Prognosis of patients after organ transplantation during active COVID-19 infection

Prognosis of patients after organ transplantation during active COVID-19 infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognosis of patients after organ transplantation during active COVID-19 infection Olga Piątek-Dalewska, Krzysztof Kuziemski, Petra M. Grešner, Alicja Zielińska, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7023466/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 11 You are reading this latest preprint version Abstract Background: Solid organ transplant recipients represent one of the most clinically vulnerable populations in the ongoing COVID-19 pandemic. Chronic immunosuppression and multiple comorbidities significantly increase the risk of severe disease and death. Despite global advances in vaccination and therapy, practical tools for early risk assessment in this group remain limited. This study addresses a critical gap by identifying simple and universally available clinical markers that can be used at hospital admission to predict short-term outcomes in transplant recipients with COVID-19. Methods: We retrospectively analyzed a cohort of solid organ transplant recipients hospitalized with confirmed COVID-19 at the University Clinical Center in Gdańsk, Poland, between 2020 and 2022. Patients were admitted either to a general COVID-19 ward or to an intensive care unit based on disease severity. Data were collected from electronic medical records and included demographic characteristics, chest imaging, and routine laboratory tests: lymphocyte count, serum creatinine, aspartate aminotransferase, C-reactive protein, and procalcitonin. Statistical analyses were performed to evaluate the association of these variables with in-hospital mortality. Results: Our analysis identified that elevated levels of serum creatinine, aspartate aminotransferase, C-reactive protein, and procalcitonin were significantly associated with increased risk of in-hospital mortality. Conversely, a higher lymphocyte count at admission was associated with better survival. These markers are widely available, inexpensive, and can be assessed during the initial diagnostic workup. Conclusions: This study offers a practical, evidence-based approach to early risk stratification in transplant recipients with COVID-19. The identified laboratory markers are not only simple and accessible but also clinically meaningful, providing frontline clinicians with actionable information during the early stages of care. Given the ongoing threat of COVID-19 and the likelihood of future viral outbreaks, these findings have broad implications for managing immunosuppressed patients. The study contributes valuable insight to a currently underserved area of transplant medicine and has strong potential to inform clinical practice across diverse healthcare settings. Trial registration not applicable COVID-19 organ transplantation immunosuppression prognostic markers mortality lymphocytes creatinine procalcitonin C-reactive protein (CRP) SARS-CoV-2 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Viral infections, especially of the respiratory system, have been a threat to the health of the population for many years. In recent years, the World has seen the emergence of new and rapidly spreading viruses e.g. MERS (Middle East Respiratory Syndrom Coronavirus), hMPV (human metapneumovirus). This group has recently been joined by infections with the SARS-CoV-2 virus ( Severe Acute Respiratory Syndrome Coronavirus 2), which has permanently remained in our population. The first SARS-CoV-2 infection was detected in November 2019 in Wuhan, the capital of Hubei Province in China, with a population of twelve million people. In March 2020, the coronavirus pandemic spread to Europe, North America, and other continents. After over 3 years, on the 5th of May 2023, the WHO (World Health Organization) lifted the global pandemic status [ 1 ]. COVID-19 (coronavirus disease 2019) has changed the previous opinions about the spread of infectious diseases. It significantly impacted health and survival in the population, contributing to excess deaths. Despite the retraction of the pandemic status, COVID-19 continues to be a significant clinical and social problem. The initial stages of the pandemic were characterized by multiple early and late organ complications, respiratory failure, and substantial mortality. In the subsequent stages of the pandemic, effective new drugs and widely available preventive vaccinations were introduced, gradually reducing complications and significantly reducing mortality. Despite this, new cases are still being reported. The disease manifests with a broad, variable range of symptoms and varying severity. However, the symptoms of COVID-19 infection are varied: from an asymptomatic course with minor symptoms of a respiratory infection to severe lung failure requiring intensive care. In extreme cases, it can still lead to multi-organ failure and death [ 2 ]. The course and prognosis are influenced by advanced age, male gender [ 3 ], comorbidities, particularly the co-occurrence of obesity, metabolic syndrome, diabetes mellitus, hypertension, chronic kidney disease, circulatory and lung diseases, and cancer [ 4 ]. Organ transplant recipients create a group that is particularly susceptible to full-blown disease and poorer prognosis. These patients are subject to long-term immunosuppression, in whom comorbidities are much more frequently noted [ 5 – 7 ]. Long-term maintenance immunosuppression with the use of a calcineurin inhibitor, on the one hand, weakens the immune system and prolongs the time of virus elimination but on the other hand, it reduces the intensity of the generalized inflammatory reaction caused by the virus. Reducing the inflammatory reaction may limit the occurrence of ARDS (acute respiratory distress syndrome), i.e. the most severe complications in this disease [ 8 ]. The global burden of COVID-19 cases prompts research to understand how risk factors, comorbidities may influence COVID-19 disease severity, ICU (Intensive Care Unit) admissions, and mortality. Organ transplant recipients experience an increased risk of respiratory and multi-organ failure during hospitalization due to generalized inflammation not only in the lungs but also in other organs [ 5 – 7 ]. For this reason, it is essential to identify available markers that affect early prognosis in this group of patients. The aim of the study was to identify simple and useful morphological and biochemical blood markers, as well as radiological features, which could predict the risk of death and short-term prognosis in organ transplant recipients hospitalized with COVID-19. Material and methods A retrospective analysis of selected morphological, biochemical, and radiological parameters influencing early prognosis in organ transplant recipients hospitalized in the acute phase of COVID-19 infection was conducted. For this purpose, data from patients hospitalized at the University Clinical Center in Gdańsk, Poland (UCC) were used. Laboratory test results were analyzed according to the standard established by the UCC Gdańsk laboratory. The UCC is the largest academic hospital in northern Poland, with the highest reference level, and it hospitalizes patients requiring multi-specialist care. The hospital is one of the leading transplant centers for heart, kidney, lung, and liver organs. During the COVID-19 pandemic from 2020 to 2022, two hospital wards were designated at the UCC for hospitalized COVID-19 patients. Patients who did not require intubation were hospitalised in the stable COVID-19 ward (COVID-S), which, depending on the stage of the pandemic and demand, had 50 to 75 beds. Patients in unstable, life-threatening conditions requiring intubation and intensive care were admitted to the COVID-Intensive Care Unit (COVID-ICU). Group demographics The study included organ transplant recipients aged 18 years or older with a diagnosis of COVID-19 confirmed by a PCR (polymerase chain reaction) test, clinical symptoms of acute lower respiratory tract infection, and significant comorbidities. The main criteria for hospitalization in the COVID-S ward included: 1) oxygen peripheral saturation (SpO 2 ) lower than 95% requiring oxygen therapy, 2) symptoms such as shortness of breath, severe cough, syncope, fatigue, diarrhea with SpO 2 equal to or higher than 95%, 3) complications of COVID-19 infection requiring hospitalization with SpO 2 equal or higher than 95%, 4) maintaining continuity of medical care in patients who had tested positive for SARS-CoV-2 during hospitalization in another ward [ 6 ]. For patients with confirmed COVID-19 requiring intubation, at least two of the below-presented criteria, including criterion number 3, had to be met: 1. Exacerbation of respiratory failure regardless of non-invasive ventilation methods as a result of: a. oxygen peripheral saturation and partial oxygen pressure remaining persistently low, leading to retention of carbon dioxide and respiratory acidosis or b. intolerance to non-invasive ventilation, that is, acute dyspnea, increased breathing effort, anxiety with poor response to pharmacotherapy. 2. Surgery, injury, or cardiopulmonary resuscitation requiring intubation. 3. The patient required intensive care and could benefit from hospitalization in the ICU as determined by an anesthesiologist according to national guidance [ 9 ]. Early-phase post-myocardial infarction patients, patients after ischemic stroke and gastrointestinal hemorrhage, as well as patients requiring dialysis or administration of pressor amines were not admitted to COVID- ICU unless they required intubation and mechanical ventilation. Instead, they were treated in COVID- S ward. Advanced metastatic cancer and DNR (do not resuscitation) disqualified patients from transfer to the ICU. Patients were ready for discharge from COVID-ICU: 1. After discontinuation of mechanical ventilation or 2. If further medical care could be continued in the COVID-S or pulmonology ward [ 10 ]. Experimental design Upon admission to the COVID-S ward, blood tests (complete blood count, hemoglobin concentration, biochemical analysis, C-reactive protein (CRP), D-dimer, creatinine, glomerular filtration rate (GFR), procalcitonin (PCT), B-type natriuretic peptide (BNP), liver enzymes: aspartate aminotransferase (AST), alanine aminotransferase (ALT) were performed in all patients. Chest radiographs were performed according to indications (High-Resolution Computed Tomography (HRCT), angio-CT, or chest X-ray), or tests already conducted in other wards were accepted. Radiographs were performed only when the result could change the course of action, or COVID-19 complications were suspected. The severity of symptoms was assessed using the CRB-65 scale (confusion, respiratory rate, blood pressure, 65 years of age) [ 11 – 12 ]. The CRB-65 scale was used to assess the severity of pneumonia. Depending on the clinical situation, individual tests were repeated or supplemented with subsequent ones when necessary. The severity of changes in the lung parenchyma was assessed according to the generally accepted CO-RADS scale (COVID-19 Reporting and Data System) [ 13 ]. In subsequent analyses, morphological and biochemical blood parameters from the beginning, middle part, and end part of hospitalization were considered individually for each patient, depending on the time of hospitalization. In analyzed radiological images, ground glass opacity, paving stones, consolidation in the lung parenchyma, inflammatory changes, fluid in the pleural cavities, nodules, and pulmonary embolism were considered. The length of hospitalization, the number of comorbidities, and the degree of progression were assessed along with their previous treatment included in the additional materials - Table A. Considering the available treatment against COVID-19, complications during hospitalization and the number of deaths. The treatment was modified according to the current state of knowledge and the guidelines of the Polish Society of Infectious Diseases [ 14 ]. Complications related to COVID-19 considered in the study included : -death, -prolonged hospitalization > 10 days, -need for respiratory therapy, -need for dialysis in patients after kidney transplantation in the COVID-S Ward, -radiological changes in the lung parenchyma related to