Development and validation of a simple web-based tool for early prediction of COVID-19-associated death in kidney transplant recipients.
Am J Transplant
; 22(2): 610-625, 2022 02.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1367287
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.
Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Trasplante de Riñón
/
COVID-19
Tipo de estudio:
Estudio de cohorte
/
Estudios diagnósticos
/
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Tópicos:
Covid persistente
Límite:
Humanos
Idioma:
Inglés
Revista:
Am J Transplant
Asunto de la revista:
Trasplante
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
Ajt.16807
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