Introduction: The maximum gain in quality of life after lung transplantation (LT) is expected between six months and one year after LT, as the occurrence of chronic lung allograft dysfunction may mask the beneficial effects beyond one year. Thus, the postoperative period could be the cornerstone of graft success. We sought to describe the factors present before postoperative admission to the ICU and associated with favorable, arduous or fatal pathway within 90 days of LT.
Materials And Methods: We conducted a retrospective single-center study between January 2015 and December 2020. Using multinomial regression, we assessed the demographic, preoperative and intraoperative characteristics of patients associated with favorable (duration of postoperative mechanical ventilation < 3 days and alive at Day 90), arduous (duration of postoperative mechanical ventilation ≥ 3 days and alive at Day 90) or fatal (dead at Day 90) pathway within 90 days of LT.
Results: A total of 269 lung transplant patients were analyzed. Maximum graft cold ischemic time ≥ 6 h and intraoperative blood transfusion ≥ 3 packed red blood cells were associated with arduous and fatal pathway at Day 90, whereas intraoperative ECMO was strongly associated with fatal pathway.
Conclusion: No patient demographics influenced the postoperative pathway at Day 90. Only extrinsic factors involving graft ischemia time, intraoperative transfusion, and intraoperative ECMO determined early postoperative pathway.
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http://dx.doi.org/10.1186/s12890-022-02120-w | DOI Listing |
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December 2024
Department of Materials Science, Fudan University, Shanghai, 200438, P. R. China.
Angew Chem Int Ed Engl
January 2025
Jiangsu Key Laboratory of New Power Batteries, Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, School of Chemistry and Materials Science, Nanjing Normal University, 210023, Nanjing, China.
Comput Med Imaging Graph
September 2024
Core Research Institute of Intelligent Robots, Jeonbuk National University, Republic of Korea. Electronic address:
In the domain of Computer-Aided Diagnosis (CAD) systems, the accurate identification of cancer lesions is paramount, given the life-threatening nature of cancer and the complexities inherent in its manifestation. This task is particularly arduous due to the often vague boundaries of cancerous regions, compounded by the presence of noise and the heterogeneity in the appearance of lesions, making precise segmentation a critical yet challenging endeavor. This study introduces an innovative, an iterative feedback mechanism tailored for the nuanced detection of cancer lesions in a variety of medical imaging modalities, offering a refining phase to adjust detection results.
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July 2024
Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
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May 2024
College of Electronics and Information Engineering, Shenzhen University, the Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, China.
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