COVID-19, -lymphopenia (< 1000 ul-1), -anemia (blood hemoglobin concentration 100 mg/l), -high creatinine concentration (> 1.3 mg/dl). This study was conducted with the approval of the Ethics Committee of the Medical University of Gdansk, Poland (NKBBN/327/2022). The informed consent was waived due to the retrospective nature of the study. Statistical analysis Quantitative data were presented as either mean +/- standard deviation (SD) or median with interquartile range (IQR), depending on the normality of data distribution, which was checked using the Shapiro-Wilk W test. Nominal and/or ordinal data were presented as both absolute and relative counts (percentages). Simple correlations between two quantitative variables were sought through Spearman’s range correlation method, with the strength of correlation being expressed using Spearman’s correlation coefficient (r SP ). In the case of correlation analysis between two ordinal variables, Kendall’s tau correlation was used instead. Simple between-group differences were tested for statistical significance using the Mann-Whitney U test. Associations between the occurrence of COVID-19 complications and the length of the patient’s hospitalization were analyzed utilizing Fisher’s exact test, while Cox proportional hazard regression modelling was used to identify plausible demise risk predictors. Statistical significance was defined as p < 0.05. Data analysis was carried out using R [ 15 ]. RESULTS Six hundred sixty-six patients (253 (38%) women; 413 (62%) men), initially admitted to the internal medicine and surgical clinics in the UCC Gdańsk, were hospitalized in the COVID-S wards between November 2, 2020, and March 7, 2022. The diagnosis of COVID-19 was established before transferring to the COVID-S ward. A detailed analysis was performed on a group of thirty-eight transplant patients who were hospitalized in the COVID-S ward. There were nine women and twenty-nine men in the group of transplant recipients. The type of organ transplant is presented in Table 1 . The median age of patients was 54 (IQR: 47.25–63.5). The median length of hospitalization in the COVID-S and COVID-ICU wards was 11.5 (IQR: 9.3- 18.8) days. Table 1 Number and percentage of transplant recipients with corresponding mortality stratified by organ type. All Tx Kidney Tx Liver Tx Heart Tx Lungs Tx Number of patients (%) 38 (100%) 26 (68%) 2 (5%) 4 (10.5%) 6 (15.7%) Number of deaths (%) 13(34%) 9 (24%) 1 (2.6%) 0 (0%) 3 (7.9%) On average, organ transplant recipients with active COVID-19 infection included in the study received 3 (IQR: 3–5; min-max: 0–6) therapeutic procedures (antibiotic therapy, antiviral therapy, steroid therapy, convalescent plasma, ventilation support, respiratory therapy) - the list of treatments included in the additional materials - Table B. The study found that, on average, organ transplant recipients diagnosed with an active COVID-19 infection had three comorbidities. The distributions of their frequencies are detailed in Table 2 . Cardiovascular and kidney diseases were the most prevalent among the patients. The interquartile range (IQR) for these comorbidities was between 2 and 5, highlighting the significant variability in health status among organ transplant recipients during the pandemic. Table 2 Frequency of individual comorbidities in the study group. The included organ transplant patients with active COVID-19 infection had a mean of 7 (median; IQR: 5.25-7; min-max: 2–12) COVID-19-related complications. Table 3 . presents detailed COVID-19-related complications in the study group. Additional laboratory parameters analyzed during hospitalization are included in the supplementary materials—Table C. Feature N absent (%) present (%) Smoking 38 36 (95) 2 (5) Hypertension 38 11 (29) 27 (71) Coronary heart disease/ Myocardial infraction in history 38 33 (87) 5 (13) Heart failure 38 31 (82) 7 (18) Arrhythmia 38 33 (87) 5 (13) Valve diseases 38 35 (92) 3 (8) Venous thromboembolism 38 36 (95) 2 (5) Diabetes mellitus type 1 or 2 38 23 (61) 15 (39) Chronic kidney diseases 38 13 (34) 25 (66) Hemodialysis 26 13 (50) 13 (50) Obstructive lung diseases 38 36 (95) 2 (5) Cerebral infarction in history 38 35 (92) 3 (8) Table 3 Complications associated with COVID-19 and their incidence in the study group of patients. NUMBER COMPLICATION n No (%) Yes (%) 1 Death 38 25 (65,8) 13 (34,2) 2 Dialysis in the COVID ward 26 22(84,6) 4 (15,4) 3 RADIOLOGICAL CHANGES DESCRIBED IN COMPUTED TOMOGRAPHY Fluid in CT 28 19 (67,9) 9 (32,1) Frosted glass 28 7 (25,0) 21 (75,0) Paving stones 28 22 (78,6) 6 (21,4) Consolidations in the CT 32 8 (25,0) 24 (75,0) Embolism in angio-CT 20 19 (95,0) 1 (5,0) Other changes 10 0 (0) 10 (100) 4 CHANGES IN LABORATORY TESTS (1) Lymphopenia [ x10^9/l] 38 14 (36,8) 24(63,2) (2) Lymphopenia [ x10^9/l] 36 15 (41,7) 21(58,3) (3) Lymphopenia [x10^9/l] 36 17 (47,2) 19(52,8) (1) Hg (< 10) [g/dl] 38 26 (68,4) 12(31,6) (2) Hg (< 10) [g/dl] 36 21 (58,3) 15(41,7) (3) Hg ( 100) [mg/l] 37 23 (62,2) 14(37,8) (2) CRP (> 100) [mg/l] 34 25 (73,5) 9(26,5) (3) CRP (> 100) [mg/l] 34 27 (79,4) 7(20,6) (1) Creatinine (> 1.3) [mg/dl] 38 5 (13,2) 33(86,8) (2) Creatinine (> 1.3) [mg/dl] 35 8 (22,9) 27(77,1) (3) Creatinine (> 1.3) [mg/dl] 34 10 (29,4) 24(70,6) 5 Length of hospitalization Hospitalization (> 10 days) 38 14(36,8) 24(63,2) 6 NECESSITY OF INTUBATION AND RESPIRATORY THERAPY Respiratory therapy 38 27(71,1) 11(28,9) Legend: Changes in laboratory tests. Measurements performed according to the time of hospitalization. (1) First measurement - beginning of hospitalization. (2) Second measurement - middle of hospitalization. (3) Third measurement - end of hospitalization. The relationship between the number of comorbidities and complications associated with COVID-19. In organ transplant recipients, a weak but statistically significant correlation between the number of comorbidities and the number of complications they experienced was observed (tau = 0.26, R 2 = 0.068; p < 0.05); Fig. 1 . However, the number of comorbidities did not correlate significantly with the degree of lung involvement ; Fig. 2 A. Similarly, following the dichotomization of patients into two groups with respect to median number of comorbidities (n = 3), no statistically significant differences in the degree of lung involvement on radiographic examinations were found between the groups (42% [35% − 48%] vs. 24% [15% − 50%]; Fig. 2 The relationship between the degree of lung parenchyma involvement and the number of complications associated with COVID-19. A positive but statistically insignificant correlation was observed between the number of complications associated with COVID-19 and the degree of lung parenchyma involvement (r SP = 0.52; NS ); Fig. 3 A. However, following the dichotomization of patients into two groups with respect to a median number of complications (n = 7), patients with more than seven different complications showed a marginally increased degree of lung parenchyma involvement on radiological examinations compared to patients with the number of complications below the median (> 7 vs. <=7 complications 55% vs. 20%; p = 0.055); Fig. 3 B. Relationship between the length of hospitalization and the occurrence of complications associated with COVID-19. Longer hospital stays (more than 10 days) were found to be significantly associated with lower concentrations of creatinine in blood plasma (greater than 1.3 mg/dl) measured at the end of the hospitalization period (OR = 0.0, 95%CI: 0.0-0.7; p < 0.05); Table 4 . The results regarding the association of the length of hospitalization with other laboratory tests are presented in the additional materials. Table D. Table 4 The relationship between the length of hospitalization and the concentration of creatinine measured at the beginning (1), middle (2) and end (3) of hospitalization. Association short/no short/yes long/no long/yes OR [95% CI] p Hospitalization ( 1.3) [mg/dl] 1 13 4 20 0.39 [0.01–4.59] 0.633 Hospitalization ( 1.3)[mg/dl] 1 10 7 17 0.25 [0-2.46] 0.387 Hospitalization ( 1.3)[mg/dl] 0 11 10 13 0 [0-0.7] 0.014 Identification of prognostic factors of COVID-19-related death. The Cox proportional hazard regression model showed that the risk of death in COVID-19 patients increases significantly with increasing plasma creatinine concentrations, regardless of the time at which this increase was observed (beginning of hospitalization: HR = 1.4, 95% CI: 1.1–1.7, p = 0.003; mid-hospitalization: HR = 1.5, 95% CI: 1.1–1.8, p = 0.004; end of hospitalization: HR = 2.3, 95% CI: 1.5–3.7, p = 0.0001). Additionally, the risk of death was shown to increase significantly with the increase in other biochemical parameters, such as AST (HR = 1.02, 95% CI: 1.00-1.04; p = 0.016) and PCT (HR = 1.26, 95% CI: 1.07–1.48; p = 0.006) measured at the beginning of hospitalization and with the increase in CRP (HR = 1.01, 95% CI: 1.00-1.01; p = 0.04) and PCT (HR = 1.3, 95% CI: 1.0-1.7; p = 0.052) measured during hospitalization. The risk of death increases significantly also with increasing CRB-65 (HR = 2.6, 95% CI: 1.3–5.2; p = 0.007). Contrary to this the risk of death decreased significantly with increasing GFR.mdrd (glomerular filtration rate - Modification Of Diet In Renal Disease; HR = 0.89, 95% CI: 0.82–0.96; p = 0.005) and GFR CKD (glomerular filtration rate Chronic Kidney Disease; HR = 0.89, 95% CI: 0.81–0.96; p = 0.005) in plasma at the beginning of hospitalization, as well as with increasing lymphocyte count in the middle (HR = 0.22, 95% CI: 0.05-1.00; p = 0.051) and at the end of hospitalization (HR = 0.3, 95% CI: 0.1–0.9; p = 0.0323). Figure 4 presents observed values of odds ratios (OR) for all plausible prognostic factors of COVID-19-associated demise evaluated in this study. The impact of anemia and lymphopenia on COVID-19-related deaths among kidney transplant patients with active COVID-19 infection. The risk of death in kidney transplant patients with active COVID-19 infection significantly decreased with increasing lymphocyte count at the end of hospitalization (HR = 0.04, 95% CI: 0.0-0.80, p < 0.05). No similar association was demonstrated for hemoglobin concentration regardless of the measurement time. The results are shown in Fig. 5 . Correlation between CRP in blood plasma and procalcitonin. Statistically significant positive correlations were shown between CRP and procalcitonin concentration in blood. At the beginning of hospitalization, the correlation between CRP and PCT was moderate (RSP = 0.40; p < 0.05), while in the middle of hospitalization it was strong (RSP = 0.72; p < 0.05). Figures showing these correlations can be found in additional materials - Figure A. DISCUSSION In the acute stage of COVID-19 infection, the respiratory system is mainly involved, but other organs, including transplanted organs, are also directly affected. The virus enters target cells by endocytosis through the angiotensin-converting enzyme 2 (ACE2) receptor associated with the cell membrane. ACE2 is present in the lungs, kidneys, heart, and bowels [ 16 ]. This has a fundamental prognostic significance for patients after organ transplantation with concomitant acute COVID-19 infection. In COVID-19, the expression of the ACE2 receptor is significantly reduced. The result is an increased concentration of angiotensin II (A2). A2 has a pro-inflammatory effect, increases neutrophil infiltration in organs and the release of pro-inflammatory cytokines. Consequently, the permeability of blood vessels increases, damaging end organs such as kidneys, liver, heart, and lungs [ 16 – 18 ]. We conducted a retrospective single-center study to analyze factors influencing early hospital mortality in patients with COVID-19. The clinical course and prognosis of patients hospitalized with COVID-19 is very diverse and uncertain. This study assessed patients hospitalised due to COVID-19 after solid organ transplantation (SOT). In Poland, the kidney is the most frequently transplanted solid organ, so patients after kidney transplantation constitute the majority. In 2020, 717 kidney transplants were performed in Poland from deceased donors and 31 from living donors; in 2021, 709 kidney transplants were performed from deceased donors and 44 from living donors [ 19 ]. Patients who undergo transplantation face a high risk of severe infections, including opportunistic infections. The chronic need to use immunosuppressive