Next Article in Journal
Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach
Next Article in Special Issue
Health Research and Education during and after the COVID-19 Pandemic: An Australian Clinician and Researcher Perspective
Previous Article in Journal
Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors
Previous Article in Special Issue
Outcome of Newborns with Confirmed or Possible SARS-CoV-2 Vertical Infection—A Scoping Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diagnostics
Volume 13
Issue 2
10.3390/diagnostics13020259
diagnostics-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

Soluble ST2 as a Useful Biomarker for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients

by
Mikyoung Park
1,
Mina Hur
2,*,
Hanah Kim
2,
Chae Hoon Lee
3,
Jong Ho Lee
3,
Hyung Woo Kim
3,
Minjeong Nam
4 and
Seungho Lee
5
1
Department of Laboratory Medicine, Eunpyeong St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 03312, Republic of Korea
2
Department of Laboratory Medicine, Konkuk University School of Medicine, Seoul 05030, Republic of Korea
3
Department of Laboratory Medicine, Yeungnam University College of Medicine, Daegu 42415, Republic of Korea
4
Department of Laboratory Medicine, Korea University Anam Hospital, Seoul 02841, Republic of Korea
5
Department of Preventive Medicine, College of Medicine, Dong-A University, Busan 49201, Republic of Korea
*
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(2), 259; https://doi.org/10.3390/diagnostics13020259
Submission received: 20 November 2022 / Revised: 31 December 2022 / Accepted: 5 January 2023 / Published: 10 January 2023
(This article belongs to the Special Issue SARS-CoV-2 Identification by Diagnostic/Medical Laboratory: Methods, Statistics, Interesting Cases)
Browse Figures
Review Reports Versions Notes

Abstract

:
Soluble suppression of tumorigenesis-2 (sST2) is an emerging biomarker for sepsis as well as for heart failure. We investigated the prognostic utility of sST2 for predicting clinical outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients. In a total of 52 hospitalized COVID-19 patients, sST2 levels were measured using the ichroma ST2 assay (Boditech Med Inc., Chuncheon-si, Gang-won-do, Republic of Korea). Clinical outcomes included intensive care unit (ICU) admission, ventilator use, extracorporeal membrane oxygenation (ECMO) use, and 30-day mortality. sST2 was analyzed according to clinical outcomes. sST2, sequential organ failure assessment (SOFA) score, critical disease, and 4C mortality score were compared using the receiver operating characteristic (ROC) curve and Kaplan–Meier methods for clinical outcomes. The sST2 level differed significantly according to ICU admission, ventilator use, ECMO use, and 30-day mortality (all p < 0.05). On ROC curve analysis, sST2 predicted ICU admission, ventilator use, ECMO use, and 30-day mortality comparable to SOFA score but significantly better than critical disease. sST2 predicted ICU admission, ventilator use, and ECMO use significantly better than the 4C mortality score. On Kaplan–Meier survival analysis, hazard ratios (95% confidence interval) were 8.4 (2.7–26.8) for sST2, 14.8 (3.0–71.7) for SOFA score, 1.8 (0.5–6.5) for critical disease, and 11.7 (3.4–40.1) for 4C mortality score. This study demonstrated that sST2 could be a useful biomarker to predict ICU admission, ventilator use, ECMO use, and 30-day mortality in hospitalized COVID-19 patients. sST2 may be implemented as a prognostic COVID-19 biomarker in clinical practice.

1. Introduction

The coronavirus disease 2019 (COVID-19) pandemic has caused globally over six million deaths as of November 8, 2022, since its outbreak occurred in December 2019 [1,2]. The clinical spectrum of COVID-19 ranges from asymptomatic to critical disease [3]. Critical disease can present an acute respiratory distress syndrome (ARDS), sepsis, septic shock, thromboembolism, or multi-organ failure (MOF) [3,4,5]. MOF can occur in the lungs, heart, liver, kidney, and nervous system and may also present with hematological abnormalities [4]. The sequential organ failure assessment (SOFA) score is used as a severity index for assessing sepsis in COVID-19 [3,5]. Recently, 4C mortality score was developed as a prognostic index to predict the in-hospital mortality of COVID-19 [6]. 4C mortality score was calculated using age, sex, comorbidities, respiratory rate, peripheral oxygen saturation on room air, Glasgow Coma Scale, levels of urea, and C-reactive protein (CRP), and it predicted the in-hospital mortality better than the SOFA score [6].
When severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells through the angiotensin-converting enzyme 2 (ACE2) receptor, it releases inflammatory cytokines, including interleukin (IL)-1, IL-6, or tissue necrosis factor-alpha (TNF-α) [4,5,7,8]. Overproduced cytokines induce the cytokine storm leading to MOF or death [4,7,8]. COVID-19 can cause cardiovascular disease (CVD), including heart failure (HF), and it can lead to sudden cardiac death [3,4,9,10]. COVID-19 patients with HF have a greater risk of poor outcomes in COVID-19 [3,9,10,11]. In COVID-19, MOF, ICU admission, and in-hospital mortality were significantly higher in the HF group than in the non-HF group [11]. Cardiac biomarkers could play an important role in predicting the prognosis of COVID-19 [12]. Traditional biomarkers such as natriuretic peptides and troponins were associated with COVID-19 disease severity and mortality [10,12,13].
Soluble suppression of tumorigenesis-2 (sST2), the soluble isoform of ST2, a member of the IL-1 superfamily, is an emerging prognostic biomarker for sepsis as well as for HF [12,13,14,15,16,17]. sST2 is not only released by vascular congestion but also by inflammatory and pro-fibrotic stimuli [14]. Previous studies suggested that sST2 might be a prognostic biomarker to predict the 30-day and in-hospital mortality of sepsis [14,15,16,17]. When IL-33 binds to sST2, IL-33/sST2 inhibits the release of anti-inflammatory cytokines, followed by the activation and release of inflammatory cytokines like IL-6 and TNF-α, leading to inflammation [13,14,15,16,17,18]. In addition, previous studies have reported that sST2 would predict COVID-19 disease severity and prognosis [18,19,20,21,22,23,24,25,26,27,28,29,30]. The sST2 level increased according to COVID-19 disease severity [23,25]. sST2 was associated with intensive care unit (ICU) admission, ventilator use, thrombosis, and mortality in COVID-19 [22,24,25,26,27,28,29,30].
Our previous studies showed that the biomarker-based approach would be useful for predicting clinical outcomes in COVID-19 compared with a clinical index including SOFA score and COVID-19 disease severity (critical disease) [5,31]. To the best of our knowledge, few studies have evaluated the prognostic performance of sST2 compared with clinical assessments in COVID-19 [29]. We aimed to explore the prognostic utility of sST2 for predicting clinical outcomes in hospitalized COVID-19 patients compared with clinical assessments such as SOFA score, critical disease, and 4C mortality score. In addition, we explored sST2 levels according to the 4C mortality score.

