Articles: mortality.
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Critical care medicine · Jun 2024
Multicenter Study Observational StudyIntubation Decision Based on Illness Severity and Mortality in COVID-19: An International Study.
To evaluate the impact of intubation timing, guided by severity criteria, on mortality in critically ill COVID-19 patients, amidst existing uncertainties regarding optimal intubation practices. ⋯ In severe COVID-19 cases, an early intubation strategy, guided by specific severity criteria, is associated with a reduced risk of death. These findings underscore the importance of timely intervention based on objective severity assessments.
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Anesthesia and analgesia · Jun 2024
Multicenter Study Comparative Study Observational StudyA Prospective Multicenter Comparison of Trauma and Injury Severity Score, American Society of Anesthesiologists Physical Status, and National Surgical Quality Improvement Program Calculator's Ability to Predict Operative Trauma Outcomes.
Trauma outcome prediction models have traditionally relied upon patient injury and physiologic data (eg, Trauma and Injury Severity Score [TRISS]) without accounting for comorbidities. We sought to prospectively evaluate the role of the American Society of Anesthesiologists physical status (ASA-PS) score and the National Surgical Quality Improvement Program Surgical Risk-Calculator (NSQIP-SRC), which are measurements of comorbidities, in the prediction of trauma outcomes, hypothesizing that they will improve the predictive ability for mortality, hospital length of stay (LOS), and complications compared to TRISS alone in trauma patients undergoing surgery within 24 hours. ⋯ TRISS predicts mortality better than ASA-PS and NSQIP-SRC in trauma patients undergoing surgery within 24 hours. The TRISS mortality predictive ability is not improved when combined with ASA-PS or NSQIP-SRC. However, NSQIP-SRC was the most accurate predictor of LOS and complications.
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Semin Respir Crit Care Med · Jun 2024
ReviewThe Oral-Lung Microbiome Axis in Connective Tissue Disease-Related Interstitial Lung Disease.
Connective tissue disease-related interstitial lung disease (CTD-ILD) is a frequent and serious complication of CTD, leading to high morbidity and mortality. Unfortunately, its pathogenesis remains poorly understood; however, one intriguing contributing factor may be the microbiome of the mouth and lungs. ⋯ Here, we review the existing data demonstrating oral and lung microbiota dysbiosis and possible contributions to the development of CTD-ILD in rheumatoid arthritis, Sjögren's syndrome, systemic sclerosis, and systemic lupus erythematosus. We identify several areas of opportunity for future investigations into the role of the oral and lung microbiota in CTD-ILD.
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Healthcare-associated infections (HCAIs) in patients admitted with acute conditions remain a major challenge to healthcare services. Here, we assessed the impact of HCAIs acquired within 7-days of acute stroke on indicators of care-quality outcomes and dependency. Data were prospectively collected (2014-2016) from the Sentinel Stroke National Audit Programme for 3309 patients (mean age = 76.2 yr, SD = 13.5) admitted to four UK hyperacute stroke units (HASU). ⋯ Compared to patients without UTI or pneumonia, those with either or both of these HCAIs were more likely to have prolonged stay (> 14-days) on HASU: 5.1 (3.8-6.8); high risk of malnutrition: 3.6 (2.9-4.5); palliative care: 4.5 (3.4-6.1); in-hospital mortality: 4.8 (3.8-6.2); disability at discharge: 7.5 (5.9-9.7); activity of daily living support: 1.6 (1.2-2.2); and discharge to care-home: 2.3 (1.6-3.3). In conclusion, HCAIs acquired within 7-days of an acute stroke led to prolonged hospitalisation, adverse health consequences and risk of care-dependency. These findings provide valuable information for timely intervention to reduce HCAIs, and minimising subsequent adverse outcomes.
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The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for patients. The objective of this study was to develop a machine learning-based predictive model for invasive fungal infection in patients during their intensive care unit (ICU) stay. ⋯ Importance: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.