drugs reduces the proper immune defence, thus infection with the SARS-CoV2 virus more often leads to multi-organ complications and death in these patients. Transplanted organs may be the target of the SARS-CoV-2 virus. In these cases, inflammation occurs in the microcirculation vessels, and the inflammatory state is mediated by a cytokine storm, leading to multiorgan failure and patient death [ 20 ]. The results obtained indicate that elevated plasma creatinine levels may represent a significant prognostic factor in patients following organ transplantation. Regardless of the timing during hospitalization when the increase was observed, higher creatinine levels were consistently associated with an increased risk of mortality. This suggests that renal dysfunction—whether occurring early or later during the hospital stay—has important prognostic implications and may reflect the overall severity of the clinical condition. An interesting and somewhat paradoxical finding was the inverse relationship observed between elevated creatinine levels and longer hospitalization, defined as a stay exceeding 10 days. While creatinine is typically considered a marker of impaired kidney function and deteriorating clinical status, in our cohort, elevated levels may have indicated earlier recognition of critical illness. This, in turn, could have led to more rapid clinical decision-making and intensified management, including earlier transfer to the intensive care unit. As a result, these patients, despite their serious condition, may have received more prompt treatment, potentially leading to a shorter overall hospital stay. While this interpretation requires further investigation, it may carry significant clinical implications, highlighting the value of early identification of high-risk patients. Our findings also suggest that in addition to creatinine, other biochemical and clinical markers may serve as predictors of poor outcomes. At the beginning of hospitalization, elevated levels of aspartate aminotransferase (AST), procalcitonin (PCT), and higher CRB-65 scores were associated with worse prognosis. These parameters may reflect ongoing inflammatory processes, organ injury, or burden on the respiratory and cardiovascular systems—findings consistent with previous studies in transplant recipients and patients with severe infections. In the middle phase of hospitalization, persistently elevated CRP and PCT levels—both classic markers of inflammation and infection—were also associated with reduced survival. Their continued elevation may signal inadequate treatment response or the development of infectious complications. Notably, increased lymphocyte counts at the end of hospitalization were associated with better prognosis, possibly indicating an improvement in immune system function and more effective control of the disease process. In transplant recipients, whose immune responses are deliberately modulated by immunosuppressive therapy, this parameter may hold particular prognostic and clinical significance. These findings emphasize the importance of dynamic patient assessment at various stages of hospitalization and underline the need for further prospective studies to validate these observations and develop consistent diagnostic and therapeutic strategies for this patient population. The rapid increase in creatinine levels in people with COVID-19 infection is likely due to the virus accumulating in the kidneys through blood spread, leading to renal cell necrosis [ 21 ]. In patients with high creatinine levels, a more severe course of the underlying COVID-19 disease was observed. Our analysis showed that dialysis therapy conducted before admission to COVID-19 wards and dialysis in COVID-S did not affect the occurrence of COVID-19 complications. Another key biomarker of the risk of death in the study group was increased AST concentration. The mechanism of liver dysfunction in COVID-19 is secondary damage to hepatocytes by a systemic inflammatory response and the use of hepatotoxic drugs, including antiviral drugs, used to treat COVID-19. Similar damage to hepatocytes may be generated by hypoxemia, which is a characteristic feature of full-blown COVID-19 [ 22 – 24 ]. Another marker of an unfavourable prognosis was an increase in CRP and PCT levels. Both indicators are associated with the development of inflammation. CRP correlates with the severity of the disease [ 25 – 27 ]. An increase in PCT level is related to bacterial infection, which may be a late complication of COVID-19. Known factors that trigger PCT synthesis include bacterial toxins and proinflammatory cytokines, particularly interleukin-6, a marker of the cytokine storm [ 25 ]. In the case of complicated viral COVID-19 infection, PCT levels increase and indicate bacterial infection, requiring treatment intensification by including appropriate antibiotics. Bacterial superinfections are challenging to diagnose and often lead to serious complications, including pneumonia and acute respiratory distress syndrome (ARDS). Published studies show that secondary bacterial infections in critically ill COVID-19 patients significantly increase mortality [ 26 ]. CRP and PCT are considered the most effective and sensitive biomarkers in predicting the progression of Covid-19 disease [ 27 – 28 ]. A characteristic feature at the beginning of COVID-19 infection is a low lymphocyte count. Their increase is observed as the general condition improves and the response to treatment is favorable. It has been shown that the risk of death among transplant patients decreases significantly with the increase in lymphocytes at the end of the hospitalization period. An increase in the concentration of lymphocytes can be another prognostic indicator of the course of infection and early risk of death. In transplant patients, several additional diseases often coexist. The type and severity of diseases may affect early and long-term prognosis [ 29 ]. It has been shown that the coexistence of chronic kidney disease may be an independent risk factor for death [ 30 – 33 ]. Our study also analyzed comorbidity. Interestingly, we did not confirm that the number of these diseases significantly correlated with the degree of lung parenchyma involvement in radiological examinations, even though the marginally increased (p = 0.055) degree of lung parenchyma involvement among patients with more than 7 (median) comorbidities may warrant further investigation. The type of radiological examination and the need to use contrast often depend on the degree of renal function. Chest CT with contrast medium was performed when there was a significant suspicion of pulmonary embolism. Inthe transplant recipients, due to coexisting renal failure, chest CT without contrast was performed if indicated. In some patients, due to their serious health condition and high creatinine concentration, radiological examinations were not performed. Additionally, a 6-point radiological CORADS scale was introduced for diagnosing COVID-19, which assessed inflammatory changes in the lung parenchyma. The assessment of patients with a positive PCR result was problematic. A positive PCR result classified patients as CODARS 6, i.e., the most advanced stage of the disease, even without changes in the lung parenchyma. This approach significantly influenced the assessment of patients and therapeutic options [ 34 – 35 ]. Along with the stages of the pandemic, variable treatment was used as per current guidelines. In Poland, the Polish Society of Epidemiologists and Infectious Disease Physicians' guidelines were widely used. These guidelines comprehensively assessed diagnostic and therapeutic options depending on the disease stage and the pandemic period [ 14 ]. Advanced treatment reflected the patient's serious condition or the occurrence of complications that required treatment. In the study group, secondary bacterial superinfections, including those caused by opportunistic bacteria, were common, necessitating treatment escalation and broad-spectrum antibiotic therapy. If profound anemia was detected, treatment with blood products was initiated. In people with increasing respiratory failure, HFNO (High-Flow Nasal Oxygen) and NIV (non-invasive mechanical ventilation) were used, and patients with critical respiratory and multi-organ failure were referred to the ICU-COVID department. In the analyzed study, the form of treatment was not interpreted as a factor increasing the risk of death due to COVID-19. LIMITATIONS OF THE STUDY The study has several limitations. First, it was a single-center retrospective study. Second, the analyzed group of patients was relatively small due to the specificity of the studied cohort. Third, the study did not consider the time since the transplantation of a given organ and the duration of immunosuppressive treatment, which could have affected the prognosis of the patients. Fourth, patients were admitted to COVID-S from various internal medicine and surgical clinics. The patients stayed in isolated shared rooms with other people. Such action could have contributed to the faster occurrence of complications complicating the disease. CONCLUSION Analysis of hospitalization of patients after organ transplantation with active COVID-19 allows us to determine key information that may be useful in the diagnosis of viral diseases, as well as in determining the prognosis of patients during viral infections in the future. Rapid diagnosis and intensive multi-specialist treatment in patients with immunosuppression is crucial. It is difficult to predict the exact prognosis of these patients. We have shown that the increase in creatinine, AST, CRP, and PCT levels may be associated with an increased risk of death in the group of organ transplant recipients with acute COVID-19 infection. On the other hand, the increase in lymphocytes translates into a better prognosis for patients in this group. Abbreviations MERS (Middle East Respiratory Syndrom Coronavirus); hMPV (human metapneumovirus); SARS-CoV-2 virus ( Severe Acute Respiratory Syndrome Coronavirus 2); WHO (World Health Organization); COVID-19 (coronavirus disease 2019); ARDS (acute respiratory distress syndrome); ICU (Intensive Care Unit); UCC (University Clinical Center); COVID-ICU (COVID-Intensive Care Unit); PCR (polymerase chain reaction); DNR (do not resuscitation); CRP (C-reactive protein); GFR (glomerular filtration rate); PCT (procalcitonin); BNP (B-type natriuretic peptide); AST (aspartate aminotransferase); ALT (alanine aminotransferase); HRCT (High-Resolution Computed Tomography); CRB-65 scale (confusion, respiratory rate, blood pressure, 65 years of age); CO-RADS (COVID-19 Reporting and Data System); SD (standard deviation); IQR (interquartile range); GFR.mdrd (glomerular filtration rate - Modification Of Diet In Renal Disease); GFR CKD (glomerular filtration rate Chronic Kidney Disease); HRs (Hazard ratios); ACE2 (angiotensin-converting enzyme 2); A2 (angiotensin II); SOT (solid organ transplantation); HFNO (High-Flow Nasal Oxygen); NIV (non-invasive mechanical ventilation). Declarations Ethics approval and consent to participate Approval was obtained from the ethics committee of the Medical University of Gdansk. Ethics approval number: NKBBN/327/2022. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Conflict of interest The authors have no financial or proprietary interest in any material discussed in this article. Funding The authors did not receive support from any organization for the submitted work. Authors' contributions PDO, KK were involved in the writing of the manuscript and in the interpretation of the results. PMG performed the statistical analysis. PDO, ZA were involved in the data collection process and the study coordination. ST database sharing. GM linguistic verification. All authors read and approved the final manuscript. Acknowledgements Not applicable. 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J Clin Med. 2022;11:622. Henkens MTHM, Raafs AG, Verdonschot JAJ, et al. Age is the main determinant of COVID-19 related in-hospital mortality with minimal impact of pre-existing comorbidities, a retrospective cohort study. BMC Geriatr. 2022;22:1–11. Goodman KE, Magder LS, Baghdadi JD, et al. Impact of Sex and Metabolic Comorbidities on Coronavirus Disease 2019 (COVID-19) Mortality Risk Across Age Groups: 66 646 Inpatients Across 613 U.S. Hospitals. 