Morgenthaler, Timothy I.

Objective: To describe interventions that target patient, provider, and system barriers to sedative-hypnotic (SH) deprescribing in the community and suggest strategies for healthcare teams. Data Sources: Ovid MEDLINE ALL and EMBASE Classic + EMBASE (March 10, 2021). Study Selection and Data Extraction: English-language studies in primary care settings. Data Synthesis: 20 studies were themed as patient-related and prescriber inertia, physician skills and awareness, and health system constraints. Patient education strategies reduced SH dose for 10% to 62% of participants, leading to discontinuation in 13% to 80% of participants. Policy interventions reduced targeted medication use by 10% to 50%. Relevance to Patient Care and Clinical Practice: Patient engagement and empowerment successfully convince patients to deprescribe chronic SHs. Quality improvement strategies should also consider interventions directed at prescribers, including education and training, drug utilization reviews, or computer alerts indicating a potentially inappropriate prescription by medication, age, dose, or disease. Educational interventions were effective when they facilitated patient engagement and provided information on the harms and limited evidence supporting chronic use as well as the effectiveness of alternatives. Decision support tools were less effective than prescriber education with patient engagement, although they can be readily incorporated in the workflow through prescribing software. Conclusions: Several strategies with demonstrated efficacy in reducing SH use in community practice were identified. Education regarding SH risks, how to taper, and potential alternatives are essential details to provide to clinicians, patients, and families. The strategies presented can guide community healthcare teams toward reducing the community burden of SH use.

Background The prevalence of excessive daytime sleepiness (EDS) in the general population is reported to be 9 to 28 percent. Various social and biological factors have been reported to contribute to the development of EDS, such as cardiovascular disease, metabolic disorders as well as socioeconomic status. Medically, EDS is associated with poorer prognosis in patients with sleep disorders. However, whether sleepiness is an independent negative prognostic indicator in patients without sleep apnea is poorly understood. Objective To evaluate the relationship between self‐reported excessive daytime sleepiness (assessed by Epworth Sleepiness Scale) and the risk of all‐cause mortality in patients without obstructive sleep apnea (OSA). Methods We performed a retrospective cohort study in a large registry of patients seen at the Mayo Clinic Center for Sleep Medicine between November 17 th , 2009 and April 15 th , 2017. All patients received a diagnostic polysomnography and had completed Epworth Sleepiness Scale (ESS). The primary outcome of all‐cause mortality risk was computed using the Kaplan‐Meier analysis and Cox‐proportional hazard model with age, sex, body mass index (BMI), baseline comorbidities and sleep measurements as confounders. Results A total of 2746 non‐OSA patients were identified. The overall median age (IQR) was computed as 53 (40, 63) with 45% of patients identifying as males. The median follow‐up time was approximately 2.3 years. In a univariate analysis, daytime sleepiness was associated with the female sex, younger age and higher BMI. No significant differences were observed in vascular related diseases between sleepy and non‐sleepy patients. EDS was not significantly associated with all‐cause mortality with an adjusted hazard ratio of 1.23 (95% CI 0.86, 1.76; p =0.257). In addition, there was no significant association of EDS and the risk of all‐cause mortality even after stratifying by sex, age and BMI in non‐OSA patients. Conclusion EDS is not associated with all‐cause mortality in non‐OSA patients. Further prospective studies are needed to confirm this finding. Support or Funding Information This research was supported by NIH HL65176 and NIH HL114024. Kaplan–Meier survival curve of 8 years all‐cause mortality of non‐OSA patients with and without excessive daytime sleepiness (EDS) at baseline. image Kaplan–Meier survival curve of 8 years all‐cause mortality of non‐OSA patients with and without excessive daytime sleepiness (EDS) at baseline. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Abstract Introduction Obstructive sleep apnea (OSA) affects 50–79% of children with Down Syndrome (CDS) prompting the development of guidelines to increase early detection of OSA. Cross-sectional survey based data shows that CDS have higher rates of bedtime resistance, sleep anxiety, night waking and parasomnias, which are also under-recognized. However, due to increased survival of CDS it may be that OSA treated in childhood returns or worsens, or that CDS may develop other sleep disorders as their life experience and exposure to comorbidities expands. Little is known about sleep disorders across the life span of CDS and screening guidelines leave a gap beyond early childhood. We determined to enhance understanding of respiratory and non-respiratory sleep disorders in a community population of CDS. Methods A retrospective population based observational study of CDS born between 1995–2011 was performed using the Rochester Epidemiology Project database. Medical records from all encounters through July 2020 were reviewed to identify sleep disorders. Sleep diagnoses, sleep test results, and treatments aimed at sleep disorders were recorded. Results 94 CDS were identified with 85 providing consent for research. 54 out of 85 individuals were diagnosed with OSA with 26 diagnosed prior to age 4 and 25 undergoing polysomnography prior to treatment. 26 individuals underwent polysomnography following surgery of which 16 continued to have clinically significant OSA requiring further treatment with secondary surgery, CPAP or anti-inflammatory therapy. Other sleep disorders observed included insomnia (n=16), restless leg syndrome (n=7), periodic limb movement disorder (n=10), idiopathic hypersomnia (n=1), nightmares (n=1), nocturnal enuresis (n=1), bruxism (n=1) and delayed sleep phase disorder (n=1). Most non-OSA sleep disorders were diagnosed during OSA evaluation by sleep medicine providers. However, many children were on melatonin without a formal sleep disorder diagnosis. Conclusion Both OSA and other sleep disorders remain under-diagnosed in CDS. This may be due to lack of validated screening tools that can be administered at the primary care level. Screening recommendations should consider the longitudinal nature of OSA in CDS and the presence of non-respiratory sleep disorders. Adenotonsillectomy is not as effective in CDS and postsurgical polysomnography is warranted along with long term follow-up to assess for further treatment needs. Support (if any):

