Assessing survival time of heart failure patients: using Bayesian approach

Ashine, Tafese; Muleta, Geremew; Tadesse, Kenenisa

doi:10.1186/s40537-021-00537-4

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Parametric models play a crucial role in Bayesian survival analysis, as they form the foundation for much of the actual Bayesian analysis. Among the popular parametric survival models, the log-normal model stands out 26 . The log-normal distribution is particularly relevant when the cause of death or failure results from the accumulation of additive damages over time 27 .…”

Section: Methodsmentioning

confidence: 99%

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

Alinia,

Ahmadi,

Mohammadi

et al. 2024

Sci Rep

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Colorectal cancer is a prevalent malignancy with global significance. This retrospective study aimed to investigate the influence of stage and tumor site on survival outcomes in 284 colorectal cancer patients diagnosed between 2001 and 2017. Patients were categorized into four groups based on tumor site (colon and rectum) and disease stage (early stage and advanced stage). Demographic characteristics, treatment modalities, and survival outcomes were recorded. Bayesian survival modeling was performed using semi-competing risks illness-death models with an accelerated failure time (AFT) approach, utilizing R 4.1 software. Results demonstrated significantly higher time ratios for disease recurrence (TR = 1.712, 95% CI 1.489–2.197), mortality without recurrence (TR = 1.933, 1.480–2.510), and mortality after recurrence (TR = 1.847, 1.147–2.178) in early-stage colon cancer compared to early-stage rectal cancer. Furthermore, patients with advanced-stage rectal cancer exhibited shorter survival times for disease recurrence than patients with early-stage colon cancer. The interaction effect between the disease site and cancer stage was not significant. These findings, derived from the optimal Bayesian log-normal model for terminal and non-terminal events, highlight the importance of early detection and effective management strategies for colon cancer. Early-stage colon cancer demonstrated improved survival rates for disease recurrence, mortality without recurrence, and mortality after recurrence compared to other stages. Early intervention and comprehensive care are crucial to enhance prognosis and minimize adverse events in colon cancer patients.

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Section: Methodsmentioning

confidence: 99%

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

Alinia,

Ahmadi,

Mohammadi

et al. 2024

Sci Rep

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“…Ashine et. al., [8] used the Bayesian method with Deviance Information Criteria as the model selection scheme. The model with least deviance value is the preferred one.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

Mishra¹

2022

j.electron.electromedical.eng.med.inform

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Heart Failure, an ailment in which the heart isn’t functioning as effectively as it should, causing in an insufficient cardiac output. The effectual functioning of the human body is dependent on how well the heart is able to pump oxygenated, and nutrient rich blood to the tissues and cells. Heart failure falls into the category of cardiovascular diseases - the disorders of the heart and blood vessels. One of the leading causes of global deaths resulting in an estimated 17.9 million deaths globally every year. The condition of heart failure results out of structural changes to the cardiac muscles majorly in the left ventricle. The weakened muscles cause the ventricle to lose its ability to contract completely. Since the left ventricle generates the required pressure for blood circulation, any kind of a failure condition results in the reduction of cardiac power output. This study aims to conduct a thorough survival analysis and survival prediction on the data of 299 patients classified into the class III/IV of heart failure and diagnosed with left ventricular systolic dysfunction. Survival analysis involves the study of the effect of a mediation assessed by measuring the number of subjects survived after that mediation over a period of time. The time starting from a distinct point to the occurrence of a certain event, for example death is known as survival time and the corresponding analysis is known as survival analysis. The analysis was performed using the methods of Kaplan-Meier (KM) estimates and Cox Potential Hazard regression. KM plots showed the survival estimates as a function of each clinical feature and how each feature at various levels affect survival over the period of time. Cox regression modelled the hazard of death event around the clinical features used for the study. As a result of the analysis, ejection fraction, serum creatinine, time and age were identified as highly significant and major risk factors in the advanced stages of heart failure. Age and rise in level of serum creatinine have a deleterious effect on the survival chances. Ejection Fraction has a beneficial effect on survival and with a unit increase in the in the EF level the probability of death event decreases by ~5.2%. Higher rate of mortality is observed during the initial days post diagnosis and the hazard gradually decreases if patients have lived for a certain number of days. Hypertension and anemic condition also seem to be high risk factors. Machine learning classification models for survival prediction were built using the most significant variables found from survival analysis. SVM, decision tree, random forest, XGBoost, and LightGBM algorithm were implemented, and all the models seem to perform well enough. However, the availability of more data will make the models more stable and robust. Smart solutions, like this can reduce the risk of heart failure condition by providing accurate prognosis, survival projections, and risk predictions. Technology and data can combine together to address any disparities in treatment, design better care plan, and improve patient health outcomes. Smart health AI solutions would enhance healthcare policies, enable physicians to look beyond the conventional practices, and increase the patient satisfaction levels not only in case of heart failure conditions but healthcare in general.

