Analysis of Laboratory Markers for Severe COVID-19
Conditions: Coronavirus Infection COVID-19
Sponsor: Karaganda Medical University

 
 
 
 
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Medical University of Karaganda 
 
 
Study protocol 
 
 
 
 
 
 
                                      
                                      Analysis of laboratory markers for severe COVID-19 
                                                            SC19MI-ARICU-1T 
Version (V 1.0) from 10.03.202 4 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Karaganda, 2024 

 
 
 
 
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I. Purpose of the study and introduction. 
1. Purpose of the study : to determine the level of significance of biochemical 
markers in severe patients with coronavirus infection COVID-19 hospitalized in 
the intensive care unit, to search for possible predictor markers of the course and 
outcome of this disease . 
2. 
Research objective:  
2.1 To evaluate the indicators of biomarkers of the systemic inflammatory response in the 
systemic bloodstream in patients in serious condition with confirmed coronavirus infection. 
Based on the obtained critical levels of early markers studied at different times, it will be 
possible to predict the risks of developing an unfavorable outcome. 
2.2 Assess the structure of complications in the selected category of patients. 
2.3 Identify the presence of correlations between clinical and laboratory parameters (laboratory 
parameters and the degree of pulmonary infiltration , outcome and length of stay, age group, 
gender, presence of concomitant pathology). 
2.4 Determine critical levels of biomarkers to predict mortality. The resulting critical levels will 
be significant indicators in the dynamic assessment of the condition of patients in the ICU. 
Introduction. The manifest course of COVID-19 often involves a fairly high risk of death due to 
the frequent development of serious complications in the form of Acute respiratory distress 
syndrome (ARDS), acute respiratory failure (ARF), pulmonary embolism (PE), sepsis, shock, 
multiple organ failure (MOF), and due to the absence at the current stage of highly proven safe 
methods of etiotropic therapy. 
Of particular relevance in this regard was the search for reliable data that would make it possible 
to predict the severity and outcome, more effectively plan the management of different groups of 
patients, and provide the opportunity for timely response of medical personnel, and rational 
distribution of limited health care system resources during a pandemic. 
Thus, in a study by Moon SS et al., which included 352 patients with identified new coronavirus 
infection, it has been shown that patients aged ≥ 70 years, patients with fever on admission and 
patients with malignancy or diabetes are more likely die from COVID-19 [1]. 
In the study by Zhou F, patients' old age and some concomitant diseases, such as hypertension 
and diabetes, were identified as factors contributing to severe progression [2]. A number of 
studies confirm that a comorbid background reliably suggests a more severe course of this 
pathology, however, of greater interest, in our opinion, is the search for more dynamic indicators. 
Jang JG, in addition to diabetes mellitus, hyperthermia ≥ 37.8°C, peripheral oxygen saturation 
<92% and creatine kinase-MB (CK-MB) level > 6.3 were proposed as independent predictors of 
severe course in hospitalized patients with COVID-19. [3]. In the study by Suh H.J.[4] dyspnea 
(60.0%) and elevated CRP levels (median 7.35 mg/dL; IQR 3.17–12.02) indicated severe clinical 
course and were suggested to evaluate patients with COVID-19 for initiation of oxygen therapy. 
In a prospective study by Rong-Hui Du, in addition to comorbidities and age (age ≥65 years, pre-
existing concomitant cardiovascular or cerebrovascular diseases) as factors associated with death 
in patients with COVID-19-associated pneumonia cardiac troponin I ≥0.05 ng mL -1 and CD3 + 
CD8 + T cells ≤75 cells·μl -1 were identified. The last two factors, in particular, were predictors 
of mortality in patients with COVID-19 pneumonia[5]. 
One of the hallmarks of SARS-CoV-2 infection is induced immune dysregulation, which often 
leads to a cytokine storm and the development of multiorgan dysfunction syndrome [6]. 
Available research allows us to judge what multiorgan failure syndrome appears the leading 
cause of high mortality among intensive care patients infected withCOVID-19 [7]. This 
condition is based on an increased risk of developing disseminated intravascular coagulation 
(DIC) in patients with SARS-CoV-2, as indicated by clinical reports indicating frequent 
thrombocytopenia and increased D-dimer in infected individuals with manifest disease [8]. 