Abstract Background Although electronic health records (EHRs) are designed to improve patient safety, they have been associated with serious patient harm. An agreed-upon and standard taxonomy for classifying health information technology (HIT) related patient safety events does not exist. Objectives We aimed to develop and evaluate a taxonomy for medication-related patient safety events associated with HIT and validate it using a set of events involving pediatric patients. Methods We performed a literature search to identify existing classifications for HIT-related safety events, which were assessed using real-world pediatric medication-related patient safety events extracted from two sources: patient safety event reporting system (ERS) reports and information technology help desk (HD) tickets. A team of clinical and patient safety experts used iterative tests of change and consensus building to converge on a single taxonomy. The final devised taxonomy was applied to pediatric medication-related events assess its characteristics, including interrater reliability and agreement. Results Literature review identified four existing classifications for HIT-related patient safety events, and one was iteratively adapted to converge on a singular taxonomy. Safety events relating to usability accounted for a greater proportion of ERS reports, compared with HD tickets (37 vs. 20%, p = 0.022). Conversely, events pertaining to incorrect configuration accounted for a greater proportion of HD tickets, compared with ERS reports (63 vs. 8%, p < 0.01). Interrater agreement (%) and reliability (kappa) were 87.8% and 0.688 for ERS reports and 73.6% and 0.556 for HD tickets, respectively. Discussion A standardized taxonomy for medication-related patient safety events related to HIT is presented. The taxonomy was validated using pediatric events. Further evaluation can assess whether the taxonomy is suitable for nonmedication-related events and those occurring in other patient populations. Conclusion Wider application of standardized taxonomies will allow for peer benchmarking and facilitate collaborative interinstitutional patient safety improvement efforts.

Abstract Introduction “Spring forward,” the start of daylight savings time (DST) reduces sleep opportunity by an hour. The resulting sleep deprivation in healthcare workers can increase the potential for medical errors. We examined the change in patient safety-related adverse events (AEs) following the time change in both spring and fall. Methods Self-reported AEs that occurred 7 days prior to and following the spring and fall time changes for years 2010–2017 in a large healthcare organization were ascertained. AEs likely resulting from human errors were identified. The change in the number of AEs (all AEs or restricted to those resulting from human error) following the spring and fall time change were modeled using negative binomial mixed models using a random effect to correct for non-independent observations in consecutive. Results Over the 8 year period, there were more AEs (all and human) in the 7 days following the change in time both in spring (All: 2812 V. 2699; Human: 1902 V. 1625) and fall (All: 3207 V. 3007; Human: 2189 V. 2087). However, the only statistically significant increase was for the estimated 18% increase in human errors following time change in spring (95% CI: 6% to 34%; p = 0.004). The 18% AE increase in spring was also significantly greater than the 5% increase in AE in fall (p = 0.018). Conclusion There is a significant increase in human error related AEs following the “spring forward” clock change which can jeopardize patient safety. Based on safety considerations, DST might best be eliminated; alternatively, policy makers and healthcare organizations should evaluate measures to mitigate the increased risk during this period. Support NA

