American journal of respiratory and critical care medicine
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Am. J. Respir. Crit. Care Med. · Mar 2024
Multicenter StudyCell-Free DNA Maps Tissue Injury and Correlates with Disease Severity in Lung Transplant Candidates.
Rationale: Plasma cell-free DNA levels correlate with disease severity in many conditions. Pretransplant cell-free DNA may risk stratify lung transplant candidates for post-transplant complications. Objectives: To evaluate if pretransplant cell-free DNA levels and tissue sources identify patients at high risk of primary graft dysfunction and other pre- and post-transplant outcomes. ⋯ High pretransplant cell-free DNA increased the risk of primary graft dysfunction (odds ratio, 1.60; 95% confidence interval [CI], 1.09-2.46; P = 0.0220), and death (hazard ratio, 1.43; 95% CI, 1.07-1.92; P = 0.0171) but not chronic lung allograft dysfunction (hazard ratio, 1.37; 95% CI, 0.97-1.94; P = 0.0767). Conclusions: Lung transplant candidates demonstrate a heightened degree of tissue injury with elevated cell-free DNA, primarily originating from innate immune cells. Pretransplant plasma cell-free DNA levels predict post-transplant complications.
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Am. J. Respir. Crit. Care Med. · Mar 2024
Multicenter StudyLung Injury Prediction Model in Bone Marrow Transplantation: A Multicenter Cohort Study.
Rationale: Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives: To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. ⋯ The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782-0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831-0.914). Conclusions: A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.