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Prediction of Pulmonary Graft Dysfunction After Double-lung Transplantation (PGD3-AI Study)

NCT04643665 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 478

Last updated 2020-11-25

No results posted yet for this study

Summary

The thundering evolution of lung transplantation management during the past ten years and primary graft dysfunction (PGD) new definition have led to new predictive factors of PGD. Therefore, we retrospectively analyzed a monocentric database using a machine-learning method, to determine the predictive factors of grade 3 PGD (PGD3), defined as a PaO2/FiO2 ratio \< 200 or being under extracorporeal membrane oxygenation (ECMO) at postoperative day 3.

We included all double lung transplantation from 2012 to 2019 and excluded multi-organ transplant, cardiopulmonary bypass, or repeated transplantation during the study period for the same patient. Recipient, donor and intraoperative data were added in a gradient boosting algorithm step-by-step according to standard transplantation stages. Dataset will be split randomly as 80% training set and 20% testing set. Relationship between predictive factors and PGD3 will be represented as ShHapley Additive exPlanation (SHAP) values.

Conditions

  • Transplant Dysfunction
  • Transplantation, Lung

Interventions

PROCEDURE

Double-lung transplantation

Sponsors & Collaborators

  • Hopital Foch

    lead OTHER

Principal Investigators

  • Elisabeth Hulier Ammar, PhD · Hopital Foch

Eligibility

Min Age
12 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2012-01-01
Primary Completion
2019-12-31
Completion
2020-10-05

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04643665 on ClinicalTrials.gov