Individualized sleep promotion in acute care hospitals: Identifying factors that affect patient sleep

Background/aimOne major challenge of inpatient sleep promotion is that there is no “one-size-fits-all” intervention as patients' sleep may be bothered by different factors. A tool evaluating factors that disturb patient sleep is greatly needed as a foundation for generating a personalized action plan to address the patient's specific need for sleep. Unfortunately such tools are currently unavailable in clinical practice. In this study we developed and psychometrically evaluated a brief assessment tool for sleep disruptors important for hospitalized patients, the Factors Affecting Inpatient Sleep (FAIS) scale.MethodsThe FAIS items were developed by literature review and validated by content validity testing. A survey collected from 105 hospitalized patients was used to select the most significant sleep disruptors. Psychometric evaluation using survey data included item analysis, principal components analysis, and internal consistency reliability.ResultsThe final FAIS scale included 14 items in three subscales explaining 56.4% of the total variance: 1) emotional or physical impairment due to illness or hospitalization; 2) sleep disturbance due to discomfort or care plan schedule; 3) sleep interruption due to hospital environment or medical care. The Cronbach's alpha coefficient for the FAIS scale was 0.87, and the reliability of the subscales ranged from 0.72 to 0.81.ConclusionThe FAIS is a brief tool assessing sleep disruptors important for patients, and is empirically grounded, judged to have content validity, and has demonstrated psychometric adequacy. The FAIS scale can be used to guide the development of an individualized patient-centered sleep promotion plan.     

Artificial Intelligence Trumps TAVI2-SCORE and CoreValve Score in Predicting 1-Year Mortality Post-Transcatheter Aortic Valve Replacement

Background/purposeMachine learning has been used to predict procedural risk in patients undergoing various medical interventions and procedures. One-year mortality in patients after Transcatheter Aortic Valve Replacement (TAVR) has a wide range (from 8.5 to 24% in various studies). We sought to apply machine learning to determine predictors of one year mortality in patients undergoing TAVR.Methods/materialsA retrospective study of 1055 patients who underwent TAVR (Jan 2014–June 2017) with one-year follow up was completed. Baseline demographics, clinical, electrocardiography (ECG), Computed Tomography (CT) and echocardiography data were abstracted. Variables with near zero variance or ≥50% missing data were excluded. The Gradient Boosting Machine learning (GBM) prediction model included 163 variables and was optimized using 5-fold cross-validation repeated 10-times. The receiver operator characteristic (ROC) for the GBM model was calculated to predict one-year mortality post TAVR, and then compared to the TAVI₂-SCORE and CoreValve score.ResultsAmong 1055 TAVR patients (mean age 80.9 ± 7.9 years, 42% female), 14.02% died at one year. 78% had balloon expandable valves placed. Based on GBM, the ten most predictive variables for one-year survival were cardiac power index, hemoglobin, systolic blood pressure, INR, diastolic blood pressure, body mass index, valve calcium score, serum creatinine, aortic annulus area, and albumin. The area under ROC to predict survival for the GBM model vs TAVI₂-SCORE and CoreValve Score was 0.72 (95% CI 0.68–0.78) vs 0.56 (95%CI 0.51–0.62) and 0.53 (95% CI 0.47–0.59) respectively with p < 0.0001.ConclusionThe GBM model outperforms TAVI₂-SCORE and CoreValve Score in predicting mortality one-year post TAVR.     

Epidemiology of burn-related fatalities in Australia and New Zealand, 2009–2015

BackgroundKnowledge of the epidemiology of burn-related fatalities is limited, with most previous studies based on hospital and burn centre data only.AimsTo describe the epidemiological characteristics of all burn-related fatalities in Australia and New Zealand, and to identify any trends in burn-related fatality incidence over the study period.MethodsData from the National Coronial Information System, including data for pre-hospital and in-hospital burn-related fatality cases, was used to examine the characteristics of burn-related fatalities occurring in Australia and New Zealand from 2009 to 2015. Burn-related fatality rates per 100,000 population were estimated, and incidence trends assessed using Poisson regression analysis.ResultsOf the 310 burn-related fatalities that occurred in Australia and New Zealand, 2009–2015, 41% occurred in a pre-hospital setting. Overall, most burn-related fatality cases were fire related, occurred at home, and were of people aged 41–80 years. One quarter of all burn-related fatalities were a result of intentional self-harm. The population incidence of all burn-related fatalities combined, and for NSW, decreased over the study period.ConclusionsThis study has identified the importance of examining all burn-related fatalities. If this is not done, vulnerable population subgroups will be missed and prevention efforts poorly targeted.     

A process-environment model for mentoring undergraduate research students

Undergraduate nursing student engagement in research remains much contested. The debate centers on whether undergraduate education is preparation for application of research findings to practice versus early exposure and engagement to discovery of new knowledge focused research as is done in graduate education. We take the position that involvement in research is beneficial but mentorship is required if the endeavor is to be meaningful. In the absence of a model to guide effective mentorship for undergraduate co-researchers we synthesized the available undergraduate mentorship literature and relevant pedagogy to develop a mentorship model for use by nurse educators who undertake research with nursing students. This was applied and refined through active engagement in, and reflection on, the execution of a research project exploring peoples' experiences of mental illness. Synthesis of the evidence and reflections led to the development of a process-environment mentorship model. This model provides an evidence- and experientially-based framework for mentoring undergraduate student co-researchers.