This is the accepted version of the journal article:
Ibeas, Jose; Monill-Raya, Núria; Macias Toro, Edwar Hernando; [et al.]. «Early
arteriovenous fistula failure prediction with artificial intelligence : a new approach
with challenging results». Nephrology Dialysis Transplantation, Vol. 36, Issue
Supplement1 (M ay2021), art.gf ab103.004.DOI10.1093/ndt/gf ab103.004


This version is available at https://ddd.uab.cat/record/273472

under the terms of the               license
EARLY ARTERIOVENOUS FISTULA FAILURE PREDICTION WITH ARTIFICIAL INTELLIGENCE: A
NEW APPROACH WITH CHALLENGING RESULTS

Jose Ibeas1,2, Nuria Monill-Raya1,2, Edwar Macias3, Carolina Rubiella1,2, Joaquim Vallespin2,4, Jana Merino4, Eva Criado2,5, Josep Guitart5, Jose Vicario3,
Antoni Morell3, Javier Serrano3

1
 Parc Taulí Hospital Universitari, Nephrology, Sabadell, Spain, 2Parc Taulí Hospital Universitari, Clinical, Interventional and Computational Nephrology -
CICN, Sabadell, Spain, 3Universitat Autònoma de Barcelona, Telecomunication and Systems Engineering, Cerdanyola, Spain, 4Parc Taulí Hospital
Universitari, Vascular Surgery, Sabadell, Spain and 5Parc Taulí Hospital Universitari, Interventional Radiology, Sabadell, Spain


BACKGROUND AND AIMS: The native arteriovenous fistula is considered the vascular access
of preference, since it is directly related to major survival, reduced complications, mortality,
and costs. Still, its proper maintenance remains a challenge for nephrologists. A previous study
from our group, which used the data of 117 arteriovenous fistulas, led to identify in the
multivariable analysis age and vein diameters as predictive factors for early failure. On the
other hand, Artificial Intelligence has been established as a tool to identify relationships
between variables at deep levels, which might be unseen with more conservative methods
like classic statistics. Therefore, through a Machine Learning technique known as Random
Forest, the aim is to evaluate the same comorbidity, biological and Doppler ultrasound
variables data to identify those with a major relation with the early failure of the native
arteriovenous fistula.

METHOD: Retrospective cohort study, gathering the same data of the previous study (from
2011 to 2015): survival, ultrasound mapping (morphology and hemodynamics), comorbidities
(blood pressure, severe arteriopathy, diabetes, Charlson’s Index), and laboratory
(haemoglobin, calcium, phosphorus, PTH, ferritin, PCR). Different Artificial Intelligence
algorithms were tested, but the most suited one for the study’s aim turned out to be Random
Forest. A model was trained, dividing the data in two sets, training and validation, with an
80/20 ratio. The algorithm used 100 decision trees, with a maximum individual depth of 3
levels. The training was made with the variables that represented the 100%, 95%, 90% and
85% of impact in the fistula’s maturation from a theresold according to Gini’s Index.

RESULTS: Age 65.7 (32-88) years, male 59.8%. Hypertension 86.7%, diabetes 50.7% and
vascular disease 41.3%. The trained model obtained provided the following results in the
evaluation: accuracy 0.82, precision 0.86, AUROC 0.85, F1 0.86 (balance between precision
and predictive value). The most relevant variables by decision order were age, phosphorous,
PTH, ferritin and calcium. Morphologic and hemodynamic variables such as the vessels
diameter, Peak Systolic Velocity, Charlson’s Index or PCR, were also found to be relevant, but
in a minor level.

CONCLUSION: In comparison with classic statistics, Machine Learning techniques might create
a change of paradigm in predictive models for the patency of the vascular access for
Haemodialysis. Even though the Artificial Intelligence-based model provided the same
relevance for age in maturation failure as traditional models, other findings stand out, like the
major participation of variables related with mineral metabolism or inflammation, rather than
ultrasound based ones.