospective study, we included >18-year-old, PCR-positive for SARS-CoV2 patients hospitalized in the ICU at the Ankara Bilkent City Hospital, Turkey, between 18 March and 31 May 2020. The hospital is a multidisciplinary tertiary referral center, where the ICUs are managed by intensivists appointed as a closed system. We collected data on patients’ age, gender, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, comorbidity (diabetes, hypertension, coronary artery disease, chronic heart failure, stroke, thyroid disorders, liver disease, chronic kidney disease (CKD), chronic obstructive pulmonary disease, and cancer), length of ICU, and hospital stay from electronic records. The patients were assigned to receive one of the respiratory support methods, including mask- or high-flow nasal oxygen or invasive mechanical ventilation (IMV). The most aggressive respiratory support method was selected if a patient was managed with multiple modalities. We recorded hematological and biochemical parameters at ICU admission and further calculated EASIX with the following equation: EASIX = Lactate dehydrogenase (U/L) × creatinine (mg/dL)/platelet count (109/L) [1]. The study’s primary outcome was the mortality rate within 30 days of ICU admission. Statistical comparisons were made between the surviving and non-surviving groups in terms of EASIX.

2.2. Statistical Analysis

We analyzed data via IBM SPSS 25.0 statistical pack software (IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. IBM Corp.: Armonk, NY, USA). Where appropriate, we expressed descriptive data as numbers and percentages or mean, standard deviation, median, minimum, maximum, and interquartile range (IQR). We compare categorical variables via the chi-square test. The normality of continuous variables was tested via the Kolmogorov–Smirnov test, skewness-kurtosis, and graphical methods (histogram, Q-Q Plot, stem and leaf, and boxplot). Normally and non-normally distributed continuous variables were compared through an independent samples t-test and a Mann–Whitney U test, respectively. We used the ROC curve (Receiver Operating Characteristic) method to determine the distinctiveness, the binary logistic regression for odds ratios, and Kaplan–Meier, log-rank, Breslow, and Tarone–Ware tests for survival analysis. Spearman’s rho correlation was used to evaluate the association between variables. We used a 95% confidence interval (CI) and an overall Type-1 error level of 5% to infer statistical significance. We converted the EASIX scores to log2 and evaluated the log2-EASIX. We found the study power as 93% upon the power analysis conducted with the G*Power 3.1.9.7 software (Franz Foul, Universitat Kiel, Kiel, Germany), given the effect size (d) = 0.79, α = 0.05, n1 = 58, and n2 = 31.

3. Results

We identified 89 patients who met the inclusion criteria. Table 1 shows the comparison of patient characteristics of the survivor and non-survivor groups. The mean age in the non-survivor group (69.3 ± 14.7 yrs) was higher than the survivor group (60.5 ± 20.3 yrs, p = 0.039). While 13.8% of the deceased patients had CKD, none in the survivor group had the disease. In the survivor group, 54.8% received high flow and 38.7% mask oxygen as respiratory support, while all patients in the non-survivor group needed IMV (p < 0.001). Of the 60 patients who received IMV, 58 (97%) died and 2 (3%) survived. Lactate dehydrogenase, creatinine, Troponin I, D-dimer, procalcitonin, ferritin, and IL-6 were statistically significantly higher in the non-survivor group compared to the survivor group. The non-survivor group had a significantly higher median EASIX score. The median EASIX score was 1.2 (0.7–2.0) in the survivor group and a median of 2.5 (1.6–4.2) in the non-survivor group (p < 0.001). The mean log2-EASIX was 0.2 ± 0.9 in the survivor group and 1.3 ± 1.2 in the non-survivor group (p < 0.001).
Table 2 shows that there is a significant correlation between mortality rate and EASIX score by Spearman’s rho-Correlation Test (Spearman-rho 0.317, p = 0.001 for EASIX). Table 3 shows the associations between the study variables and mortality by Binary logistic regression test. Log2-EASIX score predicts the probability of mortality significantly (OR 4.03, 95% CI: 1.62–10.04) (Nagelkerke R2 = 0.754, Table 3). Further analysis with logistic regression confirmed an increased rate of mortality with a higher log2-EASIX score (OR 2.89, 95% CI: 1.51–5.56) (Table 4).
Table 5 shows the ROC curve analysis for the variables. ROC curve analysis reveals that the cut-off value of the EASIX score is 2.05 (AUC = 0.764, p = 0.001, 95% CI: 0.662–0.847). Figure 1 shows ROC curve analysis for the log2-EASIX score, and Figure 2 shows Kaplan–Meier survival curves in COVID-19 patients according to the log2-EASIX score cut-off value.

