Research Information System
IV. Ulusal Dahiliye KOngresi, Antalya, Turkey, 22 - 25 May 2025, pp.122-126, (Summary Text)
Introduction
COVID-19 presents with a wide spectrum of clinical severity, ranging from asymptomatic cases to severe disease requiring intensive care unit (ICU) admission. Approximately 20% of hospitalised COVID-19 patients require ICU care, with severe cases often associated with acute respiratory distress syndrome (ARDS), multi-organ failure and increased mortality [1]. Identification of clinical and laboratory predictors of ICU admission is essential to optimise patient management and resource allocation [2].
Clinical characteristics such as advanced age, male sex and the presence of comorbidities including hypertension, diabetes, cardiovascular disease, chronic respiratory disease and obesity have been consistently reported as significant risk factors for ICU admission [3,4]. In addition, presenting symptoms such as dyspnea and hypoxaemia at the time of admission have been identified as strong indicators of severe COVID-19 [5,6].
Laboratory findings play a crucial role in predicting the need for intensive care. Elevated levels of inflammatory markers (e.g. C-reactive protein, procalcitonin, interleukin-6), coagulation markers (e.g. D-dimer) and markers of tissue damage (e.g. lactate dehydrogenase, liver enzymes) have been widely associated with increased severity and ICU admission [7,8]. Lymphopenia and high neutrophil-to-lymphocyte ratios are also common in critically ill patients [9].
Although many studies have shown consistent results, there are some discrepancies. For example, a cohort study in Trinidad found no significant difference in age between ICU and non-ICU groups, in contrast to the majority of global studies that emphasise older age as a major risk factor [10]. In addition, ethnic and geographical variations have been noted, with certain racial and ethnic groups having higher rates of ICU admission, possibly reflecting differences in socioeconomic factors, comorbidities and access to healthcare [11].
In light of the various variations and the global impact of the pandemic, our center has initiated a study to evaluate clinical and laboratory predictors of ICU admission, focusing on potential clinical and laboratory differences in outcomes. This research is intended to contribute valuable data to guide clinical decision-making and improve patient care strategies in our region.
Materials and Methods
Study Design and Patient Selection
This retrospective observational study was conducted by reviewing the medical records of 613 patients diagnosed with COVID-19 and treated in internal medicine and intensive care units. Exclusion criteria included: (1) Patients under 18 years of age, (2) Patients receiving outpatient treatment, (3) Patients with a hospital stay of less than 7 days, (4) Patients without a confirmed COVID-19 diagnosis by appropriate testing methods, and (5) Patients with insufficient medical records. Based on the inclusion criteria, 404 patients were included in the final analysis (Figure 1).
The study included patients who met the following criteria: (1) Accessible medical records through the automated system, (2) Age over 18 years, (3) Follow-up in the internal ICU and internal medicine wards with a COVID-19 diagnosis according to the Turkish COVID-19 Adult Patient Treatment Guide of the Ministry of Health [12].
Data Collection
The demographic, clinical, and laboratory data of the patients were systematically collected using the electronic hospital information system. The data points included age, gender, and comorbidities such as hypertension (HT), diabetes mellitus (DM), chronic kidney disease (CKD), coronary artery disease (CAD), malignancy, and chronic obstructive pulmonary disease (COPD)/asthma. Laboratory parameters included creatinine, uric acid, AST, ALT, CRP, D-dimer, white blood cell count, and neutrophils count. Patients were categorized into two groups based on ICU admission status, and the collected data were compared between the two groups .
ICU Admission Criteria
ICU admission was determined according to predefined clinical and laboratory criteria. Patients who met ICU criteria during their clinical course were transferred from medical wards. ICU admission criteria included severe respiratory failure (oxygen saturation [SpO₂] <90% on high-flow oxygen therapy or respiratory rate >30 breaths per minute), haemodynamic instability (systolic blood pressure <90 mmHg or need for vasopressors), altered mental status (Glasgow Coma Scale <13), multi-organ failure (e.g, creatinine, AST/ALT or lactate >2 mmol/L), rapidly progressive radiological findings and persistently high inflammatory markers despite optimal treatment.
