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Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia

Received: 18 October 2017     Accepted: 16 November 2017     Published: 15 December 2017
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Abstract

Background: Chronic kidney disease (CKD) with diagonesised end-stage renal disease (ESRD) is common public health problems worldwide. This study was aimed to investigate socio-economics and clinical characteristics determinants among end-stage renal disease (ESRD) population. Method: This study is a retrospective cohort design which was conducted during May 2012 to April 2016 and included 500 ESRD patients at Adama Hospital Medical College. Retrospectives data were gathered by reviewing patients’ medical and surgical wards history. The Cox PH regression and parametric survival (Weibull, Log-logistic and log normal) models were molded and compared for examining survival analysis of ESRD patient using R statistical package software. Results: The study participants are 500 ESRD patients, 72.40% were alive at the end of this study, while 27.40% were died. The survival time of ESRD Majority of patients (66.20%) were female. Log-normal model had fitted the ESRD data set best relatively among possible candidate models. The age at the time of admission to ESRD (HR=0.94, p-value < 0.05), female (HR=0.54, p-value <0.05) and family history (HR=0.45, p-value<0.05) had significantly shorter survival time of ESRD patients to mortality. Conclusion: parametric survival model with baseline hazard lognormal distribution was found appropriate to our dataset. This study identified that having ESRD with complications increases the probability of death. The family history of experiencing ESRD is a driver for being ESRD patient. Female patients had greater risk of death than males in this study. Age specific follow-up is necessary to reduce the mortality related to ESRD.

Published in Clinical Medicine Research (Volume 6, Issue 6)
DOI 10.11648/j.cmr.20170606.15
Page(s) 201-208
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Chronic Kidney Disease, Risk Factors, Parametric Models, Ethiopia

References
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Cite This Article
  • APA Style

    Mekiya Hussein, Geremew Muleta, Dinberu Seyoum, Demeke Kifle, Dechasa Bedada. (2017). Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia. Clinical Medicine Research, 6(6), 201-208. https://doi.org/10.11648/j.cmr.20170606.15

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    ACS Style

    Mekiya Hussein; Geremew Muleta; Dinberu Seyoum; Demeke Kifle; Dechasa Bedada. Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia. Clin. Med. Res. 2017, 6(6), 201-208. doi: 10.11648/j.cmr.20170606.15

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    AMA Style

    Mekiya Hussein, Geremew Muleta, Dinberu Seyoum, Demeke Kifle, Dechasa Bedada. Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia. Clin Med Res. 2017;6(6):201-208. doi: 10.11648/j.cmr.20170606.15

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  • @article{10.11648/j.cmr.20170606.15,
      author = {Mekiya Hussein and Geremew Muleta and Dinberu Seyoum and Demeke Kifle and Dechasa Bedada},
      title = {Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia},
      journal = {Clinical Medicine Research},
      volume = {6},
      number = {6},
      pages = {201-208},
      doi = {10.11648/j.cmr.20170606.15},
      url = {https://doi.org/10.11648/j.cmr.20170606.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cmr.20170606.15},
      abstract = {Background: Chronic kidney disease (CKD) with diagonesised end-stage renal disease (ESRD) is common public health problems worldwide. This study was aimed to investigate socio-economics and clinical characteristics determinants among end-stage renal disease (ESRD) population. Method: This study is a retrospective cohort design which was conducted during May 2012 to April 2016 and included 500 ESRD patients at Adama Hospital Medical College. Retrospectives data were gathered by reviewing patients’ medical and surgical wards history. The Cox PH regression and parametric survival (Weibull, Log-logistic and log normal) models were molded and compared for examining survival analysis of ESRD patient using R statistical package software. Results: The study participants are 500 ESRD patients, 72.40% were alive at the end of this study, while 27.40% were died. The survival time of ESRD Majority of patients (66.20%) were female. Log-normal model had fitted the ESRD data set best relatively among possible candidate models. The age at the time of admission to ESRD (HR=0.94, p-value < 0.05), female (HR=0.54, p-value <0.05) and family history (HR=0.45, p-value<0.05) had significantly shorter survival time of ESRD patients to mortality. Conclusion: parametric survival model with baseline hazard lognormal distribution was found appropriate to our dataset. This study identified that having ESRD with complications increases the probability of death. The family history of experiencing ESRD is a driver for being ESRD patient. Female patients had greater risk of death than males in this study. Age specific follow-up is necessary to reduce the mortality related to ESRD.},
     year = {2017}
    }
    

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  • TY  - JOUR
    T1  - Survival Analysis of Patients with End Stage Renal Disease the Case of Adama Hospital, Ethiopia
    AU  - Mekiya Hussein
    AU  - Geremew Muleta
    AU  - Dinberu Seyoum
    AU  - Demeke Kifle
    AU  - Dechasa Bedada
    Y1  - 2017/12/15
    PY  - 2017
    N1  - https://doi.org/10.11648/j.cmr.20170606.15
    DO  - 10.11648/j.cmr.20170606.15
    T2  - Clinical Medicine Research
    JF  - Clinical Medicine Research
    JO  - Clinical Medicine Research
    SP  - 201
    EP  - 208
    PB  - Science Publishing Group
    SN  - 2326-9057
    UR  - https://doi.org/10.11648/j.cmr.20170606.15
    AB  - Background: Chronic kidney disease (CKD) with diagonesised end-stage renal disease (ESRD) is common public health problems worldwide. This study was aimed to investigate socio-economics and clinical characteristics determinants among end-stage renal disease (ESRD) population. Method: This study is a retrospective cohort design which was conducted during May 2012 to April 2016 and included 500 ESRD patients at Adama Hospital Medical College. Retrospectives data were gathered by reviewing patients’ medical and surgical wards history. The Cox PH regression and parametric survival (Weibull, Log-logistic and log normal) models were molded and compared for examining survival analysis of ESRD patient using R statistical package software. Results: The study participants are 500 ESRD patients, 72.40% were alive at the end of this study, while 27.40% were died. The survival time of ESRD Majority of patients (66.20%) were female. Log-normal model had fitted the ESRD data set best relatively among possible candidate models. The age at the time of admission to ESRD (HR=0.94, p-value < 0.05), female (HR=0.54, p-value <0.05) and family history (HR=0.45, p-value<0.05) had significantly shorter survival time of ESRD patients to mortality. Conclusion: parametric survival model with baseline hazard lognormal distribution was found appropriate to our dataset. This study identified that having ESRD with complications increases the probability of death. The family history of experiencing ESRD is a driver for being ESRD patient. Female patients had greater risk of death than males in this study. Age specific follow-up is necessary to reduce the mortality related to ESRD.
    VL  - 6
    IS  - 6
    ER  - 

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Author Information
  • Department of Statistics, Haramaya University, Haramaya, Ethiopia

  • Department of Statistics, Jimma University, Jimma, Ethiopia

  • Department of Statistics, Jimma University, Jimma, Ethiopia

  • Department of Statistics, Jimma University, Jimma, Ethiopia

  • Department of Statistics, Jimma University, Jimma, Ethiopia

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