Dataset Information

ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome.

ABSTRACT: Cyclosporine A (CsA) is a substrate of P-glycoprotein, an efflux transporter encoded by the ABCB1 gene. Compared with carriers of the wild-type gene, carriers of T allelic variants in exons 21 or 26 have reduced P-glycoprotein activity and, secondarily, increased intracellular concentration of CsA; therefore, carriers of T variants might be at increased risk for CsA-related adverse events. We evaluated the associations between ABCB1 genotypes (in exons 12, 21, and 26) and CsA-related outcomes in 147 renal transplant recipients who were receiving CsA-based immunosuppression and were included in the Mycophenolate Steroids Sparing study. During a median of 65.5 mo follow-up, carriers of T allelic variants in exons 21 or 26 had a three-fold risk for delayed graft function (DGF), a trend to slower recovery of renal function and lower GFR at study end, and significantly higher incidences of new-onset diabetes and cytomegalovirus reactivation compared with carriers of the wild-type genotype. T variants in both exons 21 and 26 were independently associated with 3.8- and 3.5-fold higher risk for DGF, respectively (P = 0.022 and P = 0.034). The incidence of acute rejection and the mean CsA dose and blood levels were comparable in genotype groups. In conclusion, renal transplant recipients with T allelic variants in ABCB1 exons 21 or 26 are at increased risk for CsA-related adverse events. Genetic evaluation may help to identify patients at risk and to modulate CsA therapy to optimize graft and patient outcomes.

SUBMITTER: Cattaneo D

PROVIDER: S-EPMC2689900 | biostudies-other | 2009 Jun

REPOSITORIES: biostudies-other

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ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome.

Cattaneo Dario D Ruggenenti Piero P Baldelli Sara S Motterlini Nicola N Gotti Eliana E Sandrini Silvio S Salvadori Maurizio M Segoloni Giuseppe G Rigotti Paolo P Donati Donato D Perico Norberto N Remuzzi Giuseppe G

Journal of the American Society of Nephrology : JASN 20090521 6

Cyclosporine A (CsA) is a substrate of P-glycoprotein, an efflux transporter encoded by the ABCB1 gene. Compared with carriers of the wild-type gene, carriers of T allelic variants in exons 21 or 26 have reduced P-glycoprotein activity and, secondarily, increased intracellular concentration of CsA; therefore, carriers of T variants might be at increased risk for CsA-related adverse events. We evaluated the associations between ABCB1 genotypes (in exons 12, 21, and 26) and CsA-related outcomes in ...[more]

PMID: 19470683

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Project description:BACKGROUND:More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients' data to extract the temporal features using their structural temporal patterns, that is, trends. OBJECTIVE:This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI). METHODS:Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation. RESULTS:Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001). CONCLUSIONS:Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

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Dataset Information

ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome.

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ABCB1 genotypes predict cyclosporine-related adverse events and kidney allograft outcome.

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