Statistical Analysis
All statistical analyses were conducted using IBM SPSS version 30 (IBM®, Chicago, USA). The normality of continuous variables was assessed using the Shapiro-Wilk test and histogram visualization. Continuous variables with a normal distribution were represented as mean ± standard deviation (SD), whereas non-normally distributed data were presented as median (minimum–maximum). The Mann-Whitney U test was employed to compare non-parametric continuous variables between the ICU and non-ICU groups, while the chi-squared test was utilized for categorical variables. Logistic regression analysis was performed to identify independent predictors of ICU admission. Variables deemed significant in the univariate analysis, including age, white blood cell count, D-dimer, neutrophil count, and severity of illness, were incorporated into the regression model. The diagnostic performance of these variables was further evaluated using receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC), sensitivity, specificity, and optimal cut-off values were calculated to assess the predictive accuracy of each variable. The threshold for statistical significance was established at p<0.05 for all analyses.
Results
A total of 404 patients were included in the study. The median age of the patients was 65.9 ± 15.8 years, with 186 (46%) being female. The most common comorbidities were hypertension (57.8%), type 2 diabetes mellitus (40.6%), and chronic kidney disease (24.3%). Other recorded comorbidities included coronary artery disease (16.6%), chronic heart failure (7.4%), chronic obstructive pulmonary disease/asthma (17.3%), and malignancy (11.9%). The median hospital stay was 16.3 ± 8.2 days. Laboratory parameters showed a median creatinine level of 1.0 mg/dL (0.2-9.8 mg/dL), uric acid 6.2 mg/dL (0.9-30.9 mg/dL), AST 29 U/L (10-3021 U/L), ALT 21 U/L (5-366 U/L), white blood cell count 7610 x 10⁹/L (130-34310 x 10⁹/L), neutrophil count 5960 x 10⁹/L (100-32190 x 10⁹/L), lymphocyte count 950 x 10⁹/L (20-9300 x 10⁹/L), CRP 73 mg/dL (1-392 mg/dL), D-dimer 1029 ng/mL (169-9873 ng/mL), and ferritin 199 ng/mL (6-1500 ng/mL) (Table 1).
Table 2 presents a comparison of clinical and laboratory parameters between ICU and non-ICU groups, revealing statistically significant differences in several variables. ICU patients were older (69.8 ± 13.3 vs. 62.6 ± 15.8 years, p<0.001) and had longer hospital stays (18.8 ± 8.9 vs. 14.2 ± 7.0 days, p<0.001). Additionally, mortality rates were significantly higher in the ICU group (46.8% vs. 16.5%, p<0.001).
Among comorbidities, CKD and HT were more prevalent in ICU patients (p=0.039 and p<0.001, respectively). Other comorbidities, including CAD, CHF, type 2 DM, COPD/asthma, and malignancy, did not show statistically significant differences between groups (p>0.05).
Table 3 presents the logistic regression analysis that identifies uric acid (OR: 1.164, 95% CI: 1.013–1.336, p=0.032), neutrophil count (OR: 1.000, 95% CI: 1.000–1.000, p=0.003), and CRP (OR: 1.006, 95% CI: 1.001–1.011, p=0.027) as independent predictors of ICU admission (Hosmer-Lemeshow p=0.430). The Nagelkerke R² is 28.5%, and the Cox & Snell R² is 21.3%).
ROC analysis was performed to evaluate the diagnostic performance of clinical and laboratory parameters for predicting ICU admission. Area under the curve (AUC) values indicated that neutrophil count (AUC: 0.664), CRP (AUC: 0.654) and D-dimer (AUC: 0.620) had moderate diagnostic accuracy. Uric acid (AUC: 0.607) and ferritin (AUC: 0.596) were less predictive. Other variables, including age (AUC: 0.579), creatinine (AUC: 0.585) and hypertension (AUC: 0.570), had limited diagnostic value, while gender (AUC: 0.453) and lymphocyte count (AUC: 0.380) did not contribute significantly to the prediction of ICU admission (Figure 2).