2. Materials and Methods

2.1. Study Population

The enrollment and clinical outcomes of the study population are presented in Figure 1. From February to May 2020, a total of 396 COVID-19 patients at the Yeungnam University Medical Center (YUMC), Daegu, Republic of Korea, were studied. We excluded 255 patients who visited the outpatient clinic without routine blood tests in clinical practice or who were younger than 20 years. From 141 hospitalized patients over 20 years of age, we further excluded 89 patients without available residual ethylene-diamine-tetraacetic acid (EDTA) plasma samples after routine blood tests and known 30-day status. Finally, 52 patients were enrolled. Among the 52 patients, 44 patients were included in a previous study [5]. The study population had no specific limitations on care at enrollment. The symptom duration ranged up to 25 days. Therefore, we could not enroll the study population at the same point in the COVID-19 disease course. In a total of 52 patients, a follow-up period for 30-day status was five months from February to the end of June 2020. No additional blood sampling or intervention was performed. Medical records were reviewed to obtain demographic, clinical, and laboratory data. A total of 52 patients were treated with oxygen support, antibiotics, antiviral agents (Lopinavir/Ritonavir), or hydroxychloroquine.
The SOFA score, World Health Organization (WHO) disease severity, and 4C mortality score were assessed as described previously [3,5,6,31]. Critical disease included ARDS, sepsis, septic shock, and acute thrombosis [3,5,31]. 4C mortality score risk group was defined according to the scores; low (0–3), intermediate (4–8), high (9–14), and very high (≥15) [6]. For statistical analysis, three groups (low/intermediate, high, and very high) were analyzed due to the too small-sized low group. Clinical outcomes included ICU admission, ventilator use, ECMO use, and 30-day mortality [31].

2.2. sST2 Assay

A total of 52 residual EDTA plasma samples were collected from 52 patients at enrollment. The collected residual EDTA samples were aliquoted to avoid repeated freezing and thawing and were stored at −70 °C until measurement. Frozen samples were thawed at room temperature and gently mixed immediately before measuring sST2 levels. sST2 was assayed using the ichroma ST2 assay (Boditech Med Inc., Chuncheon-si, Gang-won-do, Republic of Korea) with an ichroma Ⅱ immuno-analyzer (Boditech Med Inc.) based on a fluorescence immunoassay. Both the ichroma ST2 assay and the ichroma Ⅱ immune-analyzer were approved by the Republic of Korean Ministry of Food and Drug Safety.
The manufacturer’s upper reference limit (URL) was 35 ng/mL, which was established as the cut-off value for predicting HF [13,15,18,21]. Analytical measurement intervals were from 3.1 to 200.0 ng/mL. sST2 levels were measured according to the manufacturer’s instructions.

2.3. Statistical Analysis

Data were presented as the number (percentage) or median (interquartile range, IQR). The Shapiro–Wilk test was used to determine the normality of data distribution. The Mann–Whitney U test was used to compare continuous variables (sST2, SOFA score, and 4C mortality score) according to the clinical outcomes. Chi-squared test or Fisher’s exact test was used to compare categorical variables (critical disease and 4C mortality risk group) according to the clinical outcomes. sST2, SOFA score, and critical disease were compared according to the 4C mortality risk group (low/intermediate, high, and very high) using the Kruskal–Wallis test or chi-squared test.
With the receiver operating characteristic (ROC) curve analysis, the area under the curve (AUC), the optimal cut-off values, sensitivity, and specificity of sST2, SOFA score, critical disease, and 4C mortality were estimated to predict clinical outcomes [32]. A 95% confidence interval (CI) of AUC was calculated using the Mann–Whitney statistic approach, which was suggested as superior to others for the small sample size [32,33]. Kaplan–Meier survival analysis was used to estimate the hazard ratio (HR) with a 95% confidence interval (CI) for the 30-day mortality of sST2, SOFA score, critical disease, 4C mortality score, and 4C mortality score group. HRs (95% CI) of high and very high groups relative to low/intermediate groups were calculated.
The sample size for the Kaplan–Meier survival analysis was estimated based on the previous study [34]. The inputs were identical to those described in our previous study, except for the alternative survival probability; analysis time t = 1 month, accrual time α = 5 months, follow-up time b = 1-month, null survival probability, S0(t) = 0.013, 0.025, or 0.026, type I error rate (α) = 0.05, and the power (1 − β) = 0.8 [5]. The alternative survival probability was set to S1(t) = 0.231 based on the 30-day mortality of this study. Using log-minus-log transformation, which was suggested for improving the accuracy of the small sample size, the estimated sample size was between 11 and 15. Accordingly, the sample size of 52 was considered sufficient to perform the Kaplan–Meier survival analysis. MedCalc Software (version 20.111, MedCalc Software, Ostend, Belgium) was used for statistical analysis. p-value < 0.05 was considered statistically significant.

