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

Habtamu Dessie; Yenefenta Wube; Belete Adelo; Eskeziaw Abebe

doi:doi:10.11648/j.ajtas.20200903.11

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

Habtamu Dessie, Yenefenta Wube, Belete Adelo, Eskeziaw Abebe

Published in American Journal of Theoretical and Applied Statistics (Volume 9, Issue 3)

Received: 7 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.

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

Keywords

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

References

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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.
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[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.
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[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.
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APA Style

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://article.sciencepublishinggroup.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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Author Information

Habtamu Dessie

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

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

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

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

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Plain Text BibTeX RIS

APA Style

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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  -

Copy | Download