INTRODUCTION
Coronavirus disease-2019 (COVID-19) is a highly contagious viral infection (1). Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demonstrates a somewhat lower mutational rate than other RNA viruses, approximately 12,800 mutations have been identified (2). The well-known variants are alpha B.1.1.7 (known as 20I/501Y.V1, VOC 202012/01), beta B.1.351 (known as 501Y.V2), and gamma P.1 (known as alpha, delta, and omicron) are the main determining responsible variants for COVID-19 infection in Türkiye World Health Organization (3). The last VOC of the SARS-CoV-2 virus is the omicron (4). Alpha, delta, and omicron are the main determining variants responsible for COVID-19 infection in Türkiye (5). As mentioned in a study by Loucera et al. (6), combining genomic data with patients’ clinical data will help us better understand the effect of mutations on the outcome of this challenging infection. To the best of our knowledge (at least in Türkiye), there are no studies assessing patients’ early admission clinical, laboratory, and radiological characteristics according to the variants of SARS-CoV-2 viruses. In this retrospective study, we attempted to study these issues in our hospital’s non-critical alpha, delta, and omicron variants infected by COVID-19 in-patients.
METHODS
This retrospective study was approved by University of Health Sciences Türkiye, Bakırköy Dr. Sadi Konuk Training and Research Hospital’s Clinical Research Ethics Committee (decision no: 2022-12-18, date: 20.06.2022). Data of the above-mentioned hospital’s medical pandemic services for COVID-19 patients were collected. According to the dates of predominance of alpha (01 April-30 June 2021), delta (01 August-30 November 2021), and omicron (01 January-30 April 2022) variants, COVID-19 patients were divided into group 1 (alpha), group 2 (delta), and group 3 (omicron), respectively.
Inclusion criteria;
1. Age >18 years old,
2. Positivity of the COVID-19 real-time reverse transcriptase polymerase chain reaction test at admission,
3.Presence of first-day admission laboratory records.
Exclusion criteria;
1.Those who were discharged at their request before completing their treatment and follow-up,
2. Taking medications that could affect routine laboratory measures (such as steroids, chemotherapy, radiotherapy, etc.) (within one month of the diagnosis of COVID-19 infection).
Behind demographic, clinical characteristics, and the outcome of the patients, their early admission laboratory and radiology investigations were recorded. In addition, comorbidities [such as hypertension (HT), diabetes mellitus (DM), ischemic heart disease, etc.] were recorded. Chronic kidney disease (CKD) stage ≥2 was also included in the analysis (7).
Chest computed tomography (CT) scoring system;
The semiquantitative CT severity scoring system was used (8). The scoring system was as follows: 0= no involvement, 1= less than 5% involvement, 2=5-25% involvement, 3=26-50% involvement, 4=51-75% involvement, and 5 more than 75% involvement. The sum of these yields a total score ranging from 0 to 25 points. A score of 0-8 is accepted as mild, 9-16 as moderate, and ≥17 as severe lung involvement.
Systemic immune-inflammation index;
This blood parameter was calculated using the formula: neutrophil × platelet (PLT)/lymphocyte (9).
Statistical Analysis
Statistical analyses were performed using the SPSS 22.0 statistical package for Windows. Our study parameters data showed a non-normal distribution. Therefore, the description of data was expressed by median and interquartile range. For categorical measures, ratios and/or percentages were used. For the comparison of the 2 groups, the Mann-Whitney U test was used. Otherwise, the Kruskal-Wallis test was used for the comparison of ≥3 groups parameters. The Games Howell test was used as a post-hoc test of the Kruskal-Wallis test. The effect size (ES) was determined using Eta square (η2) or epsilon square (ϵ2) tests, as appropriate. The values of these tests range between 0 (no association) and 1 (complete association) (1). A comparison of frequencies was performed by the chi-square test. For the degree of association, a Cramer’s V value was determined (between 0.0-1.0). A Cramer’s V value close to 0.00 indicates no association. A value >0.15 indicates a strong association, and >0.25 indicates a strong association (10). Spearman tests were also used to evaluate the correlation between quantitative variables. Regression analysis was performed by putting the presence or absence of the nominal. Also by putting laboratory parameters (median value) into 2 different logistic regression models (Model: Forward LR) (adjusting od ratio at 95% confidence interval). A p-value <0.05 was accepted as significant for all others.
Informed consent was obtained from each subject before the study. We are committed to protecting patient privacy and complying with the Declaration of Helsinki.