3. Results

The basic characteristics of the study population are summarized in Table 1. The median age (IQR) was 71.0 years (62.5–79.0), and males were 61.5% (n = 32). Among six patients with chronic cardiac disease, three patients had HF. The median sST2 level, SOFA score, and 4C mortality score were 46.6 ng/mL, 4.0, and 9.5, respectively. In WHO disease severity, critical disease was 71.2% (n = 37); sepsis and septic shock were 83.8% (n = 31) and 16.2% (n = 6), respectively. Eight sepsis and five septic shock patients had ARDS. Of a total of 52 patients, 26.9% patients admitted to ICU (n = 14). Among the 14 ICU patients, a ventilator was applied to 12 patients, and ECMO was applied to seven ventilated patients. The 30-day mortality was 23.1% (n = 12).
The sST2, SOFA score, critical disease, and 4C morality score according to clinical outcomes are presented in Table 2. The sST2 level and SOFA score differed significantly according to all clinical outcomes (all p < 0.05). The proportion of critical disease did not differ according to ventilator use and 30-day mortality, but the 4C mortality score differed significantly according to only the 30-day mortality. The sST2 level differed significantly among 4C mortality score risk groups (p = 0.042); however, the SOFA score and the proportion of critical disease did not (Table 3).
In the ROC curve analysis, the sST2 and SOFA score comparably predicted ICU admission, ventilator use, ECMO use, and 30-day mortality (sST2 vs. SOFA, all p > 0.05) (Figure 2). sST2 predicted ICU admission, ventilator use, ECMO use, and 30-day mortality better than critical disease (all p < 0.05). sST2 predicted ICU admission, ventilator use, and ECMO use better than the 4C mortality score (all p < 0.05). The sST2 and 4C mortality scores predicted comparably 30-day mortality (0.826 vs. 0.830, p = 0.963).
In the Kaplan–Meier survival analysis, the HR (95% CI) for predicting 30-day mortality was 8.4 (2.7–26.8) for sST2, 14.8 (3.0–71.7) for the SOFA score, 1.8 (0.5–6.5) for critical disease, and 11.7 (3.4–40.1) for the 4C mortality score (Figure 3). In 4C morality score risk groups, the 30-day survival probability differed significantly among low/intermediate, high, and very high groups (log-rank test, p < 0.001); HR (95% CI) was 4.3 (1.3–4.4) in the high group and 20.8 (1.9–221.2) in the very high group relative to the low/intermediate risk group (data not shown).

4. Discussion

This is the first study to explore the prognostic performance of the sST2, SOFA score, critical disease, and 4C mortality score simultaneously in hospitalized COVID-19 patients. In this study, most patients presented with critical disease. The median sST2 level of the total study population was 46.6 ng/mL, which was greater than the cut-off value for predicting HF, 35 ng/mL [13,15,18,21]. In this study, 25 of 29 patients with sST2 levels above 35 ng/mL had critical disease, and all 13 ARDS patients were included in these 25 patients. Among the 25 patients with critical disease, 20 patients presented dyspnea, which was known as the key symptom of HF [12]. It was not clear how many patients had HF because there was no further evaluation for the diagnosis of it. Thus, sST2 may have been elevated when reflecting HF, the degree of inflammation, or both. In the previous studies, the sST2 level was heterogeneous in COVID-19 [22,23,24,25,27,28,29,30]. In two previous studies, the overall sST2 levels were less than 10 ng/mL [22,23]. In other previous studies, median sST2 levels were from 48 ng/mL to 53.1 ng/mL in COVID-19 patients, similar to our data [24,28,29]. In this study, around 70% of patients had a 4C mortality score of nine or higher, belonging to the high and very high groups. Increasing age is the strongest variable in the 4C mortality score [6]. The age range from 70 to 79 is six points, and older than 80 is seven points. [6]. In this study, most of the patients were over the age of 70; therefore, most of them were given a score of six or higher.
Both the sST2 level and SOFA score were significantly associated with ICU admission, ventilator use, ECMO use, and 30-day mortality. Median sST2 levels were lower than the URL in GW patients, patients without a ventilator and/or ECMO use, and survivors; however, it was about five- to ten-fold higher in ICU patients, patients with ventilator and/or ECMO use, and non-survivors. Based on our data, sST2 seems to reflect clinical outcomes in COVID-19. Similar to our data, a higher sST2 level was significantly associated with ICU admission and mortality [24,25,28,30]. The median sST2 level in non-survivors was 107 ng/mL, and it was higher than that of survived ICU and GW patients; the median sST2 level was significantly higher in ventilated ICU patients than in GW patients [28].
In COVID-19, the mechanism for the release of sST2 is not fully understood. IL-33 is released mainly by injured epithelial alveolar cells and can be upregulated in COVID-19 [18,35]. COVID-19 induces IL-33 expression in the lungs, and activated IL-33 leads to the production of sST2 type Ⅱ pneumocytes [8,35]. In COVID-19, ARDS, septic shock, or inflammatory mediators can lead to HF through multiple mechanisms [9], and HF can induce the upregulation of sST2 in the lungs leading to sST2 secretion by type Ⅱ pneumocytes [36].
Our data demonstrated that the sST2 level reflected a 4C mortality score better than the SOFA score and critical disease. Unlike the SOFA score and critical disease, the median sST2 level increased significantly according to the 4C mortality score risk group. In-hospital mortality ranged from 0.0% to 1.7% in the low-risk group and 8.0% to 9.9% in the intermediate-risk group, which were substantially lower than that of the high- and very high-risk groups [6,37]. In line with the previous studies, the median sST2 level was normal in the low/intermediate risk group, and it was significantly lower than that of the high-risk group. Based on our data, the sST2 level seems to reflect the prognostic index for COVID-19 mortality.
In this study, the sST2 level and SOFA score comparably predicted ICU admission, ventilator use, ECMO use, and 30-day mortality. AUCs of the sST2 level was higher than that of the SOFA score for predicting ICU admission and 30-day mortality (0.878 vs. 0.865 in ICU admission; 0.826 vs. 0.716 in 30-day mortality). sST2 outperformed critical disease in ICU admission, ventilator use, ECMO use, and 30-day mortality. Although the sST2 level and 4C mortality score comparably predicted 30-day mortality, sST2 outperformed the 4C mortality score for predicting ICU admission, ventilator use, and ECMO use. HRs for the sST2, SOFA score, and 4C mortality score were significantly high except for critical disease. Based on our data, sST2 might be a useful biomarker for predicting clinical outcomes in COVID-19. Since COVID-19 patients could be aggravated rapidly, resulting in unexpected poor outcomes, it is important to predict COVID-19 prognosis earlier [31]. The WHO recommends monitoring vital signs, clinical warning scores, laboratory data, electrocardiogram, or chest imaging to detect a deteriorating COVID-19 patient or complications [3,31]. These signs may not be monitored in a timely manner. In addition, clinical assessments such as the SOFA score, WHO disease severity, and 4C mortality score need various clinical and laboratory data or complex calculations. A simple biomarker would be more objective and appropriate in the real hospital setting [31]. Based on our data, sST2 could be used as a simple, objective parameter for detecting patients with critical care demands, including ICU, ventilator, and ECMO, and predicting 30-day mortality in clinical practice [31].
sST2 was superior to CRP and IL-6 for predicting ICU admission and mortality [24]. sST2 and clinical scores comparably predicted ICU admission for ventilator use and in-hospital mortality [29]. sST2 has a low biological variability and reference change values, and it is not affected by sex, age, body mass index, atrial fibrillation, renal function, or the prior diagnosis of HF [12]. In serial measurements, the sST2 level showed highly dynamic change according to the disease course in both COVID-19 and HF [23,24,25,36]. Accordingly, sST2 may be a reliable biomarker for the serial monitoring of hospitalized COVID-19 patients [12,38]. On the other hand, it is also necessary to consider the analytical performance and difference of sST2 assays to implement sST2 as a prognostic biomarker for COVID-19 [13,38,39,40].
This study had several limitations. First, although a sample size of 52 was sufficient for the Kaplan–Meier survival analysis, it might not be sufficient for providing a meaningful result. In addition, a smaller sample size would result in a wide CI because of the margin of error. However, a previous study reported that larger sample sizes reduce variance but do not improve AUC regression [41]. Further studies with a larger sample size are needed to validate our findings. Second, the study population had a skewed distribution toward critical disease, and our data may be biased and not representative. Third, the duration from the symptom onset to routine blood tests after admission varied, and it was difficult to obtain blood samples at a fixed time during the first and second waves of the COVID-19 pandemic [5,42,43]. The heterogeneous disease course of COVID-19 may have affected our data. Fourth, we focused on the prognostic utility of sST2 for clinical outcomes in COVID-19. The prediction of HF due to COVID-19 was out of the scope of our study due to insufficient information on it.
In conclusion, this is the first study to explore the prognostic utility of sST2 for predicting clinical outcomes in hospitalized COVID-19 patients compared with SOFA score, critical disease, and 4C mortality score. sST2 predicted ICU admission, ventilator use, ECMO use, and 30-day mortality comparably to SOFA score but significantly better than critical disease. sST2 predicted ICU admission, ventilator use, and ECMO use significantly better than the 4C mortality score. sST2 could be a useful biomarker for predicting clinical outcomes in hospitalized COVID-19 patients. Further studies are needed to implement sST2 as a prognostic biomarker for COVID-19 in routine clinical practice.

