Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment
- Authors:
- Published online on: November 28, 2023 https://doi.org/10.3892/br.2023.1700
- Article Number: 12
-
Copyright: © Kokkoris et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
In addition to affecting the respiratory system, coronavirus disease 2019 (COVID-19) (1) frequently complicates itself by involving multiple organs (2-5) in its most severe forms, leading to various laboratory abnormalities. Since the early onset of the pandemic, several key laboratory characteristics have been identified in the acute phase, facilitating the evaluation of disease severity (3-5). The importance of certain laboratory abnormalities induced by COVID-19, measured on the day of hospital admission, has been extensively demonstrated in numerous articles (6-19).
However, during the period of hospitalization, patients with severe COVID-19 experience a variety of changes in clinical and laboratory measures. Routine hematological and biochemical monitoring is essential for assessing the disease severity, therapeutic options and treatment response (20). Notwithstanding, despite the increased interest in laboratory abnormalities upon hospital admission, studies exploring the evolution of laboratory parameters over the course of the disease are limited and variable, particularly in the setting of the intensive care unit (ICU). Some of these studies include a small patient population or both critically and non-critically ill patients (6,21-23), whereas the limited number of large studies available (24-28) on patients with COVID-19 admitted to the ICU have demonstrated diverse results. Furthermore, the effects of dexamethasone, recommended for the treatment of COVID-19(29), on the trajectory of various laboratory values have not been fully investigated.
The aim of the present study was to investigate the temporal trends in routine laboratory parameters characteristic of COVID-19 according to the clinical outcome, as well as the potential effects of dexamethasone treatment on patients admitted to the ICU due to COVID-19, using a large database.
Patients and methods
The present study was a single-center retrospective cohort study of prospectively collected data derived from the COVID-19 dataset (formed in March, 2020) for all critically ill patients admitted to the university ICU at ‘Evangelismos’ Hospital, a tertiary care center in Athens, Greece, between March, 2020 and December, 2021. All patients suffered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, confirmed by a real-time reverse transcriptase-polymerase chain reaction assay of nasopharyngeal swab specimens.
Demographics, comorbidities, the severity of illness upon admission to the ICU, mechanical ventilation, the length of stay in the ICU, dexamethasone treatment and the ICU mortality rates were recorded. The severity of illness was assessed using the Acute Physiology and Chronic Health Evaluation (APACHE) II (30) and Sequential Organ Failure Assessment (SOFA) (31) scoring systems. Routine laboratory tests for COVID-19, namely hematological parameters, including white blood cell (WBC) count, neutrophil count, lymphocyte count, neutrophil to lymphocyte ratio (NLR), platelet (PLT) count, fibrinogen and D-dimer concentrations, as well as the biochemical parameters, C-reactive protein (CRP), lactate dehydrogenase (LDH) and albumin, were selected on the basis of their routine use, on a daily basis. For analysis, laboratory values upon admission to the ICU, and on days 3, 5, 10, 15 and 21 post-ICU admission were used. The determination of SARS-CoV-2 variants in the patients with ICU over course of the pandemic was not performed routinely. However, the consecutive pandemic waves in Greece followed the global pattern, e.g., prior to the current Omicron wave, the predominant variants were Delta and Alpha. Taking into consideration the study period, which was March, 2020 to December, 2021, the vast majority of the cases in the present study were attributed to the Alpha and Delta variants.
The collection of anonymized data for the study was approved by the ‘Evangelismos’ Hospital Ethics Committee (Protocol No. 116/2021). Informed was obtained from all included patients.
Statistical analysis
All quantitative data are reported as the mean ± SD. The diagrams in all figures represent the mean ± SEM values. Qualitative variables are reported as number and percentage. Comparisons between the two ICU outcome groups (survivors and non-survivors) of quantitative variables were performed by using the independent samples unpaired Student's t-test. Differences between groups of patients of qualitative variables were assessed using the Chi-squared or Fisher's exact tests when appropriate. Repeated measurements were performed by using two-way repeated measures ANOVA. Whenever sphericity assumption was violated, the Greenhouse-Geisser test of within subjects effects was used. The overall mean value of each variable per outcome group, the overall mean differences of values across time, as well as the group x time interaction were estimated. In both groups, post-hoc pairwise within-subjects comparisons across time, as well as post-hoc between-subjects comparisons at each one of the six time points were performed. All P-values were adjusted for multiple comparisons using the Bonferroni correction. The SPSS statistical program (v.24; Dotmatics) was used for data analysis. A P-value <0.05 was considered to indicate a statistically significant difference.
