01 July 2024: Clinical Research
Effect of COVID-19 on Thrombosis Incidence and Patient Prognosis in Kidney Transplant Recipients
Hao Jiang1ABCDEFG*, Zhijun Cao2ABC, Li Liu3AC, Yuhua Huang2DEDOI: 10.12659/MSM.944285
Med Sci Monit 2024; 30:e944285
Abstract
BACKGROUND: Thrombosis poses a grave threat to patients undergoing kidney transplants, with a heightened risk of mortality. While previous studies have established a link between COVID-19 and thrombosis, the specific association between COVID-19 and thrombosis in this patient population remains unexplored.
MATERIAL AND METHODS: We conducted a retrospective analysis utilizing data from 394 individuals who underwent kidney transplantation within the period of September 1, 2015, to April 1, 2023. To evaluate overall survival, we employed Kaplan-Meier analysis and utilized a logistic regression model for risk analysis. Furthermore, we developed a prediction model and assessed its accuracy through calibration curves.
RESULTS: Out of the 394 patients included in our study, a total of 51 individuals experienced thrombosis, resulting in 2 deaths. Our analysis revealed that COVID-19 infection significantly increased the risk of thrombosis (odds ratio [OR] 8.60, 95% confidence interval 3.13-24.74, P<0.01). Additionally, the use of cyclosporine was found to elevate the risk of death (OR 20.86, 95% CI 7.93-59.24, P<0.01) according to multifactorial analysis. Logistic models were employed to screen variables, and predictive models were constructed based on the presence of COVID-19 infection and the usage of cyclosporine. A nomogram was developed, demonstrating promising accuracy in estimating the risk of thrombosis during internal validation, with a corrected C-index of 0.869.
CONCLUSIONS: Our study suggests that both COVID-19 infection and the use of cyclosporine can serve as reliable predictors of thrombosis risk in patients undergoing renal transplantation. Furthermore, we developed a mortality risk prediction model based on COVID-19 in assessing thrombosis.
Keywords: Kidney Transplantation, Risk Assessment, Thrombosis, Adult Multisystem Inflammatory Disease, COVID-19 Related
Introduction
It is crucial to recognize and address the issue of thrombosis following kidney transplantation. Thrombosis, which is the formation of blood clots, poses a significant concern in these patients. The risk of developing thrombosis is heightened due to various factors such as surgical trauma, immunosuppressive medications, and altered blood flow dynamics. These blood clots can obstruct blood vessels and compromise the transplanted kidney’s functioning, leading to severe complications and even graft loss. Therefore, healthcare professionals should remain vigilant in monitoring and managing thrombosis post-transplantation to ensure optimal outcomes for patients. Early detection, preventive strategies, and appropriate anticoagulation therapies are necessary to mitigate the risk of thrombotic events and promote long-term graft survival [1,2].
Various factors can lead to the development thrombosis. Additionally, thrombosis can be triggered by medication-induced conditions such as immunosuppression, chemotherapeutic agents, and vascular endothelial growth factor (VEGF) inhibitors, or by bleeding and clotting abnormalities associated with autoimmune diseases, infections, or malignant hypertension [3]. In patients undergoing bone marrow transplantation, secondary thrombosis can appear as a life-threatening complication, and its incidence after bone marrow transplantation ranges from 0.5% to 63.6%, depending on diagnosis methods [4,5]. Solid organ transplantation, notably kidney transplantation, can also result in thrombosis, which is often linked to allograft dysfunction or even allograft loss. While kidney transplantation outcomes have generally improved over time, many variables can impact graft survival. Vo et al’s study [6] of 226 patients identified thrombosis as a critical indicator of graft failure. Indeed, thrombosis has been recognized as a severe complication of kidney transplantation that involves several mechanisms, including post-transplantation medication, antibody-mediated rejection (AMR), ischemia/reperfusion injury, and endothelial damage due to viral infection [7,8]. In patients with end-stage kidney disease (ESKD) after an Acute Hemolytic Uremic Syndrome (AHUS) episode, renal transplantation outcomes are frequently complicated by disease relapse, which can occur in 20% to 70% of patients who do not receive anti-relapse therapies [9,10]. The likelihood of relapse is significantly influenced by particular complement gene abnormalities responsible for the disease [11]. Thrombosis progresses rapidly and leads to significant kidney damage and graft failure. Prompt detection and management, along with a comprehensive understanding of the underlying mechanisms, are critical for reversing thrombosis without delay [12].