2021;73:e4113–23. Banerjee D, Popoola J, Shah S, et al. COVID-19 infection in kidney transplant recipients. Kidney Int. 2020;97:1076–82. Sorci G, Faivre B, Morand S. Explaining among- country variation in COVID- 19 case fatality rate. Sci Rep. 2020;10:18909. Palus DK, Gołębiewska ME, Piątek O, et al. Analysing COVID-19 treatment outcomes in dedicated wards at a large university hospital in northern Poland: a result-based observational study. BMJ Open. 2023;13(6):e066734. Flisiak R, Horban A, Jaroszewicz J, et al. Management of SARS-CoV-2 infection: recommendations of the Polish Association of Epidemiologists and Infectiologists as of March 31, 2020. Pol Arch Intern Med. 2020;130:352–7. Lim WS, Baudouin SV, George RC, et al. British Thoracic Society guidelines for the management of community acquired pneumonia in adults: update 2009. Volume 64. Thorax; 2009. pp. iii1–55. Suppl III. Prokop M, van Everdingen W, van Rees Vellinga T, et al. COVID-19 Standardized Reporting Working Group of the Dutch Radiological Society. CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation. Radiology. 2020;296(2):E97–104. Polish Society of Epidemiologists and Infectious Disease Physicians. Recommendations. https://www.pteilchz.org.pl/informacje/rekomendacje/ . Accessed 2023. R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ Legrand M, Bell S, Forni L, et al. Pathophysiology of COVID-19-associated acute kidney injury. Nat Rev Nephrol. 2021;17(11):751–64. Bock MJ, Kuhn MA, Chinnock RE. COVID-19 diagnosis and testing in pediatric heart transplant recipients. J Heart Lung Transpl. 2021;40(9):897–9. Louis DW, Saad M, Vijayakumar S, et al. The Cardiovascular Manifestations of COVID-19. Cardiol Clin. 2022;40(3):277–85. https://www.poltransplant.org.pl/statystyka_2022.html Sullivan MK, Lees JS, Drake TM, et al. Acute kidney injury in patients hospitalized with COVID-19 from the ISARIC WHO CCP-UK Study: a prospective, multicentre cohort study. Nephrol Dial Transpl. 2022;37(2):271–84. Cheng Y, Luo R, Wang K, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020;97:829–38. 10.1016/j.kint.2020.03.005 . Prompetchara E, Ketloy C, Palaga T. Immune responses in COVID-19 and potential vaccines: lessons learned from SARS and MERS epidemic. Asian Pac J Allergy Immunol. 2020;38:1–9. Yang L, Wang W, Wang X, et al. Creg in hepatocytes ameliorates liver ischemia/reperfusion injury in a TAK1-dependent manner in mice. Hepatology. 2019;69:294–313. 10.1002. Feng G, Zheng KI, Yan Q-Q, et al. COVID-19 and liver dysfunction: current insights and emergent therapeutic strategies. J Clin Transl Hepatol. 2020;8:1–7. Jones AE, Fiechtl JF, Brown MD, et al. Procalcitonin test in the diagnosis of bacteremia: a meta-analysis. Ann Emerg Med. 2007;50(1):34–41. Patton MJ, Orihuela CJ, Harrod KS et al. COVID-19 bacteremic co-infection is a major risk factor for mortality, ICU admission, and mechanical ventilation. Crit Care (London England), 27(1), 34. Schuetz P, Albrich W, Mueller B. Procalcitonin for diagnosis of infection and guide to antibiotic decisions: past, present and future. BMC Med. 2011;9:107. Chalmers S, Khawaja A, Wieruszewski PM, et al. Diagnosis and treatment of acute pulmonary inflammation in critically ill patients: the role of inflammatory biomarkers. WJCCM. 2019;8:74–96. Silaghi-Dumitrescu R, Patrascu I, Lehene M, Bercea I. Comorbidities of COVID-19 Patients. Med (Kaunas). 2023;59(8):1393. Jdiaa SS, Mansour R, El Alayli A, et al. COVID–19 and chronic kidney disease: an updated overview of reviews. J Nephrol. 2022;35:69–85. Ortiz A, Cozzolino M, Duivenvoorden R et al. Chronic kidney disease is a key risk factor for severe COVID-19: a call to action by the ERA-EDTA 2021; 36:87–94. Gansevoort RT, Hilbrands LB. CKD is a key risk factor for COVID-19 mortality 2020; 16:705–6. Appelman B, Oppelaar JJ, Broeders L et al. Mortality and readmission rates among hospitalized COVID-19 patients with varying stages of chronic kidney disease: a multicenter retrospective cohort 2022; 12:1–8. Penha D, Pinto EG, Matos F, et al. CO-RADS: Coronavirus Classification Review. J Clin Imaging Sci. 2021;11:9. Çomoğlu Ş, Öztürk S, Topçu A, et al. The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs. Curr Med Imaging. 2022;18(4):381–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 02 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviews received at journal 01 Aug, 2025 Reviewers agreed at journal 18 Jul, 2025 Reviewers invited by journal 09 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 01 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7023466","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483141195,"identity":"5f019f56-b7a6-4323-9057-4dd6d2b875f1","order_by":0,"name":"Olga Piątek-Dalewska","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3RMQuCQBTA8XcId4vl+sLBr6CbQ+FXUQSbcnEMQgjO5b5A38Q9uMmlLXARGlpraxE6w6Htbgy6P7zjLb/hcQA22y/GPi+qoWSY1qWWODNxgTrhtFJDAhOhaEQ8h10epI2DhPFiv2ih1JLV0a2QdBgJV8p+0UGlJeHZTf2RIxG45f2OQ8YNSP4iHBMR3HllSJhERTKBVDpGRN1CY3VLLroi98cO9bd4rLldSXvYNI2Mnqd2XXo6oj4kBFLPO6kx1QtgwzcBE2Kz2Wx/1hukqTbLjybfswAAAABJRU5ErkJggg==","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":true,"prefix":"","firstName":"Olga","middleName":"","lastName":"Piątek-Dalewska","suffix":""},{"id":483141196,"identity":"89ba0ebf-4dcb-46f9-8971-ac01e6ec9684","order_by":1,"name":"Krzysztof Kuziemski","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Krzysztof","middleName":"","lastName":"Kuziemski","suffix":""},{"id":483141197,"identity":"32c0932b-0f22-4dac-99cb-2de1c8cf80b5","order_by":2,"name":"Petra M. Grešner","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Petra","middleName":"M.","lastName":"Grešner","suffix":""},{"id":483141199,"identity":"af92fb96-a643-4166-9ba4-0e54f6785a5f","order_by":3,"name":"Alicja Zielińska","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Alicja","middleName":"","lastName":"Zielińska","suffix":""},{"id":483141200,"identity":"a52209c9-6627-4a7a-b309-5d601c1baef6","order_by":4,"name":"Tomasz Stefaniak","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Tomasz","middleName":"","lastName":"Stefaniak","suffix":""},{"id":483141201,"identity":"bb55cff7-2699-40e6-b443-341f71343b6d","order_by":5,"name":"Martyna Gołębiewska","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Martyna","middleName":"","lastName":"Gołębiewska","suffix":""},{"id":483141202,"identity":"75409599-e07e-47c1-8953-46e2df6b4fde","order_by":6,"name":"Damian Palus","email":"","orcid":"","institution":"Medical University of Gdańsk","correspondingAuthor":false,"prefix":"","firstName":"Damian","middleName":"","lastName":"Palus","suffix":""}],"badges":[],"createdAt":"2025-07-01 21:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7023466/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7023466/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-12269-4","type":"published","date":"2025-12-12T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87028417,"identity":"2efd7e8c-8437-4ffd-b0ba-e285c0c68e2e","added_by":"auto","created_at":"2025-07-18 12:32:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between the number of comorbidities and the number of complications accepted in the study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/db39a979e043d84c346e4360.png"},{"id":87028419,"identity":"5bc18d03-eb9b-4a7e-b325-409eae04431b","added_by":"auto","created_at":"2025-07-18 12:32:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":86969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Correlation between the number of comorbidities and the degree of lung parenchyma involvement. (B) Differences in lung parenchyma involvement between two groups of subjects obtained following the study group dichotomization based on median number of comorbidities (n=3).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/a25a90c27d2142786edc0936.png"},{"id":87028416,"identity":"37ccac3f-6e52-4cb5-984f-283b3c2b0034","added_by":"auto","created_at":"2025-07-18 12:32:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Correlation between the degree of lung parenchyma involvement and the number of COVID-19-related complications. (B) Differences in lung parenchyma involvement between two groups of subjects obtained following the study group dichotomization based on median number of complications (n=7).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/501b2435e00600532e0d0386.png"},{"id":87032334,"identity":"ec194cd5-b364-44d9-97c4-dd76b341d6f8","added_by":"auto","created_at":"2025-07-18 12:56:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67188,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios (HRs) for demise together with corresponding 95% confidence intervals calculated for all plausible predictors of COVID-19 related death included in the study measured at the beginning (1), middle (2), and the end (3) of hospitalization.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/8c51c5f6f3903532652439f2.jpeg"},{"id":87028424,"identity":"12e80350-293b-45f2-84b7-1db2c1fb2a98","added_by":"auto","created_at":"2025-07-18 12:32:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios (HRs) for demise together with corresponding 95% confidence intervals calculated for lymphocyte count and hemoglobin concentration measured at the beginning (1), middle (2) and the end (3) of hospitalization in a group of kidney transplant patients with active COVID-19 infection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/a6efe483ce3315d09b7e58c5.png"},{"id":87029982,"identity":"86d84bc8-16d4-408c-82cd-ac6247fa480d","added_by":"auto","created_at":"2025-07-18 12:40:01","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":180685,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Result section.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/4cce3b3043e7410661e05133.jpeg"},{"id":98243713,"identity":"e9e76422-530b-4757-87b0-c406ae67ec16","added_by":"auto","created_at":"2025-12-15 16:10:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2007886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7023466/v1/e0b59514-16c2-4ffd-b489-28675b0efc8e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognosis of patients after organ transplantation during active COVID-19 infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eViral infections, especially of the respiratory system, have been a threat to the health of the population for many years. In recent years, the World has seen the emergence of new and rapidly spreading viruses e.g. MERS (Middle East Respiratory Syndrom Coronavirus), hMPV (human metapneumovirus). This group has recently been joined by infections with the SARS-CoV-2 virus ( Severe Acute Respiratory Syndrome Coronavirus 2), which has permanently remained in our population.\u003c/p\u003e\u003cp\u003eThe first SARS-CoV-2 infection was detected in November 2019 in Wuhan, the capital of Hubei Province in China, with a population of twelve million people. In March 2020, the coronavirus pandemic spread to Europe, North America, and other continents. After over 3 years, on the 5th of May 2023, the WHO (World Health Organization) lifted the global pandemic status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. COVID-19 (coronavirus disease 2019) has changed the previous opinions about the spread of infectious diseases. It significantly impacted health and survival in the population, contributing to excess deaths. Despite the retraction of the pandemic status, COVID-19 continues to be a significant clinical and social problem. The initial stages of the pandemic were characterized by multiple early and late organ complications, respiratory failure, and substantial mortality. In the subsequent stages of the pandemic, effective new drugs and widely available preventive vaccinations were introduced, gradually reducing complications and significantly reducing mortality.