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“…Survival models can be written into a latent Gaussian model which allows us to perform Bayesian inference using integrated nested Laplace approximations 19 . Survival analysis consists of a great body of work using latent Gaussian models and it is one of the statistical models on which INLA has been successfully applied 23 , 24 . The main advantage of INLA over MCMC techniques is its simplicity of computation 24 .…”

Section: Introductionmentioning

confidence: 99%

Bayesian parametric modeling of time to tuberculosis co-infection of HIV/AIDS patients at Jimma Medical Center, Ethiopia

Umeta

Yermosa

Dufera

2022

Sci Rep

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Tuberculosis is the most common opportunistic infection among HIV/AIDS patients, including those following Antiretroviral Therapy treatment. The risk of tuberculosis infection is higher in people living with HIV/AIDS than in people who are free from HIV/AIDS. Many studies focused on prevalence and determinants of tuberculosis in HIV/AIDS patients without taking into account the censoring aspects of the time to event data. Therefore, this study was undertaken with aim to model time to tuberculosis co-infection of HIV/AIDS patients under follow-up at Jimma Medical Center, Ethiopia using Bayesian parametric survival models. A data of a retrospective cohort of 421 HIV/AIDS patients under follow-up from January 2016 to December 2020 until active tuberculosis was diagnosed or until the end of the study was collected from Jimma Medical Center, Ethiopia. The analysis of the data was performed using R-INLA software package. In order to identify the risk factors which have association with tuberculosis co-infection survival time, Bayesian parametric accelerated failure time survival models were fitted to the data using Integrated Nested Laplace Approximation methodology. About 26.37% of the study subjects had been co-infected with tuberculosis during the study period. Among the parametric accelerated failure time models, the Bayesian log-logistic accelerated failure time model was found to be the best fitting model for the data. Patients who lived in urban areas had shorter tuberculosis co-infection free survival time compared to those who lived in rural areas with an acceleration factor of 0.2842. Patients who smoke and drink alcohol had also shorter tuberculosis co-infection survival time than those who do not smoke and drink alcohol respectively. Patients with advanced WHO clinical stages(Stage III and IV), bedridden functional status, low body mass index and severe anemic status had shorter tuberculosis co-infection survival time. Place of residence, smoking, drinking alcohol, larger family size, advanced clinical stages(Stage III and Stage IV), bedridden functional status, CD4 count ($$\le $$ ≤ 200 cells/mm3 and 200–349 cells/mm3), low body mass index and low hemoglobin are the factors that lead to shorter tuberculosis co-infection survival time in HIV/AIDS patients. The findings of the study suggested us to forward the recommendations to modify patients’ life style, early screening and treatment of opportunistic diseases like anemia , as well as effective treatment and management of tuberculosis and HIV co-infection are important to prevent tuberculosis and HIV co-infection.

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Assessing survival time of heart failure patients: using Bayesian approach

Cited by 5 publications

References 26 publications

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

Exploring the impact of stage and tumor site on colorectal cancer survival: Bayesian survival modeling

A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning Techniques

Bayesian parametric modeling of time to tuberculosis co-infection of HIV/AIDS patients at Jimma Medical Center, Ethiopia

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