Chlan, Linda L

Abstract Rationale Mechanically ventilated intensive care unit patients receive sedatives based on clinician observations of arousal and motor activity with potential for negative consequences and poor mitigation of symptoms. Objectives To test the preliminary efficacy of patient self-managed sedative therapy compared to usual care of nurse-administered sedation on self-reported anxiety, duration of mechanical ventilation, and delirium incidence (CAM-ICU). Methods A two-group, open-label, randomized, multicenter clinical trial in 5 Midwestern hospitals enrolled 161 patients receiving invasive mechanical ventilation who were willing and able to use a push-button device to self-administer sedation for up to 7 days. Patients were randomized 1:1 using center-stratified blocks to usual care (n = 81) or intervention (n = 80) with self-administered dexmedetomidine. Intervention patients were allowed 3 self-administered dexmedetomidine boluses per hour (0.25 mcg/kg) with 20-minute lock-outs. The initial basal infusion (0.2 mcg/kg/hr) was adjusted by nurses between 0.1-0.7 mcg/kg/hr based on patient-delivered boluses in the prior 2 hours. Analysis consisted of linear mixed-effects model for comparisons of anxiety ratings over time and duration of mechanical ventilation. Incidence of delirium was analyzed using generalized estimating equations. Measurement and Main Results Sample characteristics had a female (54.7%) and Caucasian preponderance (92.5%) with a median age of 62 years on protocol for a median of 88 hours. Nine intervention patients were extubated prior to protocol initiation. Patient self-reported three times daily anxiety ratings with a visual analog scale (VAS) ranged from 0-100 over time but were not significantly different between groups in either an intention-to-treat (ITT) or per protocol (PP) analysis (p = 0.94, p = 0.30 respectively). While intervention patients had shorter duration of MV [(mean 50.8(52.8) vs. usual care 68.7(58.2) hours), p = 0.02 ITT], PP analysis resulted in no difference between groups (mean 56.4 (53.0) vs. 68.7(58.2), p = 0.23). 10.2% of intervention patients developed new incidence of delirium vs. 8.5% of usual care which was not significantly different between groups by either ITT (p = 0.65) or PP analyses (p = 0.58). Conclusions Intervention patients had similar outcomes compared to usual care patients. Self-administration of sedative therapy is a safe, novel patient-centered intervention warranting further rigorous investigation for anxiety self-management.

Music is one nonpharmacological intervention to reduce anxiety and stress for mechanically ventilated patients. Efficient delivery of a music listening intervention can be enhanced through digital tools such as a mobile application (app) loaded onto an electronic tablet device. The objective of this study is to describe the iterative development and deployment of a novel app (Soundese) to deliver, record, and retrieve data associated with a two-arm randomized, blinded clinical trial testing music listening intervention compared to control silence condition on delirium severity among critically ill intensive care unit (ICU) older adult patients receiving mechanical ventilatory support. The Soundese mobile app was developed to deliver either experimental music listening intervention or a silence control listening condition to a sample of older adults receiving mechanical ventilatory support in the ICU and retrieve all protocol data. The Soundese app was developed using the Swift software language and is compatible with all iOS devices. The Soundese app consists of two components: (1) a mobile app that delivers the assigned, blinded listening intervention from an iPad through headphones to each subject and automatically logs each listening session, its duration, the randomization arm, and uploads these data to a server, and (2) an analysis app that generates a spreadsheet with summarized data of the respective listening session, music details, and reports for further analyses. A Dropbox application programmer interface enabled the secure storage of files on a designated Dropbox account. After initial field testing and iterative development changes based on research staff feedback, the Soundese app delivers the assigned experimental listening condition or silence control condition when deployed remotely in the field. The app’s mobile nature allows for immediate and automatic data capture, which is summarized for statistical analysis. There is no need for any manual recording of any intervention data by busy ICU staff, including listening time or music selections. The Soundese mobile app efficiently delivers the research protocol with fidelity and collects the necessary data for an ICU-based clinical trial. The app may be useful in other clinical trials testing music listening interventions in various settings or for deploying other audio-based interventions.

