ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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).
ABSTRACT
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.