BACKGROUND: Despite the significant morbidity associated with renal failure after lung transplantation (LTx), no predictive models currently exist. Accordingly, the purpose of this study was to develop a preoperative risk score based on recipient-, donor-, and transplant-specific characteristics to predict postoperative acute renal failure in candidates for transplantation. METHODS: The United Network of Organ Sharing (UNOS) database was queried for adult patients (≥ 18 years of age) undergoing LTx between 2005 and 2012. The population was randomly divided into derivation (80%) and validation (20%) cohorts. The primary outcome of interest was new-onset renal failure. Variables predictive of acute renal failure (exploratory p value < 0.2) within the derivation cohort were incorporated into a multivariable logistic regression model. Odds ratios were used to assign values to the independent predictors of postoperative renal failure to construct the risk stratification score (RSS). RESULTS: During the study period, 10,963 patients underwent lung transplantation, and the incidence of renal failure was 5.5% (598 patients). Baseline recipient-, donor-, and transplant-related factors were similar between the cohorts. Eighteen covariates were included in the multivariable model, and 10 were assigned values based on their relative odds ratios (ORs). Scores were stratified into 3 groups, with an observed rate of acute renal failure of 3.1%, 5.3%, and 15.6% in the low-, moderate-, and high-risk groups, respectively. The incidence of renal failure was found to be significantly increased in the highest risk group (p < 0.001). Furthermore, the risk model's predicted rates of renal failure highly correlated with actual rates observed in the population (r = 0.86). CONCLUSIONS: We introduce a novel and simple RSS that is highly predictive of renal failure after LTx.
BACKGROUND: Despite the significant morbidity associated with renal failure after lung transplantation (LTx), no predictive models currently exist. Accordingly, the purpose of this study was to develop a preoperative risk score based on recipient-, donor-, and transplant-specific characteristics to predict postoperative acute renal failure in candidates for transplantation. METHODS: The United Network of Organ Sharing (UNOS) database was queried for adult patients (≥ 18 years of age) undergoing LTx between 2005 and 2012. The population was randomly divided into derivation (80%) and validation (20%) cohorts. The primary outcome of interest was new-onset renal failure. Variables predictive of acute renal failure (exploratory p value < 0.2) within the derivation cohort were incorporated into a multivariable logistic regression model. Odds ratios were used to assign values to the independent predictors of postoperative renal failure to construct the risk stratification score (RSS). RESULTS: During the study period, 10,963 patients underwent lung transplantation, and the incidence of renal failure was 5.5% (598 patients). Baseline recipient-, donor-, and transplant-related factors were similar between the cohorts. Eighteen covariates were included in the multivariable model, and 10 were assigned values based on their relative odds ratios (ORs). Scores were stratified into 3 groups, with an observed rate of acute renal failure of 3.1%, 5.3%, and 15.6% in the low-, moderate-, and high-risk groups, respectively. The incidence of renal failure was found to be significantly increased in the highest risk group (p < 0.001). Furthermore, the risk model's predicted rates of renal failure highly correlated with actual rates observed in the population (r = 0.86). CONCLUSIONS: We introduce a novel and simple RSS that is highly predictive of renal failure after LTx.
Authors: Michael G S Shashaty; Caitlin M Forker; Todd A Miano; Qufei Wu; Wei Yang; Michelle L Oyster; Mary K Porteous; Edward E Cantu; Joshua M Diamond; Jason D Christie Journal: Clin Transplant Date: 2019-08-28 Impact factor: 2.863
Authors: M Alrawashdeh; R Zomak; M A Dew; S Sereika; M K Song; J M Pilewski; A DeVito Dabbs Journal: Am J Transplant Date: 2016-10-27 Impact factor: 8.086
Authors: Scott M Sutherland; Lakhmir S Chawla; Sandra L Kane-Gill; Raymond K Hsu; Andrew A Kramer; Stuart L Goldstein; John A Kellum; Claudio Ronco; Sean M Bagshaw Journal: Can J Kidney Health Dis Date: 2016-02-26
Authors: David Paul Baird; Fraser Rae; Christina Beecroft; Katherine Gallagher; Stephanie Sim; Robert Vaessen; Emily Wright; Samira Bell Journal: BMJ Open Qual Date: 2019-03-29