Individuals afflicted with the coronavirus disease, commonly known as COVID-19, display coagulation that are like other systemic coagulation maladies linked with severe infections like disseminated intravascular coagulation. Furthermore, in COVID-19 patients, pulmonary embolism and deep vein thrombosis are common occurrences [13]. Pulmonary embolism and deep vein thrombosis are common occurrences in COVID-19 patients, with incidence rates varying based on the severity of the disease and availability of routine ultrasound investigations; they are more frequently observed among patients hospitalized in the intensive care unit (ICU) [14]. However, there is no existing research on the correlation between thrombosis and COVID-19.
The focus of this investigation was to retrospectively analyze the risk factors linked to the development of thrombosis in renal transplant patients during the COVID-19 pandemic. Additionally, we aimed to identify possible prognostic indicators for these patients. We also developed a predictive model to assess the risk of thrombosis occurrence by considering factors such as neo-coronary infection and the use of cyclosporine. The nomograms that we created are user-friendly tools that clinicians can utilize to make informed decisions based on reliable evidence.
Material and Methods
PATIENTS:
This real-world study retrospectively analyzed data from 394 patients who underwent kidney transplantation at the First Affiliated Hospital of Soochow University between September 1, 2015, and April 1, 2023. Of these patients, 29 were diagnosed with thrombosis based on imaging evidence. Patients received postoperative anticoagulation with low molecular weight heparin. All patients who developed postoperative thrombosis had deep vein thrombosis, and there were no cases of pulmonary embolism. Patients were identified as having COVID-19 if they tested positive through both imaging and virological assessments. All the patient contracted COVID-19 after the surgery. We obtained consent from all patients or their immediate family members and conducted our research program according to the guidelines set forth by the Ethics Committee of Soochow University and the Declaration of Helsinki (Figure 1).
STATISTICAL ANALYSIS:
Sample size assessment was conducted using NCSS-PASS software (version 12.0), with a power of 0.99 and α set at 0.5. Missing values (≤5.0%) were estimated through the random forest method utilizing the ‘mice’ package in RStudio (R version 4.0.2). Categorical variables were represented as proportions and matched by the κ2 test. Normally and skewed distributed variables were presented as median with mean±standard deviation and quartile range for continuous variables. Comparisons between groups were conducted using one-way ANOVA (normal) or Kruskal-Wallis tests (skewed), supplemented with appropriate post hoc testing for pairwise comparisons among the 4 stages. Mortality was displayed cumulatively using Kaplan-Meier curves and resolved via log-rank testing. Univariate and multivariate survival responses of OS were adjusted using Cox-regression models to estimate OS, and forest plots were utilized to visualize the significance of prognosis by covariate. Constrained cubic spline alignments were performed using Harrell’s regression modeling R package ‘rms.’ To establish prognostic risk, Cox multifactor regression models were employed to identify risk factors (variants with p-values below 0.05 were included in the model). The weight of each variant was quantified, Nomograms were generated, and internal validation was performed via 1001 bootstrapping. A calibration test estimated the concordance of the derived model. Decision curve analysis was applied to evaluate the clinical benefit of the model compared to traditional prognostic scoring based on clinical benefit. Log-rank tests and KM curves were used to analyze associations between factors and survival outcomes. Statistical analyses were conducted using RStudio (R version 4.0.2) and the following R packages: “rms”, “ggplot2”, “risk regression”, “PredictABLE,” and “survminer.”
Results
The study included a cohort of 394 patients who underwent kidney transplantation between September 2015 and April 2023. The follow-up period revealed an overall mortality rate of 0.7%. Among the enrolled patients, 251 (64.0%) were male, and the median age was 42 years (range: 35–51 years). Preexisting chronic conditions included hypertension in approximately 353 (90.0%) patients, while diabetes mellitus and hyperuricemia were present in 38 (10.0%) and 31 (8%) patients, respectively. Chronic glomerulonephritis was observed in 65 (16%) patients, with anti-rejection medications comprising cyclosporine in 62 (15.7%) patients and tacrolimus in 332 (84.3%) patients. 82 (21.0%) patients experienced renal dysfunction, and 51 (13.0%) patients contracted COVID-19 infection. Notably, thrombosis occurred in 29 (7.3%) cases (Table 1).
