ArticleJournal of thoracic disease2024
Risk factors and prognostic modeling in bridging extracorporeal membrane oxygenation patients before lung transplantation.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Lactate-to-albumin ratio improves prediction of perioperative mortality in lung transplant patients.Frontiers in medicine · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The increased use of preoperative extracorporeal membrane oxygenation (ECMO) as a life support system before lung transplantation demands a better understanding of the associated prognostic factors. This study aims to discern the critical factors influencing the survival outcomes of ECMO patients and design a prognostic model tailored to this patient group. Methods: We retrospectively gathered and analyzed baseline and clinical data of patients who underwent preoperative bridging ECMO before lung transplantation from the United Network for Organ Sharing (UNOS) database. Univariate and multivariate Cox regression analyses were conducted and a prognostic model was generated to identify the independent factors influencing survival outcomes in these patients. The predictive model was cross-validated using the k-fold method where k=5. Results: Our study included 1,202 patients. Both single and multiple analyses showed that age over 51 years, high body mass index (BMI), a history of dialysis before transplantation, donor hypertension, prolonged cold ischemia time, and high serum total bilirubin are adverse prognostic factors for the survival of ECMO-bridged lung transplant patients. Using the multivariate analysis, we created a prognosis model and a nomogram to predict 1-year post-transplant survival, with a receiver operating characteristic (ROC) curve area of 0.760 in internal validation. The 1-year survival rate calibration curve supported the nomogram's accuracy. Conclusions: This study involved the development of a survival prognosis model for patients undergoing lung transplantation with preoperative ECMO bridging, which was validated through extensive data analysis. The prognosis model exhibited high accuracy and predictive capability, effectively predicting the survival outcomes of patients both pre- and post-surgery.
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