Abstract Background The symptom burden associated with cancer and its treatment can negatively affect patients’ quality of life and survival. Symptom-focused collaborative care model (CCM) interventions can improve outcomes, but only if patients engage with them. We assessed the receptivity of severely symptomatic oncology patients to a remote nurse-led CCM intervention. Methods In a pragmatic, cluster-randomized, stepped-wedge trial conducted as part of the National Cancer Institute IMPACT Consortium (E2C2, NCT03892967), patients receiving cancer care were asked to rate their sleep disturbance, pain, anxiety, emotional distress, fatigue, and limitations in physical function. Patients reporting at least 1 severe symptom (≥7/10) were offered phone consultation with a nurse symptom care manager (RN SCM). Initially, patients had to “opt-in” to receive a call, but the protocol was later modified so they had to “opt-out” if they did not want a call. We assessed the impact of opt-in vs opt-out framing and patient characteristics on receptiveness to RN SCM calls. All statistical tests were 2-sided. Results Of the 1204 symptom assessments (from 864 patients) on which at least 1 severe symptom was documented, 469 (39.0%) indicated receptivity to an RN SCM phone call. The opt-out period (odds ratio [OR] = 1.61, 95% confidence interval [CI] = 1.12 to 2.32, P = .01), receiving care at a tertiary care center (OR = 3.59, 95% CI = 2.18 to 5.91, P < .001), and having severe pain (OR = 1.80, 95% CI = 1.24 to 2.62, P = .002) were associated with statistically significantly greater willingness to receive a call. Conclusions Many severely symptomatic patients were not receptive to an RN SCM phone call. Better understanding of reasons for refusal and strategies for improving patient receptivity are needed.

AbstractAims and ObjectivesThe purpose of this paper is to describe a model to guide nursing science in a clinical practice‐based setting. Exemplars are provided to highlight the application of this nursing research model, which can be applied to other clinical settings that aim to fill evidence gaps in the literature.BackgroundNurse scientists are well positioned to develop new knowledge aimed at identifying global health solutions to multiple disparities. The generation and application of this knowledge are essential to inform and guide professional nursing practice. While a number of evidence‐based practice models exist to guide the integration of literature findings and other sources of evidence into practice, there is a need for additional models that serve as a guide and focus for the conduct of research in distinct scientific areas in practice‐based settings.DesignModel development and description.MethodsMayo Clinic is a large, comprehensive healthcare system with a mission to address unmet patient needs through practice, research and education. PhD‐prepared nurse scientists engage in practice‐based research as an integral component of Mayo Clinic's mission. A practice‐based nursing research model was developed with the intent to advance nursing research in a clinical setting.ResultsThe components of the Mayo Clinic Nursing Research model include symptom science, self‐management science and caregiving science. The generation of nursing science is focused on addressing needs of patients with complex health conditions, inclusive of caregivers.ConclusionsWhile clinical settings provide rich opportunities for the conduct of research, priorities need to be established in which to focus scientific endeavours. The Mayo Clinic Nursing Research model may be applicable to nurses around the globe who are engaged in the generation of knowledge to guide practice.Relevance to Clinical PracticeThe Mayo Clinic Nursing Research model can be used by nurse scientists embedded in healthcare settings to address clinically relevant questions, advance the generation of new nursing knowledge and ultimately improve the health and well‐being of patients and caregivers.

Background: Pharmacogenomic (PGx) testing has the potential to provide information on specific drug-metabolizing enzymes that may lead to an absence, reduction, or increase in medication effect in patients. There is a paucity of prospective studies examining PGx testing in the intensive care unit (ICU) setting. Research Aims: To (1) obtain a PGx panel in a sample of cardiovascular (CV) surgical patients with a planned ICU stay and identify phenotypes, and (2) identify PGx variants that may inform treatment regimens and may warrant prescribing adjustments. Design and Methods: Descriptive, single cohort cross-sectional design. Adult (≥18 years) CV patients with an anticipated postoperative ICU stay were enrolled from a large Midwestern tertiary academic medical center. Eligible patients provided informed consent at the time of their CV clinic appointment; PGx testing was then ordered. Pharmacogenomic testing consisted of the Focused Pharmacogenomics panel which included 10 genes and 55 medications. Results: Of the 272 patients screened, 100 (68% male) patients completed PGx testing (mean age 66.2 ± 9.6 years, mean Acute Physiology, Age and Chronic Health Evaluation III score 76.1 ± standard deviation). Pharmacogenomic results were available in the medical record within a median of 52.4 hours (interquartile range: 33.4-80.3). Pharmacogenomic testing results identified 5 CYP2C19 poor metabolizers, 26 CYP2C19 rapid metabolizers, 5 CYP2C19 ultrarapid metabolizers, 6 CYP2D6 poor metabolizers, 5 CYP2D6 poor to intermediate metabolizers, and 2 CYP2D6 rapid metabolizers identified. Overall, 98% of patients had actionable or potentially actionable PGx results, including 82% for warfarin, 65% for propafenone, 65% for tramadol, 46% for oxycodone, 45% for metoprolol, 33% for clopidogrel, 32% for proton pump inhibitors, 25% for statins, and 12% for haloperidol. Conclusions: A significant portion of patients had identified genetic variants that may warrant changes in medication management during and after CV-ICU stay. It remains to be seen if PGx testing leads to improvements in ICU patient outcomes.