When examining various indicators, notable differences in distribution between patients with thrombosis and those without thrombosis were observed. Thrombosis patients had a higher percentage of diabetes compared to non-thrombosis patients (24.0% vs 8.0%,
Furthermore, Kaplan-Meier curves in Figure 2A indicated a substantial increase in cumulative death incidence among renal transplant patients who developed thrombosis, in comparison to those without thrombosis (log-rank
According to the results presented in Table 2, univariate analysis identified several risk factors for thrombosis in renal transplant patients, including diabetes, post-transplant use of cyclosporine anti-rejection medication, perioperative COVID-19 infection, and high pre-transplant creatinine levels. Further multivariate analysis confirmed that COVID-19 infection (8.60 [3.13, 24.74],
To develop a predictive model for thrombosis after renal transplantation, we utilized multivariate LASSO regression. The results indicated that the model exhibited a better fit when perioperative COVID-19 infection, post-transplant use of cyclosporine, pre-transplant creatinine level, and prevalence of diabetes mellitus were employed as predictor variables. The model achieved optimal performance with 4 predictor variables (Figure 3A, 3B).
Following LASSO regression and multifactorial analysis, we developed a nomogram model to predict the occurrence of thrombosis after renal transplantation (Figure 4A). To internally validate this prediction model, we utilized the bootstrap validation method and obtained a C-statistic of 0.869 for estimating the risk of death. In the validation cohort, the nomogram demonstrated an estimated C-statistic of 0.869 for overall survival (OS). Additionally, a well-calibrated curve is presented in the estimates section of Figure 4B, indicating the reliability of the nomogram’s predictions.
Discussion
Our study aimed to investigate the relationship between thrombosis and COVID-19 infection in kidney transplant patients. To achieve this, we analyzed data from a cohort of 394 patients who underwent kidney transplantation between 2015 and 2023. Our analysis revealed that COVID-19 infection was identified as a significant risk factor for developing thrombosis in these patients. Additionally, the post-transplant use of cyclosporine was associated with an increased risk of death. Building upon these findings, we developed a prediction model utilizing COVID-19 infection and cyclosporine use as predictors to forecast the risk of thrombosis in kidney transplant patients. The performance of our prediction model was excellent, demonstrating high accuracy with a corrected C-index of 0.869 in estimating the risk of thrombosis.
Our study discovered that the use of cyclosporine increases the risk of thrombosis following renal transplantation. This often presents as acute kidney injury within hours or days after drug administration, and drug-induced thrombosis can also be observed [15, 16]. Previous studies have established a correlation between the calpain inhibitor (CNI), cyclosporine, and de novo thrombosis [17], but the exact mechanism by which cyclosporine leads to the development of thrombosis remains unclear.
One proposed mechanism contributing to thrombosis’s development is an altered balance of vasoactive peptides, caused by increased release of vasoconstrictors (thromboxane and endothelin) and decreased production of vasodilators (prostaglandins and prostacyclin). This leads to arterial vasoconstriction and endothelial injury secondary to renal ischemia [18,19]. Increased platelet procoagulant activity and antifibrinolytic activity may also play a role following CNI use, particularly when the endothelium is already injured by ischemia-reperfusion injury, AMR, or other mechanisms [20,21]. Furthermore, an experimental study conducted by Renner et al showed that cyclosporine induces the release of endothelial microparticles that activate complement alternative pathway (AP), which has been widely implicated in causing thrombosis [22].
