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

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Abstract

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.

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Acknowledgments

The authors would like to thank the nurses at Robert Debré Pediatric Nephrology Department for their help in assessing dialysis parameters during this study.

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Authors and Affiliations

  1. Pediatric Nephrology Department, Robert Debré Hospital, 48 Boulevard Sérurier, 75019, Paris, France

    Olivier Niel, Paul Bastard, Charlotte Boussard, Julien Hogan, Thérésa Kwon & Georges Deschênes

Authors
  1. Olivier Niel
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  2. Paul Bastard
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  3. Charlotte Boussard
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  4. Julien Hogan
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  5. Thérésa Kwon
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  6. Georges Deschênes
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Corresponding author

Correspondence to Olivier Niel.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Niel, O., Bastard, P., Boussard, C. et al. Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis. Pediatr Nephrol 33, 1799–1803 (2018). https://doi.org/10.1007/s00467-018-4015-2

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  • DOI: https://doi.org/10.1007/s00467-018-4015-2

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