 
 
 
 
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Abnormal coagulation in patients with COVID-19 is characterized by increased levels of 
fibrinogen and D-dimer in parallel with an increase in inflammatory markers. At the same time, 
an increase in prothrombin time and time of partial activation of thrombin, in combination with 
thrombocytopenia, suggests activation of the coagulation pathway with the development of 
consumptive coagulopathy due to an excessive inflammatory response. These processes cause 
microcirculatory dysfunction and the development of systemic microangiitis and extensive 
microthrombosis, which ultimately leads to multi-organ dysfunction syndrome (MODS) [7,8,9]. 
It is worth noting the difficulty of drawing up an optimal management plan for patients with such 
complications, the treatment of which should be carried out in an intensive care unit. It is even 
more difficult to timely assess the risk of developing these complications for timely transfer the 
patients to the department of anesthesiology, resuscitation and intensive care (ICU). 
Therefore, in addition to the data already accumulated to date, the authors of this work conducted 
a retrospective analysis to determine the clinical profile of patients with COVID-19 requiring 
intensive care, as well as to search for available and reliable predictors of the course and 
outcome of the disease for this cohort of patients. 
 
1. Moon SS, Lee K, Park J, Yun S, Lee YS, Lee DS. Clinical Characteristics and Mortality 
Predictors of COVID-19 Patients Hospitalized at Nationally-Designated Treatment Hospitals. 
J Korean Med Sci. 2020 Sep ;35 (36):e328. https://doi.org/10.3346/jkms.2020.35.e328 
2. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients 
with COVID-19 in Wuhan, China: a retrospective cohort study [published correction appears 
in Lancet. 2020 Mar 28;395(10229):1038] [published correction appears in Lancet. 2020 Mar 
28;395(10229):1038]. 
Lancet 
. 
2020 
;395 
(10229):1054-1062. 
doi:10.1016/S0140-
6736(20)30566-3 https://pubmed.ncbi.nlm.nih.gov/32171076/ 
3. Jang JG, Hur J, Hong KS, Lee W, Ahn JH. Prognostic Accuracy of the SIRS, qSOFA , and 
NEWS for Early Detection of Clinical Deterioration in SARS-CoV-2 Infected Patients. J 
Korean Med Sci. 2020 Jun ;35 (25):e234. https://doi.org/10.3346/jkms.2020.35.e234 
4. Suh HJ, Kim DH, Heo EY, Lee HW, Lee JK, Lee CS, Kim M, Jeon YD, Chung JW, Kim 
YK, Shin PJ, Lee MS, Kang JS, Lee MJ, Kim BN, Park SW. Clinical Characteristics of 
COVID-19: Clinical Dynamics of Mild Severe Acute Respiratory Syndrome Coronavirus 2 
Infection Detected by Early Active Surveillance. J Korean Med Sci. 2020 Aug;35(32):e297. 
https://doi.org/10.3346/jkms.2020.35.e297 
5. Rong - Hui Du , Li - Rong Liang , Cheng - Qing Yang , Wen Wang , Tan - Ze Cao , Ming Li , 
Guang - Yun Guo , Juan Du , Chun - LanZheng , Qi Zhu , Ming Hu , Xu - Yan Li , PengPeng 
, Huan - Zhong Shi . Predictors of mortality for patients with COVID-19 pneumonia caused 
by SARS-CoV-2: a prospective cohort study. European Respiratory Journal May 2020, 55 (5) 
2000524; 
DOI: 
10.1183/13993003.00524-2020. 
https://erj.ersjournals.com/content/55/5/2000524 
6. Rasizadeh , R., Aghbash , P. S., Nahand , J. S. et al. SARS-CoV-2-associated organ failure 
and inflammation: a focus on the role of cellular and viral microRNAs. Virol J 20, 179 
(2023). 
https://doi.org/10.1186/s12985-023-02152-6 
https://virologyj.biomedcentral.com/articles/10.1186/s12985-023-02152-6 
7. Zhao W, Li H, Li J, Xu B, Xu J. The mechanism of multiple organ dysfunction syndrome in 
patients with COVID-19. J Med Virol . 2022; 94: 1886-1892. doi:10.1002/jmv.27627 
https://onlinelibrary.wiley.com/doi/10.1002/jmv.27627 
8. XunLi ,LuwenWang,ShaonanYan,FanYang,LongkuiXiang,JilingZhu,BoShen,Zuojiong Gong. 
Clinical characteristics of 25 death cases with COVID-19: A retrospective review of medical 
records in a single medical center, Wuhan, China. International Journal of Infectious 
Diseases, Elsevier, May 2020. https://doi.org/10.1016/j.ijid.2020.03.053 

 
 
 
 
