Background: Overweight is defined with a body mass index (BMI) >25. A BMI >25 is known as an independent risk factor for increased morbidity and mortality. The influence of an increased BMI on the development of diabetes and on survival after heart transplantation (HTX) was investigated.
Methods: A total of 137 patients (116 men, 21 women), who underwent HTX at our Department from 1986 to 2002, were included in the study. For group stratification, the pre-operative BMI values were taken (group I: BMI 25). Groups were compared for primary disease, age and sex, development of renal failure, development of diabetes, and survival. The probability of survival and the freedom-from-diabetes interval were calculated by the use of Kaplan-Meier method.
Results: No significant differences between groups I and II were found concerning primary disease, age and sex, and occurrence of renal failure. There was a tendency towards increased survival (p = 0.18) in group I. Patients of group II developed diabetes after HTX more frequently than those of group I (p < 0.001). Cox regression revealed that pre-operative BMI >25 is a highly significant independent risk factor for post-operative development of diabetes mellitus (DM) (p < 0.001).
Conclusion: Overweight prior to HTX appears to negatively influence long-term survival after HTX, although this difference did not reach statistical significance. Pre-operative overweight is a significant and independent risk factor for the development of post-transplant diabetes.
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Developmental and Cellular Biology, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
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Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.
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