As previously mentioned, while thrombosis diagnosed based on a kidney biopsy from a patient undergoing CNI therapy is often attributed to CNIs, evidence suggests that this is not always the case. Over 95% of kidney transplant recipients use CNIs to maintain immunosuppression, but only a small percentage develop thrombosis, indicating that other underlying predisposing factors may exist in these patients [23]. A USRDS-based study has documented a significantly higher incidence of thrombosis in patients who did not receive initial maintenance CNI treatment compared to those who received CNI maintenance therapy (11.9 versus 5.0 episodes/1000 person-years) [24]. Other studies have also questioned the involvement of CNIs in the development of thrombosis. Satoskar et al demonstrated that temporary or permanent discontinuation of CNIs after the diagnosis of thrombosis associated with AMR had no impact on graft outcomes [25].
There is increasing evidence indicating that mammalian target of rapamycin (mTOR) inhibitors, including sirolimus and everolimus, used alone or in combination with cyclosporine, may be associated with the pathogenesis of de novo thrombosis [7,26,27]. The risk of thrombosis is even higher when a combination of CNI/mTORi is utilized compared to either medication alone. In a study conducted at Ohio State University, biopsy material reviewed following an immunosuppressive regimen based on a combination of sirolimus and cyclosporine with steroid avoidance showed an incidence of de novo thrombosis of 3.6% in biopsies without evidence of AMR [25]. Fortin et al evaluated the association between new immunosuppressive regimens and the risk of thrombosis in a cohort study involving 368 patients who received a kidney or kidney-pancreas transplant. They assessed 4 immunosuppressive regimens as potential risk factors for thrombosis and observed the highest risk in groups where CNIs and mTORi were used together [26].
Our study also found that COVID-19 is a significant factor in the development of thrombosis. However, previous studies have indicated that thrombosis is closely linked to abnormalities in the complement system. While the risk factors mentioned above are typical in transplant recipients, only a small percentage of kidney graft recipients develop thrombosis, suggesting that some underlying factors may predispose patients to this condition. Le Quintrec et al demonstrated that genetic complement abnormalities may represent risk factors for de novo thrombosis after kidney transplantation [28]. They conducted screening for mutations in genes that encode CFH and complement factor I (CFI) and membrane cofactor protein (MCP) in 24 kidney transplant recipients who experienced de novo thrombosis. Out of the 24 patients, 6 (25%) had low complement C3 (C3) or low complement factor B (CFB) levels, indicating AP of complement activation. Additionally, a mutation in either the CFH or CFI genes was identified in 7 of the 24 patients (29%), with 2 having a mutation in both genes. Conversely, no mutation was identified in the 25 kidney transplant recipients without de novo thrombosis or in the 100 healthy controls. The CFH and CFI genes encode factor H and factor I proteins, respectively; these are the 2 key plasma regulators of complement AP. Factor H functions as a cofactor of factor I in the proteolytic inactivation of C3b, by accelerating the decay of C3 convertase complex C3bBb and competing with FB to bind to C3b [29]. Genetic defects in these complement regulator genes can therefore predispose to uncontrolled AP activity in the presence of triggering factors such as ischemia-reperfusion injury, an immune response, and immunosuppressive drugs that injure the graft endothelium and activate the complement pathway, resulting in thrombosis.
Patients with COVID-19 were found to exhibit the same abnormal complement levels as those with thrombosis. Several complement factors such as C3 [30], C5 [30], C5a [31], and soluble C5b-9 [32] displayed increased levels in the plasma of COVID-19 patients, and were linked to the severity of the disease. As a result, an unblinded, non-randomized clinical study was conducted, where 80 ICU-treated patients with severe COVID-19 were given eculizumab – an inhibitor of C5 – in conjunction with standard care or standard care alone. The addition of eculizumab led to a significant improvement in both 15-day survival rates and oxygenation [33]. In line with the significant correlation between plasma complement factors and the severity of COVID-19, prospective observational research involving 134 patients diagnosed with the disease found higher activation levels of the complement system (C5a and soluble C5b-9) in critically ill individuals. These results suggest that such factors may have prognostic implications [34]. Likewise, another prospective study comprising a cohort of 219 individuals diagnosed with COVID-19 discovered that the patients who died exhibited increased levels of C5a [35]. Therefore, complement inhibitors such as eculizumab may be an essential treatment option for thrombosis.