\u003c/p\u003e\u003cp\u003eDespite this, new cases are still being reported. The disease manifests with a broad, variable range of symptoms and varying severity. However, the symptoms of COVID-19 infection are varied: from an asymptomatic course with minor symptoms of a respiratory infection to severe lung failure requiring intensive care. In extreme cases, it can still lead to multi-organ failure and death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The course and prognosis are influenced by advanced age, male gender [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], comorbidities, particularly the co-occurrence of obesity, metabolic syndrome, diabetes mellitus, hypertension, chronic kidney disease, circulatory and lung diseases, and cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Organ transplant recipients create a group that is particularly susceptible to full-blown disease and poorer prognosis. These patients are subject to long-term immunosuppression, in whom comorbidities are much more frequently noted [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Long-term maintenance immunosuppression with the use of a calcineurin inhibitor, on the one hand, weakens the immune system and prolongs the time of virus elimination but on the other hand, it reduces the intensity of the generalized inflammatory reaction caused by the virus. Reducing the inflammatory reaction may limit the occurrence of ARDS (acute respiratory distress syndrome), i.e. the most severe complications in this disease [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe global burden of COVID-19 cases prompts research to understand how risk factors, comorbidities may influence COVID-19 disease severity, ICU (Intensive Care Unit) admissions, and mortality. Organ transplant recipients experience an increased risk of respiratory and multi-organ failure during hospitalization due to generalized inflammation not only in the lungs but also in other organs [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For this reason, it is essential to identify available markers that affect early prognosis in this group of patients.\u003c/p\u003e\u003cp\u003eThe aim of the study was to identify simple and useful morphological and biochemical blood markers, as well as radiological features, which could predict the risk of death and short-term prognosis in organ transplant recipients hospitalized with COVID-19.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eA retrospective analysis of selected morphological, biochemical, and radiological parameters influencing early prognosis in organ transplant recipients hospitalized in the acute phase of COVID-19 infection was conducted. For this purpose, data from patients hospitalized at the University Clinical Center in Gdańsk, Poland (UCC) were used. Laboratory test results were analyzed according to the standard established by the UCC Gdańsk laboratory. The UCC is the largest academic hospital in northern Poland, with the highest reference level, and it hospitalizes patients requiring multi-specialist care. The hospital is one of the leading transplant centers for heart, kidney, lung, and liver organs. During the COVID-19 pandemic from 2020 to 2022, two hospital wards were designated at the UCC for hospitalized COVID-19 patients. Patients who did not require intubation were hospitalised in the stable COVID-19 ward (COVID-S), which, depending on the stage of the pandemic and demand, had 50 to 75 beds. Patients in unstable, life-threatening conditions requiring intubation and intensive care were admitted to the COVID-Intensive Care Unit (COVID-ICU).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGroup demographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included organ transplant recipients aged 18 years or older with a diagnosis of COVID-19 confirmed by a PCR (polymerase chain reaction) test, clinical symptoms of acute lower respiratory tract infection, and significant comorbidities. The main criteria for hospitalization in the COVID-S ward included:\u003c/p\u003e\n\u003cp\u003e1) oxygen peripheral saturation (SpO\u003csub\u003e2\u003c/sub\u003e) lower than 95% requiring oxygen therapy,\u003c/p\u003e\n\u003cp\u003e2) symptoms such as shortness of breath, severe cough, syncope, fatigue, diarrhea with SpO\u003csub\u003e2\u003c/sub\u003e equal to or higher than 95%,\u003c/p\u003e\n\u003cp\u003e3) complications of COVID-19 infection requiring hospitalization with SpO\u003csub\u003e2\u003c/sub\u003e equal or higher than 95%,\u003c/p\u003e\n\u003cp\u003e4) maintaining continuity of medical care in patients who had tested positive for SARS-CoV-2 during hospitalization in another ward [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFor patients with confirmed COVID-19 requiring intubation, at least two of the below-presented criteria, including criterion number 3, had to be met:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. Exacerbation of respiratory failure regardless of non-invasive ventilation methods as a result of:\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ea. oxygen peripheral saturation and partial oxygen pressure remaining persistently low, leading to retention of carbon dioxide and respiratory acidosis or\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003eb. intolerance to non-invasive ventilation, that is, acute dyspnea, increased breathing effort, anxiety with poor response to pharmacotherapy.\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e2. Surgery, injury, or cardiopulmonary resuscitation requiring intubation.\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e3. The patient required intensive care and could benefit from hospitalization in the ICU as determined by an anesthesiologist according to national guidance [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eEarly-phase post-myocardial infarction patients, patients after ischemic stroke and gastrointestinal hemorrhage, as well as patients requiring dialysis or administration of pressor amines were not admitted to COVID- ICU unless they required intubation and mechanical ventilation. Instead, they were treated in COVID- S ward. Advanced metastatic cancer and DNR (do not resuscitation) disqualified patients from transfer to the ICU.\u003c/p\u003e\n\u003cp\u003ePatients were ready for discharge from COVID-ICU:\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. After discontinuation of mechanical ventilation or\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e2. If further medical care could be continued in the COVID-S or pulmonology ward [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExperimental design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon admission to the COVID-S ward, blood tests (complete blood count, hemoglobin concentration, biochemical analysis, C-reactive protein (CRP), D-dimer, creatinine, glomerular filtration rate (GFR), procalcitonin (PCT), B-type natriuretic peptide (BNP), liver enzymes: aspartate aminotransferase (AST), alanine aminotransferase (ALT) were performed in all patients. Chest radiographs were performed according to indications (High-Resolution Computed Tomography (HRCT), angio-CT, or chest X-ray), or tests already conducted in other wards were accepted. Radiographs were performed only when the result could change the course of action, or COVID-19 complications were suspected. The severity of symptoms was assessed using the CRB-65 scale (confusion, respiratory rate, blood pressure, 65 years of age) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. The CRB-65 scale was used to assess the severity of pneumonia.\u003c/p\u003e\n\u003cp\u003eDepending on the clinical situation, individual tests were repeated or supplemented with subsequent ones when necessary. The severity of changes in the lung parenchyma was assessed according to the generally accepted CO-RADS scale (COVID-19 Reporting and Data System) [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn subsequent analyses, morphological and biochemical blood parameters from the beginning, middle part, and end part of hospitalization were considered individually for each patient, depending on the time of hospitalization. In analyzed radiological images, ground glass opacity, paving stones, consolidation in the lung parenchyma, inflammatory changes, fluid in the pleural cavities, nodules, and pulmonary embolism were considered. The length of hospitalization, the number of comorbidities, and the degree of progression were assessed along with their previous treatment included in the additional materials - Table A. Considering the available treatment against COVID-19, complications during hospitalization and the number of deaths. The treatment was modified according to the current state of knowledge and the guidelines of the Polish Society of Infectious Diseases [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComplications related to COVID-19 considered in the study included\u003c/strong\u003e:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e-death,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-prolonged hospitalization\u0026thinsp;\u0026gt;\u0026thinsp;10 days,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-need for respiratory therapy,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-need for dialysis in patients after kidney transplantation in the COVID-S Ward,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-radiological changes in the lung parenchyma related to COVID-19,\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-lymphopenia (\u0026lt;\u0026thinsp;1000 ul-1),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-anemia (blood hemoglobin concentration\u0026thinsp;\u0026lt;\u0026thinsp;10 g/dl),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-high CRP concentration in blood plasma (\u0026gt;\u0026thinsp;100 mg/l),\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e-high creatinine concentration (\u0026gt;\u0026thinsp;1.3 mg/dl).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study was conducted with the approval of the Ethics Committee of the Medical University of Gdansk, Poland (NKBBN/327/2022). The informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eQuantitative data were presented as either mean +/- standard deviation (SD) or median with interquartile range (IQR), depending on the normality of data distribution, which was checked using the Shapiro-Wilk W test. Nominal and/or ordinal data were presented as both absolute and relative counts (percentages). Simple correlations between two quantitative variables were sought through Spearman\u0026rsquo;s range correlation method, with the strength of correlation being expressed using Spearman\u0026rsquo;s correlation coefficient (r\u003csub\u003eSP\u003c/sub\u003e). In the case of correlation analysis between two ordinal variables, Kendall\u0026rsquo;s tau correlation was used instead. Simple between-group differences were tested for statistical significance using the Mann-Whitney \u003cem\u003eU\u003c/em\u003e test. Associations between the occurrence of COVID-19 complications and the length of the patient\u0026rsquo;s hospitalization were analyzed utilizing Fisher\u0026rsquo;s exact test, while Cox proportional hazard regression modelling was used to identify plausible demise risk predictors. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Data analysis was carried out using R [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003eSix hundred sixty-six patients (253 (38%) women; 413 (62%) men), initially admitted to the internal medicine and surgical clinics in the UCC Gdańsk, were hospitalized in the COVID-S wards between November 2, 2020, and March 7, 2022. The diagnosis of COVID-19 was established before transferring to the COVID-S ward. A detailed analysis was performed on a group of thirty-eight transplant patients who were hospitalized in the COVID-S ward. There were nine women and twenty-nine men in the group of transplant recipients. The type of organ transplant is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of patients was 54 (IQR: 47.25\u0026ndash;63.5). The median length of hospitalization in the COVID-S and COVID-ICU wards was 11.5 (IQR: 9.3-\u003c/p\u003e\u003cp\u003e18.8) days.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber and percentage of transplant recipients with corresponding mortality stratified by organ type.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll Tx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKidney Tx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLiver Tx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHeart Tx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLungs Tx\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4 (10.