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Analytic Morphomics Predict Outcomes After Lung Transplantation.

Michael J Pienta1, Peng Zhang2, Brian A Derstine2

  • 1Section of Thoracic Surgery, University of Michigan Medical School, Ann Arbor, Michigan.

The Annals of Thoracic Surgery
|December 5, 2017
PubMed
Summary
This summary is machine-generated.

Pretransplantation CT scans reveal morphomic factors like reduced subcutaneous fat and bone density predict poor lung transplant outcomes. These findings aid in better patient selection and risk assessment for improved survival.

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Area of Science:

  • Radiology and Imaging
  • Transplantation Medicine
  • Quantitative Anatomy

Background:

  • Lung transplantation outcomes are influenced by recipient factors.
  • Standard pretransplantation computed tomography (CT) scans offer a potential source of predictive data.
  • Morphomic analysis of CT scans can quantify anatomical characteristics.

Purpose of the Study:

  • To identify morphomic factors on pretransplantation CT scans associated with lung transplant outcomes.
  • To evaluate the predictive value of body composition and dimensions on post-transplant results.
  • To explore the relationship between CT-derived morphometrics and patient survival, ventilation duration, and length of stay.

Main Methods:

  • Retrospective review of 200 lung transplant recipients (2003-2014).
  • Morphomic analysis of pretransplantation CT scans, including fat, muscle, bone, and lung characteristics.
  • Statistical analysis (univariate and multivariable) of morphomic factors against clinical outcomes.

Main Results:

  • Decreased subcutaneous fat and lung density predicted survival.
  • Increased bone mineral density and body area correlated with shorter hospital stays.
  • Factors like Zubrod score and need for supplemental oxygen were associated with prolonged ventilation and stay.

Conclusions:

  • Morphomic factors indicating lower metabolic reserve and frailty are independent predictors of adverse lung transplant outcomes.
  • Pretransplantation CT-based morphomic analysis can enhance recipient selection and risk stratification.
  • Quantitative anatomical assessment may optimize lung transplant candidate evaluation.