4. Discussion

Among the COVID-19 patients admitted to our ICU in the first wave of the pandemic, those with a high EASIX score were approximately 3 times more likely to die than those with a low EASIX score. Luft et al. [10] reported that EASIX > 2 significantly predicted death in 126 patients with COVID-19 admitted to the hospital between February 2020 and September 2020. Pérez-García et al. [11], in their study of COVID-19, reported that patients with high EASIX values had high 28-day mortality. They imply the critical role of endotheliopathy in COVID-19 pathophysiology. The authors also recommend that this simple and inexpensive tool be used in triage during the pandemic when health services are overloaded and resources are scarce [11].
Kalicińska et al. [12] retrospectively reviewed COVID-19 patients hospitalized between March 2020 and March 2021 in Poland and reported mortality prediction via EASIX with 73.4% specificity and 67.6% sensitivity. We observed a linear relationship between increased lactate dehydrogenase, creatinine, troponin I, prothrombin time, D-dimer, procalcitonin, ferritin, and IL-6 levels and an increased risk of death. These findings are consistent with other studies reporting the association of laboratory parameters with mortality [13,14,15,16,17,18,19,20,21].
The elderly are one of the critical factors that increase the mortality rate of patients with COVID-19. Deng et al. [22], in their study of 82,719 COVID-19 cases in China from 29 December 2019 to 17 April 2020, reported that older age (OR 1.14 per year) was an independent risk factor for mortality. Carey et al. [23] found higher mortality of patients presenting to a healthcare facility for any reason (0.35%) between 18 March and 19 May 2020 compared to the same period in 2015 (0.24%) in the UK. They reported higher mortality in ≥80-year-old patients in 2020 than in previous years. Maximiano et al. [24], in their prospective study of 3590 adult hospitalized patients between 1 March 2020 and 1 September 2020 in Switzerland, reported that the elderly, kidney disease, chronic respiratory disease, cardiovascular disease, cancer, and dementia were associated with an increased risk of in-hospital mortality. Docherty et al. [25] analyzed clinical data of 20,133 COVID-19 patients admitted to hospital between 6 February and 19 April 2020 in England, Wales, and Scotland and reported a positive association of chronic cardiac disease, uncomplicated diabetes, non-asthmatic chronic pulmonary disease, and CKD with a high in-hospital mortality rate. Similar to the findings of these studies, we observed higher mean age and the presence of CKD in the non-survivor patient group in our study. Having chronic systemic diseases or cancer has been shown to be associated with reduced survival in COVID-19 patients. In contrast, those without comorbidities have a higher survival rate [26,27].
In the first wave of the COVID-19 pandemic, mortality in cases requiring invasive mechanical ventilation was reported as 97% in Wuhan, 71% in Washington, and 75% in Seattle [28,29,30]. Graselli et al. [31], in their almost 4000 COVID-19 ICU patients in Italy between 20 February and 22 April 2020 reported an in-hospital mortality rate of 1715 patients initially admitted to the hospital was 53.4% and a higher hazard ratio for mortality in those who were managed with IMV than that in non-invasively ventilated patients. Our study included the patients who required the most severe third-level ICU, most of them being older and featuring many comorbidities with an excessive need for IMV. Similar to the study mentioned above by Graselli et al., we observed more prolonged ICU stays in the non-survivor group. The meta-analysis by Lim et al. [32] reported the overall case fatality rate in critically ill patients with COVID-19 who underwent IMV as 45%, reaching up to >80% in some centers.
In our study results, the major predictor of mortality was the use of invasive mechanical ventilation. We applied invasive mechanical ventilation to the patients with the worst clinical presentation during the pandemic. Also, individuals in the non-survival group might have complications caused by invasive mechanical ventilation. The most important pulmonary complication of invasive mechanical ventilation in patients with COVID-19 is barotrauma. Data from COVID-19 patients admitted to the ICU in France, Switzerland, and Belgium between 25 February 2020 and 4 May 2020 were analyzed by the COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators. In patients who received invasive mechanical ventilation within the first 24 h of admission to the ICU, plateau and driving pressures were statistically significantly higher in those who died compared to those who survived, while static compliance was lower [33]. Mechanical ventilation may lead to ventilator-induced lung injury associated with barotrauma. Lung parenchymal tears, pneumothorax, and pneumomediastinum were reported in patients with severe COVID-19 who developed ARDS [34]. Driving pressure was statistically significantly higher in COVID-19 patients with barotrauma [35]. The incidence of barotrauma was four times higher in COVID-19 patients receiving invasive mechanical ventilation than in those receiving noninvasive ventilation. Eventually, in COVID-19 patients with barotrauma, the mortality rate was also higher [36].
Ventilator-associated pneumonia is a significant pulmonary complication often observed in critically ill patients with COVID-19 who receive invasive mechanical ventilation. The incidence of ventilator-associated pneumonia is approximately four times higher in COVID-19 patients compared to those without COVID-19 [37]. The development of this pneumonia has been implicated in the adverse effects of the immune system on the SARS-CoV-2 virus. Poor prognosis in COVID-19 patients with severe clinical manifestations, such as hyperinflammation, ARDS, and multiorgan failure, is linked to compromised immune cell functions [38]. Furthermore, coronary syndromes, cardiac injury, myocarditis, cardiomyopathy, increased pulmonary artery pressure, blunted hypoxic pulmonary vasoconstriction, right ventricular dysfunction, heart failure, and arrhythmias may cause a high mortality rate during invasive mechanical ventilation in critically ill patients with COVID-19 [39,40,41].
EASIX indirectly measures endothelial activation and predicts the occurrence of transplantation-related vascular complications such as sinusoidal obstruction syndrome and transplant-associated thrombotic microangiopathy. In adult patients receiving allogenic hematopoietic cell transplantation, the patient group with high EASIX scores was shown to have inferior overall survival compared to the patient group with low EASIX scores. C5b-9 levels were found to be statistically significantly associated with EASIX [42]. It is suggested that there may be a relationship between endothelial function and complement system activation. Log2-EASIX score of 3 and above at day 7 after allogeneic stem cell transplantation predicts graft-versus-host disease [43]. The proposed possible mechanism is that SARS-CoV-2 activates the complement system by causing C5b-9 formation. While C5b-9 neutralizes viruses, pores form on the endothelial membrane. Cytokines are released from endothelial cells, causing inflammation. In addition, outgoing von Willebrand factor multimers start platelet adhesion and trigger microthrombogenesis [44,45,46]. This mechanism may be a logical explanation for the association between high EASIX scores and life-threatening clinical forms of COVID-19.

