Laser ablation of placental anastomoses in twin-to-twin transfusion syndrome: preoperative predictors of death by recursive partitioning. Academic Article uri icon

Overview

abstract

  • OBJECTIVE: The aim of this study was to develop a simple clinical algorithm for prediction of donor and recipient death using 'yes'or 'no' questions through the process of recursive partitioning for patients undergoing laser therapy for twin to twin transfusion syndrome (TTTS). The intent was to identify a subset of patients with very high specificity to whom clinical decisions would be simplified. METHOD: Secondary analysis of data retrospectively collected from laser procedures was performed for TTTS at NAFTNet centers from 2002 to 2009. Preoperative factors associated with donor and recipient death were identified by recursive partitioning regression analysis. Classification And Regression Trees (CARTs) were developed to refine specificity for prediction of death. RESULTS: There were 466 TTTS patients from eight centers. CARTs were obtained for prediction of donor death. Improved specificity was achieved through recursive partitioning as demonstrated in receiver operator characteristic curves for prediction of death of the donor. There was less than optimal predictive ability for prediction of death in the recipient, as demonstrated by lack of generation of CARTs. CONCLUSION: Recursive partitioning improves the specificity and refines the prediction of donor fetal and neonatal demise in TTTS treated with laser therapy. This has the potential to improve therapeutic choices and refine counseling regarding outcomes.

authors

  • Skupski, Daniel W
  • Luks, Francois I
  • Papanna, Ramesha
  • Walker, Martin
  • Bebbington, Michael
  • Ryan, Greg
  • O'Shaughnessy, Richard
  • Moldenhauer, Julie
  • Bahtiyar, Ozan

publication date

  • February 6, 2013

Research

keywords

  • Arteriovenous Anastomosis
  • Fetofetal Transfusion
  • Laser Therapy
  • Placenta

Identity

Scopus Document Identifier

  • 84874546562

Digital Object Identifier (DOI)

  • 10.1002/pd.4059

PubMed ID

  • 23386469

Additional Document Info

volume

  • 33

issue

  • 3