ArticleBMC cardiovascular disorders2025
Bayesian joint modelling of multiple longitudinal biomarkers and survival time of cardiac patients under clinical follow-up.
Article in BMC cardiovascular disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
introductionCardiac disease (CD) is a leading cause of death worldwide. Longitudinal studies often involve multiple patient biomarkers measured over time. Simultaneous monitoring of these biomarkers alongside time-to-death outcomes is crucial for understanding disease progression and informing clinical decision-making.
methodsThis study apply Bayesian joint model (BJM) to analyze multiple longitudinal biomarkers alongside time-to-death data and identify factors influencing the survival of cardiac patients. The data comes from Cardiac Center-Ethiopia, which comprises 323 children diagnosed with cardiac disease. The data contains biomarkers; systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate (PR) alongside time-to-death outcomes.
resultsThe Bayesian Joint Model with current value and slope association structures provided the best fit for the data. The findings indicated that both the current levels and the rate of change in biomarkers were significantly associated with patient survival. Lower biomarker levels were linked to a higher probability of survival, whereas elevated levels were associated with an increased risk of mortality. Factors such as low oxygen saturation, uncorrected cardiac surgery, reduced ejection fraction, and lower hemoglobin levels negatively impacted biomarker profiles and shortened survival time. Additionally, patients with congenital heart conditions and those experiencing undernutrition exhibited lower survival probabilities.
conclusionsThe findings underscore the crucial role of identifying and utilizing biomarkers to improve survival of patients. We recommend the use of BJM with current value and slope association structures for analyzing longitudinal and time-to-event data to identify factors influencing the survival of patients.
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