5. Limitations

Our study has some limitations, which should be taken into account when interpreting the findings. This study is retrospective, and the groups are heterogeneous. The lack of legal regulation regarding end-of-life decisions is important, which may have influenced our results, especially with regard to the high mortality rates observed. While evaluating this study, it needs to be considered that there is no legal regulation regarding end-of-life decisions in our country. No “do not resuscitate,” withholding, withdrawal, or triage protocols were applied during patient admission to our ICU. We admitted any patient with very advanced age, with end-stage kidney disease, or end-stage malignancies to the ICU. The severely ill patients in our ICU may contribute to our high mortality rates.

6. Conclusions

There is an association between EASIX scores and mortality in COVID-19 patients admitted to the ICU. Being elderly, having CKD, the need for invasive mechanical ventilation, as well as high lactate dehydrogenase, creatinine, troponin I, D-dimer, procalcitonin, ferritin, and IL-6 levels are risk factors indicating poor outcomes. Critically ill COVID-19 patients with higher EASIX scores have a worse prognosis. Therefore, EASIX scores have the ability to predict mortality. Predicting a poor prognosis can help end-of-life decisions. It can also help with the efficient use of resources during pandemics. Also, EASIX can be a valuable tool for demonstrating endotheliopathy in many pathologies. The availability of EASIX with cheap and simple calculation is an important advantage. Other pathogens or events other than SARS-CoV-2 affecting the endothelium can be investigated with EASIX. Large-scale studies on EASIX, which is an important score indicating endothelial activation, are needed in the future.

Author Contributions

Conceptualization: D.G. and A.L.; Methodology: D.G. and S.I.; Software: D.G. and A.L.; Validation: D.G., A.L., and S.I.; Formal analysis: D.G. and A.L.; Investigation: D.G.; Resources: D.G. and S.I.; Data curation: D.G. and A.L.; Writing—original draft preparation: D.G.; Writing—review and editing: D.G., A.L, and S.I.; Visualization: D.G.; Supervision: S.I.; Project administration: D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Ethics Committee of Ankara Bilkent City Hospital (Approval decision number: E1-22-2414, Date 23 February 2022).

Informed Consent Statement

For a retrospective study, informed consent from each volunteer is not required in our institution. Ethics Committee approval is sufficient for the use of retrospective data in this way.