Discussion
In our research, we identified several clinical and laboratory signs associated with patients suffering from COVID-19 who ultimately require ICU admission. Specifically, we noted that higher neutrophil counts, increased levels of CRP, and elevated uric acid are key factors that can independently predict the need for ICU care. Additionally, ROC analysis demonstrated that neutrophil count, CRP, and D-dimer had moderate diagnostic accuracy for predicting severe disease requiring intensive care. The findings of this study provide valuable insights into the risk stratification of COVID-19 patients, allowing for better management and allocation of critical care resources.
Similar to our findings, a meta-analysis of 25 studies including 5350 COVID-19 patients showed that elevated CRP and D-dimer levels were associated with an increased risk of severe disease and ICU admission (13). A different study in Qatar revealed that high CRP levels independently predict ICU admission, raising the chances by fourfold (14). Consistently, a systematic review reported that CRP, neutrophil count, and D-dimer were among the strongest predictors of severe COVID-19 outcomes (15).
Our study also highlighted the role of chronic CKD and hypertension as significant comorbidities associated with ICU admission. This is supported by studies from different regions, including an Iranian cohort where hypertension and chronic illnesses like CKD were also significant risk factors (4). Unlike our findings, earlier research has shown varying outcomes regarding how COPD influences COVID-19 severity (17).
This study showed that while older age correlated with ICU admissions, it was not a significant independent predictor in the multivariate analysis. This finding partly aligns with extensive research suggesting that advanced age is a strong indicator of severe outcomes; however, its influence might be affected by several factors, including clinical presentation and comorbidities (18).
The diagnostic accuracy of laboratory parameters in our research is consistent with previous studies. Our ROC analysis indicated a moderate predictive capability for neutrophil count (AUC: 0.664) and CRP (AUC: 0.654). A study from Trinidad found that CRP acts as a vital predictor for ICU admissions (10). However, variables like gender and lymphocyte count, which showed low diagnostic accuracy in our study, have also shown variable performance in other analyses (19).
Serum uric acid levels have the potential to be a prognostic marker in COVID-19 patients. In particular, low uric acid levels have been found to be significantly associated with the severe course, need for intensive care and mortality of COVID-19 in multiple independent cohorts [20,21]. High uric acid levels have also been associated with unfavourable outcomes in some studies [22]. However, this association usually reflects the comorbid risk factors of the patients. A comprehensive study from Japan supports the existence of a U-shaped risk association for uric acid and shows that both low and high levels are associated with severe COVID-19 outcomes [20]. In this study, while elevated uric acid was an independent predictor of ICU admission, its diagnostic performance was limited.
Severe COVID-19 is characterized by a hyperinflammatory response, often referred to as a “cytokine storm,” which is marked by significantly elevated levels of inflammatory markers such as interleukin-6 (IL-6), CRP, and ferritin (23, 24). This excessive immune response contributes to a prothrombotic state, leading to coagulation abnormalities including increased D-dimer and fibrinogen levels, as well as platelet dysfunction (25). These changes underpin the high incidence of thrombotic complications in critically ill COVID-19 patients, with studies reporting venous thromboembolism rates as high as 20-30% in ICU settings (26). We also demonstrated that elevated CRP and D-dimer levels were significant predictors of ICU admission, aligning with broader evidence that thrombotic and inflammatory markers are associated with severe outcomes and increased mortality in COVID-19 patients (27).
Our study has several limitations that may affect our interpretation of the results. One important consideration is that our research is based on past observations, which may introduce selection bias and limit our ability to establish clear cause-and-effect relationships. Additionally, because the study occurred at a single center, this may affect the generalizability of the results to other populations or healthcare settings. Although the sample size was adequate for primary analyses, it may limit the analytical power for subgroup studies and the identification of rare predictors of ICU admission. Additionally, we did not include all potential predictors, such as radiological findings or more specific biomarkers (like interleukin-6 and procalcitonin), which might offer further insights into assessing ICU risk. Finally, conducting external validation of our findings in other cohorts would enhance the reliability of the predictive models created in this study.
In conclusion, our research is in line with global studies from around the world. It highlights the importance of inflammatory markers in predicting when individuals may need to be admitted to the ICU for COVID-19. The minor variations in predictive factors like COPD or age may indicate regional and population differences, emphasizing the need for localized risk assessment tools in managing COVID-19 patients.