RESULTS
The final analysis was performed with 2,090 patients. The female/male ratio and median (minimum-maximum) age of them were 938 (44.90%)/1152 (55.10%), and 63.00 (18.00-97.00) years old, respectively. The numbers of alpha, delta, and omicron variants were 701, 699, and 690, respectively. A comparison of the study parameters between alpha (group 1), delta (group 2), and omicron (group 3) mutant patients is shown in Table 1. As seen in this table, the median age of group 3 patients was significantly higher than that of the other 2 groups (p<0.05, both, and ES =0.53). On the other hand, the female/male ratio and presence of DM in group 1 patients were significantly lower than those in groups 2 and 3 (p<0.05, all, and ES was 0.10, and 0.36, respectively). In addition, group 2 patients had a significantly lower rate of HT and cardiovascular disease (CVD) than the other 2 groups (p<0.05, all, and ES was 0.10, and 0.09, respectively). The CKD rate of group patients was higher than that of the other two groups (p<0.05, and ES =0.11). Although the rate of patients with no comorbidities was lowest in group 1, the rate of patients with 1, 2, and ≥3 comorbidities was significantly lower in group 2 (p<0.05, all, and ES =0.35). Regarding the hospital stay period and outcome, group 1 patients had the longest hospital stay and highest mortality rate than the other two groups (p<0.05, both, and ES was 0.81, and 0.12, respectively).
A comparison of the study parameters of our study of COVID-19 patients (n=2,090) according to the outcome of survival (n=1,704) or death (n=386) is shown in Table 2. Table 2 presents a comparison of the study parameters for our study of COVID-19 patients. The total number of patients in the study was 2,090, out of which 1,704 survived and 386 unfortunately passed away. Those who died were significantly older than those who survived this infection (p<0.05, ES =1.99). The ratio of the F/M ratio of the dead patients was lower than that of the survived patients (154/232 versus 784/920, respectively, p<0.05 and EF =0.047). Regarding the comorbidities, the presence rates of HT, CKD, and CVD in the dead group were higher than those in the survived group (p<0.05, all, and ES was 0.056, 0.069, and 0.074, respectively). On the other hand, the rate of the presence of DM was higher in the surviving group but not reached a statistical significance (p>0.05). Comparison according to the number of comorbidities showed a non-significant difference between the surviving and dead patient groups (p>0.05). The presence of severe chest CT findings at admission and hospital stay period of the dead patients was higher than the survived patients, while the early admission %SO2 levels showed an opposite pattern (p<0.05, all, and ES was 0.233 and 0383, 0.389, respectively). Regarding the early admission laboratory blood tests measure, the median Hgb level eosinophils, lymphocytes, and PLT counts were significantly higher in the survived, and the median remaining blood test levels were significantly higher in the dead patients’ group (for the details see Table 2). Table 2 provides detailed information about the study parameters in relation to the outcome of survival or death among COVID-19 patients. The results indicate that the presence of severe chest CT findings upon admission and the duration of hospital stay were more frequent in patients who did not survive compared with those who survived (p<0.05). Conversely, the levels of early admission %SO2 (oxygen saturation) showed the opposite trend, being higher in the survival group (p<0.05). The ES for these associations were 0.233 and 0.38. Regarding the early admission laboratory blood tests, the median levels of hemoglobin (Hgb), eosinophils, lymphocytes, and PLT counts were significantly higher in the group of patients who survived, whereas the median levels of the remaining blood tests were significantly higher in the group of patients who died. Further details can be found in Table 2.
The regression analysis of parameters that could affect the outcome is shown in Table 3. The mortality risk is 1.94 times higher in patients with alpha variants. There is a 1.25-fold mortality risk in the delta, but it was not significant (p>0.05); 1.70 times in those with severe chest CT finding, 2.70 times in the presence of CKD, 1.02 times in mortality risk with one unit increase in length of stay, 0.92 times in mortality when income saturation increases by one unit, lactate dehydrogenase (LDH), C-reactive protein (CRP), D-dimer increases by n units mortality risk increases by 1,002, 1,006, 1.04, respectively. Table 3 displays the results of the regression analysis conducted to examine the parameters that could impact the outcome. The findings reveal that individuals with alpha variants of COVID-19 have a 1.94 times higher risk of mortality. Similarly, there was a 1.25-fold mortality risk associated with the delta variant, although this finding did not reach statistical significance (p>0.05). Moreover, the presence of severe chest CT findings was linked to a 1.70-fold higher mortality risk. Patients with CKD face a significantly elevated mortality risk of 2.70 times. Additionally, for every unit increase in the length of hospital stay, there is a 1.02 times higher mortality risk. Conversely, a one-unit increase in oxygen saturation levels leads to a mortality risk of 0.92 times. Furthermore, the mortality risk increased by 1,002, 1,006, and 1.04 times with each unit increase in LDH, CRP, and D-dimer levels, respectively. These results provide important insights into the various factors that can influence mortality outcomes.
