IntroductionThe perioperative period is high risk for older adults. Depression and anxiety are common perioperative problems, frequently coexisting with cognitive impairment. Older patients with these conditions are more likely than younger patients to experience postoperative delirium, long hospital stays, poor quality of life and rehospitalisation. These experiences can, in turn, exacerbate anxiety and depressive symptoms. Despite these risks, little is known about how to treat perioperative anxiety and depression among older adults.Methods and analysisWe designed a feasibility study of a perioperative mental health intervention bundle to improve perioperative mental health, specifically depression and anxiety. The overarching goals of this study are twofold: first, to adapt and refine an intervention bundle comprised of behavioural activation and medication optimisation to meet the needs of older adults within three surgical patient populations (ie, orthopaedic, oncological and cardiac); and second, to test the feasibility of study procedures and intervention bundle implementation. Quantitative data on clinical outcomes such as depression, anxiety, quality of life, delirium, falls, length of stay, hospitalisation and pain will be collected and tabulated for descriptive purposes. A hybrid inductive–deductive thematic approach will be employed to analyse qualitative feedback from key stakeholders.Ethics and disseminationThe study received approval from the Washington University Institutional Review Board. Results of this study will be presented in peer-reviewed journals, at professional conferences, and to our perioperative mental health advisory board.Trial registration numberNCT05110690.
BACKGROUND: Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians. METHODS: Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis. RESULTS: During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5). CONCLUSIONS: Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted. (Anesth Analg 2024;138:804-13) KEY POINTS• Question: What design considerations are necessary to create a machine learning dashboard that anesthesiology clinicians find useful and usable to help them in order to estimate patient risk for postoperative complications? • Findings: A user interface with electronic health record integration, explanations that permit identification of modifiable predictors, and a simple layout can result in high usability rating...
Background: Postoperative depressive symptoms are associated with pain, readmissions, death, and other undesirable outcomes. Ketamine produces rapid but transient antidepressant effects in the perioperative setting. Longer infusions confer lasting antidepressant activity in patients with treatment-resistant depression, but it is unknown whether a similar approach may produce a lasting antidepressant effect after surgery. This protocol describes a pilot study that will assess the feasibility of conducting a larger scale randomized clinical trial addressing this knowledge gap. Methods: This single-center, double-blind, placebo-controlled pilot trial involves the enrollment of 32 patients aged 18 years or older with a history of depression scheduled for surgery with planned intensive care unit admission. On the first day following surgery and extubation, participants will be randomized to an intravenous eight-hour infusion of either ketamine (0.5 mg kg-1 over 10 minutes followed by a continuous rate of 0.3 mg kg-1 h-1) or an equal volume of normal saline. Depressive symptoms will be quantified using the Montgomery-Asberg Depression Rating Scale preoperatively and serially up to 14 days after the infusion. To detect ketamine-induced changes on overnight sleep architecture, a wireless headband will be used to record electroencephalograms preoperatively, during the study infusion, and after infusion. The primary feasibility endpoints will include the fraction of patients approached who enroll, the fraction of randomized patients who complete the study infusion, and the fraction of randomized patients who complete outcome data collection. Conclusions: This pilot study will evaluate the feasibility of a future large comparative effectiveness trial of ketamine to reduce depressive symptoms in postsurgical patients. Registration: K-PASS is registered on ClinicalTrials.gov: NCT05233566; registered February 10, 2022.
IntroductionMillions of patients receive general anaesthesia for surgery annually. Crucial gaps in evidence exist regarding which technique, propofol total intravenous anaesthesia (TIVA) or inhaled volatile anaesthesia (INVA), yields superior patient experience, safety and outcomes. The aim of this pilot study is to assess the feasibility of conducting a large comparative effectiveness trial assessing patient experiences and outcomes after receiving propofol TIVA or INVA.Methods and analysisThis protocol was cocreated by a diverse team, including patient partners with personal experience of TIVA or INVA. The design is a 300-patient, two-centre, randomised, feasibility pilot trial. Patients 18 years of age or older, undergoing elective non-cardiac surgery requiring general anaesthesia with a tracheal tube or laryngeal mask airway will be eligible. Patients will be randomised 1:1 to propofol TIVA or INVA, stratified by centre and procedural complexity. The feasibility endpoints include: (1) proportion of patients approached who agree to participate; (2) proportion of patients who receive their assigned randomised treatment; (3) completeness of outcomes data collection and (4) feasibility of data management procedures. Proportions and 95% CIs will be calculated to assess whether prespecified thresholds are met for the feasibility parameters. If the lower bounds of the 95% CI are above the thresholds of 10% for the proportion of patients agreeing to participate among those approached and 80% for compliance with treatment allocation for each randomised treatment group, this will suggest that our planned pragmatic 12 500-patient comparative effectiveness trial can likely be conducted successfully. Other feasibility outcomes and adverse events will be described.Ethics and disseminationThis study is approved by the ethics board at Washington University (IRB# 202205053), serving as the single Institutional Review Board for both participating sites. Recruitment began in September 2022. Dissemination plans include presentations at scientific conferences, scientific publications, internet-based educational materials and mass media.Trial registration numberNCT05346588.
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