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Utility of gene expression profiling score variability to predict clinical events in heart transplant recipients.

ABSTRACT: Gene expression profiling test scores have primarily been used to identify heart transplant recipients who have a low probability of rejection at the time of surveillance testing. We hypothesized that the variability of gene expression profiling test scores within a patient may predict risk of future events of allograft dysfunction or death.Patients from the IMAGE study with rejection surveillance gene expression profiling tests performed at 1- to 6-month intervals were selected for this cohort study. Gene expression profiling score variability was defined as the standard deviation of an individual's cumulative test scores. Gene expression profiling ordinal score (range, 0-39), threshold score (binary value=1 if ordinal score ? 34), and score variability were studied in multivariate Cox regression models to predict future clinical events.Race, age at time of transplantation, and time posttransplantation were significantly associated with future events in the univariate analysis. In the multivariate analyses, gene expression profiling score variability, but not ordinal scores or scores over threshold, was independently associated with future clinical events. The regression coefficient P values were <0.001, 0.46, and 0.773, for gene expression profiling variability, ordinal, and threshold scores, respectively. The hazard ratio for a 1 unit increase in variability was 1.76 (95% CI, 1.4-2.3).The variability of a heart recipient's gene expression profiling test scores over time may provide prognostic utility. This information is independent of the probability of acute cellular rejection at the time of testing that is rendered from a single ordinal gene-expression profiling test score.

SUBMITTER: Deng MC

PROVIDER: S-EPMC3983476 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Utility of gene expression profiling score variability to predict clinical events in heart transplant recipients.

Deng Mario C MC Elashoff Barbara B Pham Michael X MX Teuteberg Jeffrey J JJ Kfoury Abdallah G AG Starling Randall C RC Cappola Thomas P TP Kao Andrew A Anderson Allen S AS Cotts William G WG Ewald Gregory A GA Baran David A DA Bogaev Roberta C RC Shahzad Khurram K Hiller David D Yee James J Valantine Hannah A HA

Transplantation 20140301 6

<h4>Background</h4>Gene expression profiling test scores have primarily been used to identify heart transplant recipients who have a low probability of rejection at the time of surveillance testing. We hypothesized that the variability of gene expression profiling test scores within a patient may predict risk of future events of allograft dysfunction or death.<h4>Method</h4>Patients from the IMAGE study with rejection surveillance gene expression profiling tests performed at 1- to 6-month interv ...[more]

PMID: 24637869

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Project description:A single non-invasive gene expression profiling (GEP) test (AlloMap®) is often used to discriminate if a heart transplant recipient is at a low risk of acute cellular rejection at time of testing. In a randomized trial, use of the test (a GEP score from 0-40) has been shown to be non-inferior to a routine endomyocardial biopsy for surveillance after heart transplantation in selected low-risk patients with respect to clinical outcomes. Recently, it was suggested that the within-patient variability of consecutive GEP scores may be used to independently predict future clinical events; however, future studies were recommended. Here we performed an analysis of an independent patient population to determine the prognostic utility of within-patient variability of GEP scores in predicting future clinical events.We defined the GEP score variability as the standard deviation of four GEP scores collected ?315 days post-transplantation. Of the 737 patients from the Cardiac Allograft Rejection Gene Expression Observational (CARGO) II trial, 36 were assigned to the composite event group (death, re-transplantation or graft failure ?315 days post-transplantation and within 3 years of the final GEP test) and 55 were assigned to the control group (non-event patients). In this case-controlled study, the performance of GEP score variability to predict future events was evaluated by the area under the receiver operator characteristics curve (AUC ROC). The negative predictive values (NPV) and positive predictive values (PPV) including 95 % confidence intervals (CI) of GEP score variability were calculated.The estimated prevalence of events was 17 %. Events occurred at a median of 391 (inter-quartile range 376) days after the final GEP test. The GEP variability AUC ROC for the prediction of a composite event was 0.72 (95 % CI 0.6-0.8). The NPV for GEP score variability of 0.6 was 97 % (95 % CI 91.4-100.0); the PPV for GEP score variability of 1.5 was 35.4 % (95 % CI 13.5-75.8).In heart transplant recipients, a GEP score variability may be used to predict the probability that a composite event will occur within 3 years after the last GEP score.Clinicaltrials.gov identifier NCT00761787.

Project description:Background and objectivesTertiary hyperparathyroidism in kidney allograft recipients is associated with bone loss, allograft dysfunction, and cardiovascular mortality. Accurate pretransplant risk prediction of tertiary hyperparathyroidism may support individualized treatment decisions. We aimed to develop an integer score system that predicts the risk of tertiary hyperparathyroidism using machine learning algorithms.Design, setting, participants, & measurementsWe used two separate cohorts: a derivation cohort with the data of kidney allograft recipients (n=669) who underwent kidney transplantation at Severance Hospital, Seoul, Korea between January 2009 and December 2015 and a multicenter registry dataset (the Korean Cohort Study for Outcome in Patients with Kidney Transplantation) as an external validation cohort (n=542). Tertiary hyperparathyroidism was defined as post-transplant parathyroidectomy. The derivation cohort was split into 75% training set (n=501) and 25% holdout test set (n=168) to develop prediction models and integer-based score.ResultsTertiary hyperparathyroidism requiring parathyroidectomy occurred in 5% and 2% of the derivation and validation cohorts, respectively. Three top predictors (dialysis duration, pretransplant intact parathyroid hormone, and serum calcium level measured at the time of admission for kidney transplantation) were identified to create an integer score system (dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level [DPC] score; 0-15 points) to predict tertiary hyperparathyroidism. The median DPC score was higher in participants with post-transplant parathyroidectomy than in those without (13 versus three in derivation; 13 versus four in external validation; P<0.001 for all). Pretransplant dialysis duration, pretransplant serum parathyroid hormone level, and pretransplant calcium level score predicted post-transplant parathyroidectomy with comparable performance with the best-performing machine learning model in the test set (area under the receiver operating characteristic curve: 0.94 versus 0.92; area under the precision-recall curve: 0.52 versus 0.47). Serial measurement of DPC scores (≥13 at least two or more times, 3-month interval) during 12 months prior to kidney transplantation improved risk classification for post-transplant parathyroidectomy compared with single-time measurement (net reclassification improvement, 0.28; 95% confidence interval, 0.02 to 0.54; P=0.03).ConclusionsA simple integer-based score predicted the risk of tertiary hyperparathyroidism in kidney allograft recipients, with improved classification by serial measurement compared with single-time measurement.Clinical trial registry name and registration numberKorean Cohort Study for Outcome in Patients with Kidney Transplantation (KNOW-KT), NCT02042963 PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_06_10_CJN15921221.mp3.

Dataset Information

Utility of gene expression profiling score variability to predict clinical events in heart transplant recipients.

Publications

Utility of gene expression profiling score variability to predict clinical events in heart transplant recipients.

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