Author Contributions

Conceptualization, M.H.; Data curation, M.P.; Formal analysis, M.P. and M.H.; Investigation, M.P.; Methodology, M.H.; Project administration, M.H.; Resources, H.K., C.H.L., J.H.L., H.W.K., M.N. and S.L.; Supervision, M.H.; Visualization, M.P. and M.H.; Writing—original draft, M.P. and M.H.; Writing—review and editing, M.P., M.H., H.K., C.H.L., J.H.L., H.W.K., M.N. and S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institution Review Board of YUMC (IRB FILE No. 2020-12-004, date of approval 11 December 2020).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hong, K.H.; Lee, S.W.; Kim, T.S.; Huh, H.J.; Lee, J.; Kim, S.Y.; Park, J.S.; Kim, G.J.; Sung, H.; Roh, K.H.; et al. Guidelines for Laboratory Diagnosis of Coronavirus Disease 2019 (COVID-19) in Korea. Ann. Lab. Med. 2020, 40, 351–360. [Google Scholar] [CrossRef] [PubMed]
  2. World Health Organization. WHO Coronavirus (COVID-19) Dashboard. Available online: https://covid19.who.int/table (accessed on 8 November 2022).
  3. Word Health Organization: Living Guidance for Clinical Management of COVID-19. Available online: https://www.who.int/publications/i/item/WHO-2019-nCoV-clinical-2021-2 (accessed on 8 November 2022).
  4. Mokhtari, T.; Hassani, F.; Ghaffari, N.; Ebrahimi, B.; Yarahmadi, A.; Hassanzadeh, G. COVID-19 and Multiorgan Failure: A Narrative Review on Potential Mechanisms. J. Mol. Histol. 2020, 51, 613–628. [Google Scholar] [CrossRef] [PubMed]
  5. Park, M.; Hur, M.; Kim, H.; Lee, C.H.; Lee, J.H.; Kim, H.W.; Nam, M. Prognostic Utility of Procalcitonin, Presepsin, and the VACO Index for Predicting 30-day Mortality in Hospitalized COVID-19 Patients. Ann. Lab. Med. 2022, 42, 406–414. [Google Scholar] [CrossRef] [PubMed]
  6. Knight, S.R.; Ho, A.; Pius, R.; Buchan, I.; Carson, G.; Drake, T.M.; Dunning, J.; Fairfield, C.J.; Gamble, C.; Green, C.A.; et al. Risk Stratification of Patients Admitted to Hospital with COVID-19 Using the ISARIC WHO Clinical Characterisation Protocol: Development and Validation of the 4C Mortality Score. BMJ 2020, 370, m3339. [Google Scholar] [CrossRef] [PubMed]
  7. Morgan, P.; Arnold, S.J.; Hsiao, N.W.; Shu, C.W. A Closer Look at Dexamethasone and the SARS-CoV-2-Induced Cytokine Storm: In Silico Insights of the First Life-Saving COVID-19 Drug. Antibiotics 2021, 10, 1507. [Google Scholar] [CrossRef]
  8. Nagashima, S.; Dutra, A.A.; Arantes, M.P.; Zeni, R.C.; Klein, C.K.; de Oliveira, F.C.; Piper, G.W.; Brenny, I.D.; Pereira, M.R.C.; Stocco, R.B.; et al. COVID-19 and Lung Mast Cells: The Kallikrein–Kinin Activation Pathway. Int. J. Mol. Sci. 2022, 23, 1714. [Google Scholar] [CrossRef]
  9. Bader, F.; Manla, Y.; Atallah, B.; Starling, R.C. Heart Failure and COVID-19. Heart Fail Rev. 2021, 26, 1–10. [Google Scholar] [CrossRef]
  10. Tajbakhsh, A.; Gheibi Hayat, S.M.; Taghizadeh, H.; Akbari, A.; Inabadi, M.; Savardashtaki, A.; Johnston, T.P.; Sahebkar, A. COVID-19 and Cardiac injury: Clinical Manifestations, Biomarkers, Mechanisms, Diagnosis, Treatment, and Follow up. Expert Rev. Anti. Infect. Ther. 2021, 19, 345–357. [Google Scholar] [CrossRef]
  11. Sokolski, M.; Trenson, S.; Sokolska, J.M.; D’Amario, D.; Meyer, P.; Poku, N.K.; Biering-Sørensen, T.; Højbjerg Lassen, M.C.; Skaarup, K.G.; Barge-Caballero, E.; et al. Heart Failure in COVID-19: The Multicentre, Multinational PCHF-COVICAV Registry. ESC Heart Fail. 2021, 8, 4955–4967. [Google Scholar] [CrossRef]
  12. Aleksova, A.; Sinagra, G.; Beltrami, A.P.; Pierri, A.; Ferro, F.; Janjusevic, M.; Gagno, G. Biomarkers in the Management of Acute Heart Failure: State of the Art and Role in COVID-19 era. ESC Heart Fail. 2021, 8, 4465–4483. [Google Scholar] [CrossRef]
  13. Aimo, A.; Januzzi, J.L.; Bayes-Genis, A.; Vergaro, G.; Sciarrone, P.; Passino, C.; Emdin, M. Clinical and Prognostic Significance of sST2 in Heart Failure: JACC Review Topic of the Week. J. Am. Coll. Cardiol. 2019, 74, 2193–2203. [Google Scholar] [CrossRef]
  14. Yang, H.S.; Kim, H.J.; Shim, H.J.; Kim, S.J.; Hur, M.; Di Somma, S. Soluble ST2 and Troponin I Combination: Useful Biomarker for Predicting Development of Stress Cardiomyopathy in Patients Admitted to the Medical Intensive Care Unit. Heart Lung 2015, 44, 282–288. [Google Scholar] [CrossRef]
  15. Hur, M.; Kim, H.; Kim, H.J.; Yang, H.S.; Magrini, L.; Marino, R.; Cardelli, P.; Somma, S.D.; GREAT Network. Soluble ST2 Has a Prognostic Role in Patients With Suspected Sepsis. Ann. Lab. Med. 2015, 35, 570–577. [Google Scholar] [CrossRef] [Green Version]