Data Availability Statement

Data are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Luft, T.; Benner, A.; Jodele, S.; Dandoy, C.E.; Storb, R.; Gooley, T.; Sandmaier, B.M.; Becker, N.; Radujkovic, A.; Dreger, P.; et al. EASIX in patients with acute graft-versus-host disease: A retrospective cohort analysis. Lancet Haematol. 2017, 4, e414–e423. [Google Scholar] [CrossRef] [PubMed]
  2. Luft, T.; Benner, A.; Terzer, T.; Jodele, S.; Dandoy, C.E.; Storb, R.; Kordelas, L.; Beelen, D.; Gooley, T.; Sandmaier, B.M.; et al. EASIX and mortality after allogeneic stem cell transplantation. Bone Marrow Transpl. 2020, 55, 553–561. [Google Scholar] [CrossRef] [PubMed]
  3. Pagliuca, S.; Michonneau, D.; de Fontbrune, F.S.; del Galy, A.S.; Xhaard, A.; Robin, M.; de Latour, R.P.; Socie, G. Allogeneic reactivity–mediated endothelial cell complications after HSCT: A plea for consensual definitions. Blood Adv. 2019, 3, 2424–2435. [Google Scholar] [CrossRef] [PubMed]
  4. Song, G.-Y.; Jung, S.-H.; Kim, K.; Kim, S.J.; Yoon, S.E.; Lee, H.S.; Kim, M.; Ahn, S.-Y.; Ahn, J.-S.; Yang, D.-H.; et al. Endothelial activation and stress index (EASIX) is a reliable predictor for overall survival in patients with multiple myeloma. BMC Cancer 2020, 20, 803. [Google Scholar] [CrossRef]
  5. Goeijenbier, M.; van Wissen, M.; van de Weg, C.; Jong, E.; Gerdes, V.; Meijers, J.; Brandjes, D.; van Gorp, E. Review: Viral infections and mechanisms of thrombosis and bleeding. J. Med. Virol. 2012, 84, 1680–1696. [Google Scholar] [CrossRef]
  6. Bonaventura, A.; Vecchié, A.; Dagna, L.; Martinod, K.; Dixon, D.L.; Van Tassell, B.W.; Dentali, F.; Montecucco, F.; Massberg, S.; Levi, M.; et al. Endothelial dysfunction and immunothrombosis as key pathogenic mechanisms in COVID-19. Nat. Rev. Immunol. 2021, 21, 319–329. [Google Scholar] [CrossRef]
  7. Fox, S.E.; Akmatbekov, A.; Harbert, J.L.; Li, G.; Brown, J.Q.; Heide, R.S.V. Pulmonary and cardiac pathology in African American patients with COVID-19: An autopsy series from New Orleans. Lancet Respir. Med. 2020, 8, 681–686. [Google Scholar] [CrossRef]
  8. Carsana, L.; Sonzogni, A.; Nasr, A.; Rossi, R.S.; Pellegrinelli, A.; Zerbi, P.; Rech, R.; Colombo, R.; Antinori, S.; Corbellino, M.; et al. Pulmonary post-mortem findings in a series of COVID-19 cases from northern Italy: A two-centre descriptive study. Lancet Infect. Dis. 2020, 20, 1135–1140. [Google Scholar] [CrossRef]
  9. Wichmann, D.; Sperhake, J.P.; Lütgehetmann, M.; Steurer, S.; Edler, C.; Heinemann, A.; Heinrich, F.; Mushumba, H.; Kniep, I.; Schröder, A.S.; et al. Autopsy Findings and Venous Thromboembolism in Patients With COVID-19: A Prospective Cohort Study. Ann. Intern. Med. 2020, 173, 268–277. [Google Scholar] [CrossRef]
  10. Luft, T.; Wendtner, C.-M.; Kosely, F.; Radujkovic, A.; Benner, A.; Korell, F.; Kihm, L.; Bauer, M.F.; Dreger, P.; Merle, U. EASIX for Prediction of Outcome in Hospitalized SARS-CoV-2 Infected Patients. Front. Immunol. 2021, 12, 634416. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  11. Pérez-García, F.; Bailén, R.; Torres-Macho, J.; Fernández-Rodríguez, A.; Jiménez-Sousa, M.Á.; Jiménez, E.; Pérez-Butragueño, M.; Cuadros-González, J.; Cadiñanos, J.; García-García, I.; et al. Age-Adjusted Endothelial Activation and Stress Index for Coronavirus Disease 2019 at Admission Is a Reliable Predictor for 28-Day Mortality in Hospitalized Patients With Coronavirus Disease 2019. Front. Med. 2021, 8, 736028. [Google Scholar] [CrossRef] [PubMed]
  12. Kalicińska, E.; Biernat, M.; Rybka, J.; Zińczuk, A.; Janocha-Litwin, J.; Rosiek-Biegus, M.; Morawska, M.; Waszczuk-Gajda, A.; Drozd-Sokołowska, J.; Szukalski, Ł.; et al. Endothelial Activation and Stress Index (EASIX) as an Early Predictor for Mortality and Overall Survival in Hematological and Non-Hematological Patients with COVID-19: Multicenter Cohort Study. J. Clin. Med. 2021, 10, 4373. [Google Scholar] [CrossRef] [PubMed]
  13. Henry, B.M.; de Oliveira, M.H.S.; Benoit, S.; Plebani, M.; Lippi, G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): A meta-analysis. Clin. Chem. Lab. Med. 2020, 58, 1021–1028. [Google Scholar] [CrossRef] [PubMed]