Results
Patient characteristics
A total of 733 patients (mean age, 65±13 years; 68% males) were included in the present study. The mean APACHE II and SOFA scores upon admission to the ICU were 15±6 and 6±3, respectively. The crude ICU mortality rate was 45%. Of these patients, 561 (76%) were treated with dexamethasone at a dose of 6 mg daily for up to 10 days. The characteristics of the patients the whole cohort upon admission in, as well as according to clinical outcomes, are presented in Table I.
Temporal trends of hematological parameters
The overall mean value of WBC per outcome group did not differ significantly. The overall mean differences in WBC values over time were significant: F(3.8, 858)=6.64, P=0.001. The group x time interaction was also significant: F(3.8, 858)=3.6, P=0.008. Post hoc between-subject comparisons at each one of the six time points revealed a significant difference in the mean WBC between the two outcome groups (survival and non-survival) on day 21 (-3.5x103/µl, P=0.001) (Fig. 1A).
The overall mean value of neutrophils per outcome group differed significantly: F(1.226)=5.9, P=0.016. The overall mean differences in neutrophils values across time were significant: F (3.7, 852)=3.86, P=0.005. The group x time interaction was also significant: F(3.7, 873)=3.2, P=0.014. Post-hoc between-subject comparisons at each one of the six time points revealed a significant difference in the mean number of neutrophils between the two outcome groups (survival and non-survival) on day 21 (-3,6x103/µl, P=0.001) (Fig. 1B).
The overall mean value of lymphocytes per outcome group did not differ significantly. The overall mean differences in lymphocyte values over time were significant: F(1.7, 389)=12.3, P=0.001. However, the group x time interaction was significant: F(1.7, 389)=3.6, P=0.03. Post-hoc between-subject comparisons at each one of the six time points revealed a significant difference in the mean lymphocyte values between the two outcome groups (survival and non-survival) on day 10 (0.19, P=0.01) and on day 15 (-0.18x103/µl, P=0.01) (Fig. 1C).
The overall mean value of NLR per outcome group differed significantly: F(1,226)=15, P=0.001. The overall mean differences in NLR values over time were significant: F(4.2, 952)=21.8, P=0.001. However, the group x time interaction did not differ significantly. Both groups exhibited distinct trends in the NLR over time, with a significant increase observed in non-survivors compared to survivors. Post hoc between-subject comparisons at each one of the six time points revealed significant differences in the mean NLR between the two outcome groups (survival and non-survival) on day 3 (-3.3, P=0.05), day 5 (-4.2, P=0.004), day 10 (-4.9, P=0.001), day 15 (-3.1, P=0.05) and day 21 (-4.5, P=0.001) (Fig. 1D).
The overall mean value of PLT per outcome group differed significantly F(1.228)=11.6, P=0.001). The overall mean differences in PLT values over time were significant: F(3.4, 779)=4.12, P=0.004. The group x time interaction was also significant: F(3.5, 779)=4.7, P=0.001. Both groups exhibited distinct trends in PLT levels over time, with a significant decrease in non-survivors compared to survivors. Post-hoc between-subject comparisons at each one of the six time points revealed significant mean PLT differences between the two outcome groups (survival and non-survival) on day 5 (31x103/µl, P=0.02), day 10 (42x103/µl, P=0.004), day 15 (35x103/µl, P=0.01) and day 21 (77x103/µl, P=0.001) (Fig. 1E).
Temporal trends of biochemical parameters
The overall mean value of fibrinogen per outcome group did not differ significantly. The overall mean differences in fibrinogen values over time was significant: F(3.9, 879)=218, P<0.001. The group x time interaction was also significant: F(3.9, 879)=4.9, P=0.001. Both groups exhibited parallel trends in fibrinogen levels over time, apart from the values on day 21. Post-hoc between-subject comparisons at each one of the six time points revealed significant mean fibrinogen difference between the two outcome groups (survival and non-survival) on day 21 (98 mg/dl, P=0.001) (Fig. 2A).