References
1. Chang, R., Elhusseiny, K. M., Yeh, Y.-C., & Sun, W.-Z. (2021). COVID-19 ICU and mechanical ventilation patient characteristics and outcomes—A systematic review and meta-analysis. PLOS One, 16(2), e0246318.
2. Pijls, B. G., Jolani, S., Atherley, A., Derckx, R. T., Dijkstra, J. I. R., Franssen, G. H. L., ... & Zeegers, M. P. (2021). Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies. BMJ Open, 11(1), e044640.
3. Jain, V., & Yuan, J. M. (2020). Predictive symptoms and comorbidities for severe COVID-19 and intensive care unit admission: a systematic review and meta-analysis. International Journal of Public Health, 65(5), 533–546.
4. Huang, I., Pranata, R., Lim, M. A., Oehadian, A., & Alisjahbana, B. (2021). Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evidence-Based Medicine, 26(3), 107–108.
5. Alkhawaja, R., Kardousha, A., Awaisu, A., Abou-Ali, A., Khatib, M., Aboukamar, M., & Al-Hail, M. (2024). Early predictors of intensive care unit admission among COVID-19 patients in Qatar. Frontiers in Public Health, 12, 1278046.
6. Zhao, Z., Chen, A., Hou, W., Graham, J. M., Li, H., Richman, P. S., ... & Duong, T. Q. (2020). Prediction model and risk scores of ICU admission and mortality in COVID-19. PLOS One, 15(7), e0236618.
7. Gopaul, C. D., Ventour, D., & Thomas, D. (2022). Laboratory predictors for COVID-19 Intensive Care Unit admissions in Trinidad and Tobago. Dialogues in Health, 1, 100022.
8. Magesh, S., John, D., Li, W. T., Li, Y., Mattingly-App, A., Jain, S., ... & Chang, E. Y. (2021). Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic review and meta-analysis. JAMA Network Open, 4(11), e2134147.
9. Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA, 323(11), 1061-1069.
10. Gopaul, C. D., Ventour, D., & Thomas, D. (2022). Laboratory predictors for COVID-19 Intensive Care Unit admissions in Trinidad and Tobago. Dialogues in Health, 1, 100022.
11. Yancy, C. W. (2020). COVID-19 and African Americans. JAMA, 323(19), 1891-1892.
12. COVID 19 Turkey epidemic management. Available at: https://covid19.saglik.gov.tr/TR-66393/covid-19-salgin-yonetimi-ve-calisma-rehberi.html Accessed at 27/02/2025.
13. Huang, I., Pranata, R., Lim, M. A., Oehadian, A., & Alisjahbana, B. (2020). C-reactive protein, procalcitonin, D-dimer, and ferritin in severe coronavirus disease 2019: A meta-analysis. Therapeutic Advances in Respiratory Disease, 14, 1753466620937175.
14. Abuyousef, S., Alnaimi, S., Omar, N. E., et al. (2024). Early predictors of intensive care unit admission among COVID-19 patients in Qatar: A case–control study. Frontiers in Public Health, 12, 1278046.
15. Abate, S. M., Ali, S. A., Mantfardo, B., & Basu, B. (2020). Rate of Intensive Care Unit admission and outcomes among patients with coronavirus: A systematic review and meta-analysis. PLOS ONE, 15(7), e0235653.
16. Sharifi, F., Mehrolhassani, M. H., Ahmadi Gohari, M., et al. (2023). Clinical risk factors of need for intensive care unit admission of COVID-19 patients: A cross-sectional study. Archives of Academic Emergency Medicine, 11(1), e15.
17. Lee, J. (2022). Age, sex, and race/ethnicity in clinical outcomes among patients hospitalized with COVID-19, 2020. Frontiers in Medicine, 9, 850536.
18. Singer, A. J., Morley, E. J., Meyers, K., et al. (2020). Predictors of ICU care and ventilation in COVID-19 patients. Annals of Emergency Medicine, 76(4), 394–404.
19. van Klaveren, D., Rekkas, A., Alsma, J., et al. (2021). COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop models for ICU admission prediction. BMJ Open, 11(9), e051468.