  14. Henry, B.M.; Aggarwal, G.; Wong, J.; Benoit, S.; Vikse, J.; Plebani, M.; Lippi, G. Lactate dehydrogenase levels predict coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis. Am. J. Emerg. Med. 2020, 38, 1722–1726. [Google Scholar] [CrossRef]
  15. Malik, P.; Patel, U.; Mehta, D.; Patel, N.; Kelkar, R.; Akrmah, M.; Gabrilove, J.L.; Sacks, H. Biomarkers and outcomes of COVID-19 hospitalisations: Systematic review and meta-analysis. BMJ Evid. Based Med. 2021, 26, 107–108. [Google Scholar] [CrossRef]
  16. 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]
  17. Goyal, P.; Choi, J.J.; Pinheiro, L.C.; Schenck, E.J.; Chen, R.; Jabri, A.; Satlin, M.J.; Campion, T.R., Jr.; Nahid, M.; Ringel, J.B.; et al. Clinical Characteristics of Covid-19 in New York City. N. Engl. J. Med. 2020, 382, 2372–2374. [Google Scholar] [CrossRef]
  18. Gandini, O.; Criniti, A.; Ballesio, L.; Giglio, S.; Galardo, G.; Gianni, W.; Santoro, L.; Angeloni, A.; Lubrano, C. Serum Ferritin is an independent risk factor for Acute Respiratory Distress Syndrome in COVID-19. J. Infect. 2020, 81, 979–997. [Google Scholar] [CrossRef]
  19. Cao, P.; Wu, Y.; Wu, S.; Wu, T.; Zhang, Q.; Zhang, R.; Wang, Z.; Zhang, Y. Elevated serum ferritin level effectively discriminates severity illness and liver injury of coronavirus disease 2019 pneumonia. Biomarkers 2021, 26, 207–212. [Google Scholar] [CrossRef]
  20. Qeadan, F.; Tingey, B.; Gu, L.Y.; Packard, A.H.; Erdei, E.; Saeed, A.I. Prognostic Values of Serum Ferritin and D-Dimer Trajectory in Patients with COVID-19. Viruses 2021, 13, 419. [Google Scholar] [CrossRef]
  21. Mahroum, N.; Alghory, A.; Kiyak, Z.; Alwani, A.; Seida, R.; Alrais, M.; Shoenfeld, Y. Ferritin—From iron, through inflammation and autoimmunity, to COVID-19. J. Autoimmun. 2022, 126, 102778. [Google Scholar] [CrossRef] [PubMed]
  22. Deng, X.; Yang, J.; Wang, W.; Wang, X.; Zhou, J.; Chen, Z.; Li, J.; Chen, Y.; Yan, H.; Zhang, J.; et al. Case Fatality Risk of the First Pandemic Wave of Coronavirus Disease 2019 (COVID-19) in China. Clin. Infect. Dis. 2021, 73, e79–e85. [Google Scholar] [CrossRef] [PubMed]
  23. Carey, I.M.; Cook, D.G.; Harris, T.; DeWilde, S.; Chaudhry, U.A.R.; Strachan, D.P. Risk factors for excess all-cause mortality during the first wave of the COVID-19 pandemic in England: A retrospective cohort study of primary care data. PLoS ONE 2021, 16, e0260381. [Google Scholar] [CrossRef] [PubMed]
  24. Sousa, F.M.; Roelens, M.; Friker, B.; Thiabaud, A.; Iten, A.; Cusini, A.; Flury, D.; Buettcher, M.; Zucol, F.; Balmelli, C.; et al. Risk factors for severe outcomes for COVID-19 patients hospitalised in Switzerland during the first pandemic wave, February to August 2020: Prospective observational cohort study. Swiss Med. Wkly. 2021, 151, w20547. [Google Scholar] [CrossRef]
  25. Docherty, A.B.; Harrison, E.M.; Green, C.A.; Hardwick, H.E.; Pius, R.; Norman, L.; Holden, K.A.; Read, J.M.; Dondelinger, F.; Carson, G.; et al. Features of 20 133 UK patients in hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: Prospective observational cohort study. BMJ 2020, 369, m1985. [Google Scholar] [CrossRef]
  26. Demirelli, D.S.; Genc, G.; Basarir, C.I.; Bulut, S. Comparison of Clinical Characteristics of COVID-19-Related and Unrelated Acute Stroke Patients During the COVID-19 Pandemic in Turkey. Sisli Etfal Hast. Tip Bul. 2022, 56, 55–61. [Google Scholar] [CrossRef]
  27. Okuyucu, M.; Ozturk, O.; Atay, M.H.; Gullu, Y.T.; Temocin, F.; Terzi, O. Clinical Evaluation of Patients with COVID-19 Within the Framework of Comorbidities. Sisli Etfal Hast. Tip Bul. 2022, 56, 311–317. [Google Scholar] [CrossRef]
  28. Wang, Y.; Lu, X.; Li, Y.; Chen, H.; Chen, T.; Su, N.; Huang, F.; Zhou, J.; Zhang, B.; Yan, F.; et al. Clinical Course and Outcomes of 344 Intensive Care Patients with COVID-19. Am. J. Respir. Crit. Care Med. 2020, 201, 1430–1434. [Google Scholar] [CrossRef]
  29. Arentz, M.; Yim, E.; Klaff, L.; Lokhandwala, S.; Riedo, F.X.; Chong, M.; Lee, M. Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State. JAMA 2020, 323, 1612–1614. [Google Scholar] [CrossRef]
  30. Bhatraju, P.K.; Ghassemieh, B.J.; Nichols, M.; Kim, R.; Jerome, K.R.; Nalla, A.K.; Greninger, A.L.; Pipavath, S.; Wurfel, M.M.; Evans, L.; et al. Covid-19 in Critically Ill Patients in the Seattle Region—Case Series. N. Engl. J. Med. 2020, 382, 2012–2022. [Google Scholar] [CrossRef]
