Modeling Survival Time to Death among Stroke Patients at Jimma University Medical Center, Southwest Ethiopia: A Retrospective Cohort Study.
Bikiltu Wakuma NegasaTeramaj Wongel WotaleMesfin Esayas LelishoLegesse Kassa DebushoKibrealem SisayWubishet GezimuPublished in: Stroke research and treatment (2023)
The Weibull accelerated failure time model better described the time to death of the stroke patients' data set than other distributions used in this study. Patients' age, atrial fibrillation, alcohol consumption, being diagnosed with hemorrhagic types of stroke, having hypertension, and having diabetes mellitus were found to be factors shortening survival time to death for stroke patients. Hence, healthcare professionals need to thoroughly follow the patients who pass risk factors. Moreover, patients need to be educated about lifestyle modifications.
Keyphrases
- end stage renal disease
- atrial fibrillation
- newly diagnosed
- risk factors
- ejection fraction
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- metabolic syndrome
- cardiovascular disease
- patient reported outcomes
- coronary artery disease
- venous thromboembolism
- insulin resistance
- acute coronary syndrome
- machine learning
- deep learning
- blood brain barrier
- big data
- oral anticoagulants
- free survival
- data analysis