The overall mean value of LDH per outcome group differed significantly: F(1,227)=5.3, P=0.02. The overall mean differences in LDH values across time were significant: F(1.4, 323)=6.9, P=0.004. The group x time interaction was also significant: F(1.4, 323)=5.4, P=0.01. Both groups exhibited parallel trends in LDH levels across time, apart from the values on day 21. Post hoc between-subject comparisons at each one of the six time points revealed significant difference in the mean LDH values between the two outcome groups (survival and non-survival) on day 21 (-364 IU/l, P=0.008) (Fig. 2B).
The overall mean value of CRP per outcome group differed significantly: F(1,228)=18.9, P=0.001. The overall mean differences in CRP values over time were significant: F(4,936)=10.9, P=0.001. The group x time interaction was also significant: F(4,936)=7.9, P=0.001. Both groups exhibited distinct trends in CRP levels over time, with a significant increase in non-survivors compared to survivors. Post hoc between-subject comparisons at each one of the six time points revealed significant mean CRP differences between the two outcome groups (survival and non-survival) on day 10 (-2.9 mg/dl, P=0.03), day 15 (-4 mg/dl, P=0.002) and day 21 (-9 mg/dl, P=0.001) (Fig. 2C).
The overall mean value of albumin per outcome group differed significantly: F(1,272=20, P=0.001. The overall mean differences in albumin values across time were significant: F(1.2,287)=28.8, P=0.001. The group x time interaction was not significant. Both groups exhibited parallel, declining, though distinct trends in albumin levels over time, with a significant decrease in non-survivors compared to survivors. Post-hoc between-subject comparisons at each one of the six time points revealed significant mean albumin differences between the two outcome groups (survival and non-survival) on day 5 (0.12 g/dl, P=0.02), day 10 (0.13 g/dl, P=0.008), day 15 (0.23 g/dl, P=0.001) and day 21 (0.44 g/dl, P=0.001) (Fig. 2D).
Sensitivity analysis. Effects of dexamethasone on temporal trends of laboratory parameters in the whole population
To determine the effects dexamethasone on the laboratory data, two-way repeated measures analyses for the same parameters that were analyzed above were performed. The patients were categorized into two treatment groups, according to the administration of dexamethasone or not. Only the overall mean values of NLR, fibrinogen and CRP per treatment group differed significantly: [F(1,237)=6.81, P=0.01], [F(1,234)=7.38, P=0.006] and [F(1, 240)=7.37, P=0.007], respectively. More specifically, both fibrinogen and CRP exhibited declining trends over time in the dexamethasone group as compared to the group of patients who did not receive dexamethasone. On the contrary, NLR exhibited an increasing trend (Fig. 3).
Effects of dexamethasone on the temporal trends of laboratory parameters in non-survivors. Only the overall mean value of NLR per treatment group differed significantly [F(1, 104)=6.17, P=0.015], exhibiting an increasing trend over time in the dexamethasone group as compared to the group of patients who did not receive dexamethasone (Fig. 4A). No significant difference was observed in the fibrinogen and CRP levels between the two treatment groups. However, CRP x dexamethasone interaction was significant [F(4,429)=3.75, P=0.005]. More specifically, CRP exhibited an initially decreasing trend during the first 10 days in the dexamethasone group and thereafter, an increasing trajectory (Fig. 4B).
Effects of dexamethasone on temporal trends of laboratory parameters in survivors. Only the overall mean values of fibrinogen and CRP per treatment group differed significantly: [F(1,120)=9.32, P=0.003] and [F(1,121)=7.73, P=0.006)], respectively. They both exhibited a decreasing trajectory over time in the dexamethasone group as compared to the group of patients who did not receive dexamethasone (Fig. 4C and D). There was no significant difference in NLR between the two treatment groups.
