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Lung transplantation survival estimates are traditionally reported as fixed 1-, 5-, and 10-year mortality rates. Alternatively, this study aims to demonstrate how conditional survival models can provide useful prognostic information tailored to the time a recipient has already survived from the date of transplantation. Recipient data was obtained from the Organ Procurement and Transplantation Network database. Data from 24,820 adult recipients over age 18 who received a lung transplant between 2002 and 2017 were included in the study. Five-year observed conditional survival estimates were calculated by recipient age, sex, race, transplant indication, transplant type ( i.e. , single or double), and renal function at the time of transplantation. Significant variability exists in conditional survival following lung transplantation. Each specific recipient characteristic significantly impacted conditional survival during at least one time point in the first 5 years. Younger age and double lung transplantation were the two most positive predictors of improved conditional survival consistently throughout the 5-year study period. Conditional survival in lung transplantation recipients changes over time and across recipient characteristics. Hazards of mortality are not fixed and need to be dynamically evaluated as a function of time. Conditional survival calculations can provide more accurate prognostic predictions than unconditional survival estimates.
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http://dx.doi.org/10.1097/MAT.0000000000001975 | DOI Listing |
Int J Clin Oncol
September 2025
Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-8655, Japan.
Background: Limited data are available on relative survival (RS) among cancer survivors enrolled in private cancer insurance in Japan. Additionally, the incidence of second primary cancers or recurrences, as applicable, after a certain period remains unclear.
Methods: We analyzed 8,846 cancer survivors, including carcinoma in situ, aged 15-79 years, enrolled in private cancer insurance between April 2005 and September 2021, and diagnosed before April 2022.
J Viral Hepat
October 2025
Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
Discontinuing antivirals in chronic hepatitis B virus (HBV) 'e' antigen negative infection can enhance HBV surface antigen (HBsAg) loss but risks complications. We modelled the clinical impact of discontinuing antivirals in chronic HBV. We developed a Markov state model with Monte Carlo simulation of chronic HBV to compare continuation of antiviral therapy with 3 strategies of cessation and reinitiation for: (1) virologic relapse, (2) clinical relapse, or (3) hepatitis flare.
View Article and Find Full Text PDFAging Cell
September 2025
Department of Cell Systems & Anatomy, University of Texas Health San Antonio, San Antonio, Texas, USA.
The Hippo signaling pathway is a key regulator of cell growth and cell survival, and hyperactivation of the Hippo pathway has been implicated in neurodegenerative diseases such as Huntington's disease. However, the role of Hippo signaling in Alzheimer's disease (AD) remains unclear. We observed that hyperactivation of Hippo signaling occurred in the AD model 5xFAD mice.
View Article and Find Full Text PDFJ Am Coll Surg
September 2025
Division of Trauma/Surgical Critical Care, University of Tennessee Health Science Center, Memphis, Tennessee.
Background: Gastrointestinal bleeding (GiB) is associated with hypoperfusion, cytokine release, and alterations to the mucosal barrier frequently seen in the critical care population. Risk factors in the population at large have been well-studied, but few have specifically addressed the unique circumstances surrounding critically ill trauma patients. We aimed to evaluate the incidence and risk factors for GiB in the trauma critical care population.
View Article and Find Full Text PDFStat Med
September 2025
Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Background: Binary endpoints measured at two timepoints-such as pre- and post-treatment-are common in biomedical and healthcare research. The Generalized Bivariate Bernoulli Model (GBBM) provides a specialized framework for analyzing such bivariate binary data, allowing for formal tests of covariate-dependent associations conditional on baseline outcomes. Despite its potential utility, the GBBM remains underutilized due to the lack of direct implementation in standard statistical software.
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