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of deaths (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOn average, organ transplant recipients with active COVID-19 infection included in the study received 3 (IQR: 3\u0026ndash;5; min-max: 0\u0026ndash;6) therapeutic procedures (antibiotic therapy, antiviral therapy, steroid therapy, convalescent plasma, ventilation support, respiratory therapy) - the list of treatments included in the additional materials - Table B.\u003c/p\u003e\u003cp\u003eThe study found that, on average, organ transplant recipients diagnosed with an active COVID-19 infection had three comorbidities. The distributions of their frequencies are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Cardiovascular and kidney diseases were the most prevalent among the patients. The interquartile range (IQR) for these comorbidities was between 2 and 5, highlighting the significant variability in health status among organ transplant recipients during the pandemic.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eFrequency of individual comorbidities in the study group.\u003c/b\u003e The included organ transplant patients with active COVID-19 infection had a mean of 7 (median; IQR: 5.25-7; min-max: 2\u0026ndash;12) COVID-19-related complications. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. presents detailed COVID-19-related complications in the study group. Additional laboratory parameters analyzed during hospitalization are included in the supplementary materials\u0026mdash;Table C.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eabsent (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epresent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease/ Myocardial infraction in history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrhythmia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eValve diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVenous thromboembolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus type 1 or 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemodialysis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (50)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstructive lung diseases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2 (5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral infarction in history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35 (92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComplications associated with COVID-19 and their incidence in the study group of patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNUMBER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCOMPLICATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDeath\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (65,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13 (34,2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eDialysis in the COVID ward\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22(84,6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4 (15,4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eRADIOLOGICAL CHANGES DESCRIBED IN COMPUTED TOMOGRAPHY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFluid in CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (67,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9 (32,1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrosted glass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (25,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21 (75,0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePaving stones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (78,6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (21,4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsolidations in the CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (25,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24 (75,0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmbolism in angio-CT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19 (95,0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (5,0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther changes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eCHANGES IN LABORATORY TESTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLymphopenia [ x10^9/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (36,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24(63,2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLymphopenia [ x10^9/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15 (41,7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21(58,3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLymphopenia [x10^9/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17 (47,2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19(52,8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHg (\u0026lt;\u0026thinsp;10) \u0026nbsp;[g/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26 (68,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12(31,6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHg (\u0026lt;\u0026thinsp;10) \u0026nbsp;[g/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21 (58,3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15(41,7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHg (\u0026lt;\u0026thinsp;10) \u0026nbsp;[g/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22 (61,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14(38,9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCRP (\u0026gt;\u0026thinsp;100) [mg/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (62,2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14(37,8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCRP (\u0026gt;\u0026thinsp;100) [mg/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25 (73,5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9(26,5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCRP (\u0026gt;\u0026thinsp;100) [mg/l]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (79,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7(20,6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCreatinine (\u0026gt;\u0026thinsp;1.3) [mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (13,2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33(86,8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCreatinine (\u0026gt;\u0026thinsp;1.3) [mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (22,9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27(77,1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCreatinine (\u0026gt;\u0026thinsp;1.3) [mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10 (29,4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24(70,6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLength of hospitalization\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHospitalization (\u0026gt;\u0026thinsp;10 days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14(36,8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24(63,2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eNECESSITY OF INTUBATION AND RESPIRATORY THERAPY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRespiratory therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27(71,1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11(28,9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLegend:\u003c/p\u003e\u003cp\u003eChanges in laboratory tests. Measurements performed according to the time of hospitalization.\u003c/p\u003e\u003cp\u003e(1) First measurement - beginning of hospitalization.\u003c/p\u003e\u003cp\u003e(2) Second measurement - middle of hospitalization.\u003c/p\u003e\u003cp\u003e(3) Third measurement - end of hospitalization.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe relationship between the number of comorbidities and complications associated with COVID-19.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn organ transplant recipients, a weak but statistically significant correlation between the number of comorbidities and the number of complications they experienced was observed (tau\u0026thinsp;=\u0026thinsp;0.26, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.068; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHowever, the number of comorbidities did not correlate significantly with the degree of lung involvement ; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. Similarly, following the dichotomization of patients into two groups with respect to median number of comorbidities (n\u0026thinsp;=\u0026thinsp;3), no statistically significant differences in the degree of lung involvement on radiographic examinations were found between the groups (42% [35% \u0026minus;\u0026thinsp;48%] vs. 24% [15% \u0026minus;\u0026thinsp;50%]; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe relationship between the degree of lung parenchyma involvement and the number of complications associated with COVID-19.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA positive but statistically insignificant correlation was observed between the number of complications associated with COVID-19 and the degree of lung parenchyma involvement (r\u003csub\u003eSP\u003c/sub\u003e = 0.52; \u003cem\u003eNS\u003c/em\u003e); Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. However, following the dichotomization of patients into two groups with respect to a median number of complications (n\u0026thinsp;=\u0026thinsp;7), patients with more than seven different complications showed a marginally increased degree of lung parenchyma involvement on radiological examinations compared to patients with the number of complications below the median (\u0026gt;\u0026thinsp;7 vs. \u0026lt;=7 complications 55% vs. 20%; p\u0026thinsp;=\u0026thinsp;0.055); Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eRelationship between the length of hospitalization and the occurrence of complications associated with COVID-19.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLonger hospital stays (more than 10 days) were found to be significantly associated with lower concentrations of creatinine in blood plasma (greater than 1.3 mg/dl) measured at the end of the hospitalization period (OR\u0026thinsp;=\u0026thinsp;0.0, 95%CI: 0.0-0.7; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results regarding the association of the length of hospitalization with other laboratory tests are presented in the additional materials. Table D.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe relationship between the length of hospitalization and the concentration of creatinine measured at the beginning (1), middle (2) and end (3) of hospitalization.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssociation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eshort/no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshort/yes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003elong/no\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003elong/yes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR [95% CI]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization (\u0026lt;\u0026thinsp;10 days) vs. Creatinine (1) (\u0026gt;\u0026thinsp;1.3) [mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.39 [0.01\u0026ndash;4.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization (\u0026lt;\u0026thinsp;10 days) vs. Creatinine (2) (\u0026gt;\u0026thinsp;1.3)[mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.25 [0-2.46]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization (\u0026lt;\u0026thinsp;10 days) vs. Creatinine (3) (\u0026gt;\u0026thinsp;1.3)[mg/dl]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0 [0-0.7]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of prognostic factors of COVID-19-related death.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Cox proportional hazard regression model showed that the risk of death in COVID-19 patients increases significantly with increasing plasma creatinine concentrations, regardless of the time at which this increase was observed (beginning of hospitalization: HR\u0026thinsp;=\u0026thinsp;1.4, 95% CI: 1.1\u0026ndash;1.7, p\u0026thinsp;=\u0026thinsp;0.003; mid-hospitalization: HR\u0026thinsp;=\u0026thinsp;1.5, 95% CI: 1.1\u0026ndash;1.8, p\u0026thinsp;=\u0026thinsp;0.004; end of hospitalization: HR\u0026thinsp;=\u0026thinsp;2.3, 95% CI: 1.5\u0026ndash;3.7, p\u0026thinsp;=\u0026thinsp;0.0001).