Plasma kallikrein predicts primary graft dysfunction after heart transplant

Nicholas P. Giangreco, Guillaume Lebreton, Susan Restaino, Mary Jane Farr, Emmanuel Zorn, Paolo C. Colombo, Jignesh Patel, Ryan Levine, Lauren Truby, Rajesh Kumar Soni, Pascal Leprince, Jon Kobashigawa, Nicholas P. Tatonetti, Barry M. Fine

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. Methods: Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. Results: Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66–0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92–0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. Conclusions: Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.

Original languageEnglish (US)
Pages (from-to)1199-1211
Number of pages13
JournalJournal of Heart and Lung Transplantation
Volume40
Issue number10
DOIs
StatePublished - Oct 2021
Externally publishedYes

Keywords

  • exosomes
  • machine learning
  • primary graft dysfunction

ASJC Scopus subject areas

  • Surgery
  • Pulmonary and Respiratory Medicine
  • Cardiology and Cardiovascular Medicine
  • Transplantation

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