  16. Kim, H.; Hur, M.; Moon, H.-W.; Yun, Y.-M.; Di Somma, S. Multi-Marker Approach Using Procalcitonin, Presepsin, Galectin-3, and Soluble Suppression of Tumorigenicity 2 for the Prediction of Mortality in Sepsis. Ann. Intensive Care 2017, 7, 27. [Google Scholar] [CrossRef] [Green Version]
  17. Zhang, J.; Chen, Z.; Ma, M.; He, Y. Soluble ST2 in Coronary Artery Disease: Clinical Biomarkers and Treatment Guidance. Front. Cardiovasc. Med. 2022, 9, 924461. [Google Scholar] [CrossRef]
  18. Marino, L.; Concistrè, A.; Suppa, M.; Galardo, G.; Rosa, A.; Bertazzoni, G.; Pugliese, F.; Letizia, C.; Petramala, L. Prognostic Role of sST2 in Acute Heart Failure and COVID-19 Infection—A Narrative Review on Pathophysiology and Clinical Prospective. Int. J. Mol. Sci. 2022, 23, 8230. [Google Scholar] [CrossRef]
  19. Khan, S.; Rasool, S.T.; Ahmed, S.I. Role of Cardiac Biomarkers in COVID-19: What Recent Investigations Tell Us? Curr. Probl. Cardiol. 2021, 46, 100842. [Google Scholar] [CrossRef]
  20. Ragusa, R.; Basta, G.; Del Turco, S.; Caselli, C. A Possible Role for ST2 as Prognostic Biomarker for COVID-19. Vascul. Pharmacol. 2021, 138, 106857. [Google Scholar] [CrossRef]
  21. Miftode, R.S.; Petriș, A.O.; Onofrei Aursulesei, V.; Cianga, C.; Costache, I.I.; Mitu, O.; Miftode, I.L.; Șerban, I.L. The Novel Perspectives Opened by ST2 in the Pandemic: A Review of Its Role in the Diagnosis and Prognosis of Patients with Heart Failure and COVID-19. Diagnostics 2021, 11, 175. [Google Scholar] [CrossRef]
  22. Wendt, R.; Lingitz, M.T.; Laggner, M.; Mildner, M.; Traxler, D.; Graf, A.; Krotka, P.; Moser, B.; Hoetzenecker, K.; Kalbitz, S.; et al. Clinical Relevance of Elevated Soluble ST2, HSP27 and 20S Proteasome at Hospital Admission in Patients with COVID-19. Biology 2021, 10, 1186. [Google Scholar] [CrossRef]
  23. Zeng, Z.; Hong, X.-Y.; Li, Y.; Chen, W.; Ye, G.; Li, Y.; Luo, Y. Serum-soluble ST2 as a Novel Biomarker Reflecting Inflammatory Status and Illness Severity in Patients With COVID-19. Biomark. Med. 2020, 14, 1619–1629. [Google Scholar] [CrossRef] [PubMed]
  24. Sánchez-Marteles, M.; Rubio-Gracia, J.; Peña-Fresneda, N.; Garcés-Horna, V.; Gracia-Tello, B.; Martínez-Lostao, L.; Crespo-Aznárez, S.; Pérez-Calvo, J.I.; Giménez-López, I. Early Measurement of Blood sST2 Is a Good Predictor of Death and Poor Outcomes in Patients Admitted for COVID-19 Infection. J. Clin. Med. 2021, 10, 3534. [Google Scholar] [CrossRef]
  25. Abers, M.S.; Delmonte, O.M.; Ricotta, E.E.; Fintzi, J.; Fink, D.L.; de Jesus, A.A.A.; Zarember, K.A.; Alehashemi, S.; Oikonomou, V.; Desai, J.V.; et al. An Immune-based Biomarker Signature is Associated With Mortality in COVID-19 patients. JCI Insight 2021, 6, e144455. [Google Scholar] [CrossRef] [PubMed]
  26. Umesh, M.; Singaravelu, V.; Daulatabad, V.; Kamble, P.; Singhal, A.; John, N.A.; John, J. An Overview of Prognostic Value of Neurologic and Cardiac Biomarkers in Patients With COVID-19 Sequelae. Horm. Mol. Biol. Clin. Investig. 2022, in press. [Google Scholar] [CrossRef] [PubMed]
  27. Motloch, L.J.; Jirak, P.; Gareeva, D.; Davtyan, P.; Gumerov, R.; Lakman, I.; Tataurov, A.; Zulkarneev, R.; Kabirov, I.; Cai, B.; et al. Cardiovascular Biomarkers for Prediction of In-hospital and 1-Year Post-discharge Mortality in Patients with COVID-19 Pneumonia. Front Med. 2022, 9, 906665. [Google Scholar] [CrossRef]
  28. Omland, T.; Prebensen, C.; Jonassen, C.; Svensson, M.; Berdal, J.E.; Seljeflot, I.; Myhre, P.L. Soluble ST2 Concentrations Associate With In-hospital Mortality and Need for Mechanical Ventilation in Unselected Patients With COVID-19. Open Heart 2021, 8, e001884. [Google Scholar] [CrossRef]
  29. Rubio-Gracia, J.; Sánchez-Marteles, M.; Garcés-Horna, V.; Martínez-Lostao, L.; Ruiz-Laiglesia, F.; Crespo-Aznarez, S.; Peña-Fresneda, N.; Gracia-Tello, B.; Cebollada, A.; Carrera-Lasfuentes, P.; et al. Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients. J. Clin. Med. 2021, 10, 5478. [Google Scholar] [CrossRef]
  30. Cabrera-Garcia, D.; Miltiades, A.; Yim, P.; Parsons, S.; Elisman, K.; Mansouri, M.T.; Wagener, G.; Harrison, N.L. Plasma Biomarkers Associated With Survival and Thrombosis in Hospitalized COVID-19 Patients. Int. J. Hematol. 2022, 116, 937–946. [Google Scholar] [CrossRef]
