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Prediction models for primary graft dysfunction after lung transplantation: Systematic review
Ruying Zhao1, Xiaofan Bu2, Rongrong Fan3
1The Laminar Flow Research Ward, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, China.
Transplantation Reviews (Orlando, Fla.)
|March 15, 2026
Summary
Accurate prediction of primary graft dysfunction (PGD) after lung transplantation (LTx) is crucial. Current PGD prediction models show high risk of bias and lack clinical validation, necessitating improved methodology and external validation for practical use.
Area of Science:
- Transplantation research
- Medical prediction modeling
- Pulmonary medicine
Background:
- Primary graft dysfunction (PGD) is a major cause of mortality after lung transplantation (LTx).
- Accurate PGD prediction is vital for timely intervention and improved patient outcomes.
Conclusions:
- Urgent need for improved adherence to reporting guidelines (TRIPOD) and rigorous methodology (PROBAST).
- Current PGD models require further development, external validation, and clinical integration.
- Future research must focus on robust design and validation for clinical decision-making.