The nomogram utilized in our study provides an intuitive method for presenting a risk prediction model. We successfully developed a risk model that can individually predict the probability of thrombosis in kidney transplant patients, considering factors such as COVID-19 infection, pre-transplant creatinine levels, and cyclosporine use. The accuracy of the model in assessing the likelihood of thrombosis in patients was remarkably high. Notably, this is the first prediction model for thrombosis that incorporates the impact of COVID-19 infection. Implementing this model enables the identification of high-risk patients with lower survival rates who may benefit from targeted treatment strategies.
However, it is important to acknowledge certain limitations of our study. Firstly, further validation is required to confirm the influence of COVID-19 infection on thrombosis through in vitro experiments. Additionally, it is crucial to continue investigating the molecular mechanisms underlying the development of thrombosis in kidney transplant patients after surgery. Secondly, the predictive performance of the nomogram should be validated using a larger sample size. Therefore, we recommend initiating prospective studies to validate and refine the predictive capabilities of our model before proceeding further.
Conclusions
Our study highlights that COVID-19 infection, pre-transplant creatinine levels, and cyclosporine use can serve as independent risk factors for predicting the development of thrombosis following renal transplantation. The nomogram developed based on these factors has shown promising accuracy in evaluating the likelihood of thrombosis after renal transplantation.
Figures
Figure 1. The patient screening process involved following a flowchartPatients were screened for eligibility based on the criteria outlined in the flowchart. Patients who withdrew from treatment or had missing key information were excluded, leaving 394 cases included in this study. 
Figure 2. Kaplan-Meier curves for different subgroups of renal transplant patients(A) Kaplan-Meier curves for overall survival in patients with and without thrombosis after renal transplantation. (B) Kaplan-Meier curves for overall survival in patients with and without perioperative COVID-19 infection. 
Figure 3. Texture feature selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model(A) The LASSO model’s adjustment parameter (λ) was selected by cross-validating 10-fold with the least criterion. The area under the receiver operating characteristic (AUC) curve was plotted against log(λ), with dotted vertical lines drawn at the optimal values determined by the minimum criterion and one standard error of the minimum criterion (1-SE criterion). The best λ value (1-SE criterion) was chosen based on the 10-fold cross-validation results. (B) The LASSO coefficient profiles for 6 texture features were generated for the log(λ) sequence. The vertical lines were drawn on the values selected by the 10-fold cross-validation method, where the best λ led to 3 non-zero coefficients. 
Figure 4. Thrombosis risk prediction with internal validation plots(A) Nomogram for predicting the risk of thrombosis in renal transplant patients. (B) Validity of calibration curves in estimating patient prognosis. 
References
1. Arnold DM, Patriquin CJ, Nazy I, Thrombotic microangiopathies: A general approach to diagnosis and management: CMAJ, 2017; 189(4); E153–E59
2. Tsai HM, The molecular biology of thrombotic microangiopathy: Kidney Int, 2006; 70(1); 16-23
3. George JN, Nester CM, Syndromes of thrombotic microangiopathy: N Engl J Med, 2014; 371(19); 1847-48
4. George JN, Li X, McMinn JR, Thrombotic thrombocytopenic purpura-hemolytic uremic syndrome following allogeneic HPC transplantation: A diagnostic dilemma: Transfusion, 2004; 44(2); 294-304
5. Stavrou E, Lazarus HM, Thrombotic microangiopathy in haematopoietic cell transplantation: An update: Mediterr J Hematol Infect Dis, 2010; 2(3); e2010033
