Previously we established a prediction model for graft intolerance syndrome requiring graft nephrectomy in patients with late kidney graft failure. The aim of this study is to determine generalizability of this model in an independent cohort. The validation cohort included patients with late kidney graft failure between 2008 and 2018. Primary outcome is the prognostic performance of our model, expressed as the area under the receiver operating characteristic curve (ROC-AUC), in the validation cohort. In 63 of 580 patients (10.9%) a graft nephrectomy was performed because of graft intolerance. The original model, which included donor age, graft survival and number of acute rejections, performed poorly in the validation cohort (ROC-AUC 0.61). After retraining of the model using recipient age at graft failure instead of donor age, the model had an average ROC-AUC of 0.70 in the original cohort and of 0.69 in the validation cohort. Our original model did not accurately predict the graft intolerance syndrome in a validation cohort. However, a retrained model including recipient age at graft failure instead of donor age performed moderately well in both the development and validation cohort enabling identification of patients with the highest and lowest risk of graft intolerance syndrome.
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http://dx.doi.org/10.3389/ti.2023.11147 | DOI Listing |
Over the last decade, Hippo signaling has emerged as a major tumor-suppressing pathway. Its dysregulation is associated with abnormal expression of and -family genes. Recent works have highlighted the role of YAP1/TEAD activity in several cancers and its potential therapeutic implications.
View Article and Find Full Text PDFEClinicalMedicine
February 2025
Department of Breast and Gynaecological Surgery, Institut Curie, Paris, France.
Background: Randomized clinical trials (RCTs) are fundamental to evidence-based medicine, but their real-world impact on clinical practice often remains unmonitored. Leveraging large-scale real-world data can enable systematic monitoring of RCT effects. We aimed to develop a reproducible framework using real-world data to assess how major RCTs influence medical practice, using two pivotal surgical RCTs in gynaecologic oncology as an example-the LACC (Laparoscopic Approach to Cervical Cancer) and LION (Lymphadenectomy in Ovarian Neoplasms) trials.
View Article and Find Full Text PDFJ Inflamm Res
January 2025
Department of Hematology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan Province, People's Republic of China.
Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Critical Care Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China.
Background: Rhabdomyolysis (RM) frequently gives rise to diverse complications, ultimately leading to an unfavorable prognosis for patients. Consequently, there is a pressing need for early prediction of survival rates among RM patients, yet reliable and effective predictive models are currently scarce.
Methods: All data utilized in this study were sourced from the MIMIC-IV database.
Front Immunol
January 2025
Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.
Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital.
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