Gender based survival prediction models for heart failure patients: A case study in Pakistan.

<h4>Objectives</h4>The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction.<h4>Methods</h4>A lasso approach was used to decide...

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Main Authors: Faisal Maqbool Zahid, Shakeela Ramzan, Shahla Faisal, Ijaz Hussain
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2019-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0210602
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spelling doaj-fbe4906074c048648c7e250baddc666d2021-03-04T10:36:38ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01142e021060210.1371/journal.pone.0210602Gender based survival prediction models for heart failure patients: A case study in Pakistan.Faisal Maqbool ZahidShakeela RamzanShahla FaisalIjaz Hussain<h4>Objectives</h4>The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction.<h4>Methods</h4>A lasso approach was used to decide the informative predictors for building semi-parametric proportional hazards Cox model. Separate models were built for all patients [N = 299], male patients [Nmale = 194 (64.88%)], and female patients [Nfemale = 105 (35.12%)], to observe the risk factors associated with the individual's risk of death. The likelihood- ratio test was used to test the goodness of fit of the selected model, and the C-index was used to assess the predictive performance of the selected model(s) with respect to the overall model with all observed risk factors.<h4>Results</h4>The survival prediction model for females is notably different from that for males. For males, smoking, diabetes, and anaemia, whereas for females, ejection fraction, sodium, and platelets count are non-informative with zero regression coefficients. The goodness of fit of the selected models with respect to the general model with all observed risk factors is tested using the likelihood-ratio test. The results are in favor of the selected models with p-values 0.51,0.61, and 0.70 for all patients, male patients, and female patients, respectively. The same values of C-index for the full model and the selected models for overall data, for males, and for females (0.72, 0.73, and 0.77 for overall data, male data, and female data, respectively) indicate that the selected models are as good as the corresponding overall models regarding their predictive performance.<h4>Conclusion</h4>There is a substantial difference in the survival prediction models for heart failure (HF) of male and female patients in this study. More studies are needed in Pakistan for confirming this striking male-female difference regarding the potential risk factors to predict survival with heart failure.https://doi.org/10.1371/journal.pone.0210602
collection DOAJ
language English
format Article
sources DOAJ
author Faisal Maqbool Zahid
Shakeela Ramzan
Shahla Faisal
Ijaz Hussain
spellingShingle Faisal Maqbool Zahid
Shakeela Ramzan
Shahla Faisal
Ijaz Hussain
Gender based survival prediction models for heart failure patients: A case study in Pakistan.
PLoS ONE
author_facet Faisal Maqbool Zahid
Shakeela Ramzan
Shahla Faisal
Ijaz Hussain
author_sort Faisal Maqbool Zahid
title Gender based survival prediction models for heart failure patients: A case study in Pakistan.
title_short Gender based survival prediction models for heart failure patients: A case study in Pakistan.
title_full Gender based survival prediction models for heart failure patients: A case study in Pakistan.
title_fullStr Gender based survival prediction models for heart failure patients: A case study in Pakistan.
title_full_unstemmed Gender based survival prediction models for heart failure patients: A case study in Pakistan.
title_sort gender based survival prediction models for heart failure patients: a case study in pakistan.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2019-01-01
description <h4>Objectives</h4>The objective of this study was to build and assess the performance of survival prediction models using the gender-specific informative risk factors for patients with left ventricular systolic dysfunction.<h4>Methods</h4>A lasso approach was used to decide the informative predictors for building semi-parametric proportional hazards Cox model. Separate models were built for all patients [N = 299], male patients [Nmale = 194 (64.88%)], and female patients [Nfemale = 105 (35.12%)], to observe the risk factors associated with the individual's risk of death. The likelihood- ratio test was used to test the goodness of fit of the selected model, and the C-index was used to assess the predictive performance of the selected model(s) with respect to the overall model with all observed risk factors.<h4>Results</h4>The survival prediction model for females is notably different from that for males. For males, smoking, diabetes, and anaemia, whereas for females, ejection fraction, sodium, and platelets count are non-informative with zero regression coefficients. The goodness of fit of the selected models with respect to the general model with all observed risk factors is tested using the likelihood-ratio test. The results are in favor of the selected models with p-values 0.51,0.61, and 0.70 for all patients, male patients, and female patients, respectively. The same values of C-index for the full model and the selected models for overall data, for males, and for females (0.72, 0.73, and 0.77 for overall data, male data, and female data, respectively) indicate that the selected models are as good as the corresponding overall models regarding their predictive performance.<h4>Conclusion</h4>There is a substantial difference in the survival prediction models for heart failure (HF) of male and female patients in this study. More studies are needed in Pakistan for confirming this striking male-female difference regarding the potential risk factors to predict survival with heart failure.
url https://doi.org/10.1371/journal.pone.0210602
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