6. Vo AA, Sinha A, Haas M, Choi J, Factors predicting risk for antibody-mediated rejection and graft loss in highly human leukocyte antigen sensitized patients transplanted after desensitization: Transplantation, 2015; 99(7); 1423-30
7. Langer RM, Van Buren CT, Katz SM, Kahan BD, De novo hemolytic uremic syndrome after kidney transplantation in patients treated with cyclosporine-sirolimus combination: Transplantation, 2002; 73(5); 756-60
8. Ponticelli C, De novo thrombotic microangiopathy. An underrated complication of renal transplantation: Clin Nephrol, 2007; 67(6); 335-40
9. Artz MA, Steenbergen EJ, Hoitsma AJ, Renal transplantation in patients with hemolytic uremic syndrome: High rate of recurrence and increased incidence of acute rejections: Transplantation, 2003; 76(5); 821-26
10. Loirat C, Fremeaux-Bacchi V, Hemolytic uremic syndrome recurrence after renal transplantation: Pediatr Transplant, 2008; 12(6); 619-29
11. Le Quintrec M, Zuber J, Moulin B, Complement genes strongly predict recurrence and graft outcome in adult renal transplant recipients with atypical hemolytic and uremic syndrome: Am J Transplant, 2013; 13(3); 663-75
12. Imanifard Z, Liguori L, Remuzzi G, TMA in kidney transplantation: Transplantation, 2023; 101(11); 2329-40
13. Iba T, Levy JH, Levi M, Connors JM, Thachil J, Coagulopathy of coronavirus disease 2019: Crit Care Med, 2020; 48(9); 1358-64
14. Cui S, Chen S, Li X, Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia: J Thromb Haemost, 2020; 18(6); 1421-24
15. Al-Nouri ZL, Reese JA, Terrell DR, Drug-induced thrombotic microangiopathy: A systematic review of published reports: Blood, 2015; 125(4); 616-18
16. Reese JA, Bougie DW, Curtis BR, Drug-induced thrombotic microangiopathy: Experience of the Oklahoma Registry and the BloodCenter of Wisconsin: Am J Hematol, 2015; 90(5); 406-10
17. Noris M, Remuzzi G, Thrombotic microangiopathy after kidney transplantation: Am J Transplant, 2010; 10(7); 1517-23
18. Ramirez C, Olmo A, O’Valle F, Role of intrarenal endothelin 1, endothelin 3, and angiotensin II expression in chronic cyclosporin A nephrotoxicity in rats: Exp Nephrol, 2000; 8(3); 161-72
19. Kahan BD, The era of cyclosporine: Twenty years forward, twenty years back: Transplant Proc, 2004; 36(2 Suppl); 5S-6S
20. Tomasiak M, Rusak T, Gacko M, Stelmach H, Cyclosporine enhances platelet procoagulant activity: Nephrol Dial Transplant, 2007; 22(6); 1750-56
21. Sahin G, Akay OM, Bal C, The effect of calcineurin inhibitors on endothelial and platelet function in renal transplant patients: Clin Nephrol, 2011; 76(3); 218-25
22. Renner B, Klawitter J, Goldberg R, Cyclosporine induces endothelial cell release of complement-activating microparticles: J Am Soc Nephrol, 2013; 24(11); 1849-62
23. Abbas F, El Kossi M, Kim JJ, Thrombotic microangiopathy after renal transplantation: Current insights in de novo and recurrent disease: World J Transplant, 2018; 8(5); 122-41
24. Reynolds JC, Agodoa LY, Yuan CM, Abbott KC, Thrombotic microangiopathy after renal transplantation in the United States: Am J Kidney Dis, 2003; 42(5); 1058-68
25. Satoskar AA, Pelletier R, Adams P, De novo thrombotic microangiopathy in renal allograft biopsies-role of antibody-mediated rejection: Am J Transplant, 2010; 10(8); 1804-11
26. Fortin MC, Raymond MA, Madore F, Increased risk of thrombotic microangiopathy in patients receiving a cyclosporin-sirolimus combination: Am J Transplant, 2004; 4(6); 946-52
27. Sartelet H, Toupance O, Lorenzato M, Sirolimus-induced thrombotic microangiopathy is associated with decreased expression of vascular endothelial growth factor in kidneys: Am J Transplant, 2005; 5(10); 2441-47
28. Le Quintrec M, Lionet A, Kamar N, Complement mutation-associated de novo thrombotic microangiopathy following kidney transplantation: Am J Transplant, 2008; 8(8); 1694-701
29. Noris M, Remuzzi G, Glomerular diseases dependent on complement activation, including atypical hemolytic uremic syndrome, membranoproliferative glomerulonephritis, and C3 glomerulopathy: Core curriculum 2015: Am J Kidney Dis, 2015; 66(2); 359-75
