Survival Analysis of Acute Myocardial Infarction Patients Using the Kumaraswamy-Logistic Model and Kaplan-Meier Estimation
DOI:
https://doi.org/10.62933/32m8tn21Keywords:
Acute Myocardial Infarction, Survival Analysis, Kumaraswamy-Logistic Model, Kaplan-Meier Estimation, Risk AssessmentAbstract
Acute Myocardial Infarction (AMI) remains a leading cause of morbidity and mortality worldwide, necessitating robust survival analyses to better understand prognostic factors and improve patient outcomes. This study applies the Kumaraswamy-logistic survival regression model and the Kaplan-Meier estimator to analyze survival trends in AMI patients using the Worcester Heart Attack Study dataset. The dataset comprises survival times for AMI patients, with censoring and covariates such as age, sex, and body mass index (BMI). The Kumaraswamy-logistic model provided maximum likelihood estimates (MLEs) of model parameters, revealing significant associations between survival time and specific covariates. Factors such as advanced age, male sex, and obesity were identified as significant risk factors negatively impacting survival. The Kaplan-Meier analysis revealed strong short-term survival probabilities, with survival rates at 98% and 79.6% at 6 and 187 days, respectively. However, long-term survival declined significantly, dropping to 15.8% by 2421 days. Increasing standard errors and widening confidence intervals in the Kaplan-Meier estimates underscored reduced precision in long-term survival predictions due to smaller sample sizes and higher variability. Comparative analysis between the two approaches showed that the Kumaraswamy-logistic model effectively captured the influence of covariates on survival probabilities, complementing the empirical insights from the Kaplan-Meier method. This study highlights the importance of integrating parametric and non-parametric methods for survival analysis to provide a comprehensive understanding of AMI survival trends and prognostic factors. The findings emphasize the need for targeted interventions to improve long-term outcomes, particularly for high-risk groups, and underscore the utility of advanced survival models in clinical decision-making and patient care strategies.
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Copyright (c) 2025 Ibrahim Abubakar Sadiq, Yahaya Zakari, Sani Ibrahim Doguwa, Mannir Isiya, Suleiman Yahaya Suleiman, Ayodamola Hephzibah Ajayi, Yahaya Yahaya Gambo, Wisdom Samuel (Author)
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