American Journal of Theoretical and Applied Statistics

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Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals

Received: 07 October 2019    Accepted: 16 April 2020    Published: 28 April 2020
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Abstract

Congestive heart failure is a complex clinical syndrome of functional or structural impairment in the heart. Nowadays heart failure is common and increasing in the world and researches on this area is limited. Therefore the aim of the present study was to analyze and quantify the impact of modelling heart failure survival allowing for covariates with time varying effects known to be independent predictors of overall mortality in this clinical setting. A retrospective cohort study was conducted on CHF patients who were on treatment follow up at both WGH and DRH from January 1, 2010 to December 30, 2016. A total of 487 patients were selected by using simple random sampling from the patient's medical record. Semi parametric, parametric PH models and AFT models was employed to identify the best model which shown as the real causation of factors with the outcome of CHF which is death. The Weibull accelerated failure time model result showed that the risk factors related to accelerating or decelerating the lifespan were age (TR=0.962, p=0.000), Residence (rural) (TR=1.24, p=0.019), Nutritional (Poor) (TR=0.582, p=0.000), Smoking (TR=0.774, p=0.005), Alcoholism (TR=1.394, p=0.010), Diabetes mellitus (TR=0.49, p=0.000), Hypertension (TR=0.079, p=0.019), Stroke (TR=0.799, p=0.014), Coronary Artery disease (TR=0.276, p=0.012), Tuberculosis bacillus (TR=0.103, p=0.000) as a co morbidity and the interaction between age and Tuberculosis bacillus (p=0.000), age and Coronary artery disease (p=0.041), Diabetes mellitus with Hypertension (p=0.000), Hypertension with Nutritional status (p=0.000) and age with time (p=0.000) were found statistically significant. The Weibull accelerated failure time model performed better explain the effect of predictors than other Cox and parametric PH models. Thus, researchers should use parametric AFT models to see regression varying effect covariates. Frequent monitoring and follow up of Patients with heart failure should be adopted.

DOI 10.11648/j.ajtas.20200903.11
Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3, May 2020)
Page(s) 21-36
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), 2024. Published by Science Publishing Group

Keywords

CHF, Retrospective Cohort Study, Parametric AFT Model, Censoring, Mortality

References
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[2] Ponikowski, P. A. (2014). Heart failure: Preventing disease and death worldwide. European Society of Cardiology; from: http://spo.escardio.org/eslides/view.aspx?eevtid=59&f, Available.
[3] Owusu, I. B. (2013). Prevalence and etiology of Heart Failure in Patients SeenataTeaching Hospital in Ghana. J Cardiovasc Dis Diagn, 1: 131.
[4] Tantchou, Tchoumi, Jacques, Cabraletal.(2011). Retrieved from The Pan African Medical Journal - ISSN 1937-8688.: http://www.panafrican-med-journal.com/content/article/8/11/full/.
[5] Cabral, T. S. (2011). Occurrence, etiology and challenges in the management of congestive heart failure in sub-Saharan Africa: experience of the Cardiac Centrein Shisong, Cameroon. Pan African Medical journal, 8: 11.
[6] Misganaw, A. H. -M. -M. (2014). Epidemiology of Major Non-communicable Diseases in Ethiopia. A Systematic Review. J Health Popul Nutr 32 (1), 1-13.
[7] Misganaw, A. H. -M. (2012). The Double Mortality Burden among Adults in Addis Ababa, Ethiopia, 2006-2009. Prev Chronic Dis, 9: 110-142.
[8] Azmera, h. (2015). Survival during Treatment Period of Patients with Severe Heart Failure Admitted to Intensive Care Unit (ICU) at Gondar University Hospital. American journal of health research, 257-269.
[9] Bennett, D. E. (2012). Study protocol: systematic review of the burden of heart failure in low-and middle-income countries. Bennettetat. Systematic reviews, 1: 59.
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[11] KhalilMurad, M. a. (2012). Frailty and Multiple Comorbidities in the Elderly Patient with Heart Failure: Implications for Management. Heart Fail Rev: doi: 10. 1007/s10741-011-9258-y, 581–588.
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[15] GioloSR, K. J. (2012). Survival Analysis of Patients with Heart Failure: Implications of Time-Varying Regression Effects in Modeling Mortality. PLoS ONE7.
[16] Retrieved from http://Chronic kidney disease and heart failure Bidirectional close link and common the rapeuticgoal Science Direct.html.
[17] BadveSV, RobertsMA, HawleyCM, CassA, GargAX, KrumH, TonkinA, PerkovicV.. (2011). Effects of beta adrenergic antagonists in patients with chronic kidney disease: asystematicreviewandmeta-analysis. J Am Coll Cardiol, 1152–1161.
[18] Akiomiyoshisha, y. k. (2017). The impact of nutration indices on mortality inpatients with heart failur. Open heart. bmj.(6): e37392. doi: 10. 1371/journal. pone. 0037392.
[19] Clare J Taylor, Ronan Ryan, Linda Nichols, Nicola Gale, FD Richard Hobbs, and Tom Marshall (2017). Survival following a diagnosis of heart failure in primary care. Family Practice, Vol. 34, No. 2, 161–168.
[20] Mulubirhan Tirfe, Teshome Nedi, Desalew Mekonnen and Alemseged Beyene (2020). Treatment outcome and its predictors among patients of acute heart failure at a tertiary care hospital in Ethiopia: a prospective observational study. BMC Cardio vascular Disorders 20: 16.
[21] Saifullah Nasir, M. a (2012). Congestive Heart Failure and Diabetes: Balancing Glycemic Control with Heart Failure Improvement. Winter Center for Heart Failure Research and section of Cardiology; Am J, 110.
Author Information
  • Department of Statistics, Faculty of Natural and Computational Science, Woldia University, Woldia, Ethiopia