30. Zhang Y, Han K, Du C, Carboxypeptidase B blocks ex vivo activation of the anaphylatoxin-neutrophil extracellular trap axis in neutrophils from COVID-19 patients: Crit Care, 2021; 25(1); 51
31. Carvelli J, Demaria O, Vely F, Association of COVID-19 inflammation with activation of the C5a-C5aR1 axis: Nature, 2020; 588(7836); 146-50
32. Skendros P, Mitsios A, Chrysanthopoulou A, Complement and tissue factor-enriched neutrophil extracellular traps are key drivers in COVID-19 immunothrombosis: J Clin Invest, 2020; 130(11); 6151-57
33. Annane D, Heming N, Grimaldi-Bensouda L, Eculizumab as an emergency treatment for adult patients with severe COVID-19 in the intensive care unit: A proof-of-concept study: EClinicalMedicine, 2020; 28; 100590
34. Ma L, Sahu SK, Cano M, Increased complement activation is a distinctive feature of severe SARS-CoV-2 infection: Sci Immunol, 2021; 6(59); eabh2259
35. de Bruin S, Bos LD, van Roon MA, Clinical features and prognostic factors in COVID-19: A prospective cohort study: EBioMedicine, 2021; 67; 103378
Figures
Figure 1. The patient screening process involved following a flowchartPatients were screened for eligibility based on the criteria outlined in the flowchart. Patients who withdrew from treatment or had missing key information were excluded, leaving 394 cases included in this study.Figure 2. Kaplan-Meier curves for different subgroups of renal transplant patients(A) Kaplan-Meier curves for overall survival in patients with and without thrombosis after renal transplantation. (B) Kaplan-Meier curves for overall survival in patients with and without perioperative COVID-19 infection.Figure 3. Texture feature selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model(A) The LASSO model’s adjustment parameter (λ) was selected by cross-validating 10-fold with the least criterion. The area under the receiver operating characteristic (AUC) curve was plotted against log(λ), with dotted vertical lines drawn at the optimal values determined by the minimum criterion and one standard error of the minimum criterion (1-SE criterion). The best λ value (1-SE criterion) was chosen based on the 10-fold cross-validation results. (B) The LASSO coefficient profiles for 6 texture features were generated for the log(λ) sequence. The vertical lines were drawn on the values selected by the 10-fold cross-validation method, where the best λ led to 3 non-zero coefficients.Figure 4. Thrombosis risk prediction with internal validation plots(A) Nomogram for predicting the risk of thrombosis in renal transplant patients. (B) Validity of calibration curves in estimating patient prognosis. In Press
Clinical Research
Machine Learning Models for Predicting 24-Hour Intraocular Pressure Changes: A Comparative StudyMed Sci Monit In Press; DOI: 10.12659/MSM.945483
Clinical Research
Cost Reduction in Blood Transfusions After Implementation of Decision Protocol Based on Hemoglobin and Anem...Med Sci Monit In Press; DOI: 10.12659/MSM.945854
Clinical Research
Impact of Comprehensive Preoperative Assessments on Gynecological Ambulatory Surgery Outcomes in a Chinese ...Med Sci Monit In Press; DOI: 10.12659/MSM.945771
Animal Research
Histological Evaluation of the Effects of Intra-Articular Injection of Caffeic Acid on Cartilage Repair in ...Med Sci Monit In Press; DOI: 10.12659/MSM.946845
Most Viewed Current Articles
17 Jan 2024 : Review article 6,956,165
Vaccination Guidelines for Pregnant Women: Addressing COVID-19 and the Omicron VariantDOI :10.12659/MSM.942799
Med Sci Monit 2024; 30:e942799
14 Dec 2022 : Clinical Research 1,947,492
Prevalence and Variability of Allergen-Specific Immunoglobulin E in Patients with Elevated Tryptase LevelsDOI :10.12659/MSM.937990
Med Sci Monit 2022; 28:e937990
16 May 2023 : Clinical Research 696,690
Electrophysiological Testing for an Auditory Processing Disorder and Reading Performance in 54 School Stude...DOI :10.12659/MSM.940387
Med Sci Monit 2023; 29:e940387
07 Jan 2022 : Meta-Analysis 262,693
Efficacy and Safety of Light Therapy as a Home Treatment for Motor and Non-Motor Symptoms of Parkinson Dise...DOI :10.12659/MSM.935074
Med Sci Monit 2022; 28:e935074