DISCUSSION
In our study, the ratio of female/male in Alpha variant-infected COVID-19 inpatients was significantly lower than the ratio of the other two variant-infected patient groups. On the other hand, the median age of the omicron variant group was significantly higher than that of the other two groups (p<0.05, both). Previous studies also showed a higher rate of alpha infections in males than in females (11), but the emergence of new mutant variants and/or vaccines somewhat affected these issues (12). We should mention that the rate of known comorbidities (HT, DM, CKD, and CVD) that could affect the course and outcome of this disease was also different between the study groups. This should also be considered [the presence of severe chest CT findings and mortality rate, and duration of hospital stay were significantly higher in alpha variant group patients (in comparison to the other 2 groups) (p<0.05, all, and ES were 0.32, 0.12, and 0.08, respectively)] (12,13). Regarding the laboratory parameters, although most of them were significantly different between the groups, their ES was not significantly different (Table 1).
There was a significant difference in the study parameters of patients who survived or died from COVID-19 infection. Behind the statistical significance, most of these showed a somewhat high ES (Table 2). Regression analysis of all parameters that may affect the outcome of patients. As shown in Table 3, the presence of the Alpha variant infection was one of the important determinants of mortality. This variant increased the risk of mortality by 1.25 times. Previous studies also showed a high risk of hospitalization and death in patients with alpha variant COVID-19 infections. Significant differences were observed in the study parameters between COVID-19 patients who survived and those who succumbed to the infection. These differences were not only statistically significant but also demonstrated relatively high ES, as indicated in Table 2. To further explore the factors influencing patient outcomes, a regression analysis was conducted, considering all potential parameters. The results presented in Table 3 highlight the significance of alpha variant infection as a crucial determinant of mortality. Patients infected with the alpha variant faced a 1.25-fold higher risk of mortality. This finding aligns with previous studies that have also reported a heightened risk of hospitalization and death associated with the alpha variant of COVID-19. In a commentary by Cevik and Mishra (14). The severity of this variant-related COVID-19 infection is increased with ages more than 30 years. Additionally, this severity of infection is more pronounced in patients older than 65 years. In our patient data set, age was also a predictor of outcome. The median age of those patients who died was significantly higher than that of those who survived this infection in our study patients (70 versus 64 years old, p<0.05) (Table 2). This finding is also consistent with other published studies (14,15). Lung involvement is a predictor of the severity and outcome of this viral disease (16). Our study findings also showed increased mortality with increased severity of lung involvement as detected by chest CT (Table 2 and 3) (8). Previous studies from Türkiye and other countries have shown a poor outcome of COVID-19 in CKD patients (15,17,18). Our study results also support these findings. The presence of CKD in our study patients (regardless of the type of COVID-19 variant) increased the mortality risk by 1,719 times (Table 3). Although other predictors of mortality were determined in our study, the determination of the effect of CKD on the mortality of COVID-19 is of paramount importance that could help in planning the management and/or in planning similar studies in this field.
One of the important limitations of this study is that it was retrospective. Therefore, we could not assess the effect of the type of therapy on the outcome. The management of the disease was performed according to the Turkish Ministry of Health’s guidelines applicable at the related periods and/or peaks of COVID-19 infection. The other limiting factor is not including intensive care unit (ICU) patients in this study. To decrease bias and incorrect data, we used data from our non-ICU pandemic services. This study has a notable limitation as it is retrospective in nature, which means that we were unable to evaluate the impact of different therapies on patient outcomes. The management of the disease followed the guidelines provided by the Turkish Ministry of Health during the relevant periods and peaks of COVID-19 infection. Another limitation is that the study did not include patients from the ICU. To mitigate potential biases and ensure accurate data, we relied on data obtained from non-ICU pandemic services.
CONCLUSION
Our study results determined unique useful early admission predictors of COVID-19 infection that could be used in different stages and variants of SARS-CoV-2 viral infection. These findings could be a pathfinder for clinicians and/or researchers dealing with this challenging contagious viral disease. The findings of our study have identified valuable predictors for early admission in COVID-19 infection, which can be applied across various stages and variants of SARS-CoV-2 viral infection. These results provide valuable guidance for clinicians and researchers involved in the management of this complex and highly contagious viral disease. They serve as a valuable resource for navigating the challenges posed by COVID-19.
ETHICS
Ethics Committee Approval: This retrospective study was approved by University of Health Sciences Türkiye, Bakırköy Dr. Sadi Konuk Training and Research Hospital’s Clinical Research Ethics Committee (decision no: 2022-12-18, date: 20.06.2022).
Informed Consent: Informed consent was obtained from each subject before the study.
Authorship Contributions
Surgical and Medical Practices: D.Y., F.A., İ.Ö., M.H., Concept: D.Y., F.A., B.E., M.H., Design: D.Y., F.A., B.E., M.H., Data Collection or Processing: F.K., İ.Ö., Analysis or Interpretation: F.A., B.E., F.K., M.H., Literature Search: F.A., E.Ş., F.K., İ.Ö., Y.E.Ö., H.G., M.H., Writing: F.A., E.Ş., Y.E.Ö., H.G., M.H.
Conflict of Interest: No conflict of interest was declared by the authors.
Financial Disclosure: The authors declare that this study received no financial support.