Anesthesia and analgesia
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Anesthesia and analgesia · Mar 2022
ReviewAn Overview of Commonly Used Data Sources in Observational Research in Anesthesia.
Anesthesia research using existing databases has drastically expanded over the last decade. The most commonly used data sources in multi-institutional observational research are administrative databases and clinical registries. These databases are powerful tools to address research questions that are difficult to answer with smaller samples or single-institution information. ⋯ We identified a wide range of data sources used for anesthesia-related research. Research topics ranged widely spanning questions regarding optimal anesthesia type and analgesic protocols to outcomes and cost of care both on a national and a local level. Researchers should choose their data sources based on various factors such as the population encompassed by the database, ability of the data to adequately address the research question, budget, acceptable limitations, available data analytics resources, and pipeline of follow-up studies.
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Anesthesia and analgesia · Mar 2022
ReviewHealth Services Research in Anesthesia: A Brief Overview of Common Methodologies.
The use of large data sources such as registries and claims-based data sets to perform health services research in anesthesia has increased considerably, ultimately informing clinical decisions, supporting evaluation of policy or intervention changes, and guiding further research. These observational data sources come with limitations that must be addressed to effectively examine all aspects of health care services and generate new individual- and population-level knowledge. ⋯ In this article, we provide a brief overview of common statistical methods used in health services research when using observational data sources, guidance on their interpretation, and examples of how they have been applied to anesthesia-related health services research. Methods described involve regression, propensity scoring, instrumental variables, difference-in-differences, interrupted time series, and machine learning.
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Anesthesia and analgesia · Mar 2022
ReviewThe Burden of Coronavirus Disease 2019-Related Cases, Hospitalizations, and Mortality Based on Vaccination Status and Mandated Mask Use: Statewide Data From Wisconsin and Narrative Review of the Literature.
Coronavirus disease 2019 (COVID-19) cases continue to surge in the United States with the emergence of new variants. Statewide variability and inconsistency in implementing risk mitigation strategies are widespread, particularly in regards to enforcing mask mandates and encouraging the public to become fully vaccinated. ⋯ Strict adherence to public mask use and fully vaccinated status are associated with improved COVID-19-related outcomes and can mitigate the spread, morbidity, and mortality of COVID-19. Anesthesiologists and intensivists should adhere to evidence-based guidelines in their approach and management of patients to help mitigate spread.
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Anesthesia and analgesia · Mar 2022
ReviewNarrative Medicine: Perioperative Opportunities and Applicable Health Services Research Methods.
Narrative medicine is a humanities-based discipline that posits that attention to the patient narrative and the collaborative formation of a narrative between the patient and provider is essential for the provision of health care. In this Special Article, we review the basic theoretical constructs of the narrative medicine discipline and apply them to the perioperative setting. ⋯ We then examine the importance of incorporating narrative medicine into medical education and residency training and evaluate the literature on such narrative medicine didactics. Finally, we discuss applying health services research, specifically qualitative and mixed methods, in the rigorous evaluation of the efficacy and impact of narrative medicine clinical programs and medical education curricula.
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Anesthesia and analgesia · Mar 2022
Performance of the Hypotension Prediction Index With Noninvasive Arterial Pressure Waveforms in Awake Cesarean Delivery Patients Under Spinal Anesthesia.
Arterial hypotension is common after spinal anesthesia (SA) for cesarean delivery (CD), and to date, there is no definitive method to predict it. The hypotension prediction index (HPI) is an algorithm that uses the arterial waveform to predict early phases of intraoperative hypotension. The aims of this study were to assess the diagnostic ability of HPI working with arterial waveforms detected by ClearSight system in predicting impending hypotension in awake patients, and the agreement of pressure values recorded by ClearSight with conventional noninvasive blood pressure (NIBP) monitoring in patients undergoing CD under SA. ⋯ HPI provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence in awake patients under SA. We found acceptable agreement between ClearSight MAP and NIBP MAP.