Real-Life Prevention of Breakthrough SARS-CoV-2 Infections by mRNA Vaccines in Kidney Transplant Recipients vs Hemodialysis Patients

Purpose: The increased COVID-19 severity observed in kidney transplant recipients (KTR) has been widely reported. In addition, several studies have shown a reduced humoral and cellular response after mRNA vaccination in this population compared to hemodialysis patients. However, there is currently no information on real-life clinical protection (deaths and hospitalizations), a gap that this study aims to fill. Method(s): Observational prospective study. A total population of 1336 KTR and hemodialysis patients from three dialysis units affiliated to Hospital Clinic of Barcelona, Spain, vaccinated with two doses of mRNA-1273 (Moderna) or BNT162b2 (Pfizer-BioNTech) SARS-CoV-2 mRNA vaccines. The outcomes measured were SARS-CoV-2 infection diagnosed by a positive RT-PCR fourteen days after the second vaccine dose, hospital admissions derived from infection, and a severe COVID-19 composite outcome, defined as either ICU admission, invasive and non-invasive mechanical ventilation, or death. Result(s): Six per cent (18/302) of patients on hemodialysis were infected, of whom four required hospital admission (1.3%), only one (0.3%) had severe COVID-19, and none of them died. In contrast, 4.3% (44/1034) of KTR were infected, and presented more hospital admissions (26 patients, 2.5%), severe COVID-19 (11 patients, 1.1%) or death (4 patients, 0.4%). There were no correlations on the multivariate analysis between measured outcomes and baseline characteristics nor immunosuppressive treatment. Conclusion(s): The study highlights the need for further booster doses in KTR. In contrast, the hemodialysis population appears to have an adequate clinical response to vaccination, at least up to four months from its administration.

Evolution of Cellular and Humoral Immunity During and After a Three Doses-Course of mRNA-1273 COVID-19 Vaccine in Kidney Transplant Recipients

Purpose: Seroconversion after a 2 doses of mRNA COVID-19 vaccine in kidney transplant recipients (KTR) ranges between 30 and 50% in different series. We previously demonstrated that a substantial proportion of KTRs (35%) without a humoral response, develops a cellular response after the second dose assessed by the ELISpot technique. We aim to study the evolution of both humoral and cellular response in the same cohort before and 1 month after the administration of the third dose of mRNA-1273 COVID-19 vaccine. Method(s): We included in the final analysis KTRs without evidence of previous exposure to COVID-19 and who were not infected during the course of the study and with complete data in all the time-points (n=105). The four time-points studied were at baseline before the first dose (T1), after the second dose (T2, 2 months) and before (T3, 6 months) and after (T4, 7 months) the administration of the third dose of 100mcg mRNA-1273 COVID-19 vaccine. In all the time points, IgG and IgM titre against protein S assessed by Luminex technique and cellular immunity assessed by N- and S-protein specific ELISpot were studied. Result(s): The percentage of patients with a positive humoral or cellular immunity against the S-protein were 24.8% and 51.4% after the second dose (T2). This percentages changed to 54.3% and 48.6% at 6 months (T3), respectively for IgG and S-ELISpot, in the absence of proven COVID-19. After the administration of the 3rd dose (T4) these percentages increased to 75.2% for IgG and 61.0% of S-ELISpot respectively. At multivariate analysis, the only factor that was positively associated with IgG development at T4 was S-ELIspot positivity after the 2nd dose (T2) [OR(CI) 3.14[1.10-8.96], p=0.032). Factors negatively associated with seroconversion were being transplanted during the last year [OR(CI) 0.23[0.07-0.80], P=0.021] and previous transplantation [OR(CI) 0.22[0.06-0.78], P=0.020). Conclusion(s): After a 3 doses-course of mRNA-1273 COVID-19 vaccine, three quarters of kidney transplant recipients developed finally IgG against protein S. Developing a cellular response after the second dose was positively associated with the final seroconversion, while being transplanted previously or being vaccinated during the first year after KT impacted negatively on the vaccine outcome.

HUMORAL AND CELLULAR IMMUNE RESPONSES AFTER A THREE-DOSE COURSE OF MRNA-1273 COVID-19 VACCINE IN KIDNEY TRANSPLANT RECIPIENTS: A PROSPECTIVE COHORT STUDY

BACKGROUND AND AIMS: Seroconversion after a two-dose course of mRNA COVID-19 vaccination in kidney transplant recipients ranges between 30% and 50% in different series. We previously demonstrated that a substantial proportion of patients (35%) without a humoral response, develop a cellular response after the second dose assessed by the ELISpot technique. We aim to study the evolution of both humoral and cellular responses in the same cohort before and 1 month after the administration of the third dose of 100 mcg of mRNA-1273 COVID-19 vaccine. METHOD: Final population included 129 KTRs studied at four time-points: at baseline before the first dose, after the second dose (median 42 days) and before (203 days) and after (232 days) the third dose. At all the time-points, IgG and IgM were assessed as well as N- and S-protein specific ELISpot. The main outcome was seroconversion after the third dose. RESULTS: After the second dose, 26.7% of naïve cases experienced seroconversion. Before the third dose and in the absence of clinically evident COVID-19, this percentage increased to 61.9%. After the third dose, seroconversion was observed in 80.0% of patients. S-ELISpot positivity after the second dose was significantly associated with final seroconversion [OR (95% CI) 3.14 (1.10-8.96);P = .032], while transplantation < 1 year and previous kidney transplant were negatively associated with [OR (95% CI) 0.23 (0.07-0.80);P = .021 and OR (95% CI) 0.22 (0.06-0.78);P = .020, respectively). IgG after third dose were significantly higher (P < .001) in patients who maintained S-ELISpot positivity throughout the study (34.3%) and were correlated with S-spots after the second dose (r = 0.344, P < .001). CONCLUSION: A substantial proportion of KTRs vaccinated with mRNA-1273 develops a late seroconversion after two doses and only a fifth remained seronegative after a third. Cellular immunity seems to play a major role in the development of a final strong humoral response.

