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Related Concept Videos

Cystic Fibrosis: Management01:24

Cystic Fibrosis: Management

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Cystic fibrosis (CF) is an autosomal recessive disorder that predominantly affects individuals of Northern European descent, occurring at a rate of 1 in 3500. It is caused by a genetic mutation in a gene on chromosome 7, most commonly the ΔF508 mutation, that codes for the cystic fibrosis transmembrane conductance regulator (CFTR) protein. This results in thicker mucus secretions and obstruction pathologies in multiple organs, including the lungs and sinuses.
Sinus disease and chronic...
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Cystic Fibrosis: Pathogenesis01:23

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Cystic fibrosis (CF), an autosomal recessive disorder, significantly affects the function of exocrine glands. This genetically inherited disease is characterized by the production of thick and sticky mucus, which can severely affect various organs and systems in the body.
CF is primarily caused by a genetic mutation in a chromosome 7 gene coding for the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The most common gene mutation leading to CF is the ΔF508 mutation,...
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Prediction of Postoperative Lung Graft Dysfunction During the Procedure: A Single-Center Cohort Study of Cystic

Pimnara Chuachao1, Jérome Devaquet2, Edouard Sage3

  • 1Department of Epidemiology, Data, Biostatistics, Delegation of Clinical Research and Innovation, Hôpital Foch, Suresnes, France.

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Predicting severe primary graft dysfunction (PGD3) after lung transplants in cystic fibrosis (CF) patients is possible using intraoperative data. Elevated lactate and extracorporeal membrane oxygenation (ECMO) use significantly increase PGD3 risk.

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cystic fibrosislung transplantationpredictionpulmonary graft dysfunction

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Area of Science:

  • Thoracic surgery
  • Transplantation immunology
  • Pulmonary medicine

Background:

  • Primary graft dysfunction (PGD3) is a major complication after lung transplantation, particularly in cystic fibrosis (CF) patients.
  • Accurate prediction of PGD3 using intraoperative data can guide clinical management and improve outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for severe PGD3 (PGD3) following double-lung transplantation in CF patients.
  • To identify key intraoperative variables associated with PGD3 risk.

Main Methods:

  • Retrospective single-center cohort study of CF patients undergoing double-lung transplantation (2012-2019).
  • Logistic regression model developed using 69 intraoperative variables recorded at nine time-points.
  • Model performance evaluated using area under the curve (AUC).

Main Results:

  • PGD3 occurred in 15.5% of patients.
  • The predictive model's AUC improved progressively during surgery, reaching 0.892.
  • Key predictors of increased PGD3 risk included lactate level at second pulmonary artery clamping and extracorporeal membrane oxygenation (ECMO) use at surgery's end.

Conclusions:

  • A predictive model incorporating intraoperative data can effectively forecast PGD3 risk in CF lung transplant recipients.
  • Sustained ECMO use and elevated intraoperative lactate levels are significant risk factors for PGD3.
  • This model can aid in identifying high-risk patients and optimizing perioperative care.