Date: Tuesday, June 8, 2021
Session Time: 4:30pm-5:30pm
Presentation Time: 5:00pm-5:05pm
*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.
To cite this abstract in AMA style:Revuelta I, Santos-Arteaga F, Caprio DDi, Montagud-Marrahi E, Cofan F, Torregrosa J, Bodro M, Moreno A, Ventura-Aguiar P, Cucchiari D, Esforzado N, Piñeiro G, Ugalde-Altamirano J, Campistol J, Alcaraz A, Bayès B, Poch E, Oppenheimer F, Diekmann F. A Machine Learning-Based Predictive Model for Outcome of Covid-19 in Kidney Transplant Recipients [abstract]. Am J Transplant. 2021; 21 (suppl 3). https://atcmeetingabstracts.com/abstract/a-machine-learning-based-predictive-model-for-outcome-of-covid-19-in-kidney-transplant-recipients/. Accessed June 18, 2021.
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