23. Jose, R. J., & Manuel, A. (2020). COVID-19 cytokine storm: the interplay between inflammation and coagulation. The Lancet Respiratory Medicine, 8(6), e46–e47. https://doi.org/10.1016/S2213-2600(20)30216-2
24. Savla, S. R., Prabhavalkar, K. S., & Bhatt, L. K. (2021). Cytokine storm associated coagulation complications in COVID-19 patients: Pathogenesis and Management. Expert Review of Anti-infective Therapy, 19(11), 1397–1413. https://doi.org/10.1080/14787210.2021.1908138
25. Klok, F. A., Kruip, M. J. H. A., van der Meer, N. J. M., et al. (2020). Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thrombosis Research, 191, 145–147. https://doi.org/10.1016/j.thromres.2020.04.013
26. Tang, N., Li, D., Wang, X., & Sun, Z. (2020). Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of Thrombosis and Haemostasis, 18(4), 844–847. https://doi.org/10.1111/jth.14768
27. Chaudhary, R., Bliden, K. P., Kreutz, R. P., et al. (2021). Thromboinflammatory biomarkers in COVID-19: Systematic review and meta-analysis of 17,052 patients. Mayo Clinic Proceedings: Innovations, Quality & Outcomes, 5(2), 272–e2. https://doi.org/10.1016/j.mayocpiqo.2021.01.009
| Patients (n=404) |
Age | 65.9 ± 15.8 |
Gender (F) | 186 (46%) |
Comorbidities |
|
Coronary Artery Disease (CAD) | 67 (16.6%) |
Chronic Heart Failure (CHF) | 30 (7.4%) |
Type 2 Diabetes Mellitus (T2DM) | 164 (40.6%) |
Hypertension (HT) | 233 (57.8%) |
Chronic Kidney Disease (CKD) | 98 (24.3%) |
COPD/Asthma | 70 (17.3%) |
Malignancy | 48 (11.9%) |
Length of Hospital Stay (days) a | 16.3 ± 8.2 |
ICU Admission | 186 (46%) |
Alive/Deceased (Deceased) | 123 (30.4%) |
Creatinine (mg/dl)* | 1.0 (0.2-9.8) |
Uric Acid (mg/dl)* | 6.2 (0.9-30.9) |
AST (U/L)* | 29 (10-3021) |
ALT (U/L)* | 21 (5-366) |
Leukocyte (10^9/L)* | 7610 (130-34310) |
Neutrophil (10^9/L)* | 5960 (100-32190) |
Lymphocyte (10^9/L)* | 950 (20-9300) |
CRP (mg/dl)* | 73 (1-392) |
D-dimer (ng/ml)* | 1029 (169-9873) |
Ferritin (ng/ml)* | 199 (6-1500) |
* Median (minimum-maximum), a mean ± SD Abbreviations: CAD: Coronary Artery Disease, CHF: Chronic Heart Failure, T2DM: Type 2 Diabetes Mellitus, HT: Hypertension, CKD: Chronic Kidney Disease, COPD: Chronic Obstructive Pulmonary Disease, AST: Aspartate Aminotransferase, ALT: Alanine Aminotransferase, CRP: C-Reactive Protein.
