To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXRs) for predicting patient outcomes and the need for mechanical ventilation in COVID-19 pneumonia. Our IRB-approved study included 1367 serial CXRs from 405 adult patients (mean age 65 ± 16 years) from two sites in the US (Site A) and South Korea (Site B). We recorded information pertaining to patient demographics (age, gender), smoking history, comorbid conditions (such as cancer, cardiovascular and other diseases), vital signs (temperature, oxygen saturation), and available laboratory data (such as WBC count and CRP). Two thoracic radiologists performed the qualitative assessment of all CXRs based on the RALE score for assessing the severity of lung involvement. All CXRs were processed with a commercial AI algorithm to obtain the percentage of the lung affected with findings related to COVID-19 (AI score). Independent t- and chi-square tests were used in addition to multiple logistic regression with Area Under the Curve (AUC) as output for predicting disease outcome and the need for mechanical ventilation. The RALE and AI scores had a strong positive correlation in CXRs from each site (r = 0.79-0.86; p < 0.0001). Patients who died or received mechanical ventilation had significantly higher RALE and AI scores than those with recovery or without the need for mechanical ventilation (p < 0.001). Patients with a more substantial difference in baseline and maximum RALE scores and AI scores had a higher prevalence of death and mechanical ventilation (p < 0.001). The addition of patients' age, gender, WBC count, and peripheral oxygen saturation increased the outcome prediction from 0.87 to 0.94 (95% CI 0.90-0.97) for RALE scores and from 0.82 to 0.91 (95% CI 0.87-0.95) for the AI scores. AI algorithm is as robust a predictor of adverse patient outcome (death or need for mechanical ventilation) as subjective RALE scores in patients with COVID-19 pneumonia.
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http://dx.doi.org/10.1038/s41598-020-79470-0 | DOI Listing |
BMC Neurol
January 2025
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, 1 East Jianshe Road, Zhengzhou, China.
Background: Awareness of the characteristics of glial fibrillary acidic protein autoantibody (GFAP-IgG) associated myelitis facilitates early diagnosis and treatment. We explored features in GFAP-IgG myelitis and compared them with those in myelitis associated with aquaporin-4 IgG (AQP4-IgG) and myelin oligodendrocyte glycoprotein IgG (MOG-IgG).
Methods: We retrospectively reviewed data from patients with GFAP-IgG myelitis at the First Affiliated Hospital of Zhengzhou University and Henan Children's Hospital from May 2018 to May 2023.
BMC Infect Dis
January 2025
Department of Clinical Laboratory, Key Laboratory of Clinical Laboratory Medicine of Guangxi Department of Education, the First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Background: In clinical practice, the emergence of ST11-K64 carbapenem-resistant Klebsiella pneumoniae (ST11-K64 CRKP) has become increasingly alarming. Despite this trend, limited research has been conducted to elucidate the clinical and molecular characteristics of these strains.
Objectives: This study aimed to comprehensively investigate the clinical characteristics, antimicrobial resistance patterns, resistance and virulence-associated genes, and molecular epidemiology of ST11-K64 CRKP in Southwest China.
BMC Infect Dis
January 2025
Intensive Care Unit, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Background: Risk factors for bloodstream infection in patients with COVID-19 in the intensive care unit (ICU) remain unclear. The purpose of this systematic review was to study the risk factors for BSI in patients admitted to ICUs for COVID-19.
Methods: A systematic search was performed on PubMed, EMBASE, Cochrane Library, and Web of Science up to July 2024.
Background: Paroxysmal sympathetic hyperactivity (PSH) occurs with high prevalence among critically ill patients with traumatic brain injury (TBI) and is associated with worse outcomes. The PSH-Assessment Measure (PSH-AM) consists of a Clinical Features Scale and a diagnosis likelihood tool (DLT) intended to quantify the severity of sympathetically mediated symptoms and the likelihood that they are due to PSH, respectively, on a daily basis. Here, we aim to identify and explore the value of dynamic trends in the evolution of sympathetic hyperactivity following acute TBI using elements of the PSH-AM.
View Article and Find Full Text PDFClin Lymphoma Myeloma Leuk
December 2024
Department of Intensive Care Medicine, the Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China. Electronic address:
Background: Invasive fungal disease (IFD) poses significant challenges for critically ill patients with hematological malignancies (HMs). However, there is limited research on the clinical characteristics, risk factors, and outcomes of IFD within this population.
Method: A retrospective study was conducted at a tertiary center in China.
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