Machine learning analysis of serum biomarkers for cardiovascular risk assessment in chronic kidney disease
Authors: Forné Carles , Cambray Serafi , Bermudez-Lopez Marcelino , Fernandez Elvira , Bozic Milica , Valdivielso Jose M ,   NEFRONA investigators, Regidor José Aladrén , Almirall Jaume , Ponz Esther , Coloma Jesús Arteaga , Rubio Auxiliadora Bajo , Díaz Raquel , Rodríguez Montserrat Belart , Gascón Antonio , Sanjuan Jordi Bover , Artero Josep Bronsoms , Cabezuelo Romero Juan B , Varela Jesús Calviño , Acevedo Pilar Caro , Bassa Jordi Carreras , Amenós Aleix Cases , Jiménez Elisabet Massó , López Rosario Moreno , Cigarrán Guldris Secundino , Prieto Saray López , Mongay Lourdes Comas , Comerma Isabel , Jové Teresa Compte , Izquierdo Marta Cuberes , de Álvaro Fernando , Ojanguren Covadonga Hevia , de Arriba de la Fuente Gabriel , del Pino y Pino Dolores , Diaz-Tejeiro Izquierdo Rafael , Ahijado Hormigos Francisco , Dotori Marta , Duarte Verónica , Torres Sara Estupiñan , Fernández Reyes José , Fernández Rodríguez Loreto , Fernández Guillermina , Serrano Antonio Galán , Cantón Cesar García , García Herrera Antonio L , Mena Mercedes García , Sacaluga Luis Gil , Aguilar Maria , Luis Górriz José , Loza Emma Huarte , Lerma José Luis , Cañada Antonio Liebana , Álvarez Jesús Pedro Marín , Alemany Nádia Martín , García Jesús Martín , Castelao Alberto Martínez , Martínez Villaescusa María , Martínez Isabel , Eguren Iñigo Moina , Huertos Silvia Moreno Los , Mirco Ricardo Mouzo , Vila Antonia Munar , Muñoz Díaz Ana Beatriz , Navarro González Juan F , Nieto Javier , Carreño Agustín , Fernández Enrique Novoa , Ortiz Alberto , Fernandez Beatriz , Paraíso Vicente , Fontán Miguel Pérez , Domingo Ana Peris , Haces Celestino Piñera , Garrido Dolores Prados , Velasco Mario Prieto , Marí Carmina Puig , Gorrín Maite Rivera , Rubio Esther , Ruiz Pilar , Lazo Mercedes Salgueira , Martínez Puerto Ana Isabel , Sánchez Tomero José Antonio , Sánchez José Emilio , Lorman Ramon Sans , Saracho Ramon , Sarrias Maria , Serón Daniel , Soler María José , Barrios Clara , Sousa Fernando , Toran Daniel , Molina Fernando Tornero , Carrasco José Javier Usón , Cortes Ildefonso Valera , del Perugia Merce Vilaprinyo , Virto Ruiz Rafael C , Carratalá Vicente Pallarés , Altozano Carlos Santos , Ródenas Miguel Artigao , Gil Gil Inés , Gil Francisco Adan , Criado Emilio García , Belinchón Rafael Durá , Fernández Toro Jose M , Divisón Garrote Juan A 
Published: July 2019

Background

Chronic kidney disease (CKD) patients show an increased burden of atherosclerosis and high risk of cardiovascular events (CVEs). There are several biomarkers described as being associated with CVEs, but their combined effectiveness in cardiovascular risk stratification in CKD has not been tested. The objective of this work is to analyse the combined ability of 19 biomarkers associated with atheromatous disease in predicting CVEs after 4 years of follow-up in a subcohort of the NEFRONA study in individuals with different stages of CKD without previous CVEs.

Methods

Nineteen putative biomarkers were quantified in 1366 patients (73 CVEs) and their ability to predict CVEs was ranked by random survival forest (RSF) analysis. The factors associated with CVEs were tested in Fine and Gray (FG) regression models, with non-cardiovascular death and kidney transplant as competing events.

Results

RSF analysis detected several biomarkers as relevant for predicting CVEs. Inclusion of those biomarkers in an FG model showed that high levels of osteopontin, osteoprotegerin, matrix metalloproteinase-9 and vascular endothelial growth factor increased the risk for CVEs, but only marginally improved the discrimination obtained with classical clinical parameters: concordance index 0.744 (95% confidence interval 0.609–0.878) versus 0.723 (0.592–0.854), respectively. However, in individuals with diabetes treated with antihypertensives and lipid-lowering drugs, the determination of these biomarkers could help to improve cardiovascular risk estimates.

Conclusions

We conclude that the determination of four biomarkers in the serum of CKD patients could improve cardiovascular risk prediction in high-risk individuals.