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Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.

ABSTRACT: This analysis, using data from the Brazilian kidney transplant (KT) COVID-19 study, seeks to develop a prediction score to assist in COVID-19 risk stratification in KT recipients. In this study, 1379 patients (35 sites) were enrolled, and a machine learning approach was used to fit models in a derivation cohort. A reduced Elastic Net model was selected, and the accuracy to predict the 28-day fatality after the COVID-19 diagnosis, assessed by the area under the ROC curve (AUC-ROC), was confirmed in a validation cohort. The better calibration values were used to build the applicable ImAgeS score. The 28-day fatality rate was 17% (n = 235), which was associated with increasing age, hypertension and cardiovascular disease, higher body mass index, dyspnea, and use of mycophenolate acid or azathioprine. Higher kidney graft function, longer time of symptoms until COVID-19 diagnosis, presence of anosmia or coryza, and use of mTOR inhibitor were associated with reduced risk of death. The coefficients of the best model were used to build the predictive score, which achieved an AUC-ROC of 0.767 (95% CI 0.698-0.834) in the validation cohort. In conclusion, the easily applicable predictive model could assist health care practitioners in identifying non-hospitalized kidney transplant patients that may require more intensive monitoring. Trial registration: ClinicalTrials.gov NCT04494776.

SUBMITTER: Modelli de Andrade LG

PROVIDER: S-EPMC8441938 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.

Modelli de Andrade Luis Gustavo LG de Sandes-Freitas Tainá Veras TV Requião-Moura Lúcio R LR Viana Laila Almeida LA Cristelli Marina Pontello MP Garcia Valter Duro VD Alcântara Aline Lima Cunha ALC Esmeraldo Ronaldo de Matos RM Abbud Filho Mario M Pacheco-Silva Alvaro A de Lima Carneiro Erika Cristina Ribeiro ECR Manfro Roberto Ceratti RC Costa Kellen Micheline Alves Henrique KMAH Simão Denise Rodrigues DR de Sousa Marcos Vinicius MV Santana Viviane Brandão Bandeira de Mello VBBM Noronha Irene L IL Romão Elen Almeida EA Zanocco Juliana Aparecida JA Arimatea Gustavo Guilherme Queiroz GGQ De Boni Monteiro de Carvalho Deise D Tedesco-Silva Helio H Medina-Pestana José J

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 20210902 2

This analysis, using data from the Brazilian kidney transplant (KT) COVID-19 study, seeks to develop a prediction score to assist in COVID-19 risk stratification in KT recipients. In this study, 1379 patients (35 sites) were enrolled, and a machine learning approach was used to fit models in a derivation cohort. A reduced Elastic Net model was selected, and the accuracy to predict the 28-day fatality after the COVID-19 diagnosis, assessed by the area under the ROC curve (AUC-ROC), was confirmed ...[more]

PMID: 34416075

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Project description:Background and Objectives: Kidney transplant recipients represent a unique population with metabolic abnormalities, altered nutritional and immune status, as well as an imbalanced regulation of adipocytokine metabolism. Leptin is a hormonally active protein mainly produced by fat tissue that modulates appetite, satiety, and influences growth, energy, and bone metabolism. There has been great interest in the role of this hormone in chronic kidney disease-related protein energy wasting; thus, a positive leptin correlation with body mass index and fat mass was confirmed. This study was designed to determine the association of pre and post-kidney transplant leptin concentration with nutritional status and body composition. Materials and Methods: We studied 65 kidney transplant recipients. Nutritional status was evaluated before kidney transplantation and 6 months later using three different malnutrition screening tools (Subjective Global Assessment Scale (SGA), Malnutrition Inflammation Score (MIS), and Geriatric Nutritional Risk Index (GNRI)), anthropometric measurements, and body composition (bioelectrical impedance analysis (BIA)). Demographic profile, serum leptin levels, and other biochemical nutritional markers were collected. Statistical analysis was performed with R software. Results: Median age of the studied patients was 45 years, 42% were females, and 12% had diabetes. Leptin change was associated with body weight (p < 0.001), waist circumference (p < 0.001), fat mass (p < 0.001) and body fat percentage (p < 0.001), decrease in parathyroid hormone (PTH) (p < 0.001) transferrin (p < 0.001), diabetes mellitus (p = 0.010), and residual renal function (p = 0.039), but not dependent on dialysis vintage, estimated glomerular filtration rate (eGFR), or delayed graft function at any time during the study. After adjustment for age and sex, body mass index (BMI) (p < 0.001), fat mass (p < 0.001), and body fat percentage (p < 0.001) were independent variables significantly associated with post-transplant leptin change. Lower leptin values were found both before and after kidney transplantation in the SGA B group. GNRI as a nutritional status tool was strongly positively related to changes in leptin within the 6-month follow-up period. Conclusions: Kidney transplant recipients experience change in leptin concentration mainly due to an increase in fat mass and loss of muscle mass. GNRI score as compared to SGA or MIS score identifies patients in whom leptin concentration is increasing alongside an accumulation of fat and decreasing muscle mass. Leptin concentration evaluation in combination with BIA, handgrip strength measurement, and GNRI assessment are tools of importance in defining nutrition status in the early post-kidney transplant period.

Project description:BackgroundPredicting allograft failure in kidney transplant recipients can help plan renal replacement therapy and guide patient-provider communication. The kidney failure risk equation (KFRE) accurately predicts the need for dialysis in patients with chronic kidney disease (CKD), but has not been validated in kidney transplant recipients.ObjectiveWe sought to validate the 4-variable KFRE (age, sex, estimated glomerular filtration rate [eGFR], and urine albumin-to-creatinine ratio [ACR]) for prediction of 2- and 5-year death-censored allograft failure.DesignRetrospective cohort study.SettingFour independent North American Cohorts from Ontario, Canada; Alberta, Canada; Manitoba, Canada; and Wisconsin, United States, between January 1999 and December 2017.PatientsAdult kidney transplant patients at 1-year posttransplantation.MeasurementsKidney failure risk as measured by the KFRE (eGFR, urine ACR, age, and sex).MethodsWe included all adult patients who had at least 1 serum creatinine and at least 1 urine ACR measurement approximately 1 year following kidney transplantation. The performance of the KFRE was evaluated using the area under the receiver operating characteristic curve (C-statistic). C-statistics from the 4 cohorts were meta-analyzed using random-effects models.ResultsA total of 3659 patients were included. Pooled C-statistics were good in the entire population, at 0.81 (95% confidence interval: 0.72-0.91) for the 2-year KFRE and 0.73 (0.67-0.80) for the 5-year KFRE. Discrimination improved among patients with poorer kidney function (eGFR < 45 mL/min/1.73 m2), with a C-statistic of 0.88 (0.78-0.98) for the 2-year KFRE and 0.83 (0.74-0.91) for the 5-year KFRE.LimitationsThe KFRE does not predict episodes of acute rejection and there was heterogeneity between cohorts.ConclusionsThe KFRE accurately predicts kidney failure in kidney transplant recipients at 1-year posttransplantation. Further validation in larger cohorts with longer follow-up times can strengthen the case for clinical implementation.

Dataset Information

Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.

Publications

Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.

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