Donor B-type natriuretic peptide predicts early cardiac performance after heart transplantation

Vorlat, Anne; Conraads, Viviane M.; Jorens, Philippe G.; Aerts, Sophie; Gorp, Sara Van; Vermeulen, Tom; Herck, Paul Van; Hoof, Viviane Van; Rodrigus, Inez; Vrints, Christiaan J.; Claeys, Marc J.

doi:10.1016/j.healun.2012.02.009

Cited by 26 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various methods are used to limit false-positive/negative signals and reduce variables. These mainly use stepwise regression models ( 36 , 37 ), principal component analysis (PCA) ( 38 , 39 ), T -statistic, and correlation to clinical variables ( 7 , 8 , 40 – 43 ).…”

Section: Computational Challenges and Approaches For Selecting Biomarmentioning

confidence: 99%

“…Many regression models are available to select a particular set of independent variables and the commonly used methods are stepwise techniques ( 36 , 37 ). Traditional stepwise selection alternates between forward and backward regression selection, in which variables are added or removed that meet a selection criteria setting for entry or removal, until a final subset of variables make the model saturated.…”

Section: Computational Challenges and Approaches For Selecting Biomarmentioning

confidence: 99%

See 1 more Smart Citation

Computational Models for Transplant Biomarker Discovery

Wang

Sarwal

2015

Front. Immunol.

View full text Add to dashboard Cite

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called “omics” provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key computational approaches for selecting efficiently the best subset of biomarkers from high-dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

show abstract

Section: Computational Challenges and Approaches For Selecting Biomarmentioning

confidence: 99%

Section: Computational Challenges and Approaches For Selecting Biomarmentioning

confidence: 99%

Computational Models for Transplant Biomarker Discovery

Wang

Sarwal

2015

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…BNP or NT-proBNP is another easily measureable serum biomarker that is a sensitive indicator of myocardial dysfunction. As with troponin, several authors have reported a significant correlation between elevated serum BNP (or NTproBNP) in the donor and donor heart dysfunction; however, there has been an inconsistent relationship between donor BNP and post-transplant graft function [35][36][37][38].…”

Section: Biomarkers Of Primary Graft Dysfunctionmentioning

confidence: 99%

“…The largest share of experience with any of these newer devices has been with the Levitronix CentriMag. A multicentre UK registry analysis of the use of this device to support 38 [47]. Clinical studies have suggested similar outcomes when these devices are applied to patients with post-infarction or cardiomyopathy-associated cardiogenic shock, but there is limited experience with the newer devices in severe PGD [48].…”

Section: Managementmentioning

confidence: 99%

Primary Graft Dysfunction After Heart Transplantation

et al. 2014

View full text Add to dashboard Cite

Primary graft dysfunction (PGD) is a lifethreatening complication of heart transplantation that presents as left, right, or biventricular dysfunction occurring within the first 24 hours of transplant surgery for which there is no identifiable secondary cause. Myocardial injury caused by acute catecholamine toxicity and the release of multiple proinflammatory mediators in the donor, followed by ischaemiareperfusion injury sustained during retrieval, have been considered the predominant pathogenetic processes leading to PGD. Donor, recipient, and procedural factors contribute to the development and severity of the clinical syndrome. The changing donor and recipient characteristics over the last two decades, particularly the increasing donor and recipient age, have led to heightened risk of PGD. PGD is graded from mild to severe depending on the extent of circulatory support that is required to maintain haemodynamic stability and vital organ function. While advances in acute mechanical support devices have improved the outlook for patients with PGD, the rate of mortality remains high for those with severe PGD, reaching 40 %. Potential approaches to preventing or minimising the severity of PGD include optimising donor management, donor heart preservation, and donor/recipient matching.

show abstract

“…BNP is increasingly utilized in the management of adult and pediatric HT recipients as a marker for rejection and for risk stratification of early graft performance, death, and re‐transplantation . In the complex milieu of changing clinical variables during the early transplant period, a biomarker that could aid surveillance by providing a “context” of graft recovery could be helpful.…”

mentioning

confidence: 99%

B‐type natriuretic peptide trends after pediatric heart transplantation

Sparks

Boston

Eghtesady

et al. 2014

Pediatric Transplantation

View full text Add to dashboard Cite

BNP is increasingly utilized in the management of pediatric HT recipients. Performing a retrospective single-center chart review, we sought to describe BNP changes during the first year after HT and identify factors that affect its trend. After exclusion for rejection, 316 BNP levels from 50 patients were evaluated. BNP underwent an exponential decline 120 days after HT followed by a plateau. Log10 BNP decline strongly correlated with time (r = -0.70, p < 0.0001). Initial BNP was less in pretransplant VAD (p = 0.0016) and lower post-HT inotrope use (p = 0.0043). Infant recipients, IT >4 h, and those bridged medically were associated with higher plateau BNP. Multivariable logistic regression demonstrated IT >4 h independently predicted plateau BNP in the upper quartile (OR 7.1, p = 0.02). No significant change in BNP coincided with rejection (N = 6 patients) without severe hemodynamic compromise. BNP correlated modestly with right atrial pressure (r = 0.4652, p < 0.0001) and pulmonary capillary wedge pressure (r = 0.2660, p < 0.001), but poorly with echocardiogram (r = -0.18, p = 0.003). Trending BNP could help provide insight into how the graft recovers after HT and IT >4 h independently predicted higher plateau BNP and may reflect subtle changes in graft performance.

show abstract

Donor B-type natriuretic peptide predicts early cardiac performance after heart transplantation

Cited by 26 publications

References 25 publications

Computational Models for Transplant Biomarker Discovery

Computational Models for Transplant Biomarker Discovery

Primary Graft Dysfunction After Heart Transplantation

B‐type natriuretic peptide trends after pediatric heart transplantation

Contact Info

Product

Resources

About