Discussion
The present study describes the temporal progression of routine laboratory markers, characteristic of COVID-19, in a cohort of 733 patients critically ill with COVID-19. The main findings were as follows: i) Persistently abnormal laboratory values in both survivors and non-survivors; ii) significant differences between survivors and non-survivors concerning the dynamic changes of NLR, CRP, PLT and albumin, but not of WBC and neutrophil count, fibrinogen and LDH over time; iii) significant effects of dexamethasone treatment on the temporal progression of fibrinogen and CRP values in survivors and that of NLR in non-survivors.
Similar to previous studies (28,32,33), WBC and neutrophil counts upon admission to the ICU, as well as within the first 15 days post-admission were steadily elevated, indicating persistent inflammatory activation; however, they did not differ significantly between survivors and non-survivors. Therefore, though a notable decrease in both counts was observed in survivors on day 21 after admission, the temporal changes of these variables cannot be used in prognostication. By contrast, previously (17), WBC and neutrophil counts were elevated over time only in non-survivors; however, all hospitalized, and not exclusively patients with COVID-19 in the ICU, were included in that study.
Both survivors and non-survivors had profound lymphopenia upon admission, probably reflecting the severity of COVID-19 in this critically ill population. Notably, during the stay in the ICU, both groups had persistent lymphopenia, with a nadir on day 3. Despite the gradual recovery observed thereafter, consistent with that previously reported (26), the mean lymphocyte count did not reach normal values within the 3 weeks of observation (remaining <1,500/µl). Although significantly different mean lymphocyte counts between survivors and non-survivors were observed only at two time points (days 10 and 15), lower values in non-survivors were steadily observed.
Even more impressive than the evolution of lymphocyte values was the association between the temporal trends of NLR values and survival. Highly elevated upon admission to the ICU, NLR values did not differ significantly between survivors and not survivors, in contrast to those previously reported by Zanella et al (23) and Υe et al (34). Subsequently, the slopes of NLR were distinctly different between survivors and non-survivors, with steadily higher values in non-survivors at each time point. It should be noted that in survivors, although the mean NLR values were gradually decreasing, apart from day 21, they remained elevated, namely >10, indicating, along with the non-resolving lymphopenia, a persistent inflammation. These temporal changes in NLR are consistent with those of previous studies (27,28,35), confirming the superior role of NLR, as compared to the WBC count, concerning risk stratification in the clinical context of COVID-19.
Whereas the PLT count upon admission did not differ between the two outcome groups, contrary to that reported elsewhere (23,28), their temporal trend exhibited significant differences between the two groups, with ICU survivors presenting consistently higher counts. This is in accordance with previous findings (23,28,36). It should be noted that PLT counts in the patients in the present study were maintained mostly within or higher than the normal range, indicating a hypercoagulation state that persisted over time. This finding is in accordance with that by Wendel Garcia et al (24), although they are in contrast to findings reported elsewhere showing thrombocytopenia in patients critically ill with COVID-19 (27,31). The lower counts of PLT in non-survivors relative to survivors may be attributed to their more severe inflammatory status, leading to increased PLT destruction, either mechanically through disseminated intravascular coagulation, or immunologically.
Notable differences in temporal changes were also found for CRP values. The high CRP values upon admission to the ICU gradually decreased over the first 5 days in all patients. Such a decrease has not been described in studies conducted during the first wave of the pandemic (36). This could be explained by the dexamethasone treatment which has been recommended for severe COVID-19 after the first wave (29). Accordingly, Zacharias et al (37) recently evaluated the effects of dexamethasone on the trajectory of CRP values among critically ill patients with COVID-19, demonstrating, similarly to the findings of the present study, a significant reduction in CRP in the first 3 days of treatment. Of note, in the present study, following an initial decrease, a second increase in CRP values was observed on days 10 and 15. A plausible explanation for this increase may be the onset of nosocomial infections, usually complicating the clinical course (38,39). Indeed, in patients critically ill with COVID-19, ICU-acquired bacteremia developed after a median time of 11 days after ICU admission, as shown in a previous study from the ICU (40). Both survivors and non-survivors exhibited similar trends up to and including day 10. After that day, CRP values in non-survivors continued to increase, signaling a more intense inflammatory response, whereas a tendency towards normalization was observed in survivors.