  31. Grasselli, G.; Greco, M.; Zanella, A.; Albano, G.; Antonelli, M.; Bellani, G.; Bonanomi, E.; Cabrini, L.; Carlesso, E.; Castelli, G.; et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy. JAMA Intern. Med. 2020, 180, 1345–1355. [Google Scholar] [CrossRef] [PubMed]
  32. Lim, Z.J.; Subramaniam, A.; Reddy, M.P.; Blecher, G.; Kadam, U.; Afroz, A.; Billah, B.; Ashwin, S.; Kubicki, M.; Bilotta, F.; et al. Case Fatality Rates for Patients with COVID-19 Requiring Invasive Mechanical Ventilation. A Meta-analysis. Am. J. Respir. Crit. Care Med. 2021, 203, 54–66. [Google Scholar] [CrossRef] [PubMed]
  33. 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. 2021, 47, 60–73. [Google Scholar] [CrossRef]
  34. Al-Azzawi, M.; Douedi, S.; Alshami, A.; Al-Saoudi, G.; Mikhail, J. Spontaneous Subcutaneous Emphysema and Pneumomediastinum in COVID-19 Patients: An Indicator of Poor Prognosis? Am. J. Case Rep. 2020, 21, e925557. [Google Scholar] [CrossRef] [PubMed]
  35. Donohue, K.-N.; Sivanushanthan, S.; Etling, E.; Hockstein, M.; Yohannes, S.; Clark, P. Incidence of barotrauma in patients with COVID-19 (alpha- and beta-predominant period) requiring mechanical ventilation: Single-center retrospective study. SAGE Open Med. 2023, 11, 20503121231159479. [Google Scholar] [CrossRef]
  36. Kahn, M.R.; Watson, R.L.; Thetford, J.T.; Wong, J.I.; Kamangar, N. High Incidence of Barotrauma in Patients With Severe Coronavirus Disease 2019. J. Intensiv. Care Med. 2021, 36, 646–654. [Google Scholar] [CrossRef]
  37. Maes, M.; Higginson, E.; Pereira-Dias, J.; Curran, M.D.; Parmar, S.; Khokhar, F.; Cuchet-Lourenço, D.; Lux, J.; Sharma-Hajela, S.; Ravenhill, B.; et al. Ventilator-associated pneumonia in critically ill patients with COVID-19. Crit. Care 2021, 25, 25. [Google Scholar] [CrossRef]
  38. Laing, A.G.; Lorenc, A.; del Barrio, I.D.M.; Das, A.; Fish, M.; Monin, L.; Muñoz-Ruiz, M.; McKenzie, D.R.; Hayday, T.S.; Francos-Quijorna, I.; et al. A dynamic COVID-19 immune signature includes associations with poor prognosis. Nat. Med. 2020, 26, 1623–1635. [Google Scholar] [CrossRef]
  39. Caravita, S.; Baratto, C.; Di Marco, F.; Calabrese, A.; Balestrieri, G.; Russo, F.; Faini, A.; Soranna, D.; Perego, G.B.; Badano, L.P.; et al. Haemodynamic characteristics of COVID-19 patients with acute respiratory distress syndrome requiring mechanical ventilation. An invasive assessment using right heart catheterization. Eur. J. Heart Fail. 2020, 22, 2228–2237. [Google Scholar] [CrossRef]
  40. Zou, F.; Qian, Z.; Wang, Y.; Zhao, Y.; Bai, J. Cardiac Injury and COVID-19: A Systematic Review and Meta-analysis. CJC Open 2020, 2, 386–394. [Google Scholar] [CrossRef]
  41. Khan, W.; Safi, A.; Muneeb, M.; Mooghal, M.; Aftab, A.; Ahmed, J. Complications of invasive mechanical ventilation in critically Ill Covid-19 patients—A narrative review. Ann. Med. Surg. 2022, 80, 104201. [Google Scholar] [CrossRef] [PubMed]
  42. Gavriilaki, E.; Sakellari, I.; Chatzikonstantinou, T.; Mallouri, D.; Batsis, I.; Vardi, A.; Bousiou, Z.; Koravou, E.-E.; Masmanidou, M.; Touloumenidou, T.; et al. Endothelial and Complement Activation As Predictors of Survival in Adult Allogeneic Hematopoietic Cell Transplantation. HemaSphere 2020, 5, e487. [Google Scholar] [CrossRef] [PubMed]
  43. Pedraza, A.; Salas, M.Q.; Rodríguez-Lobato, L.G.; Escribano-Serrat, S.; Suárez-Lledo, M.; Martínez-Cebrian, N.; Solano, M.T.; Arcarons, J.; Rosiñol, L.; Gutiérrez-García, G.; et al. Easix Score Correlates With Endothelial Dysfunction Biomarkers and Predicts Risk of Acute Graft-Versus-Host Disease After Allogeneic Transplantation. Transplant. Cell. Ther. 2024, 30, 187.e1–187.e12. [Google Scholar] [CrossRef]
  44. de Nooijer, A.H.; Grondman, I.; Janssen, N.A.F.; Netea, M.G.; Willems, L.; van de Veerdonk, F.L.; Giamarellos-Bourboulis, E.J.; Toonen, E.J.M.; Joosten, L.A.B.; RCI-COVID-19 study group. Complement Activation in the Disease Course of Coronavirus Disease 2019 and Its Effects on Clinical Outcomes. J. Infect. Dis. 2020, 223, 214–224. [Google Scholar] [CrossRef] [PubMed]
