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Objective To evaluate the effect of body mass index (BMI) on early prognosis of the recipients after lung transplantation. Methods Clinical data of 307 lung transplant recipients were retrospectively analyzed. According to preoperative BMI, all recipients were divided into the low (BMI <18.5 kg/m2, n=114), normal (18.5 kg/m2≤BMI <24 kg/m2, n=145) and high (BMI≥24.0 kg/m2, n=48) BMI groups, respectively. Baseline data including age, sex, blood type, BMI, preoperative complications, preoperative pulmonary hypertension, and intraoperative use of extracorporeal membrane oxygenation (ECMO) of all recipients were compared among three groups. The survival rate of all recipients was estimated by Kaplan-Meier curve and the survival curve was delineated. The differences of survival rate were analyzed by log-rank test. The 30-, 90- and 180-d mortality risk of all recipients after lung transplantation in different BMI groups was compared by multivariate Cox regression analysis. Results There were significant differences in age and sex of recipients among three groups (both P<0.05). There was a significant difference regarding the 180-d survival rate after lung transplantation among different BMI groups (P<0.05). Multivariate Cox regression analysis showed that the 90-d mortality risk after lung transplantation in the high BMI group was 2.295 times higher than that in the normal BMI group [hazard ratio (HR) 2.295, 95% confidence interval (CI) 1.064-4.947, P=0.034]. In the high BMI group, the 180-d mortality risk after lung transplantation was 2.783 times higher compared with that in the normal BMI group (HR 2.783, 95%CI 1.333-5.810, P=0.006), and the 180-d mortality risk in the low BMI group was 2.181 times higher than that in the normal BMI group (HR 2.181, 95%CI 1.124-4.232, P=0.021). Conclusions Compared with the recipients with normal BMI, their counterparts with high and low preoperative BMI have higher mortality risk early after lung transplantation. Adjusting preoperative BMI to normal range contributes to improving early prognosis of lung transplant recipients.
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Objective To analyze the prediction efficiency of scoring models at home and abroad on delayed graft function (DGF) after renal transplantation in China. Methods The clinical data of 112 donors and 220 recipients undergoing renal transplantation were prospectively analyzed. The DGF predicted by KDRI model, Jeldres model, and model of our center was compared with actual DGF incidence of renal transplant recipients. The prediction efficiency of each model was analyzed. The predictive accuracy was compared by the area under curve (AUC) of receiver operating characteristic (ROC) curve. Results The DGF incidence of 220 renal transplant recipients was 14.1% (31/220). DGF prediction using KDRI model showed that 41 cases were high risk donors, the AUC was 0.57, the sensitivity was 0.37, the specificity was 0.66, and the positive predictive value was 22%. DGF prediction using Jedres model showed that 22 cases were high risk recipients, the AUC was 0.56, the sensitivity was 0.13, the specificity was 0.92 and the positive predictive value was 20%. DGF prediction using the model of our center showed that 25 cases were high risk donors, the AUC was 0.80, the sensitivity was 0.53, the specificity was 0.84, the positive predictive value was 40%. Conclusions Compared with the KDRI and Jedres models, the prediction model of our center has higher AUC and sensitivity with a better prediction efficiency on DGF. Therefore, it is a suitable evaluation system of donors from donation after citizen's death in Chinese.
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Lung transplantation is now considered one of the effective management of end stage pulmonary diseases. This review presents the recent progress of primary graft failure in lung preservation, such as low temperature, ventilation, etc. The purpose is to provide some valid methods in clinical practice.