  31. Park, M.; Hur, M.; Kim, H.; Lee, C.H.; Lee, J.H.; Nam, M. Usefulness of KL-6 for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients. Medicina 2022, 58, 1317. [Google Scholar] [CrossRef]
  32. DeLong, E.R.; DeLong, D.M.; Clarke-Pearson, D.L. Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics 1988, 44, 837–845. [Google Scholar] [CrossRef]
  33. Feng, D.; Cortese, G.; Baumgartner, R. A Comparison of Confidence/Credible Interval Methods for the Area Under the ROC Curve for Continuous Diagnostic Tests With Small Sample Size. Stat. Methods Med. Res. 2017, 26, 2603–2621. [Google Scholar] [CrossRef]
  34. Nagashima, K.; Noma, H.; Sato, Y.; Gosho, M. Sample Size Calculations for Single-Arm Survival Studies Using Transformations of the Kaplan–Meier Estimator. Pharm. Stat. 2020, 20, 499–511. [Google Scholar] [CrossRef]
  35. Zizzo, G.; Cohen, P.L. Imperfect Storm: Is Interleukin-33 The Achilles Geel of COVID-19? Lancet Rheumatol. 2020, 2, e779–e790. [Google Scholar] [CrossRef]
  36. Pascual-Figal, D.A.; Pérez-Martínez, M.T.; Asensio-Lopez, M.C.; Sanchez-Más, J.; García-García, M.E.; Martinez, C.M.; Lencina, M.; Jara, R.; Januzzi, J.L.; Lax, A. Pulmonary Production of Soluble ST2 in Heart Failure. Circ. Heart Fail. 2018, 11, e005488. [Google Scholar] [CrossRef]
  37. Jones, A.; Pitre, T.; Junek, M.; Kapralik, J.; Patel, R.; Feng, E.; Dawson, L.; Tsang, J.L.Y.; Duong, M.; Ho, T.; et al. External Validation of the 4C Mortality Score among COVID-19 Patients Admitted to Hospital in Ontario, Canada: A Retrospective Study. Sci. Rep. 2021, 11, 18638. [Google Scholar] [CrossRef]
  38. Sarlo, F.; De Luca, C.; Moretti, G.; Urbani, A.; Baroni, S. Analytical Performance Evaluation of the New sST2 Turbidimetric Assay Implemented in Laboratory Automation Systems. Clin. Chem. Lab. Med. 2021, 60, e54–e56. [Google Scholar] [CrossRef]
  39. Dieplinger, B.; Egger, M.; Gegenhuber, A.; Haltmayer, M.; Mueller, T. Analytical and Clinical Evaluation of A Rapid Quantitative Lateral Flow Immunoassay for Measurement of Soluble ST2 in Human Plasma. Clin. Chim. Acta 2015, 451, 310–315. [Google Scholar] [CrossRef] [Green Version]
  40. Aurora, L.; Peterson, E.; Gui, H.; Zeld, N.; McCord, J.; Pinto, Y.; Cook, B.; Sabbah, H.N.; Keoki Williams, L.; Snider, J.; et al. Suppression Tumorigenicity 2 (ST2) Turbidimetric Immunoassay Compared to Enzyme-Linked Immunosorbent Assay in Predicting Survival in Heart Failure Patients with Reduced Ejection Fraction. Clin. Chim. Acta 2020, 510, 767–771. [Google Scholar] [CrossRef]
  41. Hanczar, B.; Hua, J.; Sima, C.; Weinstein, J.; Bittner, M.; Dougherty, E.R. Small-sample precision of ROC-related estimates. Bioinformatics 2010, 26, 822–830. [Google Scholar] [CrossRef] [Green Version]
  42. Kim, S.W.; Kim, S.M.; Kim, Y.K.; Kim, J.Y.; Lee, Y.M.; Kim, B.O.; Hwangbo, S.; Park, T. Clinical Characteristics and Outcomes of COVID-19 Cohort Patients in Daegu Metropolitan City Outbreak in 2020. J. Korean Med. Sci. 2021, 36, e12. [Google Scholar] [CrossRef]
  43. Jeon, J.; Han, C.; Kim, T.; Lee, S. Evolution of Responses to COVID-19 and Epidemiological Characteristics in South Korea. Int. J. Environ. Res. Public Health 2022, 19, 4056. [Google Scholar] [CrossRef] [PubMed]
Diagnostics 13 00259 g001 550
Figure 1. The enrollment and clinical outcomes of the study population. None of the study population presented severe disease in terms of WHO disease severity. The red line indicates death with regard to 30-day mortality. * Among 37 patients with critical disease, 31 patients had sepsis and six patients had septic shock. Abbreviations: WHO, World Health Organization; COVID-19, coronavirus disease 2019; YUMC, Yeoungnam University Medical Center; EDTA, ethylene-diamine-tetraacetic acid; ICU, intensive care unit; GW, general ward; ECMO, extracorporeal membrane oxygenation.
Figure 1. The enrollment and clinical outcomes of the study population. None of the study population presented severe disease in terms of WHO disease severity. The red line indicates death with regard to 30-day mortality. * Among 37 patients with critical disease, 31 patients had sepsis and six patients had septic shock. Abbreviations: WHO, World Health Organization; COVID-19, coronavirus disease 2019; YUMC, Yeoungnam University Medical Center; EDTA, ethylene-diamine-tetraacetic acid; ICU, intensive care unit; GW, general ward; ECMO, extracorporeal membrane oxygenation.