  45. Kumar, A.; Narayan, R.K.; Kumari, C.; Faiq, M.A.; Kulandhasamy, M.; Kant, K.; Pareek, V. SARS-CoV-2 cell entry receptor ACE2 mediated endothelial dysfunction leads to vascular thrombosis in COVID-19 patients. Med. Hypotheses 2020, 145, 110320. [Google Scholar] [CrossRef]
  46. Chang, J. COVID-19 Sepsis: Pathogenesis and Endothelial Molecular Mechanisms Based on “Two-Path Unifying Theory” of Hemostasis and Endotheliopathy-Associated Vascular Microthrombotic Disease, and Proposed Therapeutic Approach with Antimicrothrombotic Therapy. Vasc. Health Risk Manag. 2021, 17, 273–298. [Google Scholar] [CrossRef]
Covid 05 00089 g001
Figure 1. ROC curve analysis for the log2-EASIX score.
Figure 1. ROC curve analysis for the log2-EASIX score.
Covid 05 00089 g001
Covid 05 00089 g002
Figure 2. The Kaplan–Meier survival curves show a significant decrease in survival rate above the cutoff value of 1.032 for log2-EASIX score in COVID-19 patients admitted to the ICU.
Figure 2. The Kaplan–Meier survival curves show a significant decrease in survival rate above the cutoff value of 1.032 for log2-EASIX score in COVID-19 patients admitted to the ICU.
Covid 05 00089 g002
Table 1. Comparison of characteristics between survivors and non-survivors.
Table 1. Comparison of characteristics between survivors and non-survivors.
Survivors (n = 31)Non-Survivors (n = 58)p
Age (years) 60.5 ± 20.369.3 ± 14.70.039 b
GenderFemale13 (41.9%)30 (51.7%)0.511 a
Male18 (58.1%)28 (48.3%)
APACHE II score14.9 ± 7.931.2 ± 10.5<0.001 b
ComorbidityNo11 (35.5%)8 (13.8%)0.035 a
Yes20 (64.5%)50 (86.2%)
Diabetes mellitus9 (29.0%)24 (41.4%)0.358 a
Hypertension15 (48.4%)37 (63.8%)0.238 a
Coronary artery disease7 (22.6%)14 (24.1%)1.000 a
Chronic heart failure3 (9.7%)13 (22.4%)0.230 a
Stroke2 (6.5%)0 (0.0%)0.119 a
Thyroid disorders2 (6.5%)4 (6.9%)1.000 a
Liver disease0 (0.0%)0 (0.0%)--
Chronic kidney disease0 (0.0%)8 (13.8%)<0.001 a
Chronic obstructive pulmonary disease4 (12.9%)15 (25.9%)0.250 a
Cancer2 (6.5%)8 (13.8%)0.484 a
Length of ICU stay (days)7.0 (4.0–12.0)12.5 (6.8–21.3)0.005 c
Length of hospital stay (days)17.2 ± 9.817.8 ± 12.30.820 b
Respiratory supportInvasive mechanical ventilation2 (6.5%)58 (100.0%)<0.001 a
High-flow17 (54.8%)0 (0.0%)
Mask12 (38.7%)0 (0.0%)
Lactate dehydrogenase (U/L)449.5 ± 225.1559.6 ± 223.00.030 b
Creatinine (mg/dL)0.77 (0.60–0.90)1.09 (0.75–1.38)0.004 c
Troponin I (ng/L)5.0 (2.5–12.0)17.5 (9.3–72.0)<0.001 c
Prothrombin time (sec)12.6 (11.6–13.0)13.1 (12.6–14.6)0.006 c
Prothrombin activity (%)87.8 ± 15.376.3 ± 20.30.007 b
INR1.09 ± 0.121.21 ± 0.300.032 b
Activated partial thromboplastin time (sec)22.2 (20.6–24.6)24.6 (21.9–27.7)0.011 c
Fibrinogen (g/L)5.8 ± 1.86.2 ± 1.60.306 b
D-dimer (mg/L)1.21 (0.72–1.79)1.75 (0.99–3.58)0.033 c
Procalcitonin (µg/L)0.09 (0.05–0.17)0.22 (0.10–0.61)0.002 c
Ferritin (µg/L)355.0 (150.0–628.0)526.0 (279.8–1152.8)0.012 c
Leukocyte (×109/L)11.0 ± 5.510.7 ± 5.30.799 b
Lymphocyte (×109/L)0.72 (0.46–0.98)0.58 (0.34–0.93)0.753 c
Neutrophil (×109/L)9.6 (5.5–12.8)8.9 (5.7–12.3)0.935 c
Monocyte (×109/L)0.38 (0.24–0.54)0.34 (0.23–0.53)0.555 c
Eosinophile (×109/L)0.02 (0.01–0.05)0.02 (0.01–0.06)0.843 c
Hemoglobin (g/dL)12.8 ± 1.712.2 ± 2.00.141 b
Haematocrit (%)39.5 ± 4.238.3 ± 6.00.273 b
Platelet (×109/L)301.2 ± 124.5255.6 ± 100.70.064 b
Albumin (g/L)35.7 ± 3.434.7 ± 4.50.295 b
Lactate (mmol/L)2.5 (1.8–3.0)2.2 (1.6–2.9)0.218 c
C-reactive protein (g/L)0.07 (0.04–0.15)0.11 (0.04–0.18)0.225 c
IL-6 (pg/mL)14.1 (6.1–29.7)50.7 (17.5–154.6)0.001 c
EASIX score1.2 (0.7–2.0)2.5 (1.6–4.2)<0.001 c
Log2-EASIX score0.2 ± 0.91.3 ± 1.2<0.001 b
APACHE II: Acute Physiology and Chronic Health Evaluation II; INR: International normalized ratio; EASIX: Endothelial Activation and Stress Index. a: Chi-Square Test (n (%)), b: Independent Samples t-Test (Mean ± SD), c: Mann–Whitney U test (Median (Q1–Q3)).
Table 2. Spearman’s rho correlation test shows the relationship between study variables and risk of mortality.
Table 2. Spearman’s rho correlation test shows the relationship between study variables and risk of mortality.
Mortality
rp
Age (years)0.2370.018
APACHE II score0.636<0.001
Length of ICU stay (days)0.2640.008
Method of oxygen support−0.936<0.001
Lactate dehydrogenase (U/L)0.2420.016
Creatinine (mg/dL)0.2920.003
Troponin I (ng/L)0.350<0.001
