Background: Knowing the probability that patients have a bloodstream infection (BSI) could influence the ordering of blood cultures and interpretation of their preliminary results. Many previous BSI probability models have limited applicability and accuracy. This study used currently recommended modeling techniques and a large sample to derive and validate the Ottawa BSI Model.
Methods: At a tertiary care teaching hospital, we retrieved a random sample of 4180 adults having blood cultures in our emergency department or during the initial 48 h of the encounter. Variable selection was based on clinical experience and a systematic review of previous model performance. Model performance was measured in a temporal external validation group of 4680 patients.
Results: A total of 327 derivation patients had a BSI (8.0%). BSI risk increased with increased number of culture sets (2 sets: adjusted odds ratio [aOR] 1.52 [1.10-2.11]; 3 sets: 1.99 [0.86-4.58]); with indwelling catheter (aOR 2.07 [1.34-3.20); with increasing temperature, heart rate, and neutrophil-lymphocyte ratio; and with decreasing systolic blood pressure, platelet count, urea-creatinine ratio, and estimated glomerular filtration rate. In the temporal external validation group, model discrimination was good (c-statistic 0.71 [0.69-0.74]) and calibration was very good (integrated calibration index .016 [.010-.024]). Exclusion of validation patients with acute SARS-CoV-2 infection improved discrimination slightly (c-statistic 0.73 [0.69-0.76]).
Conclusions: The Ottawa BSI Model uses commonly available data to return an expected BSI probability for acutely ill patients. However, it cannot exclude BSI and its complexity requires computational assistance to use.
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http://dx.doi.org/10.1007/s11606-023-08407-w | DOI Listing |
Biomark Res
January 2025
Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361003, P.R. China.
Background: Disease progression within 24 months (POD24) significantly impacts overall survival (OS) in patients with follicular lymphoma (FL). This study aimed to develop a robust predictive model, FLIPI-C, using a machine learning approach to identify FL patients at high risk of POD24.
Methods: A cohort of 1,938 FL patients (FL1-3a) from seventeen centers nationwide in China was randomly divided into training and internal validation sets (2:1 ratio).
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, Shengjing Hospital of China Medical University, China (Y.Y., T.W.). Electronic address:
Rationale And Objectives: Mixed ground-glass nodules (mGGNs) are highly malignant and common nonspecific lung imaging findings. This study aimed to explore whether combining quantitative and qualitative spectral dual-layer detector-based computed tomography (SDCT)-derived parameters with serological tumor abnormal proteins (TAPs) and thymidine kinase 1 (TK1) expression enhances invasive mGGN diagnostic efficacy and to develop a joint diagnostic model.
Materials And Methods: This prospective study included patients with mGGNs undergoing preoperative triple-phase contrast-enhanced SDCT with TAP and TK1 tests.
Acad 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.
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