A downward trend in LDH in both survivors and non-survivors was observed in the present study. Although overall significantly different over time, LDH values did not differ between survivors and non-survivors, except for day 21. By contrast, significant differences in temporal changes of LDH between survivors and non-survivors were reported by Xie et al (22) and by Wendel Garcia et al (24), as well as by two additional studies (23,41), with LDH levels remaining elevated in patients with unfavorable outcomes. Of note, the sharp increase of LDH on day 21 in non-survivors in the present study is in accordance with a similar increase of LDH on the day of or a day prior to death, in the study by Chen et al (28). This, combined with the sharp decrease in fibrinogen occurring on the same day, could possibly indicate widespread tissue damage due to ischemia, in the context of disseminated intravascular coagulation secondary to late bacterial sepsis.
Finally, albumin kinetics exhibited a rapid decrease following ICU admission, regardless of outcomes. Although initially not different, serum albumin levels were persistently lower in non-survivors compared to survivors. These findings are consistent with those of previous studies (23,28,36). Notably, following an initial deterioration phase, the albumin levels improved after the 15th day in survivors, highlighting a recovery phase. Notably, such a recovery time point occurred earlier, at ~7 days after ICU admission, in the study by Su et al (42). Thus far, additional data reporting albumin kinetics in the context of COVID-19 are lacking.
Information regarding the effects of dexamethasone on the temporal progression of laboratory values in patients with COVID-19 is limited (37). Since dexamethasone is known to suppress the pro-inflammatory response, the finding that, among survivors, both fibrinogen and CRP exhibited decreasing values over time in patients treated with dexamethasone as compared to those who did not, appears plausible. On the other hand, the finding of an increasing NLR in non-survivors who received dexamethasone, compared to those who did not, could reflect their enhanced vulnerability caused be dexamethasone to secondary bacterial infections and the subsequent neutrophilia.
The present study had certain limitations, which should be mentioned. First, some laboratory tests characteristic of COVID-19, such as ferritin, D-dimer and various cytokines, were not included in the present study as the selection of parameters was based on their routine daily measurement; thus, the full laboratory spectrum of COVID-19 was not analyzed. Second, age-related differences in laboratory features, which may have played a role (43) in the interpretation of the results, were not considered. Third, as in almost all relevant studies, comparisons with non-COVID-19 ICU patients were not included. Therefore, it remains unclear whether laboratory trends in COVID-19 differ from those in other causes of infection. Nevertheless, the present large cohort study, based on prospectively collected data, reveals the importance of longitudinal data compared to a sole measurement on admission and provides further information regarding the temporal trends in laboratory parameters according to clinical outcomes, as well as the effects of dexamethasone treatment on the laboratory values, useful for prognostic models and risk stratification in this critically ill subpopulation.
In conclusion, the present study demonstrates the association between the temporal progression of routine laboratory variables and clinical outcomes in patients admitted to the ICU due to COVID-19. There were significant differences between survivors and non-survivors concerning the dynamic changes over time of NLR, CRP, PLT and albumin, but not of WBC and neutrophil count, fibrinogen or LDH. The aforementioned differences may represent distinct sub-phenotypes or endotypes of COVID-19, the further elucidation of which may potentially have a profound effect on treatment. Finally, dexamethasone treatment significantly affected the temporal progression of fibrinogen and CRP values in survivors and that of NLR and CRP in non-survivors.
Acknowledgements
Not applicable.
Funding
Funding: No funding was received.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
AKa, SKo and CR conceptualized the study. AKa, SKa, CR, VEG, AG, DAS, DK, SKo, PK, TN, CG, MAK, GD, ET, SO and AKo made a substantial contribution to data collection, interpretation and analysis, and wrote and prepared the draft of the manuscript. SKo and AKa analyzed the data and provided critical revisions. SK, AKo and CR confirm the authenticity of all the data. All authors contributed to manuscript revision, and have read and approved the final version of the manuscript.
Ethics approval and consent to participate
The present study was conducted in line with the Declaration of Helsinki and obtained approval from the ‘Evangelismos’ Hospital Ethics Committee (protocol No. 116/2021). Informed was obtained from all included patients.
Patient consent for publication
Not applicable.
Competing interests
DAS is the Editor-in-Chief for the journal, but had no personal involvement in the reviewing process, or any influence in terms of adjudicating on the final decision, for this article. The other authors declare that they have no competing interests.