Abstract Introduction Prior studies established that compliance during the early days of positive airway pressure (PAP) therapy predicts long-term adherence, which in turn relates to outcomes. However, patient impressions gathered immediately after PAP titration may be the earliest indication of adherence and could help focus efforts at improving compliance. The aim of this study was to examine whether a survey on PAP experience during polysomnography predicts adherence. Methods This prospective cohort study included PAP naïve adults with obstructive sleep apnea who had PAP titration during polysomnography and had 30 day adherence data available. Patients answered a pre-existing 5-question survey with dichotomous answers (yes/no) regarding their PAP experience (improved sleep, general discomfort, PAP related discomfort, mask discomfort, willingness to use PAP). Primary outcome was 30 day adherence (average time used per night, percent of nights used, percent of nights with &gt;4 hours use). Results We enrolled 47 patients (68% male) with average age of 62.4 years, body mass index 32.6 kg/m2, and apnea-hypopnea index 27.8/hr. Patients answered an average of 3.4±1.3 survey questions favorably. The number of favorable answers correlated with percent of nights used and percent of nights with &gt;4 hours use (P&lt;0.05). Patients with improved sleep during PAP titration had a greater percent of nights with &gt;4 hours use (82.4% vs 60.3%; P &lt;0.05). In addition, successful titration during polysomnography (less than 5 events per hour) correlated with improved adherence and less leak after 30 days. Conclusion Initial PAP titration experience influences future PAP adherence. Although there was a correlation between answers on our pre-existing questionnaire and outcomes, some questions did not contribute much for this use. We believe a next step would be to develop questions that help predict future usage and better guide therapy. Support None

Abstract Introduction We aimed to test the feasibility and acceptability of SleepWell24, a multicomponent, smartphone-delivered intervention to increase positive airway pressure (PAP) adherence among newly diagnosed OSA patients. Methods SleepWell24 targets PAP adherence along with other health behaviors through education, trouble-shooting, goal-setting, and near real-time biofeedback of PAP machine use, and sleep and physical activity levels (via Fitbit integration), and other chronic disease self-management components. Patients with a first-time diagnosis of OSA (AHI≥5) and prescribed PAP therapy were enrolled from the Centers for Sleep Medicine at Mayo Clinic in Rochester, MN and Phoenix, AZ. Patients were randomized to SleepWell24 or usual care (UC) and assessed for PAP use over 60 consecutive nights. UC patients received a Fitbit monitor to control for non-specific intervention effects related to the introduction of a new personal technology. Feasibility was assessed with recruitment and retention rates and acceptability was assessed post-intervention with the validated, 8-item Treatment Evaluation Questionnaire (TEQ; range:0-4). ANCOVA models, adjusting for age, sex, and AHI severity, compared intervention arms on acceptability ratings. Results OSA patients were consented and randomized (N=111). Before the intervention began 4 participants withdrew, 12 were lost to follow-up, and 5 could not start the trial due to durable medical equipment (DME) vendor barriers. Ninety OSA patients (n=41 SleepWell24, n=49 UC; age M±SD=57.2±12.2; 44.4% female, 61.1% AHI≥15) started the intervention, with 2 participants withdrawing, 1 becoming deceased (unrelated to treatment) and 7 with missing PAP data due to DME vendor barriers. There was no significant between-groups differences on post-treatment acceptability (SleepWell24 M±SD=2.7±1.1 vs. UC M±SD=3.1±0.9, F[1,73]=2.3, p=0.11), and 77% of SleepWell24 participants found the app to be moderately to totally acceptable. Conclusion Overall, SleepWell24 was found to be feasible for delivery in two large clinical sleep medicine centers, and patients found the app to be acceptable. A number of challenges in trial delivery were encountered that have implications for scaled-up efficacy testing: (a) partnerships with DME vendors for near real-time PAP data integration; (b) alignment with clinical practice (i.e., referral, medical record integration); and (c) patient engagement. Support National Institute of Nursing Research / National Institutes of Health: R21NR016046