ArticleChest2015
Starting a lung transplant program: a roadmap for long-term excellence.
Article in Chest, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review.Frontiers in digital health · 2025Review
- CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.medRxiv : the preprint server for health sciences · 2023Article
- A comparison of outcomes after lung transplantation between European and North American centers.The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2022Article
- Multidisciplinary collaboration: the key to advancing lung transplantation outcomes.Indian journal of thoracic and cardiovascular surgery · 2022Article
- Lung transplantation in China between 2015 and 2018.Chinese medical journal · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Lung transplantation is an effective therapy for many patients with end-stage lung disease. Few centers across the United States offer this therapy, as a successful lung transplant program requires significant institutional resources and specialized personnel. Analysis of the United Network of Organ Sharing database reveals that the failure rate of new programs exceeds 40%. These data suggest that an accurate assessment of program viability as well as a strategy to continuously assess defined quality measures is needed. As part of strategic planning, regional availability of recipient and donors should be assessed. Additionally, analysis of institutional expertise at the physician, support staff, financial, and administrative levels is necessary. In May of 2007, we started a new lung transplant program at the University of Iowa Hospitals and Clinics and have performed 101 transplants with an average recipient 1-year survival of 91%, placing our program among the top in the country for the past 5 years. Herein, we review internal and external factors that impact the viability of a new lung transplant program. We discuss the use of four prospectively identified quality measures: volume, recipient outcomes, financial solvency, and academic contribution as one approach to achieve programmatic excellence.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.