References
|
World Health Organization (WHO): Coronavirus disease (COVID2019) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed October 22, 2020. | |
|
Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, et al: Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet. 395:1054–1062. 2020.PubMed/NCBI View Article : Google Scholar | |
|
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. 180:1345–1355. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Li C, He Q, Qian H and Liu J: Overview of the pathogenesis of COVID-19 (Review). Exp Ther Med. 22(1011)2021.PubMed/NCBI View Article : Google Scholar | |
|
Feng Z, Chen Y, Wu Y, Wang J, Zhang H and Zhang W: Kidney involvement in coronavirus-associated diseases (Review). Exp Ther Med. 21(361)2021.PubMed/NCBI View Article : Google Scholar | |
|
Fukui S, Ikeda K, Kobayashi M, Nishida K, Yamada K, Horie S, Shimada Y, Miki H, Goto H, Hayashi K, et al: Predictive prognostic biomarkers in patients with COVID-19 infection. Mol Med Rep. 27(15)2023.PubMed/NCBI View Article : Google Scholar | |
|
Daher J: Endothelial dysfunction and COVID-19 (Review). Biomed Rep. 15(102)2021.PubMed/NCBI View Article : Google Scholar | |
|
Lippi G and Plebani M: Laboratory abnormalities in patients with COVID-2019 infection. Clin Chem Lab Med. 58:1131–1134. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Ye J, Jiao Y, Zhang Y, Li Z, Zeng X, Deng H and Yang M: Hematological changes in patients with COVID-19 (Review). Mol Med Rep. 22:4485–4491. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Henry BM, de Oliveira MHS, Benoit S, Plebani M and 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. 58:1021–1028. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Skevaki C, Fragkou PC, Cheng C, Xie M and Renz H: Laboratory characteristics of patients infected with the novel SARS-CoV-2 virus. J Infect. 81:205–212. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Huang Y, Zhang Y and Ma L: Meta-analysis of laboratory results in patients with severe coronavirus disease 2019. Exp Ther Med. 21(449)2021.PubMed/NCBI View Article : Google Scholar | |
|
Bohn MK, Lippi G, Horvath A, Sethi S, Koch D, Ferrari M, Wang CB, Mancini N, Steele S and Adeli K: Molecular, serological, and biochemical diagnosis and monitoring of COVID-19: IFCC taskforce evaluation of the latest evidence. Clin Chem Lab Med. 58:1037–1052. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Georgakopoulou VE, Garmpis N, Damaskos C, Valsami S, Dimitroulis D, Diamantis E, Farmaki P, Papageorgiou CV, Makrodimitri S, Gravvanis N, et al: The impact of peripheral eosinophil counts and eosinophil to lymphocyte ratio (ELR) in the clinical course of COVID-19 patients: A retrospective study. In Vivo. 35:641–648. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Georgakopoulou VE, Makrodimitri S, Triantafyllou M, Samara S, Voutsinas PM, Anastasopoulou A, Papageorgiou CV, Spandidos DA, Gkoufa A, Papalexis P, et al: Immature granulocytes: Innovative biomarker for SARS-CoV-2 infection. Mol Med Rep. 26(217)2022.PubMed/NCBI View Article : Google Scholar | |
|
Bali T, Georgakopoulou VE, Kamiliou A, Vergos I, Adamantou M, Vlachos S, Ermidis G, Sipsas NV, Samarkos M and Cholongitas E: Abnormal liver function tests and coronavirus disease 2019: A close relationship. J Viral Hepat. 30:79–80. 2023.PubMed/NCBI View Article : Google Scholar | |
|
Georgakopoulou VE, Bali T, Adamantou M, Asimakopoulou S, Makrodimitri S, Samara S, Triantafyllou M, Voutsinas PM, Eliadi I, Karamanakos G, et al: Acute hepatitis and liver injury in hospitalized patients with COVID-19 infection. Exp Ther Med. 24(691)2022.PubMed/NCBI View Article : Google Scholar | |
|
