Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis

• Published in: Pediatric nephrology (Berlin, West) Vol. 33; no. 10; pp. 1799 - 1803
• Main Authors: Niel, Olivier, Bastard, Paul, Boussard, Charlotte, Hogan, Julien, Kwon, Thérésa, Deschênes, Georges
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2018; Springer; Springer Nature B.V
• Subjects: All terrain vehicles; Antihypertensives; Artificial intelligence; Blood pressure; Brief Report; Hemodialysis; Medicine; Medicine & Public Health; Methods; Nephrologists; Nephrology; Neural networks; Pediatrics; Practice; Technology application; Urology; What's New in Dialysis; France; Bio-impedancemetry; Blood pressure; Neural network; Hemodialysis; Artificial intelligence; Dry weight
• ISSN: 0931-041X; 1432-198X; 1432-198X
• DOI: 10.1007/s00467-018-4015-2

Background Dry weight is the lowest weight patients on hemodialysis can tolerate; correct dry weight estimation is necessary to minimize morbi-mortality, but is difficult to achieve. Here, we used artificial intelligence to improve the accuracy of dry weight assessment in hemodialysis patients. Methods/Results We designed a neural network which used bio-impedancemetry, blood volume monitoring, and blood pressure values as inputs; output was artificial intelligence dry weight. Fourteen pediatric patients were switched from nephrologist to artificial intelligence dry weight. Artificial intelligence dry weight was higher (28.6%), lower (50%), or identical to nephrologist dry weight. Mean difference between artificial intelligence and nephrologist dry weights was 0.497 kg (− 1.33 to + 1.29 kg). In patients for whom artificial intelligence dry weight was lower than nephrologist dry weight, systolic blood pressure significantly decreased after dry weight decrease to artificial intelligence dry weight (77th to 60th percentile, p  = 0.022); anti-hypertensive treatments were successfully decreased or discontinued in 28.7% of cases. In patients for whom artificial intelligence dry weight was higher than nephrologist dry weight, no hypertension was observed after dry weight increase to artificial intelligence dry weight; when present, symptoms of dry weight underestimation receded. Conclusions Neural network predictions outperformed those of experienced nephrologists in most cases, proving artificial intelligence is a powerful tool for predicting dry weight in hemodialysis patients.