Jose Ramirez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jose Ramirez has a total of 46 co-authors. Here are the top co-authors by number of shared publications:
- Abbas Rana
Division of Abdominal Transplantation, Michael E. DeBakey Department of Surgery Baylor College of Medicine Houston Texas USA
3 shared publications Abbas Rana
2 shared publications- Catherine P. Seger
Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
2 shared publications Jennifer Leonard
2 shared publicationsJeremy L. Ward
2 shared publications- John R. Dawson
Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, Texas
2 shared publications
Latest Publications
- Open Access
- 7 August 2026
- Published by Elsevier in JTCVS Open
The Pediatric Heart Listing Mortality score: A score to estimate 12-month waitlist mortality risk in candidates awaiting heart transplant
- 7 August 2026
- Published by Elsevier in JTCVS Open
Open AccessThe Pediatric Heart Listing Mortality score: A score to estimate 12-month waitlist mortality risk in candidates awaiting heart transplant
- 5 May 2026
- Published by Elsevier in Heart & Lung
- Vol. 79, 102833
Improved outcomes in adult lung transplantation: a two-decade national analysis
- 5 May 2026
- Published by Elsevier in Heart & Lung
- Vol. 79, 102833
Improved outcomes in adult lung transplantation: a two-decade national analysis
- Open Access
- 19 January 2026
- Published by Wiley in Clinical Transplantation
- Vol. 40 (1) , e70454
Pediatric Lung Waitlist Score (PLWS): A Score to Predict Waitlist Time for Pediatric Lung Transplant Candidates
Background : In pediatric lung transplantation, the ability to estimate the projected waiting time could be particularly helpful for candidates and physicians. A score to predict waiting time has yet to be developed in this field. The purpose of this study was to create a score that predicts waitlist time for pediatric patients who are listed for lung transplantation. Methods : A retrospective analysis was performed with publicly available de‐identified OPTN lung transplant data from March 8, 2019, to March 8, 2025. After applying our exclusionary criteria, the sample included 218 pediatric patients, whom we divided into training and testing groups based on the implementation of the CAS. Univariable and multivariable analyses on the training group of pediatric patients identified independent recipient risk factors for the time spent on the waitlist. This model was used to make our score, the pediatric lung waitlist score (PLWS), which was then applied to the testing group to examine its validity. Results : Five recipient factors were independently associated with time to transplant: adolescence, ECMO support, residence in OPTN Region 4, residence in Region 8, and very high oxygen supplementation (>15 LPM). The resulting score demonstrated a c‐statistic of 0.71 in the training cohort and 0.73 in the testing cohort. Conclusion : We identified five recipient characteristics that significantly predict pediatric lung transplant waitlist duration and incorporated them into a prognostic score, the PLWS, derived from a Cox proportional hazards model.
- 19 January 2026
- Published by Wiley in Clinical Transplantation
- Vol. 40 (1) , e70454
Open AccessPediatric Lung Waitlist Score (PLWS): A Score to Predict Waitlist Time for Pediatric Lung Transplant Candidates
Background : In pediatric lung transplantation, the ability to estimate the projected waiting time could be particularly helpful for candidates and physicians. A score to predict waiting time has yet to be developed in this field. The purpose of this study was to create a score that predicts waitlist time for pediatric patients who are listed for lung transplantation. Methods : A retrospective analysis was performed with publicly available de‐identified OPTN lung transplant data from March 8, 2019, to March 8, 2025. After applying our exclusionary criteria, the sample included 218 pediatric patients, whom we divided into training and testing groups based on the implementation of the CAS. Univariable and multivariable analyses on the training group of pediatric patients identified independent recipient risk factors for the time spent on the waitlist. This model was used to make our score, the pediatric lung waitlist score (PLWS), which was then applied to the testing group to examine its validity. Results : Five recipient factors were independently associated with time to transplant: adolescence, ECMO support, residence in OPTN Region 4, residence in Region 8, and very high oxygen supplementation (>15 LPM). The resulting score demonstrated a c‐statistic of 0.71 in the training cohort and 0.73 in the testing cohort. Conclusion : We identified five recipient characteristics that significantly predict pediatric lung transplant waitlist duration and incorporated them into a prognostic score, the PLWS, derived from a Cox proportional hazards model.