Diagnostic management of suspected pulmonary embolism (PE) in patients with a history of venous thromboembolism (VTE) is complicated due to persistent abnormal D-dimer levels, residual embolic obstruction and higher clinical prediction rule (CPR) scores. We aimed to evaluate the safety and efficiency of the standard diagnostic algorithm consisting of a CPR, D-dimer test and computed tomography pulmonary angiography (CTPA) in this specific patient category. We performed a systematic literature search for prospective studies evaluating a diagnostic algorithm in consecutive patients with clinically suspected PE and a history of VTE. The VTE incidence rates during three-month follow-up and the number of indicated CTPAs were pooled using random effect models. Four studies concerning 1,286 patients were included with a pooled baseline PE prevalence of 36 % (95 % confidence interval [CI] 30-42). In only 217 patients (15 %; 95 %CI 11-20) PE could be excluded without CTPA. The three-month VTE incidence rate was 0.8 % (95 %CI 0.06-2.4) in patients managed without CTPA, 1.6 % (95 %CI 0.3-4.0) in patients in whom PE was excluded by CTPA and 1.4 % (95 %CI 0.6-2.7) overall. In the pooled studies, PE was safely excluded in patients with a history of VTE based on a CPR followed by a D-dimer test and/or CTPA, although the efficiency of the algorithm is relatively low compared to patients without a history of VTE.
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http://dx.doi.org/10.1160/TH14-06-0488 | DOI Listing |
BMC Cancer
January 2025
Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Peking, Beijing, 100023, People's Republic of China.
Background: Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD, 21287, USA.
Purpose: Social determinants of health including neighborhood socioeconomic status, have been established to play a profound role in overall access to care and outcomes in numerous specialized disease entities. To provide glioblastoma multiforme (GBM) patients with high-quality care, it is crucial to identify predictors of hospital length of stay (LOS), discharge disposition, and access to postoperative adjuvant chemoradiation. In this study, we incorporate a novel neighborhood socioeconomic status index (NSES) and develop three predictive algorithms for assessing post-operative outcomes in GBM patients, offering a tool for preoperative risk stratification of GBM patients.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Radiology, Southeast University Zhongda Hospital, No. 87 Dingjiaqiao Road, Gulou District, Nanjing, Jiangsu Province, China (M.Y., J.J.). Electronic address:
Rationale And Objectives: To develop radiomics and deep learning models for differentiating malignant and benign soft tissue tumors (STTs) preoperatively based on fat saturation T2-weighted imaging (FS-T2WI) of patients.
Materials And Methods: Data of 115 patients with STTs of extremities and trunk were collected from our hospital as the training set, and data of other 70 patients were collected from another center as the external validation set. Outlined Regions of interest included the intratumor and the peritumor region extending outward by 5 mm, then the corresponding radiomics features were extracted respectively.
HPB (Oxford)
December 2024
Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States. Electronic address:
Objective: We sought to develop a machine learning (ML) preoperative model to predict bile leak following hepatectomy for primary and secondary liver cancer.
Methods: An eXtreme Gradient Boosting (XGBoost) model was developed to predict post-hepatectomy bile leak using data from the ACS-NSQIP database. The model was externally validated using data from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) multi-institutional databases.
Int J Med Inform
December 2024
Neurosurgery Department, Hamad General Hospital, Qatar; Department of Clinical Academic Sciences, College of Medicine, Qatar University, Doha, Qatar; Department of Neurological Sciences, Weill Cornell Medicine, Doha, Qatar.
Introduction: Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, trust, security, and ethical governance. The development of these challenges is important for promoting the responsible adoption of AI technologies into healthcare systems.
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