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9. Valyaeva , A. A. , Zharikova , A. A. , & Sheval , E. V. ( 2023 ). SARS-CoV-2 cellular 
tropism and direct multiorgan failure in COVID-19 patients: Bioinformatic predictions, 
experimental observations, and open questions . Cell Biology International , 47 , 308–326 . 
https :// doi . org /10.1002/ cbin .11928 
 
II. Criteria for selecting study participants. 
1. Number of participants . Retrospective analysis of medical records (case histories) of 193 
patients who received treatment in the intensive care unit of the Regional Clinical Hospital of 
Karaganda . 
2. Gender distribution . The expected gender distribution is uneven, due to the lack of 
information about significant differences in laboratory markers among representatives of 
different sexes. 
3. Age. From 18 years old without age restrictions 
4. Nationality (ethnicity). Those with different national and ethnic backgrounds undergoing 
treatment at the center. 
5. Criteria for inclusion . Case histories of treated patients with severe cases confirmed by PCR 
of a nasopharyngeal smear of coronavirus infection COVID -19. 
6. Criteria for exclusion . Pregnancy. 
7. Vulnerable groups . No 
III. Methods and procedures 
1. Methods and procedures . The study plans to retrospectively analyze the medical records of 
patients who received treatment in the intensive care unit of the Regional Clinical Hospital for 
severe COVID19 infection. The retrospective analysis will evaluate the results of diagnostic 
procedures performed, including computed tomography of the chest and analysis of biochemical 
parameters, including procalcitonin, C-reactive protein, D- dimer and ferritin, identified as 
markers of the systemic inflammatory response. 
2. Data analysis and monitoring . Statistical processing of the obtained data was carried out 
using statistical software jamovi ( Computer Software , Version 2.3.26), MedCalc ( MedCalc 
Software Ltd , Ostend, Belgium), Microsoft programs Office Excel , 2016. Statistical research 
methods include: calculation of median and mode, standard deviation. In order to determine 
correlations between quantitative and qualitative data at different stages of treatment, a 
correlation analysis was carried out ( Spearman's test was used ). To evaluate selected laboratory 
markers as predictors of outcome, the ROC analysis method was selected, followed by an 
assessment of the odds ratio (OR) taking into account the obtained J Youden index and the 
associated criterion ( Associated criterion ) for each of the selected markers in relation to patient 
outcome. It is preliminary planned to compile a contingency table in relation to laboratory 
parameters and outcomes (2x2). 
Data storage and privacy. Confidentiality is ensured by encrypting the personal data of the 
subject with a digital code. Storage of information about patients is carried out in written form in 
the registration journal of the research topic, in electronic form on a computer in Microsoft 
program databases Excel and jamovi. Access to the data is available to the supervisor of the 
research and the main researcher, with the consent of the administration of the Regional Clinical 
Hospital of the city of Karaganda. 
IV. Risk/benefit assessment 
1. Degree of risk . There are no risks. 
2. Potential risk . There are no risks. 
3. Risk protection . Each patient is assigned a code to anonymize the data. 
4. Potential benefit to the participant . none 

 
 
 
 
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5. Alternatives for the participant are missing. 
V. Identification of study participants, recruitment and consent 
At the time of hospitalization and during treatment, informed consent was obtained from each 
patient for medical procedures and processing of personal data. 
1. Methods for identifying participants and recruiting them . Materials (medical histories of 
treated patients) are selected using a continuous method in accordance with the ICU-CVI patient 
register for the period 08/01/2021-02/14/2022. 
2. Consent process . Access to medical documentation was obtained with the consent of the 
administration of the Regional Clinical Hospital of Karaganda. Consent to the processing of 
patients' personal data was obtained during treatment in the intensive care unit of the Regional 
Clinical Hospital in the form of oral and written consent. 
3. Participant's condition . obtaining informed consent in person from the patient/official 
representative. 
4. Understanding . At the time of hospitalization and during treatment, informed consent was 
obtained from each patient for medical procedures and processing of personal data. 
5. Consent forms . The informed consent form was compiled according to the recommendations 
of the EC. 
6. Documentation of consent . Access to medical documentation was obtained with the consent 
of the administration of the Regional Clinical Hospital of Karaganda. Consent to the processing 
of patients' personal data was obtained during treatment in the intensive care unit of the Regional 
Clinical Hospital in the form of written consent, which is stored in the archives of the Clinic and 
in a scanned version in the database of an integrated medical information system. 
7. Participation price . There is no cost of participation for patients. 
8. Participation fee . There is no payment to the participant.