Journal of clinical anesthesia
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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.
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Observational Study
Evaluation of pre-induction dynamic arterial elastance as an adjustable predictor of post-induction hypotension: A prospective observational study.
Dynamic arterial elastance (Eadyn) has been suggested as a functional measure of arterial load. We aimed to evaluate whether pre-induction Eadyn can predict post-induction hypotension. ⋯ In our study, invasive pre-induction Eadyn during deep breathing -could predict post-induction hypotension. Despite its invasiveness, future studies will be needed to evaluate the usefulness of Eadyn as a predictor of post-induction hypotension because it is an adjustable parameter.