Journal of clinical anesthesia
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To develop, validate, and deploy models for predicting delirium in critically ill adult patients as early as upon intensive care unit (ICU) admission. ⋯ Our early prediction models based on data obtained upon ICU admission could achieve good performance in predicting delirium occurred within 48 h after ICU admission. Our 24-h models can improve delirium prediction for patients discharged >1 day after ICU admission.
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Comment Letter Randomized Controlled Trial
In response to- Effect of ultrasound-guided lung recruitment manoeuvre on perioperative atelectasis during laparoscopy in young infants: A randomised controlled trial.
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Randomized Controlled Trial
Comparing nasopharyngeal apnoeic oxygenation at 18 l/min to preoxygenation alone in obese patients - A randomised controlled study.
Investigate a low-cost, nasopharyngeal apnoeic oxygenation technique, establish its efficacy, and compare it to preoxygenation only in an obese population. The study's hypothesis was that nasopharyngeal apnoeic oxygenation at 18 l.min-1 would significantly prolong safe apnoea time compared to preoxygenation alone. ⋯ PACTR202202665252087; WC/202004/007.
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Review Meta Analysis
The effectiveness of supplemental oxygen and high-flow nasal cannula therapy in patients with obstructive sleep apnea in different clinical settings: A systematic review and meta-analysis.
To evaluate the effectiveness of supplemental oxygen therapy and high-flow nasal cannula (HFNC) therapy in patients with obstructive sleep apnea (OSA) in different clinical settings to assess its application to surgical patients in the postoperative setting. ⋯ Oxygen therapy effectively reduces AHI and increases SpO2 in patients with OSA. CPAP is more effective in reducing AHI than oxygen therapy. HFNC therapy is effective in reducing AHI. Although both oxygen therapy and HFNC therapy effectively reduce AHI, more research is needed to draw conclusions on clinical outcomes.
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Performing hip or knee arthroplasty as an outpatient surgery has been shown to be operationally and financially beneficial for selected patients. By applying machine learning models to predict patients suitable for outpatient arthroplasty, health care systems can better utilize resources efficiently. The goal of this study was to develop predictive models for identifying patients likely to be discharged same-day following hip or knee arthroplasty. ⋯ Machine learning models may utilize electronic health records to screen arthroplasty procedures for outpatient eligibility. Tree-based models demonstrated superior performance in this study.