Anesthesia and analgesia
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Anesthesia and analgesia · May 2017
A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries.
A predictive model that can identify patients who are at an increased risk for prolonged postanesthesia care unit (PACU) stay could help optimize resource utilization and case sequencing. Although previous studies identified some predictors, there is not a model that only utilizes various patients demographic and comorbidities, that are already known preoperatively, and that may affect PACU length of stay for outpatient procedures requiring the care of an anesthesiologist. ⋯ We developed a predictive model with excellent discrimination and goodness-of-fit that can help identify those at higher odds for extended PACU length of stay. This information may help optimize case-sequencing methodologies.
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Anesthesia and analgesia · May 2017
Comparative StudyCreation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery.
In medical practice today, clinical data registries have become a powerful tool for measuring and driving quality improvement, especially among multicenter projects. Registries face the known problem of trying to create dependable and clear metrics from electronic medical records data, which are typically scattered and often based on unreliable data sources. The Society for Thoracic Surgery (STS) is one such example, and it supports manually collected data by trained clinical staff in an effort to obtain the highest-fidelity data possible. As a possible alternative, our team designed an algorithm to test the feasibility of producing computer-derived data for the case of postoperative mechanical ventilation hours. In this article, we study and compare the accuracy of algorithm-derived mechanical ventilation data with manual data extraction. ⋯ There is a significant appeal to having a computer algorithm capable of calculating metrics such as total ventilator times, especially because it is labor intensive and prone to human error. By incorporating 3 different sources into our algorithm and by using preprogrammed clinical judgment to overcome common errors with data entry, our results proved to be more comprehensive and more accurate, and they required a fraction of the computation time compared with manual review.