Comparison of Outcomes of Covid-19 Between Fully Vaccinated and Unvaccinated Kidney Transplant Recipients and According to the Type of M-RNA Vaccine

Purpose: The evidence regarding the clinical effectiveness of COVID-19 vaccination in kidney transplant (KT) recipients is limited so far. Our purpose is to analyze the characteristics and outcomes of a large series of KT with COVID-19 breakthrough infection and compare them with unvaccinated patients. As a secondary objective, we analyzed the evolution according to the type of mRNA vaccine administered. Method(s): From April to October 2021, KT recipients with COVID-19, included in the COVID-19 registry of the Spanish Society of Nephrology, were analyzed. Data regarding vaccination status and type of vaccine were collected and outcomes of unvaccinated or partially vaccinated patients were compared with fully vaccinated patients. Result(s): Clinical picture was similar and survival analysis showed no differences between groups: 21.7% of fully vaccinated patients and 20.8% of unvaccinated or partially vaccinated died (p=.776). In multivariable analysis age and pneumonia were independent risk factors for death, while vaccination status was not related to mortality. These results remained similar when we excluded patients with partial vaccination as well as when we analyzed exclusively hospitalized patients. Patients vaccinated with mRNA-1273 (Moderna) (n=213) showed a significantly lower mortality than those who received BNT162b2 (Pfizer-BioNTech) vaccine (n=121) (16.4% vs. 28.8%, p=.002) (HR: 0.52, CI 95% 0.31-0.85, p=.010). Conclusion(s): COVID-19 severity in KT patients has remained high and has not improved despite receiving 2 doses of an mRNA vaccine, but the mRNA-1273 vaccine shows higher clinical effectiveness than BNT162b2 in KT recipients with breakthrough infection, so it could be considered as the first option in these patients.

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.

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.

Needs of people with kidney transplantation during the COVID 19 pandemic and its confinement, and the role of communication technologies through the “patient experience”

Purpose: During the COVID-19 pandemic, healthcare services have focused on the care of acute processes and there is a risk of not meeting the needs of complex chronic high-risk patients, such as kidney transplant patients(KT). Our goal is to understand the unmet needs/problems of KT during COVID-19 lock-down from the “patient experience” perspective, and to analyze the role of communication technologies. Methods: Development of a survey through concepts grouped into categories (units of meaning) and synthesized into metacategories extracted after interviews with 3 stable and confined KT focus groups (videoconference;verbal informed consent). Verbatim transcription of the conversation and analysis with MAXQDA software(https://www.maxqda.com/). After validation, the surveys were compared with those of the group of CJ Atchison et al in the UK general population (authorization in order). Results: Out of 1528 KTs, 947 answered (response rate:63%). 64.2% men, and 55% over 55years. Post-TR time:3m-5a. 115 concepts, 21 categories and 3 meta-categories were identified (in order of importance for patients: Role of technology (69%), Impact of lock-down (19%) and Contact with professionals(12%)). Our KTs have a serious concern about the disease (if infection, 38% believe that their life would be at risk;and 48% think that the infection would be serious), but not about their kidney disease (20.6%), 99.7% of KTs have taken measures of social distancing, and lock-down has not meant a change in their lifestyle (sleep, diet, exercise, and family or social relationships). On a scale of 1-6 (1 little-6 a lot), for the KTs the global impact has been 1.69, very different from that of the UK (p<0.05). Regarding healthcare needs, 27.7% reported having received information from the hospital, but not very useful;42.3% have stopped doing some medical control, but only 1.92% have stopped the medication. During the pandemic, 55% contacted a specialist, by telemedicine in a 84.7% (preferring by video-call). KTs understand that technology does not replace all face-to-face technology and positively value group videoconferences (therapeutic education, dietary recommendations, physical activity). Conclusions: KTs are people at risk, and they have serious concern about the disease, so they comply with lock-down, although this has not led to a significant change in their lifestyle. KTs value having easy access to professionals, information from the center, and consider technology a key role during lock-down. These learnings should profoundly change the way health professionals relate to patients, allowing an increase in the number of contacts and reducing face-to-face visits.

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.