Nosocomial infection with respiratory syncytial virus (RSV) is an important health risk in pediatric care but is largely preventable by efficient infection control measures. Commonly applied rapid antigen detection tests (RADTs) miss a considerable number of RSV-infected patients. The objective of our analysis was to evaluate whether readily available host parameters are associated with false-negative RADT, and to assess how these parameters could be applied in an optimized RSV isolation strategy.We retrospectively analyzed a cohort of 242 children under the age of 2 years hospitalized with acute respiratory tract infection to identify host parameters associated with false-negative RADT test result. We subsequently simulated the outcome of different isolation strategies based on RADT result and host parameters in view of the overall isolation efficacy.Out of 242 hospitalized patients, 134 (55%) patients were found RSV-positive by RT-PCR, whereas 108 (45%) patients were tested negative. The performance of the RADT was compared with the result obtained by reverse transcription polymerase chain reaction on the identical nasopharyngeal wash. Overall, we found that 85 patients (35%) were tested true positive, 108 (45%) were tested true negative, whereas a false-negative test result was obtained in 49 patients (20%). Duration of respiratory symptoms for >3 days and a respiratory admission diagnosis are associated with false-negative RADT result. In comparison with RADT alone, consideration of these clinical parameters and RADT result can decrease the rate of nonisolated RSV-infected patients from approximately 24% to 8% (65% RSV pretest probability).Consideration of both RADT and clinical parameters associated with false-negative RADT can result in an optimized RSV infection control policy.
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http://dx.doi.org/10.1097/MD.0000000000000144 | DOI Listing |
Int J Surg
October 2024
Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China.
Objective: To develop a model for accurate prediction of axillary lymph node (LN) status after neoadjuvant chemotherapy (NAC) in breast cancer patients with nodal involvement.
Methods: Between October 2018 and February 2024, 671 breast cancer patients with biopsy-proven LN metastasis who received NAC followed by axillary LN dissection were enrolled in this prospective, multicenter study. Preoperative ultrasound (US) images, including B-mode ultrasound (BUS) and shear wave elastography (SWE), were obtained.
Lancet Digit Health
January 2025
Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA. Electronic address:
Background: Palliative spine radiation therapy is prone to treatment at the wrong anatomic level. We developed a fully automated deep learning-based spine-targeting quality assurance system (DL-SpiQA) for detecting treatment at the wrong anatomic level. DL-SpiQA was evaluated based on retrospective testing of spine radiation therapy treatments and prospective clinical deployment.
View Article and Find Full Text PDFArtif Intell Med
December 2024
Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America; Medical Physics Graduate Program, Duke University, Durham, NC, United States of America; Department of Radiology, Duke University, Durham, NC, United States of America; Department of Biomedical Engineering, Duke University, Durham, NC, United States of America; Department of Radiation Oncology, Duke University, Durham, NC, United States of America; Department of Pathology, Duke University, Durham, NC, United States of America. Electronic address:
In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions generated by an individualized AI time-to-event model trained with fused data of low dose CT (LDCT) radiomics with patient demographics, we demonstrate its effectiveness, resulting in improved risk assessment compared to the Lung CT Screening Reporting & Data System (Lung-RADS). Subgroup-based Lung-RADS faces challenges in representing individual variations and relies on a limited set of predefined characteristics, resulting in variable predictions.
View Article and Find Full Text PDFTransl Oncol
December 2024
Saint Camillus International University of Medical and Health Sciences, Rome, Italy; Direzione Scientifica Fondazione Policlinico A. Gemelli IRCCS, Rome, Italy.
Background: Circulating tumor DNA (ctDNA) revolutionized the molecular diagnostics of lung cancer by enabling non-invasive, sensitive identification of actionable mutations. However, ctDNA analysis may be challenging due to tumor shedding variability, leading to false negative results. This study aims to understand the determinants for ctDNA shedding based on clinical characteristics of lung cancer patients, for a better interpretation of false negative results to be considered when ordering ctDNA analysis for clinical practice.
View Article and Find Full Text PDFHong Kong Med J
December 2024
Department of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong SAR, China.
Introduction: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artificial intelligence-based computer-assisted diagnosis (AI-CAD) program in symptomatic clinics in Hong Kong and analyse the impact of radio-pathological breast cancer phenotype on AI-CAD performance.
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