Georgakopoulou VE, Lembessis P, Skarlis C, Gkoufa A, Sipsas NV and Mavragani CP: Hematological abnormalities in COVID-19 disease: Association with type I interferon pathway activation and disease outcomes. Front Med (Lausanne). 9(850472)2022.PubMed/NCBI View Article : Google Scholar | |
|
Cholongitas E, Bali T, Georgakopoulou VE, Giannakodimos A, Gyftopoulos A, Georgilaki V, Gerogiannis D, Basoulis D, Eliadi I, Karamanakos G, et al: Prevalence of abnormal liver biochemistry and its impact on COVID-19 patients' outcomes: A single-center Greek study. Ann Gastroenterol. 35:290–296. 2022.PubMed/NCBI View Article : Google Scholar | |
|
Ouyang SM, Zhu HQ, Xie YN, Zou ZS, Zuo HM, Rao YW, Liu XY, Zhong B and Chen X: Temporal changes in laboratory markers of survivors and non-survivors of adult inpatients with COVID-19. BMC Infect Dis. 20(952)2020.PubMed/NCBI View Article : Google Scholar | |
|
Juneja GK, Castelo M, Yeh CH, Cerroni SE, Hansen BE, Chessum JE, Abraham J, Cani E, Dwivedi DJ, Fraser DD, et al: Biomarkers of coagulation, endothelial function, and fibrinolysis in critically ill patients with COVID-19: A single-center prospective longitudinal study. J Thromb Haemost. 19:1546–1557. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Xie J, Wu W, Li S, Hu Y, Hu M, Li J, Yang Y, Huang T, Zheng K, Wang Y, et al: Clinical characteristics and outcomes of critically ill patients with novel coronavirus infectious disease (COVID-19) in China: A retrospective multicenter study. Intensive Care Med. 46:1863–1872. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Zanella A, Florio G, Antonelli M, Bellani G, Berselli A, Bove T, Cabrini L, Carlesso E, Castelli GP, Cecconi M, et al: Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units. Intensive Care Med. 47:995–1008. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Wendel Garcia PD, Fumeaux T, Guerci P, Heuberger DM, Montomoli J, Roche-Campo F, Schuepbach RA and Hilty MP: RISC-19-ICU Investigators. Prognostic factors associated with mortality risk and disease progression in 639 critically ill patients with COVID-19 in Europe: Initial report of the international RISC-19-ICU prospective observational cohort. EClinicalMedicine. 25(100449)2020.PubMed/NCBI View Article : Google Scholar | |
|
Chen R, Sang L, Jiang M, Yang Z, Jia N, Fu W, Xie J, Guan W, Liang W, Ni Z, et al: Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China. J Allergy Clin Immunol. 146:89–100. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Lim AYH, Goh JL, Chua MCW, Heng BH, Abisheganaden JA and George PP: Temporal changes of haematological and radiological findings of the COVID-19 infection-a review of literature. BMC Pulm Med. 21(37)2021.PubMed/NCBI View Article : Google Scholar | |
|
Kokkoris S, Kanavou A, Kremmydas P, Katsaros D, Karageorgiou S, Gkoufa A, Georgakopoulou VE, Spandidos DA, Giannopoulos C, Kardamitsi M and Routsi C: Temporal evolution of laboratory characteristics in patients critically ill with COVID-19 admitted to the intensive care unit (Review). Med Int (Lond). 3(52)2023.PubMed/NCBI View Article : Google Scholar | |
|
Chen A, Zhao Z, Hou W, Singer AJ, Li H and Duong TQ: Time-to-death longitudinal characterization of clinical variables and longitudinal prediction of mortality in COVID-19 patients: A two-center study. Front Med (Lausanne). 8(661940)2021.PubMed/NCBI View Article : Google Scholar | |
|
RECOVERY Collaborative Group. Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, Staplin N, Brightling C, Ustianowski A, et al: Dexamethasone in hospitalized patients with COVID-19. N Engl J Med. 384:693–704. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Knaus WA, Wagner DP, Draper EA and Zimmerman JE: APACHE II: A severity of disease classification system. Crit Care Med. 13:818–829. 1985.PubMed/NCBI | |
