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Hemodialysis Water Parameters as Predisposing Factors for Anemia in Patients in Dialytic Treatment: Application of Mixed Regression Models

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Abstract

Contamination by metals and microbiological agents in hemodialysis water can cause clinical intercurrences in hemodialysis patients. Evaluating and assuring minimum levels of contamination from metals and microorganisms in hemodialysis water can improve patient safety. The objective of this study was to assess hemodialysis water quality in a major tertiary hospital in Brazil and investigate the relationship between hemodialysis water quality and clinical intercurrences in hemodialysis patients. A prospective-observational cohort study for evaluation of water quality parameters and clinical intercurrences present by patients (n = 52) were performed from May 2014 to April 2015. Mixed linear regression models and binary regression models were fitted for water quality parameters and patients’ clinical parameters. The increase in the levels of copper and nitrate in hemodialysis water was significant (p < 0.05) to explain anemia. Bone pain was related to age, sex (female), and hemodialysis treatment duration (years). Hypotensive episodes were related to serum sodium decrease. Aluminum in hemodialysis water was present in an average concentration higher than the permitted threshold (15.35 ± 14.53 μg/L). Cadmium, total coliforms, Escherichia coli, and endotoxins in hemodialysis water were not detected and the heterotrophic bacteria count was below the reference limit. These parameters’ concentrations varied during the study, evidencing the need for continuous monitoring.

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Funding

This study was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES).

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Authors and Affiliations

  1. Laboratory of Ecotoxicology and Environmental Parasitology, College of Nursing of Ribeirão Preto, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, São Paulo, 14040-902, Brazil

    Meire Nikaido Suzuki, Brisa Maria Fregonesi, Carolina Sampaio Machado, Guilherme Sgobbi Zagui & Susana Segura-Muñoz

  2. Department of Fundamental Nursing, College of Nursing of Ribeirão Preto, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, São Paulo, 14040-902, Brazil

    Luciana Kusumota

  3. Department of Applied Mathematics and Statistics, Institute of Mathematical and Computing Sciences, University of São Paulo, Av. Trabalhador São Carlense 400, São Carlos, São Paulo, 13560-970, Brazil

    Adriano Kamimura Suzuki

  4. Department of Internal Medicine, Faculty of Medicine of Ribeirão Preto, University of São Paulo, Av. Bandeirantes 3900, Ribeirão Preto, São Paulo, 14049-900, Brazil

    José Abrão Cardeal da Costa

  5. Laboratory of Toxicology and Environmental Health, School of Medicine, IISPV, Universitat Rovira i Virgili, Sant Llorenç 21, 43201, Reus, Catalonia, Spain

    Jordi Sierra Llopart, Martí Nadal & Jose Luis Domingo

  6. Laboratory of Soil Science, Faculty of Pharmacy, Universitat de Barcelona, Avda. Joan XXIII s/n., 08020, Barcelona, Catalonia, Spain

    Jordi Sierra Llopart

Authors
  1. Meire Nikaido Suzuki
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  2. Brisa Maria Fregonesi
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  3. Carolina Sampaio Machado
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  4. Guilherme Sgobbi Zagui
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  5. Luciana Kusumota
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  6. Adriano Kamimura Suzuki
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  7. José Abrão Cardeal da Costa
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  8. Jordi Sierra Llopart
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  9. Martí Nadal
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  10. Jose Luis Domingo
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  11. Susana Segura-Muñoz
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Corresponding author

Correspondence to Susana Segura-Muñoz.

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The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in compliance 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. The study was approved by the ethics committee of the University of São Paulo at Ribeirão Preto College of Nursing (report no. 343.500).

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Suzuki, M.N., Fregonesi, B.M., Machado, C.S. et al. Hemodialysis Water Parameters as Predisposing Factors for Anemia in Patients in Dialytic Treatment: Application of Mixed Regression Models. Biol Trace Elem Res 190, 30–37 (2019). https://doi.org/10.1007/s12011-018-1515-7

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