\u003c/p\u003e\u003cp\u003eAdditionally, the risk of death was shown to increase significantly with the increase in other biochemical parameters, such as AST (HR\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 1.00-1.04; p\u0026thinsp;=\u0026thinsp;0.016) and PCT (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.07\u0026ndash;1.48; p\u0026thinsp;=\u0026thinsp;0.006) measured at the beginning of hospitalization and with the increase in CRP (HR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00-1.01; p\u0026thinsp;=\u0026thinsp;0.04) and PCT (HR\u0026thinsp;=\u0026thinsp;1.3, 95% CI: 1.0-1.7; p\u0026thinsp;=\u0026thinsp;0.052) measured during hospitalization. The risk of death increases significantly also with increasing CRB-65 (HR\u0026thinsp;=\u0026thinsp;2.6, 95% CI: 1.3\u0026ndash;5.2; p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e\u003cp\u003eContrary to this the risk of death decreased significantly with increasing GFR.mdrd (glomerular filtration rate - Modification Of Diet In Renal Disease; HR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.82\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.005) and GFR CKD (glomerular filtration rate Chronic Kidney Disease; HR\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.81\u0026ndash;0.96; p\u0026thinsp;=\u0026thinsp;0.005) in plasma at the beginning of hospitalization, as well as with increasing lymphocyte count in the middle (HR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.05-1.00; p\u0026thinsp;=\u0026thinsp;0.051) and at the end of hospitalization (HR\u0026thinsp;=\u0026thinsp;0.3, 95% CI: 0.1\u0026ndash;0.9; p\u0026thinsp;=\u0026thinsp;0.0323). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents observed values of odds ratios (OR) for all plausible prognostic factors of COVID-19-associated demise evaluated in this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe impact of anemia and lymphopenia on COVID-19-related deaths among kidney transplant patients with active COVID-19 infection.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe risk of death in kidney transplant patients with active COVID-19 infection significantly decreased with increasing lymphocyte count at the end of hospitalization (HR\u0026thinsp;=\u0026thinsp;0.04, 95% CI: 0.0-0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No similar association was demonstrated for hemoglobin concentration regardless of the measurement time. The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation between CRP in blood plasma and procalcitonin.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStatistically significant positive correlations were shown between CRP and procalcitonin concentration in blood. At the beginning of hospitalization, the correlation between CRP and PCT was moderate (RSP\u0026thinsp;=\u0026thinsp;0.40; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while in the middle of hospitalization it was strong (RSP\u0026thinsp;=\u0026thinsp;0.72; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Figures showing these correlations can be found in additional materials - Figure A.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the acute stage of COVID-19 infection, the respiratory system is mainly involved, but other organs, including transplanted organs, are also directly affected. The virus enters target cells by endocytosis through the angiotensin-converting enzyme 2 (ACE2) receptor associated with the cell membrane. ACE2 is present in the lungs, kidneys, heart, and bowels [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This has a fundamental prognostic significance for patients after organ transplantation with concomitant acute COVID-19 infection. In COVID-19, the expression of the ACE2 receptor is significantly reduced. The result is an increased concentration of angiotensin II (A2). A2 has a pro-inflammatory effect, increases neutrophil infiltration in organs and the release of pro-inflammatory cytokines. Consequently, the permeability of blood vessels increases, damaging end organs such as kidneys, liver, heart, and lungs [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe conducted a retrospective single-center study to analyze factors influencing early hospital mortality in patients with COVID-19. The clinical course and prognosis of patients hospitalized with COVID-19 is very diverse and uncertain. This study assessed patients hospitalised due to COVID-19 after solid organ transplantation (SOT). In Poland, the kidney is the most frequently transplanted solid organ, so patients after kidney transplantation constitute the majority. In 2020, 717 kidney transplants were performed in Poland from deceased donors and 31 from living donors; in 2021, 709 kidney transplants were performed from deceased donors and 44 from living donors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients who undergo transplantation face a high risk of severe infections, including opportunistic infections. The chronic need to use immunosuppressive drugs reduces the proper immune defence, thus infection with the SARS-CoV2 virus more often leads to multi-organ complications and death in these patients. Transplanted organs may be the target of the SARS-CoV-2 virus. In these cases, inflammation occurs in the microcirculation vessels, and the inflammatory state is mediated by a cytokine storm, leading to multiorgan failure and patient death [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results obtained indicate that elevated plasma creatinine levels may represent a significant prognostic factor in patients following organ transplantation. Regardless of the timing during hospitalization when the increase was observed, higher creatinine levels were consistently associated with an increased risk of mortality. This suggests that renal dysfunction\u0026mdash;whether occurring early or later during the hospital stay\u0026mdash;has important prognostic implications and may reflect the overall severity of the clinical condition.\u003c/p\u003e\u003cp\u003eAn interesting and somewhat paradoxical finding was the inverse relationship observed between elevated creatinine levels and longer hospitalization, defined as a stay exceeding 10 days. While creatinine is typically considered a marker of impaired kidney function and deteriorating clinical status, in our cohort, elevated levels may have indicated earlier recognition of critical illness. This, in turn, could have led to more rapid clinical decision-making and intensified management, including earlier transfer to the intensive care unit. As a result, these patients, despite their serious condition, may have received more prompt treatment, potentially leading to a shorter overall hospital stay. While this interpretation requires further investigation, it may carry significant clinical implications, highlighting the value of early identification of high-risk patients.\u003c/p\u003e\u003cp\u003eOur findings also suggest that in addition to creatinine, other biochemical and clinical markers may serve as predictors of poor outcomes. At the beginning of hospitalization, elevated levels of aspartate aminotransferase (AST), procalcitonin (PCT), and higher CRB-65 scores were associated with worse prognosis. These parameters may reflect ongoing inflammatory processes, organ injury, or burden on the respiratory and cardiovascular systems\u0026mdash;findings consistent with previous studies in transplant recipients and patients with severe infections.\u003c/p\u003e\u003cp\u003eIn the middle phase of hospitalization, persistently elevated CRP and PCT levels\u0026mdash;both classic markers of inflammation and infection\u0026mdash;were also associated with reduced survival. Their continued elevation may signal inadequate treatment response or the development of infectious complications.\u003c/p\u003e\u003cp\u003eNotably, increased lymphocyte counts at the end of hospitalization were associated with better prognosis, possibly indicating an improvement in immune system function and more effective control of the disease process. In transplant recipients, whose immune responses are deliberately modulated by immunosuppressive therapy, this parameter may hold particular prognostic and clinical significance. These findings emphasize the importance of dynamic patient assessment at various stages of hospitalization and underline the need for further prospective studies to validate these observations and develop consistent diagnostic and therapeutic strategies for this patient population. The rapid increase in creatinine levels in people with COVID-19 infection is likely due to the virus accumulating in the kidneys through blood spread, leading to renal cell necrosis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In patients with high creatinine levels, a more severe course of the underlying COVID-19 disease was observed. Our analysis showed that dialysis therapy conducted before admission to COVID-19 wards and dialysis in COVID-S did not affect the occurrence of COVID-19 complications.\u003c/p\u003e\u003cp\u003eAnother key biomarker of the risk of death in the study group was increased AST concentration. The mechanism of liver dysfunction in COVID-19 is secondary damage to hepatocytes by a systemic inflammatory response and the use of hepatotoxic drugs, including antiviral drugs, used to treat COVID-19. Similar damage to hepatocytes may be generated by hypoxemia, which is a characteristic feature of full-blown COVID-19 [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother marker of an unfavourable prognosis was an increase in CRP and PCT levels. Both indicators are associated with the development of inflammation. CRP correlates with the severity of the disease [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. An increase in PCT level is related to bacterial infection, which may be a late complication of COVID-19. Known factors that trigger PCT synthesis include bacterial toxins and proinflammatory cytokines, particularly interleukin-6, a marker of the cytokine storm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In the case of complicated viral COVID-19 infection, PCT levels increase and indicate bacterial infection, requiring treatment intensification by including appropriate antibiotics. Bacterial superinfections are challenging to diagnose and often lead to serious complications, including pneumonia and acute respiratory distress syndrome (ARDS). Published studies show that secondary bacterial infections in critically ill COVID-19 patients significantly increase mortality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. CRP and PCT are considered the most effective and sensitive biomarkers in predicting the progression of Covid-19 disease [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA characteristic feature at the beginning of COVID-19 infection is a low lymphocyte count. Their increase is observed as the general condition improves and the response to treatment is favorable. It has been shown that the risk of death among transplant patients decreases significantly with the increase in lymphocytes at the end of the hospitalization period. An increase in the concentration of lymphocytes can be another prognostic indicator of the course of infection and early risk of death.\u003c/p\u003e\u003cp\u003eIn transplant patients, several additional diseases often coexist. The type and severity of diseases may affect early and long-term prognosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It has been shown that the coexistence of chronic kidney disease may be an independent risk factor for death [\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study also analyzed comorbidity. Interestingly, we did not confirm that the number of these diseases significantly correlated with the degree of lung parenchyma involvement in radiological examinations, even though the marginally increased (p\u0026thinsp;=\u0026thinsp;0.055) degree of lung parenchyma involvement among patients with more than 7 (median) comorbidities may warrant further investigation.