Background: Critically ill patients with systemic rheumatic disease (SRD) have benefited from better provision of rheumatic and critical care in recent years. Recent comprehensive data regarding in-hospital mortality rates and, most importantly, long-term outcomes are scarce.
Research Question: The aim of this study was to assess short and long-term outcome of patients with SRD who were admitted to the ICU.
Study Design And Methods: All records of patients with SRD who were admitted to ICU between 2006 and 2016 were reviewed. In-hospital and one-year mortality rates were assessed, and predictive factors of death were identified.
Results: A total of 525 patients with SRD were included. Causes of admission were most frequently shock (40.8%) and acute respiratory failure (31.8%). Main diagnoses were infection (39%) and SRD flare-up (35%). In-hospital and one-year mortality rates were 30.5% and 37.7%, respectively. Predictive factors that were associated with in-hospital and one-year mortalities were, respectively, age, prior corticosteroid therapy, simplified acute physiology score II ≥50, need for invasive mechanical ventilation, or need for renal replacement therapy. Knaus scale C or D and prior conventional disease modifying antirheumatic drug therapy was associated independently with death one-year after ICU admission.
Interpretation: Critically ill patients with SRD had a fair outcome after an ICU stay. Increased age, prior corticosteroid therapy, and severity of critical illness were associated significantly with short- and long-term mortality rates. The one-year mortality rate was also associated with prior health status and conventional disease modifying antirheumatic drug therapy.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.chest.2020.03.050 | DOI Listing |
Pilot Feasibility Stud
January 2025
Center for Healthcare Organization and Implementation Research, VA , Boston Healthcare System, 150 South Huntington Avenue, Boston, 02130, USA.
Background: Drug use trends change rapidly among youth, leaving intervention experts struggling to respond promptly. Delays in responses can lead to preventable morbidity and mortality. The COVID-19 pandemic underscored the need for implementation science to facilitate rapid, equitable responses using existing treatment and prevention efforts.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Endocrinology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, No 134 Dongjie Street, Gulou District, Fuzhou, Fujian, 350001, People's Republic of China.
Objectives: To develop a machine learning-based prediction model using clinical data from the first 24 h of ICU admission to enable rapid screening and early intervention for sepsis patients.
Methods: This multicenter retrospective cohort study analyzed electronic medical records of sepsis patients using machine learning methods. We evaluated model performance in predicting sepsis outcomes within the first 24 h of ICU admission across US and Chinese healthcare settings.
BMC Public Health
January 2025
Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, People's Republic of China.
Background: Leukemia, a group of malignant tumors, has been a significant public health concern due to its high incidence and mortality rates. This study aimed to provide an in-depth analysis of the global leukemia burden from 1990 to 2021 using the Global Burden of Disease (GBD) database, focusing on trends in incidence, mortality, and Disability-Adjusted Life Years (DALYs) across different regions, genders, and age groups including forecasting future trends.
Methods: Data were sourced from the GBD study, utilizing the Global Health Data Exchange (GHDx) query tool.
BMC Med
January 2025
Neurology Department, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200082, People's Republic of China.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!