Diagnostics 13 00259 g001
Diagnostics 13 00259 g002a 550Diagnostics 13 00259 g002b 550Diagnostics 13 00259 g002c 550
Figure 2. Receiver operating characteristic curve analysis of sST2, SOFA score, critical disease, and 4C mortality score for clinical outcomes. (a) ICU admission. (b) Ventilator use. (c) ECMO use. (d) 30-day mortality. Abbreviations: sST2—soluble suppressor of tumorigenicity 2; SOFA—sequential organ failure assessment; AUC—area under the curve; CI—confidence interval.
Figure 2. Receiver operating characteristic curve analysis of sST2, SOFA score, critical disease, and 4C mortality score for clinical outcomes. (a) ICU admission. (b) Ventilator use. (c) ECMO use. (d) 30-day mortality. Abbreviations: sST2—soluble suppressor of tumorigenicity 2; SOFA—sequential organ failure assessment; AUC—area under the curve; CI—confidence interval.
Diagnostics 13 00259 g002aDiagnostics 13 00259 g002bDiagnostics 13 00259 g002c
Diagnostics 13 00259 g003a 550Diagnostics 13 00259 g003b 550Diagnostics 13 00259 g003c 550
Figure 3. Kaplan–Meier survival analysis of sST2, SOFA score, critical disease, and 4C mortality score for 30-day mortality. (a) sST2. (b) SOFA score. (c) Critical disease. (d) 4C mortality score. Abbreviations: sST2, soluble suppressor of tumorigenicity 2; SOFA, sequential organ failure assessment; HR, hazard ratio; CI, confidence interval.
Figure 3. Kaplan–Meier survival analysis of sST2, SOFA score, critical disease, and 4C mortality score for 30-day mortality. (a) sST2. (b) SOFA score. (c) Critical disease. (d) 4C mortality score. Abbreviations: sST2, soluble suppressor of tumorigenicity 2; SOFA, sequential organ failure assessment; HR, hazard ratio; CI, confidence interval.
Diagnostics 13 00259 g003aDiagnostics 13 00259 g003bDiagnostics 13 00259 g003c
Table
Table 1. The characteristics of the study population.
Table 1. The characteristics of the study population.
VariableAll Patients (n = 52)
Age, years71.0 (62.5–79.0)
Male32 (61.5)
BMI, kg/m224.1 (22.3–25.6)
Comorbidities
 DM18 (34.6)
 Malignancy9 (17.3)
 Chronic neurological conditions7 (13.7)
 Chronic cardiac disease *6 (11.5)
Chronic respiratory disease (except asthma)5 (9.6)
 Dementia5 (9.6)
 Chronic kidney disease3 (5.8)
 Connective tissue disease3 (5.8)
 Others †3 (5.8)
Symptoms
 Fever or chilling33 (63.5)
 General weakness23 (44.2)
 Dyspnea28 (53.8)
 Cough22 (42.3)
 Sputum14 (26.9)
 Gastrointestinal ‡7 (13.5)
 Neurological ∥6 (11.5)
 Myalgia5 (9.6)
 Others 5 (9.6)
Symptom duration, days4.0 (0.5–7.5)
COVID-19 diagnosis to admission, days0.0 (0.0–2.0)
COVID-19 diagnosis to blood sampling, days3.0 (0.0–11.0)
Hospital stays, days28.0 (16.5–47.5)
Treatment
 Oxygen support42 (80.8)
 Antibiotics30 (57.7)
 Antiviral agent (Lopinavir/Ritonavir)25 (48.1)
 Hydroxychloroquine3 (5.8)
Severity assessment
 SOFA score4.0 (1.0–7.0)
 WHO disease severity
  Mild disease1 (1.9)
  Moderate disease14 (26.9)
  Severe disease0 (0.0)
  Critical disease37 (71.2)
   Sepsis/septic shock **31 (83.8)/6 (16.2)
Prognosis assessment
 4C mortality score9.5 (7.5–12.0)
  Low (0–3)2 (3.8)
  Intermediate (4–8)15 (28.8)
  High (9–14)30 (57.7)
  Very high (≥15)5 (9.6)
Clinical outcomes
 ICU admission14 (26.9)
 Ventilator use12 (23.1)
 ECMO use7 (13.5)
 30-day mortality12 (23.1)
Vital signs
 SBP, mm Hg120.0 (110.0–140.0)
 DBP, mm Hg71.5 (70.0–80.0)
 HR, beats/min82.0 (74.0–93.5)
 RR, breaths/min20.0 (20.0–23.0)
 BT, ℃37.1 (36.8–37.8)
 SpO2, %96 (94.3–97.0)
Laboratory data
 WBC, ×109/L5.9 (4.8–8.6)
 Hb, g/L120.0 (109.0–130.5)
 PLT, ×109/L199.0 (148.0–268.0)
 AST, U/L30.5 (23.0–44.0)
 ALT, U/L24.5 (14.5–37.5)
 Total bilirubin, umol/L12.7 (9.6–18.3)
 Cr, umol/L76.5 (56.6–96.4)
Lactate, mmol/L1.8 (1.2–2.2)
 CRP, mg/L36.4 (4.6–132.5)
 NT-proBNP ††, pg/mL230.2 (111.6–852.3)
 hs-TnI ‡‡, ng/mL0.01 (0.00–0.02)
 sST2, ng/mL46.6 (18.8–106.6)
