The effect of pre-heart transplant body mass index on posttransplant outcomes: An analysis of the ISHLT Registry Data.
Barbara S DoumourasChun-Po S FanBrigitte MuellerAnne I DipchandCedric ManlhiotJosef StehlikHeather J RossAna Carolina AlbaPublished in: Clinical transplantation (2019)
We evaluated the effect of pre-heart transplant body mass index (BMI) on posttransplant outcomes using the International Society for Heart and Lung Transplantation Registry. Kaplan-Meier analysis and a multivariable Cox proportional hazard regression model were used for all-cause mortality, and cause-specific hazard regression for cause-specific mortality and morbidity. We assessed 38 498 recipients from 2000 to 2014 stratified by pretransplant BMI. Ten-year survival was 56% in underweight, 59% in normal weight, 57% in overweight, 52% in obese class I, 54% in class II, and 47% in class III patients (P < 0.001). Mortality was increased in underweight (HR 1.29, 95% CI 1.24-1.35), obese class I (HR 1.19, 95% CI 1.13-1.26), class II (HR 1.20, 95% CI 1.08-1.32), and class III patients (HR 1.45, 95% CI 1.15-1.83). Obesity was independently associated with increased death from myocardial infarction, chronic rejection, infection, and renal dysfunction. An underweight BMI lead to increased death from infection, acute and chronic rejection, malignancy, and bleeding. Obese patients had a higher incidence of renal dysfunction, diabetes, stroke, acute rejection, cardiac allograft vasculopathy, and malignancy, and underweight recipients had increased acute rejection. We have shown that pretransplant obese and underweight patients have increased post-heart transplant mortality and morbidity. This has implications for candidate selection and posttransplant management.
Keyphrases
- body mass index
- weight gain
- weight loss
- obese patients
- end stage renal disease
- type diabetes
- heart failure
- metabolic syndrome
- ejection fraction
- atrial fibrillation
- physical activity
- liver failure
- risk factors
- bariatric surgery
- prognostic factors
- intensive care unit
- cardiovascular events
- machine learning
- brain injury
- patient reported