Peak post-transplant lung function in bilateral lung transplant recipients using a prediction model based on donor and recipient demographic characteristics

Michella Azar, Sheila Krishnan, Timothy E. Stump, Daniel Gutteridge, David W. Roe, Chadi Hage

Research output: Contribution to journalArticle

Abstract

Rationale: There are no reliable methods to predict lung function following lung transplantation. We sought to devise a prediction model of peak pulmonary function testing (PFT) post-transplant based on donor and recipient demographic characteristics. Methods: Single center retrospective analysis of bilateral lung transplant recipients between 2011 and 2015 without evidence of allograft dysfunction in the first year was performed. Peak PFT post-transplant was determined by serially measured FEV1 and FVC. Using the NHANES III equation, donor demographic characteristics were used to calculate predicted lung function. Multivariable linear regression helped determine which donor and recipient characteristics affected peak lung function and identify the discrepancy between donor predicted and recipient observed PFT post-transplant. Results: 146 donor/recipient patients were analyzed. 80 had obstructive lung disease, 66 had restrictive disease. Peak post-transplant FEV1 and FVC was reached in 64.30 ± 48.96 and 78.14 ± 50.68 weeks, respectively. Spirometry values peaked earlier in restrictive lung disease recipients. Higher peak FEV1 was significantly associated with younger donor age, non-African American donor race, male recipient sex, greater recipient height, underlying obstructive lung disease. Greater absolute differences between donor predicted and observed FEV1 were significantly associated with male donor sex, greater donor height, non-African-American donor race, female recipient sex, greater recipient height. Conclusions: Donor and recipient characteristics can help predict lung function post-transplant. Patients without complications in the first year post-transplant may take greater than one year to achieve peak lung function. Such predictions can help guide clinical decision making in the right setting.

Original languageEnglish (US)
Pages (from-to)29-35
Number of pages7
JournalRespiratory Medicine
Volume155
DOIs
StatePublished - Aug 1 2019

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Demography
Tissue Donors
Transplants
Lung
Obstructive Lung Diseases
Transplant Recipients
Lung Transplantation
Nutrition Surveys
Spirometry
Lung Diseases
Allografts
Linear Models

Keywords

  • Bilateral lung transplant
  • Lung function
  • Pulmonary function testing

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

Cite this

Peak post-transplant lung function in bilateral lung transplant recipients using a prediction model based on donor and recipient demographic characteristics. / Azar, Michella; Krishnan, Sheila; Stump, Timothy E.; Gutteridge, Daniel; Roe, David W.; Hage, Chadi.

In: Respiratory Medicine, Vol. 155, 01.08.2019, p. 29-35.

Research output: Contribution to journalArticle

Azar, Michella ; Krishnan, Sheila ; Stump, Timothy E. ; Gutteridge, Daniel ; Roe, David W. ; Hage, Chadi. / Peak post-transplant lung function in bilateral lung transplant recipients using a prediction model based on donor and recipient demographic characteristics. In: Respiratory Medicine. 2019 ; Vol. 155. pp. 29-35.
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abstract = "Rationale: There are no reliable methods to predict lung function following lung transplantation. We sought to devise a prediction model of peak pulmonary function testing (PFT) post-transplant based on donor and recipient demographic characteristics. Methods: Single center retrospective analysis of bilateral lung transplant recipients between 2011 and 2015 without evidence of allograft dysfunction in the first year was performed. Peak PFT post-transplant was determined by serially measured FEV1 and FVC. Using the NHANES III equation, donor demographic characteristics were used to calculate predicted lung function. Multivariable linear regression helped determine which donor and recipient characteristics affected peak lung function and identify the discrepancy between donor predicted and recipient observed PFT post-transplant. Results: 146 donor/recipient patients were analyzed. 80 had obstructive lung disease, 66 had restrictive disease. Peak post-transplant FEV1 and FVC was reached in 64.30 ± 48.96 and 78.14 ± 50.68 weeks, respectively. Spirometry values peaked earlier in restrictive lung disease recipients. Higher peak FEV1 was significantly associated with younger donor age, non-African American donor race, male recipient sex, greater recipient height, underlying obstructive lung disease. Greater absolute differences between donor predicted and observed FEV1 were significantly associated with male donor sex, greater donor height, non-African-American donor race, female recipient sex, greater recipient height. Conclusions: Donor and recipient characteristics can help predict lung function post-transplant. Patients without complications in the first year post-transplant may take greater than one year to achieve peak lung function. Such predictions can help guide clinical decision making in the right setting.",
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N2 - Rationale: There are no reliable methods to predict lung function following lung transplantation. We sought to devise a prediction model of peak pulmonary function testing (PFT) post-transplant based on donor and recipient demographic characteristics. Methods: Single center retrospective analysis of bilateral lung transplant recipients between 2011 and 2015 without evidence of allograft dysfunction in the first year was performed. Peak PFT post-transplant was determined by serially measured FEV1 and FVC. Using the NHANES III equation, donor demographic characteristics were used to calculate predicted lung function. Multivariable linear regression helped determine which donor and recipient characteristics affected peak lung function and identify the discrepancy between donor predicted and recipient observed PFT post-transplant. Results: 146 donor/recipient patients were analyzed. 80 had obstructive lung disease, 66 had restrictive disease. Peak post-transplant FEV1 and FVC was reached in 64.30 ± 48.96 and 78.14 ± 50.68 weeks, respectively. Spirometry values peaked earlier in restrictive lung disease recipients. Higher peak FEV1 was significantly associated with younger donor age, non-African American donor race, male recipient sex, greater recipient height, underlying obstructive lung disease. Greater absolute differences between donor predicted and observed FEV1 were significantly associated with male donor sex, greater donor height, non-African-American donor race, female recipient sex, greater recipient height. Conclusions: Donor and recipient characteristics can help predict lung function post-transplant. Patients without complications in the first year post-transplant may take greater than one year to achieve peak lung function. Such predictions can help guide clinical decision making in the right setting.

AB - Rationale: There are no reliable methods to predict lung function following lung transplantation. We sought to devise a prediction model of peak pulmonary function testing (PFT) post-transplant based on donor and recipient demographic characteristics. Methods: Single center retrospective analysis of bilateral lung transplant recipients between 2011 and 2015 without evidence of allograft dysfunction in the first year was performed. Peak PFT post-transplant was determined by serially measured FEV1 and FVC. Using the NHANES III equation, donor demographic characteristics were used to calculate predicted lung function. Multivariable linear regression helped determine which donor and recipient characteristics affected peak lung function and identify the discrepancy between donor predicted and recipient observed PFT post-transplant. Results: 146 donor/recipient patients were analyzed. 80 had obstructive lung disease, 66 had restrictive disease. Peak post-transplant FEV1 and FVC was reached in 64.30 ± 48.96 and 78.14 ± 50.68 weeks, respectively. Spirometry values peaked earlier in restrictive lung disease recipients. Higher peak FEV1 was significantly associated with younger donor age, non-African American donor race, male recipient sex, greater recipient height, underlying obstructive lung disease. Greater absolute differences between donor predicted and observed FEV1 were significantly associated with male donor sex, greater donor height, non-African-American donor race, female recipient sex, greater recipient height. Conclusions: Donor and recipient characteristics can help predict lung function post-transplant. Patients without complications in the first year post-transplant may take greater than one year to achieve peak lung function. Such predictions can help guide clinical decision making in the right setting.

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