Ramiro Fernandez1, Niloufar Safaeinili1, Chitaru Kurihara1, David D Odell1, Manu Jain2, Malcolm M DeCamp1, G R Scott Budinger2, Ankit Bharat3. 1. Division of Thoracic Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Ill. 2. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill. 3. Division of Thoracic Surgery, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, Ill; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Ill. Electronic address: abharat@nm.org.
Abstract
BACKGROUND: The association of body mass index (BMI) with survival after lung transplantation remains controversial, owing to conflicting evidence in the literature. Previous reports have used traditional BMI categories, included patients who underwent transplantation before implementation of the lung allocation score (LAS), or were limited by single-center experiences. Here we evaluated the association of individual BMI units with short-term and long-term mortality in a large national database following implementation of the LAS. METHODS: The Scientific Registry of Transplant Recipients database was used to collect data for 17,233 adult lung transplantations performed between May 2005 and June 2016. The primary outcome was all-cause mortality at 90 days and 1 year posttransplantation. Logistic regression modeling was used to independently predict mortality per BMI unit, adjusting for donor and recipient factors. RESULTS: BMI was an independent predictor of mortality at both 90 days and 1 year. At 90 days, a BMI of 25 was associated with the lowest predicted probability of death (0.053; 95% confidence interval [CI], 0.047-0.049), with increased odds of mortality at BMI ≤20 and ≥28. At 1 year, a BMI of 26 was associated with the lowest predicted probability of death (0.12; 95% CI, 0.11-0.13), with increased odds of mortality at BMI ≤24 and ≥28. CONCLUSIONS: Each individual BMI unit has a quantifiable effect on posttransplantation survival, and the patterns of effect do not fit into the predefined BMI categories. The mortality risk associated with BMI should be considered by transplant centers when making listing decisions and by regulatory bodies for estimating expected outcomes.
BACKGROUND: The association of body mass index (BMI) with survival after lung transplantation remains controversial, owing to conflicting evidence in the literature. Previous reports have used traditional BMI categories, included patients who underwent transplantation before implementation of the lung allocation score (LAS), or were limited by single-center experiences. Here we evaluated the association of individual BMI units with short-term and long-term mortality in a large national database following implementation of the LAS. METHODS: The Scientific Registry of Transplant Recipients database was used to collect data for 17,233 adult lung transplantations performed between May 2005 and June 2016. The primary outcome was all-cause mortality at 90 days and 1 year posttransplantation. Logistic regression modeling was used to independently predict mortality per BMI unit, adjusting for donor and recipient factors. RESULTS: BMI was an independent predictor of mortality at both 90 days and 1 year. At 90 days, a BMI of 25 was associated with the lowest predicted probability of death (0.053; 95% confidence interval [CI], 0.047-0.049), with increased odds of mortality at BMI ≤20 and ≥28. At 1 year, a BMI of 26 was associated with the lowest predicted probability of death (0.12; 95% CI, 0.11-0.13), with increased odds of mortality at BMI ≤24 and ≥28. CONCLUSIONS: Each individual BMI unit has a quantifiable effect on posttransplantation survival, and the patterns of effect do not fit into the predefined BMI categories. The mortality risk associated with BMI should be considered by transplant centers when making listing decisions and by regulatory bodies for estimating expected outcomes.
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