Feature | ICU (+) (n=186) | ICU (-) (n=218) | p-value |
Age | 69.8 ± 13.3 | 62.6 ± 15.8 | <0.001 |
Gender (Female) | 79 (42.5%) | 107 (49.1%) | 0.184 |
Comorbidities |
|
|
|
Coronary Artery Disease (CAD) | 35 (18.8%) | 32 (14.7%) | 0.274 |
Chronic Kidney Disease (CKD) | 54 (29%) | 44 (20.2%) | 0.039 |
Chronic Heart Failure (CHF) | 12 (6.5%) | 18 (8.3%) | 0.482 |
Type 2 Diabetes Mellitus (T2DM) | 76 (40.9%) | 88 (40.6%) | 0.950 |
Hypertension (HT) | 124 (66.7%) | 109 (50.2%) | <0.001 |
COPD/Asthma | 33 (17.7%) | 37 (17.1%) | 0.855 |
Malignancy | 26 (14%) | 22 (10.1%) | 0.235 |
Length of Hospital Stay (days) a | 18.8 ± 8.9 | 14.2 ± 7.0 | <0.001 |
Alive/Deceased (Deceased) | 87 (46.8%) | 36 (16.5%) | <0.001 |
Creatinine (mg/dl)* | 1.1 (0.4-5.3) | 1.0 (0.3-9.8) | 0.072 |
Uric Acid (mg/dl)* | 6.0 (2.0-30.9) | 5.1 (0.9-18.1) | 0.011 |
AST (U/L)* | 31.5 (14-3021) | 32.5 (13-178) | 0.008 |
ALT (U/L)* | 21.5 (5-366) | 20 (5-219) | 0.306 |
Neutrophil (10^9/L)* | 6760 (1070-32190) | 4600 (100-11110) | 0.001 |
Lymphocyte (10^9/L)* | 795 (190-8680) | 1100 (20-3320) | <0.001 |
Ferritin (ng/ml)* | 301 (6-1500) | 166 (7-1500) | 0.002 |
CRP (mg/dl)* | 102 (3-378) | 56 (1-302) | 0.002 |
D-dimer (ng/ml)* | 1358 (188-4563) | 978 (179-4241) | <0.001 |
* Median (minimum-maximum), a mean ± SD Abbreviations: ICU: Intensive Care Unit, CAD: Coronary Artery Disease, CHF: Chronic Heart Failure, T2DM: Type 2 Diabetes Mellitus, HT: Hypertension, CKD: Chronic Kidney Disease, COPD: Chronic Obstructive Pulmonary Disease, AST: Aspartate Aminotransferase, ALT: Alanine Aminotransferase, CRP: C-Reactive Protein.
Variable | B | S.E. | OR | 95% Confidence Interval | p-value |
Age | 0.002 | 0.016 | 1.002 | (0.971-1.034) | 0.891 |
Gender | -0.284 | 0.377 | 0.752 | (0.360-1.574) | 0.450 |
Creatinine | -0.324 | 0.188 | 0.723 | (0.501-1.046) | 0.085 |
Uric Acid | 0.151 | 0.070 | 1.164 | (1.013-1.336) | 0.032* |
AST | 0.000 | 0.002 | 1.000 | (0.997-1.003) | 0.992 |
Neutrophil | 0.000 | 0.000 | 1.000 | (1.000-1.000) | 0.003* |
Lymphocyte | 0.000 | 0.000 | 1.000 | (0.999-1.000) | 0.090 |
Ferritin | 0.000 | 0.001 | 1.000 | (0.999-1.001) | 0.400 |
CRP | 0.006 | 0.003 | 1.006 | (1.001-1.011) | 0.027* |
D-dimer | 0.000 | 0.000 | 1.000 | (1.000-1.000) | 0.294 |
Hypertension | 0.687 | 0.381 | 1.988 | (0.943-4.192) | 0.071 |
Chronic Kidney Disease (CKD) | -0.210 | 0.490 | 0.811 | (0.310-2.117) | 0.668 |
* p<0.05 Abbreviations: CKD: Chronic Kidney Disease, CRP: C-Reactive Protein, AST: Aspartate Aminotransferase , Hosmer-Lemeshow p=0.430, Omnibus Test χ²=44.5, p<0.001, Nagelkerke R²=28.5%, Cox & Snell R²=21.3%.
Figure 1. The flowchart of the patient selection process, ICU; Intensive care unit.
Figure 2. ROC Curve of Variables. Age (AUC: 0.623), CKD (0.501), HT (0.570), D-dimer (0.620), AST (AUC: 0.522), Neutrophil (AUC: 0.655), Lymphocyte (AUC: 0.384), Ferritin (AUC: 0.561), CRP (AUC: 0.633), Uric Acid (AUC: 0.645), Creatinine (AUC: 0.619), Gender (0.453). Abbreviations: ROC; Receiver Operating Characteristics, AUC; Area Under Curve, CKD: Chronic Kidney Disease, AST; Aspartate Aminotransferase, CRP; C-Reactive Protein.
Figure 1. The flowchart of the patient selection process, ICU; Intensive care unit.