  • Department of Statistics, Faculty of Natural and Computational Science, Woldia University, Woldia, Ethiopia

  • Department of Statistics, Faculty of Natural and Computational Science, Woldia University, Woldia, Ethiopia

  • Department of Midwifery, Faculty of Natural and Computational Science, Woldia University, Woldia, Ethiopia

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    Habtamu Dessie, Yenefenta Wube, Belete Adelo, Eskeziaw Abebe. (2020). Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals. American Journal of Theoretical and Applied Statistics, 9(3), 21-36. https://doi.org/10.11648/j.ajtas.20200903.11

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    ACS Style

    Habtamu Dessie; Yenefenta Wube; Belete Adelo; Eskeziaw Abebe. Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals. Am. J. Theor. Appl. Stat. 2020, 9(3), 21-36. doi: 10.11648/j.ajtas.20200903.11

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    AMA Style

    Habtamu Dessie, Yenefenta Wube, Belete Adelo, Eskeziaw Abebe. Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals. Am J Theor Appl Stat. 2020;9(3):21-36. doi: 10.11648/j.ajtas.20200903.11

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  • @article{10.11648/j.ajtas.20200903.11,
      author = {Habtamu Dessie and Yenefenta Wube and Belete Adelo and Eskeziaw Abebe},
      title = {Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {9},
      number = {3},
      pages = {21-36},
      doi = {10.11648/j.ajtas.20200903.11},
      url = {https://doi.org/10.11648/j.ajtas.20200903.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20200903.11},
      abstract = {Congestive heart failure is a complex clinical syndrome of functional or structural impairment in the heart. Nowadays heart failure is common and increasing in the world and researches on this area is limited. Therefore the aim of the present study was to analyze and quantify the impact of modelling heart failure survival allowing for covariates with time varying effects known to be independent predictors of overall mortality in this clinical setting. A retrospective cohort study was conducted on CHF patients who were on treatment follow up at both WGH and DRH from January 1, 2010 to December 30, 2016. A total of 487 patients were selected by using simple random sampling from the patient's medical record. Semi parametric, parametric PH models and AFT models was employed to identify the best model which shown as the real causation of factors with the outcome of CHF which is death. The Weibull accelerated failure time model result showed that the risk factors related to accelerating or decelerating the lifespan were age (TR=0.962, p=0.000), Residence (rural) (TR=1.24, p=0.019), Nutritional (Poor) (TR=0.582, p=0.000), Smoking (TR=0.774, p=0.005), Alcoholism (TR=1.394, p=0.010), Diabetes mellitus (TR=0.49, p=0.000), Hypertension (TR=0.079, p=0.019), Stroke (TR=0.799, p=0.014), Coronary Artery disease (TR=0.276, p=0.012), Tuberculosis bacillus (TR=0.103, p=0.000) as a co morbidity and the interaction between age and Tuberculosis bacillus (p=0.000), age and Coronary artery disease (p=0.041), Diabetes mellitus with Hypertension (p=0.000), Hypertension with Nutritional status (p=0.000) and age with time (p=0.000) were found statistically significant. The Weibull accelerated failure time model performed better explain the effect of predictors than other Cox and parametric PH models. Thus, researchers should use parametric AFT models to see regression varying effect covariates. Frequent monitoring and follow up of Patients with heart failure should be adopted.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Modelling Time to Mortality with Congestive Heart Failure: A Case Study in Wollo General and Referral Government Hospitals
    AU  - Habtamu Dessie
    AU  - Yenefenta Wube
    AU  - Belete Adelo
    AU  - Eskeziaw Abebe
    Y1  - 2020/04/28
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ajtas.20200903.11
    DO  - 10.11648/j.ajtas.20200903.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 21
    EP  - 36
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20200903.11
    AB  - Congestive heart failure is a complex clinical syndrome of functional or structural impairment in the heart. Nowadays heart failure is common and increasing in the world and researches on this area is limited. Therefore the aim of the present study was to analyze and quantify the impact of modelling heart failure survival allowing for covariates with time varying effects known to be independent predictors of overall mortality in this clinical setting. A retrospective cohort study was conducted on CHF patients who were on treatment follow up at both WGH and DRH from January 1, 2010 to December 30, 2016. A total of 487 patients were selected by using simple random sampling from the patient's medical record. Semi parametric, parametric PH models and AFT models was employed to identify the best model which shown as the real causation of factors with the outcome of CHF which is death. The Weibull accelerated failure time model result showed that the risk factors related to accelerating or decelerating the lifespan were age (TR=0.962, p=0.000), Residence (rural) (TR=1.24, p=0.019), Nutritional (Poor) (TR=0.582, p=0.000), Smoking (TR=0.774, p=0.005), Alcoholism (TR=1.394, p=0.010), Diabetes mellitus (TR=0.49, p=0.000), Hypertension (TR=0.079, p=0.019), Stroke (TR=0.799, p=0.014), Coronary Artery disease (TR=0.276, p=0.012), Tuberculosis bacillus (TR=0.103, p=0.000) as a co morbidity and the interaction between age and Tuberculosis bacillus (p=0.000), age and Coronary artery disease (p=0.041), Diabetes mellitus with Hypertension (p=0.000), Hypertension with Nutritional status (p=0.000) and age with time (p=0.000) were found statistically significant. The Weibull accelerated failure time model performed better explain the effect of predictors than other Cox and parametric PH models. Thus, researchers should use parametric AFT models to see regression varying effect covariates. Frequent monitoring and follow up of Patients with heart failure should be adopted.
    VL  - 9
    IS  - 3
    ER  - 

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