|
Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, Reinhart CK, Suter PM and Thijs LG: The SOFA (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. On behalf of the working group on sepsis-related problems of the European society of intensive care medicine. Intensive Care Med. 22:707–710. 1996.PubMed/NCBI View Article : Google Scholar | |
|
Zheng Y, Sun LJ, Xu M, Pan J, Zhang YT, Fang XL, Fang Q and Caiet HL: Clinical characteristics of 34 COVID-19 patients admitted to intensive care unit in Hangzhou, China. J Zhejiang Univ Sci B. 21:378–387. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z, Xiong Y, et al: Clinical characteristics of 138 hospitalized patients with 2019 novel Coronavirus-infected pneumonia in Wuhan, China. JAMA. 323:1061–1069. 2020.PubMed/NCBI View Article : Google Scholar | |
|
Ye W, Chen G, Li X, Lan X, Ji C, Hou M, Zhang D, Zeng G, Wang Y, Xu C, et al: Dynamic changes of D-dimer and neutrophil-lymphocyte count ratio as prognostic biomarkers in COVID-19. Respir Res. 21(169)2020.PubMed/NCBI View Article : Google Scholar | |
|
Zhou S, Yang Y, Zhang X, Li Z, Liu X, Hu C, Chen C, Wang D and Peng Z: Clinical course of 195 critically ill COVID-19 patients: A retrospective multicenter study. Shock. 54:644–651. 2020.PubMed/NCBI View Article : Google Scholar | |
|
van Oers JAH, Kluiters Y, Bons JAP, de Jongh M, Pouwels S, Ramnarain D, de Lange DW, de Grooth HJ and Girbes ARJ: Endothelium-associated biomarkers mid-regional proadrenomedullin and C-terminal proendothelin-1 have good ability to predict 28-day mortality in critically ill patients with SARS-CoV-2 pneumonia: A prospective cohort study. J Crit Care. 66:173–180. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Zacharias H, Mungara R, Wilson AP, Singer M and Arulkumaran N: The utility of CRP with the use of dexamethasone and Tocilizumab in critically ill patients with COVID-19. J Crit Care. 70(154053)2022.PubMed/NCBI View Article : Google Scholar | |
|
Du Q, Zhang D, Hu W, Li X, Xia Q, Wen T and Jia H: Nosocomial infection of COVID-19: A new challenge for healthcare professionals (Review). Int J Mol Med. 47(31)2021.PubMed/NCBI View Article : Google Scholar | |
|
Susan M, Susan R, Lazar V, Bagiu IC, Mihu AG, Bagiu RV, Ionescu A, Iana AN, Dehelean CA, Lighezan D and Marti DT: COVID-19 association with multidrug-resistant bacteria superinfections: Lessons for future challenges. Exp Ther Med. 25(254)2023.PubMed/NCBI View Article : Google Scholar | |
|
Kokkoris S, Papachatzakis I, Gavrielatou E, Ntaidou T, Ischaki E, Malachias S, Vrettou C, Nichlos C, Kanavou A, Zervakis D, et al: ICU-acquired bloodstream infections in critically ill patients with COVID-19. J Hosp Infect. 107:95–97. 2021.PubMed/NCBI View Article : Google Scholar | |
|
Montrucchio G, Sales G, Rumbolo F, Palmesino F, Fanelli V, Urbino R, Filippini C, Mengozzi G and Brazzi L: Effectiveness of mid-regional pro-adrenomedullin (MR-proADM) as prognostic marker in COVID-19 critically ill patients: An observational prospective study. PLoS One. 16(e0246771)2021.PubMed/NCBI View Article : Google Scholar | |
|
Su C, Hoffman KL, Xu Z, Sanchez E, Siempos II, Harrington JS, Racanelli AC, Plataki M, Wang F and Schenck EJ: Evaluation of albumin kinetics in critically ill patients with coronavirus disease 2019 compared to those with sepsis-induced acute respiratory distress syndrome. Crit Care Explor. 3(e0589)2021.PubMed/NCBI View Article : Google Scholar | |
|
Vakili S, Savardashtaki A, Jamalnia S, Tabrizi R, Nematollahi MH, Jafarinia M and Akbari H: Laboratory findings of COVID-19 infection are conflicting in different age groups and pregnant women: A literature review. Arch Med Res. 51:603–607. 2020.PubMed/NCBI View Article : Google Scholar |