Prothrombin time (sec)0.2790.005
Prothrombin activity (%)−0.2710.007
INR0.2780.005
Activated partial thromboplastin time (sec)0.2370.018
D-dimer (mg/L)0.2380.017
Procalcitonin (µg/L)0.2960.003
Ferritin (µg/L)0.2120.035
IL-6 (pg/mL)0.3130.002
EASIX score0.3170.001
Log2-EASIX score0.3170.001
APACHE II: Acute Physiology and Chronic Health Evaluation II; INR: International normalized ratio; EASIX: Endothelial Activation and Stress Index.
Table 3. Binary Logistic Regression Test shows the predictors of mortality.
Table 3. Binary Logistic Regression Test shows the predictors of mortality.
Risk FactorBSEWaldOdds95% CIp *
Age (years)0.0360.0212.8601.040.99–1.080.091
APACHE II0.2230.05715.1141.251.12–1.40<0.001
Length of ICU stay (days)0.1360.0614.9291.151.02–1.290.026
Prothrombin time (sec)0.5620.3822.1611.750.83–3.710.142
Log2-EASIX score1.3940.4668.9544.031.62–10.040.003
Constant−16.7796.1457.457 0.006
APACHE II: Acute Physiology and Chronic Health Evaluation II; EASIX: Endothelial Activation and Stress Index. * Binary Logistic Regression Test (presented only for the variables remaining in the model), Nagelkerke R2 = 0.754, Hosmer and Lemeshow Test = 0.759.
Table 4. Logistic regression analysis using the Backward Stepwise method shows the relationship between mortality and log2-EASIX score.
Table 4. Logistic regression analysis using the Backward Stepwise method shows the relationship between mortality and log2-EASIX score.
Risk FactorBSEWaldOdds95% CIp *
APACHE II0.1860.04219.6301.201.11–1.31<0.001
Log2-EASIX score1.0620.33310.1552.891.51–5.560.001
Constant−4.3341.05216.9720.010.00–0.00<0.001
APACHE II, Acute Physiology and Chronic Health Evaluation II; EASIX, Endothelial Activation and Stress Index. * Binary Logistic Regression Test, Nagelkerke R2 = 0.649, Hosmer and Lemeshow Test = 0.425.
Table 5. Receiver operating characteristic (ROC) curve analysis for variables.
Table 5. Receiver operating characteristic (ROC) curve analysis for variables.
AUC95% CICut-OffSensitivitySpecificityYouden Index+PV−PVp-Value
Age (years)0.6340.525–0.733>5584.548.40.32975.462.50.055
APACHE II score0.8860.801–0.943>1887.974.20.62186.476.70.001
Length of ICU stay (days)0.6810.574–0.776>1156.974.20.31180.547.90.002
Lactate dehydrogenase (U/L)0.6740.566–0.769>47155.277.40.32682.148.00.005
Creatinine (mg/dL)0.6880.581–0.782>0.965.577.40.42984.454.50.001
Troponin I (ng/L)0.7540.651–0.839>975.974.20.50184.662.20.001
Prothrombin time (sec)0.6770.569–0.772>13.150.080.70.30782.946.30.003
Prothrombin activity (%)0.6680.560–0.764≤79.758.674.20.32881.048.90.005
INR0.6590.551–0.757>1.148.380.70.28982.445.50.006
aPTT (sn)0.6640.556–0.761>25.248.380.70.28982.445.50.005
D-dimer (mg/L)0.6380.529–0.737>1.4962.171.00.33080.050.00.025
Procalcitonin (µg/L)0.7010.595–0.794>0.1463.874.20.38082.252.30.001
Ferritin (µg/L)0.6620.554–0.759>68943.183.90.27083.344.10.008
IL-6 (pg/mL)0.7150.609–0.806>26.567.274.20.41483.054.8<0.001
EASIX score0.7640.662–0.847>2.0570.783.90.54689.160.50.001
Log2-EASIX score0.7640.662–0.847>1.03270.783.90.54689.160.50.001
APACHE II: Acute Physiology and Chronic Health Evaluation II; ICU: Intensive care unit; INR: International normalized ratio; aPTT: Activated partial thromboplastin time; EASIX: Endothelial Activation and Stress Index.
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

Gokcinar, D.; Lafci, A.; Izdes, S. Endothelial Activation and Stress Index (EASIX) to Predict the Outcome of Patients with COVID-19. COVID 2025, 5, 89. https://doi.org/10.3390/covid5060089

AMA Style

Gokcinar D, Lafci A, Izdes S. Endothelial Activation and Stress Index (EASIX) to Predict the Outcome of Patients with COVID-19. COVID. 2025; 5(6):89. https://doi.org/10.3390/covid5060089

Chicago/Turabian Style

Gokcinar, Derya, Ayse Lafci, and Seval Izdes. 2025. "Endothelial Activation and Stress Index (EASIX) to Predict the Outcome of Patients with COVID-19" COVID 5, no. 6: 89. https://doi.org/10.3390/covid5060089

APA Style

Gokcinar, D., Lafci, A., & Izdes, S. (2025). Endothelial Activation and Stress Index (EASIX) to Predict the Outcome of Patients with COVID-19. COVID, 5(6), 89. https://doi.org/10.3390/covid5060089

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop