Journal of clinical anesthesia
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Review Meta Analysis
Validity of non-contact methods for diagnosis of Obstructive Sleep Apnea: a systematic review and meta-analysis.
Obstructive Sleep Apnea (OSA) is associated with increased perioperative cardiac, respiratory and neurological complications. Pre-operative OSA risk assessment is currently done through screening questionnaires with high sensitivity but poor specificity. The objective of this study was to evaluate the validity and diagnostic accuracy of portable, non-contact devices in the diagnosis of OSA as compared with polysomnography. ⋯ Available data indicate contactless methods have high pooled sensitivity and specificity for OSA diagnosis with moderate to high level of evidence. Future research is needed to evaluate these tools in the perioperative setting.
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Randomized Controlled Trial
Large volume acute normovolemic hemodilution in patients undergoing cardiac surgery with intermediate-high risk of transfusion: A randomized controlled trial.
To investigate whether large volume acute normovolemic hemodilution (L-ANH), compared with moderate acute normovolemic hemodilution (M-ANH), can reduce perioperative allogeneic blood transfusion in patients with intermediate-high risk of transfusion during cardiac surgery with cardiopulmonary bypass (CPB). ⋯ Compared with M-ANH, L-ANH during cardiac surgery inclined to be associated with reduced perioperative RBC transfusion and the volume of RBC transfusion was inversely proportional to the volume of ANH. In addition, LANH during cardiac surgery was associated with a lower incidence of postoperative excessive bleeding.
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Multicenter Study
Development and validation of a machine learning ASA-score to identify candidates for comprehensive preoperative screening and risk stratification.
The ASA physical status (ASA-PS) is determined by an anesthesia provider or surgeon to communicate co-morbidities relevant to perioperative risk. Assigning an ASA-PS is a clinical decision and there is substantial provider-dependent variability. We developed and externally validated a machine learning-derived algorithm to determine ASA-PS (ML-PS) based on data available in the medical record. ⋯ We created and validated a machine learning physical status based on preoperatively available data. The ability to identify patients at high risk early in the preoperative process independent of the provider's decision is a part of the process we use to standardize the stratified preoperative evaluation of patients scheduled for ambulatory surgery.