Influence of vitamin d status on the prognosis of COVID 19 in patients with kidney transplant

Purpose: The protective role of Vitamin D as an immunomodulator has been demonstrated in different pathologies included the viral etiology. This effect has been described by different mechanisms among these acting as an immunoprotein inducer, participates in growth and cell differentiation and acts as a mediator of apoptosis. Some evidence suggests that it could influence the SARS-COV 2 infection and its prognosis. Kidney transplant (KT) patients are more susceptible to 25 (OH) VitD (Calcidiol) deficiencies. The purpose of this study is to evaluate the Vitamin D status in transplant patients who have been diagnosed with COVID-19 and its possible correlation with prognosis. Methods: It is an observational, retrospective, cross-sectional and descriptive study that includes kidney transplant patients diagnosed with COVID-19 and with serum 25 (OH) Vit D Results: 79 patients were evaluated. The mean age was 58 years, 60.8% were men. 86% were KT, 11% were simultaneous pancreas and kidney transplant (SPK). 39 (48%) presented neumonia, 22 (28%)flu-like syndrom. 14 (17%) asyntomatic and 2(2,5%) fever. From this patients 39,2% had not changes in the antiinflamatorie therapy, 20.3%, required increased dose of corticosteroids, and 30.4% required methylprednisolone bolus or initiation of anti-interleukin therapy. The mean of Vit D was 21.41+/- 11% we found that 52% has Vit D <20 ng/ dl. 25% between 20 -30 ng / dl and 21,51% > 30 ng / dl In 32 patientes who required intensification of treatment we found that 73% had Vit D levels <20 ng. 11 patients need an citical care unit, of these 62.5% had levels below <20 ng / dl. There were 12 deaths. 66% of deaths had vitamin D values <20 ng / dl. Conclusions: We were able to observe that vitamin D levels could influence in the prognosis of SARS-COV 2 infection. Vitamin D deficiency was found in a high percentage of transplant patients with COVID 19. Low levels of 25 OH Vit D were evidenced in patients who required greater intensification of antiinflamatory treatment and in deaths.

A machine learning-based predictive model for outcome of COVID-19 in kidney transplant recipients

Purpose: Health systems need tools to deal with COVID-19, especially for high-risk population,such as transplant recipients. Predictive models are necessary to improve management of patients and optimize resources. Methods: A retrospective study of hospitalized transplant patients due to COVID-19 was evaluated(March 3-April 24,2020). Admission data were integrated to develop a prediction model to evaluate a composite-event defined as Intensive Care Unit admission or intensification treatment with antiinflamatory agents. Predictions were made using a Data Envelopment Analysis(DEA)-Artificial Neural Network(ANN) hybrid, whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Results: Of 1006 recipients with a planned or an unscheduled visit during the observation period, thirty-eight were admitted due to COVID-19. Twenty-five patients(63.2%) exhibited poor clinical course(mortality rate:13.2%), within a mean of 12 days of admission stay. Cough as a presenting symptom(P=0.000), pneumonia(P=0.011), and levels of LDH(P=0.031) were admission factors associated with poor outcomes. The prediction hybrid model working with a set of 17 input variables displays an accuracy of 96.3%, outperforming any competing model, such as logistic regression(65.5%) and Random forest(denoted by Bagged Trees,44.8%). Moreover, the prediction model allows us to categorize the evolution of patients through the values at hospital admission. Conclusions: The prediction model based in Data Envelopment Analysis-Artificial Neural Network hybrid forecasts the progression towards severe COVID-19 disease with an accuracy of 96.3%, and may help to guide COVID-19 management by identification of key predictors that permit a sustainable distribution of resources in a patient-centered model. Improving efficiency and patient parformance in the AAN with DEA, we can get high accurancy even with no-big cohorts. (Table Presented).

A machine learning-based predictive model for outcome of COVID-19 in kidney transplant recipients

Background: Health systems need tools to deal with COVID-19, especially for highrisk population, such as transplant recipients. Predictive models are necessary to improve management of patients and optimize resources. Methods: A retrospective study of hospitalized transplant patients due to COVID-19 was evaluated(March 3-April 24,2020). Admission data were integrated to develop a prediction model to evaluate a composite-event defined as Intensive Care Unit admission or intensification treatment with antiinflamatory agents. Predictions were made using a Data Envelopment Analysis(DEA)-Artificial Neural Network(ANN) hybrid, whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Results: Of 1006 recipients with a planned or an unscheduled visit during the observation period, thirty-eight were admitted due to COVID-19. Twenty-five patients(63.2%) exhibited poor clinical course(mortality rate:13.2%), within a mean of 12 days of admission stay. Cough as a presenting symptom(P=0.000), pneumonia(P=0.011), and levels of LDH(P=0.031) were admission factors associated with poor outcomes. The prediction hybrid model working with a set of 17 input variables displays an accuracy of 96.3%, outperforming any competing model, such as logistic regression(65.5%) and Random forest(denoted by Bagged Trees, 44.8%). Moreover, the prediction model allows us to categorize the evolution of patients through the values at hospital admission. Conclusions: The prediction model based in Data Envelopment Analysis-Artificial Neural Network hybrid forecasts the progression towards severe COVID-19 disease with an accuracy of 96.3%, and may help to guide COVID-19 management by identification of key predictors that permit a sustainable distribution of resources in a patient-centered model.