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Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis

BACKGROUND: Assessment of infection-related mortality remains inadequate in patients undergoing peritoneal dialysis. This study was performed to develop a risk model for predicting the 2-year infection-related mortality risk in patients undergoing peritoneal dialysis. METHODS: The study cohort compr...

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Autores principales: Tsujikawa, Hiroaki, Tanaka, Shigeru, Matsukuma, Yuta, Kanai, Hidetoshi, Torisu, Kumiko, Nakano, Toshiaki, Tsuruya, Kazuhiko, Kitazono, Takanari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426225/
https://www.ncbi.nlm.nih.gov/pubmed/30893369
http://dx.doi.org/10.1371/journal.pone.0213922
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author Tsujikawa, Hiroaki
Tanaka, Shigeru
Matsukuma, Yuta
Kanai, Hidetoshi
Torisu, Kumiko
Nakano, Toshiaki
Tsuruya, Kazuhiko
Kitazono, Takanari
author_facet Tsujikawa, Hiroaki
Tanaka, Shigeru
Matsukuma, Yuta
Kanai, Hidetoshi
Torisu, Kumiko
Nakano, Toshiaki
Tsuruya, Kazuhiko
Kitazono, Takanari
author_sort Tsujikawa, Hiroaki
collection PubMed
description BACKGROUND: Assessment of infection-related mortality remains inadequate in patients undergoing peritoneal dialysis. This study was performed to develop a risk model for predicting the 2-year infection-related mortality risk in patients undergoing peritoneal dialysis. METHODS: The study cohort comprised 606 patients who started and continued peritoneal dialysis for 90 at least days and was drawn from the Fukuoka Peritoneal Dialysis Database Registry Study in Japan. The patients were registered from 1 January 2006 to 31 December 2016 and followed up until 31 December 2017. To generate a prediction rule, the score for each variable was weighted by the regression coefficients calculated using a Cox proportional hazard model adjusted by risk factors for infection-related mortality, including patient characteristics, comorbidities, and laboratory data. RESULTS: During the follow-up period (median, 2.2 years), 138 patients died; 58 of them of infectious disease. The final model for infection-related mortality comprises six factors: age, sex, serum albumin, serum creatinine, total cholesterol, and weekly renal Kt/V. The incidence of infection-related mortality increased linearly with increasing total risk score (P for trend <0.001). Furthermore, the prediction model showed adequate discrimination (c-statistic = 0.79 [0.72–0.86]) and calibration (Hosmer–Lemeshow test, P = 0.47). CONCLUSION: In this study, we developed a new model using clinical measures for predicting infection-related mortality in patients undergoing peritoneal dialysis.
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spelling pubmed-64262252019-04-02 Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis Tsujikawa, Hiroaki Tanaka, Shigeru Matsukuma, Yuta Kanai, Hidetoshi Torisu, Kumiko Nakano, Toshiaki Tsuruya, Kazuhiko Kitazono, Takanari PLoS One Research Article BACKGROUND: Assessment of infection-related mortality remains inadequate in patients undergoing peritoneal dialysis. This study was performed to develop a risk model for predicting the 2-year infection-related mortality risk in patients undergoing peritoneal dialysis. METHODS: The study cohort comprised 606 patients who started and continued peritoneal dialysis for 90 at least days and was drawn from the Fukuoka Peritoneal Dialysis Database Registry Study in Japan. The patients were registered from 1 January 2006 to 31 December 2016 and followed up until 31 December 2017. To generate a prediction rule, the score for each variable was weighted by the regression coefficients calculated using a Cox proportional hazard model adjusted by risk factors for infection-related mortality, including patient characteristics, comorbidities, and laboratory data. RESULTS: During the follow-up period (median, 2.2 years), 138 patients died; 58 of them of infectious disease. The final model for infection-related mortality comprises six factors: age, sex, serum albumin, serum creatinine, total cholesterol, and weekly renal Kt/V. The incidence of infection-related mortality increased linearly with increasing total risk score (P for trend <0.001). Furthermore, the prediction model showed adequate discrimination (c-statistic = 0.79 [0.72–0.86]) and calibration (Hosmer–Lemeshow test, P = 0.47). CONCLUSION: In this study, we developed a new model using clinical measures for predicting infection-related mortality in patients undergoing peritoneal dialysis. Public Library of Science 2019-03-20 /pmc/articles/PMC6426225/ /pubmed/30893369 http://dx.doi.org/10.1371/journal.pone.0213922 Text en © 2019 Tsujikawa et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Tsujikawa, Hiroaki
Tanaka, Shigeru
Matsukuma, Yuta
Kanai, Hidetoshi
Torisu, Kumiko
Nakano, Toshiaki
Tsuruya, Kazuhiko
Kitazono, Takanari
Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title_full Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title_fullStr Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title_full_unstemmed Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title_short Development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
title_sort development of a risk prediction model for infection-related mortality in patients undergoing peritoneal dialysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6426225/
https://www.ncbi.nlm.nih.gov/pubmed/30893369
http://dx.doi.org/10.1371/journal.pone.0213922
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