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Modelos de sobrevivência paramétricos para estudo da mortalidade associada à insuficiência cardíaca
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Resumo(s)
Heart failure is the leading cause of hospitalizations worldwide. It is a cardiovascular disease in which the heart is unable to pump the necessary amount of blood throughout the body, and the body may cease to function normally. Although there is no cure, early diagnosis and appropriate treatment allow patients to have a better quality of life. Therefore, it is essential to understand the risk factors that contribute to the development of this disease. In Pakistan, the incidence of cardiovascular disease has been increasing. Poor access to healthcare, unhealthy dietary choices, regional conflicts, and the occurrence of natural disasters are some examples of factors contributing to this increase. This study aims to identify the factors that influence the survival time of patients with heart failure treated at the Institute of Cardiology at Allied Hospital in Faisalabad, Pakistan, between April and December 2015. This data comes from a research article published in Pakistan, which aimed to calculate death rates due to heart failure and identify the main risk factors using the Cox model. This study aims to analyze the data using the R statistical computing software with the survival package, using parametric regression models from survival analysis to identify the factors that influence survival time. The preliminary analysis used the Kaplan-Meier estimate and the log-rank test. Age, ejection fraction, creatinine, blood pressure, anemia, CPK and sodium were found as significant risk factors for mortality among heart failure patients.
Descrição
Tese de Mestrado, Estatística e Investigação Operacional, 2025, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
Heart failure Cox model Parametric models Survival analysis