Data are represented as a number (percentage) or median (interquartile range, IQR). * Chronic cardiac disease includes coronary artery disease (n = 3) and heart failure (n = 3). † Others include clinician-defined obesity (n = 2) and liver disease (n = 1). ‡ Gastrointestinal symptoms include anorexia, nausea, diarrhea, and abdominal distension/pain. ∥ Neurological symptoms include headache, dizziness, mental change, and motor weakness. Other symptoms include hemoptysis, rhinorrhea, chest pain, nasal congestion, and sore throat. ** 13 patients (sepsis [n = 8], septic shock [n = 5]) had ARDS. One sepsis patient without ARDS had acute thrombosis (acute stroke). †† NT-proBNP levels were obtained from 38 patients at enrollment. ‡‡ hs-TnI levels were obtained from 37 patients at enrollment. Abbreviations: BMI, body mass index; DM, diabetes mellitus; COVID-19, coronavirus disease 2019; SOFA, sequential organ failure assessment; WHO, World Health Organization; ICU, intensive care unit; ECMO, extracorporeal membrane oxygenation; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; RR, respiratory rate; BT, body temperature; SpO2, peripheral oxygen saturation; WBC, white blood cell; Hb, hemoglobin; PLT, platelets; AST, aspartate aminotransferase; ALT, alanine aminotransferase; Cr, creatinine; CRP, C-reactive protein; NT-proBNP, N-terminal pro-B-type natriuretic peptide; hs-TnI, high-sensitivity troponin I; sST2, soluble suppressor of tumorigenicity 2; ARDS, acute respiratory distress syndrome.
Table
Table 2. sST2, SOFA score, critical disease, and 4C mortality score according to clinical outcomes.
Table 2. sST2, SOFA score, critical disease, and 4C mortality score according to clinical outcomes.
VariablesICU AdmissionVentilator UseECMO Use30-Day Mortality
Yes
(n = 14)		No
(n = 38)		p *Yes
(n = 12)		No
(n = 40)		p *Yes
(n = 7)		No
(n = 45)		p *Yes
(n = 12)		No
(n = 40)		p *
sST2, ng/mL242.7
(77.8–378.0)		28.0
(16.3–59.5)		<0.001295.1
(137.3–412.5)		28.1
(16.3–59.1)		<0.001252.5
(146.9–667.9)		30.0
(17.6–76.9)		0.002154.8
(59.1–426.6)		28.1
(16.3–79.2)		<0.001
SOFA score7.5
(7.0–9.0)		3.0
(1.0–4.0)		<0.0018.0
(7.0–9.5)		3.0
(0.5–4.0)		<0.0018.0
(7.3–9.0)		3.0 (1.0–5.0)<0.0017.5
(3.0–9.5)		4.0
(0.5–5.5)		0.023
Critical disease13 (92.9)24 (63.2)<0.04312 (100.0)25 (62.5)0.0117 (100.0)30 (66.7)0.09310 (83.3)27 (67.5)0.470
4C mortality score10.5
(8.0–14.0)		9.0
(7.0–12.0)		0.09911.5
(9.0–14.0)		9.0
(7.0–11.5)		0.03911.0
(8.5–12.8)		9.0
(7.0–12.0)		0.36713.0
(11.5–15.5)		9.0 (7.0–10.0)<0.001
  Low/intermediate4 (28.6)13 (34.2)0.7653 (25.0)14 (35.0)0.5792 (28.6)15 (33.3)0.5821 (8.3)16 (40.0)0.002
  High8 (57.1)22 (57.9)7 (58.3)23 (57.5)5 (71.4)25 (55.6)7 (58.3)23 (57.5)
  Very high2 (14.3)3 (7.9)2 (16.7)3 (7.5)0 (0.0)5 (11.1)4 (33.3)1 (2.5)
Data were represented as a number (percentage) or median (IQR). * p-values were calculated using the Mann–Whitney test, chi-squared test, or Fisher’s exact test. Abbreviations: sST2, soluble suppressor of tumorigenicity 2; SOFA, sequential organ failure assessment; ICU, intensive care unit; ECMO, extracorporeal membrane oxygenation.
Table
Table 3. sST2, SOFA score, and critical disease according to 4C mortality score risk group.
Table 3. sST2, SOFA score, and critical disease according to 4C mortality score risk group.
VariablesLow/Intermediate (n = 17)High (n = 30)Very High (n = 5)p *
sST2, ng/mL19.2 (12.9–58.7)51.8 (22.9–118.3)76.6 (49.5–347.8)0.042 †
SOFA score3.0 (0.0–6.3)4.0 (2.0–7.0)8.0 (3.0–10.3)0.093
Critical disease9 (52.9)23 (76.7)5 (100.0)0.073
Data were represented as a number (percentage) or median (IQR). * p-values were calculated using the Kruskal–Wallis test or chi-squared test among low/intermediate, high, and very high groups. † p-values between two groups calculated using Mann–Whitney test; Low/intermediate vs. High, p = 0.030; Low/intermediate vs. Very high, p = 0.055; High vs. Very high, p = 0.409. Abbreviations: sST2, soluble suppressor of tumorigenicity 2; SOFA, sequential organ failure assessment.
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

Park, M.; Hur, M.; Kim, H.; Lee, C.H.; Lee, J.H.; Kim, H.W.; Nam, M.; Lee, S. Soluble ST2 as a Useful Biomarker for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients. Diagnostics 2023, 13, 259. https://doi.org/10.3390/diagnostics13020259

AMA Style

Park M, Hur M, Kim H, Lee CH, Lee JH, Kim HW, Nam M, Lee S. Soluble ST2 as a Useful Biomarker for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients. Diagnostics. 2023; 13(2):259. https://doi.org/10.3390/diagnostics13020259

Chicago/Turabian Style

Park, Mikyoung, Mina Hur, Hanah Kim, Chae Hoon Lee, Jong Ho Lee, Hyung Woo Kim, Minjeong Nam, and Seungho Lee. 2023. "Soluble ST2 as a Useful Biomarker for Predicting Clinical Outcomes in Hospitalized COVID-19 Patients" Diagnostics 13, no. 2: 259. https://doi.org/10.3390/diagnostics13020259

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