\u003c/p\u003e\u003cp\u003eThe type of radiological examination and the need to use contrast often depend on the degree of renal function. Chest CT with contrast medium was performed when there was a significant suspicion of pulmonary embolism. Inthe transplant recipients, due to coexisting renal failure, chest CT without contrast was performed if indicated. In some patients, due to their serious health condition and high creatinine concentration, radiological examinations were not performed. Additionally, a 6-point radiological CORADS scale was introduced for diagnosing COVID-19, which assessed inflammatory changes in the lung parenchyma. The assessment of patients with a positive PCR result was problematic. A positive PCR result classified patients as CODARS 6, i.e., the most advanced stage of the disease, even without changes in the lung parenchyma. This approach significantly influenced the assessment of patients and therapeutic options [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Along with the stages of the pandemic, variable treatment was used as per current guidelines. In Poland, the Polish Society of Epidemiologists and Infectious Disease Physicians' guidelines were widely used. These guidelines comprehensively assessed diagnostic and therapeutic options depending on the disease stage and the pandemic period [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Advanced treatment reflected the patient's serious condition or the occurrence of complications that required treatment. In the study group, secondary bacterial superinfections, including those caused by opportunistic bacteria, were common, necessitating treatment escalation and broad-spectrum antibiotic therapy. If profound anemia was detected, treatment with blood products was initiated. In people with increasing respiratory failure, HFNO (High-Flow Nasal Oxygen) and NIV (non-invasive mechanical ventilation) were used, and patients with critical respiratory and multi-organ failure were referred to the ICU-COVID department. In the analyzed study, the form of treatment was not interpreted as a factor increasing the risk of death due to COVID-19.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLIMITATIONS OF THE STUDY\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study has several limitations. First, it was a single-center retrospective study. Second, the analyzed group of patients was relatively small due to the specificity of the studied cohort. Third, the study did not consider the time since the transplantation of a given organ and the duration of immunosuppressive treatment, which could have affected the prognosis of the patients. Fourth, patients were admitted to COVID-S from various internal medicine and surgical clinics. The patients stayed in isolated shared rooms with other people. Such action could have contributed to the faster occurrence of complications complicating the disease.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eAnalysis of hospitalization of patients after organ transplantation with active COVID-19 allows us to determine key information that may be useful in the diagnosis of viral diseases, as well as in determining the prognosis of patients during viral infections in the future. Rapid diagnosis and intensive multi-specialist treatment in patients with immunosuppression is crucial. It is difficult to predict the exact prognosis of these patients. We have shown that the increase in creatinine, AST, CRP, and PCT levels may be associated with an increased risk of death in the group of organ transplant recipients with acute COVID-19 infection. On the other hand, the increase in lymphocytes translates into a better prognosis for patients in this group.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMERS (Middle East Respiratory Syndrom Coronavirus);\u003c/p\u003e\n\u003cp\u003ehMPV (human metapneumovirus);\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2 virus (\u0026nbsp;Severe Acute Respiratory Syndrome Coronavirus 2);\u003c/p\u003e\n\u003cp\u003eWHO (World Health Organization);\u003c/p\u003e\n\u003cp\u003eCOVID-19 (coronavirus disease 2019);\u003c/p\u003e\n\u003cp\u003eARDS (acute respiratory distress syndrome);\u003c/p\u003e\n\u003cp\u003eICU (Intensive Care Unit);\u003c/p\u003e\n\u003cp\u003eUCC (University Clinical Center);\u003c/p\u003e\n\u003cp\u003eCOVID-ICU (COVID-Intensive Care Unit);\u003c/p\u003e\n\u003cp\u003ePCR (polymerase chain reaction);\u003c/p\u003e\n\u003cp\u003eDNR (do not resuscitation);\u003c/p\u003e\n\u003cp\u003eCRP (C-reactive protein);\u003c/p\u003e\n\u003cp\u003eGFR (glomerular filtration rate);\u003c/p\u003e\n\u003cp\u003ePCT (procalcitonin);\u003c/p\u003e\n\u003cp\u003eBNP (B-type natriuretic peptide);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAST (aspartate aminotransferase);\u003c/p\u003e\n\u003cp\u003eALT (alanine aminotransferase);\u003c/p\u003e\n\u003cp\u003eHRCT (High-Resolution Computed Tomography);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRB-65 scale (confusion, respiratory rate, blood pressure, 65 years of age);\u003c/p\u003e\n\u003cp\u003eCO-RADS (COVID-19 Reporting and Data System);\u003c/p\u003e\n\u003cp\u003eSD (standard deviation);\u003c/p\u003e\n\u003cp\u003eIQR \u0026nbsp;(interquartile range);\u003c/p\u003e\n\u003cp\u003eGFR.mdrd (glomerular filtration rate - Modification Of Diet In Renal Disease);\u003c/p\u003e\n\u003cp\u003eGFR CKD (glomerular filtration rate Chronic Kidney Disease);\u003c/p\u003e\n\u003cp\u003eHRs (Hazard ratios);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACE2 (angiotensin-converting enzyme 2);\u003c/p\u003e\n\u003cp\u003eA2 (angiotensin II);\u003c/p\u003e\n\u003cp\u003eSOT (solid organ transplantation);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHFNO (High-Flow Nasal Oxygen);\u003c/p\u003e\n\u003cp\u003eNIV (non-invasive mechanical ventilation).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval was obtained from the ethics committee of the Medical University of Gdansk. Ethics approval number: NKBBN/327/2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or proprietary interest in any material discussed in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePDO, KK were involved in the writing of the manuscript and in the interpretation of the results. PMG performed the statistical analysis. PDO, ZA \u0026nbsp;were involved in the data collection process and the study coordination. ST database sharing. GM linguistic verification. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO Director-General's opening remarks at the media briefing \u0026ndash; 5 May 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/speeches/item/who-director-general-s-opening-remarks-at-the-media-briefing---5-may-2023\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/speeches/item/who-director-general-s-opening-remarks-at-the-media-briefing---5-may-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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WJCCM. 2019;8:74\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilaghi-Dumitrescu R, Patrascu I, Lehene M, Bercea I. Comorbidities of COVID-19 Patients. Med (Kaunas). 2023;59(8):1393.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJdiaa SS, Mansour R, El Alayli A, et al. COVID\u0026ndash;19 and chronic kidney disease: an updated overview of reviews. J Nephrol. 2022;35:69\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrtiz A, Cozzolino M, Duivenvoorden R et al. Chronic kidney disease is a key risk factor for severe COVID-19: a call to action by the ERA-EDTA 2021; 36:87\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGansevoort RT, Hilbrands LB. CKD is a key risk factor for COVID-19 mortality 2020; 16:705\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAppelman B, Oppelaar JJ, Broeders L et al. Mortality and readmission rates among hospitalized COVID-19 patients with varying stages of chronic kidney disease: a multicenter retrospective cohort 2022; 12:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePenha D, Pinto EG, Matos F, et al. CO-RADS: Coronavirus Classification Review. J Clin Imaging Sci. 2021;11:9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Ccedil;omoğlu Ş, \u0026Ouml;zt\u0026uuml;rk S, Top\u0026ccedil;u A, et al. The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs. Curr Med Imaging. 2022;18(4):381\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, organ transplantation, immunosuppression, prognostic markers, mortality, lymphocytes, creatinine, procalcitonin, C-reactive protein (CRP), SARS-CoV-2","lastPublishedDoi":"10.21203/rs.3.rs-7023466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7023466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSolid organ transplant recipients represent one of the most clinically vulnerable populations in the ongoing COVID-19 pandemic. Chronic immunosuppression and multiple comorbidities significantly increase the risk of severe disease and death. Despite global advances in vaccination and therapy, practical tools for early risk assessment in this group remain limited. This study addresses a critical gap by identifying simple and universally available clinical markers that can be used at hospital admission to predict short-term outcomes in transplant recipients with COVID-19.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe retrospectively analyzed a cohort of solid organ transplant recipients hospitalized with confirmed COVID-19 at the University Clinical Center in Gdańsk, Poland, between 2020 and 2022. Patients were admitted either to a general COVID-19 ward or to an intensive care unit based on disease severity. Data were collected from electronic medical records and included demographic characteristics, chest imaging, and routine laboratory tests: lymphocyte count, serum creatinine, aspartate aminotransferase, C-reactive protein, and procalcitonin. Statistical analyses were performed to evaluate the association of these variables with in-hospital mortality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur analysis identified that elevated levels of serum creatinine, aspartate aminotransferase, C-reactive protein, and procalcitonin were significantly associated with increased risk of in-hospital mortality. Conversely, a higher lymphocyte count at admission was associated with better survival. These markers are widely available, inexpensive, and can be assessed during the initial diagnostic workup.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study offers a practical, evidence-based approach to early risk stratification in transplant recipients with COVID-19. The identified laboratory markers are not only simple and accessible but also clinically meaningful, providing frontline clinicians with actionable information during the early stages of care. Given the ongoing threat of COVID-19 and the likelihood of future viral outbreaks, these findings have broad implications for managing immunosuppressed patients. The study contributes valuable insight to a currently underserved area of transplant medicine and has strong potential to inform clinical practice across diverse healthcare settings.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrial registration\u003c/b\u003e\u003c/p\u003e\u003cp\u003enot applicable\u003c/p\u003e","manuscriptTitle":"Prognosis of patients after organ transplantation during active COVID-19 infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 12:31:56","doi":"10.21203/rs.3.rs-7023466/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T12:34:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T10:15:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T20:36:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69652388968726976844935478267468422568","date":"2025-09-23T18:44:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182245068123803606514619365918408508171","date":"2025-09-21T16:28:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-01T06:57:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174050630107042413597335961016037231839","date":"2025-07-18T13:50:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T19:40:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-02T23:38:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-02T23:37:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-07-01T20:54:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec5950c2-d7ce-4e46-9bb9-f797c12a24de","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-7023466","link":"https://doi.org/10.1186/s12879-025-12269-4","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-12-12 15:57:53","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-07-18 12:31:56","video":"","vorDoi":"10.1186/s12879-025-12269-4","vorDoiUrl":"https://doi.org/10.1186/s12879-025-12269-4","workflowStages":[]},"version":"v1","identity":"rs-7023466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7023466","identity":"rs-7023466","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}