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Winners of the Outstanding Dissertation and DNP Project Awards

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2016 Winners


Erin Downey, DNP
Duke University
Durham, NC

Implementation of a Patient Agreement for Opioids and Stimulants in a Primary Care Practice
Chair - Paula Tanabe, PhD

DESCRIPTION OF DNP PRoject

The dramatic increase in the consumption of controlled substances in North Carolina and across the nation has created a public health crisis with epidemic levels of medication diversion, misuse, abuse, unintentional overdose and death. Primary care providers are the principal prescribers of controlled substances and therefore at greatest risk of encountering patients that abuse medications. Guidelines recommend patient agreements with specific monitoring requirements when prescribing Schedule II medications (opioids and stimulants). Studies have focused solely on opioids and excluded stimulants and adherence to all recommended monitoring requirements has not been fully evaluated in the literature. Patient agreements were not previously used in the project practice site.

Implement a Schedule II controlled substance patient agreement and measure fidelity to components of the agreement.

A quality improvement framework using Plan-Do-Check-Act was used to design and implement the project. An opioid and stimulant prescribing policy and patient agreement was developed from sample agreements and based upon existing evidence with input from the practice partners. The policy applied to all patients aged 19 and over prescribed a long-term Schedule II medication for the chronic conditions of pain and/or attention deficit hyperactivity disorder. Examples of Schedule II narcotics include: hydrocodone, hydromorphone, oxycodone, fentanyl and methadone. Stimulants include: amphetamine, methamphetamine, and methylphenidate. All providers in the clinic received education about the policy. Adherence to the following outcome measures (elements of the protocol) were evaluated monthly: patient signed Schedule II agreement on file, prescription monitoring program (pmp) checked prior to writing a Schedule II prescription, urine toxicology screens, and prescriptions written without a mandatory visit. Monthly feedback was given to the providers over the course of the project. Modifications to improve adherence were made as needed. Outcomes were compared seven months pre- to seven months post-implementation of the patient agreement. Wilcoxon signed rank test and McNemar test were used to analyze differences in adherence between the pre and post implementation time period.

Fifty patients met study criteria and were included in the analysis. The mean (SD) age was 50.7 (16.3). The majority of patients were white (96.0%) and female (62.0%). An almost equal proportion of patients received medication for chronic pain (50%) and attention deficit hyperactivity disorder (46%). Four percent of participants received medication for both diagnoses. The percent of guideline adherence to each outcome improved from pre to post implementation: Signed agreement in chart (0%, 94%); urine screen guideline (5.3%, 71.1%); pmp checks (11.3%, 99.0%); prescriptions written without a visit guideline deviation, (20.6%, 0). All changes were significant (p < .001).

Implementation of a Schedule II controlled substance patient agreement and prescribing policy in a small primary care practice was feasible and adherence to the policy was excellent over a 7-month period.

Dissemination: The manuscript is under review for publication in the Journal of Family Medicine and Primary Care. The journal audience is family practice providers who frequently struggle with the complexity of prescribing chronic opioids. The project results will be shared at an evidence-based research conference scheduled for September 2016 at UCLA.

Continued implementation: All providers in the practice continue to follow the policy and are initiating patient agreements for new patients as well as following all aspects of the protocol. Monitoring for adherence is ongoing.

Sustainability and cost: The cost of implementation was not measured directly, but has proven to be minimal. In North Carolina, the prescription-monitoring program is state funded with free access to registered users. All project-associated tasks such as presentation and explanation of the patient agreement and urine collection were completed within the normal workday and did not require additional man-hours. In March 2016, the Centers for Disease Control and Prevention (CDC) issued the Guideline for Prescribing Opioids, http://www.cdc.gov/mmwr/volumes/65/rr/rr6501e1.htm.

The protocol we implemented included almost all aspects of the guideline, which should increase the probability of associated costs being covered by insurance. All of these factors enhance the project’s sustainability.




Sarah Farabi, PhD
University of Illinois at Chicago
Chicago, IL

Sleep, Glucose Variability and Cardiovascular Disease Risk in Young Adults with Type 1 Diabetes
Chair - Mariann Piano, PhD

DESCRIPTION OF Dissertation

People with type 1 diabetes mellitus (T1DM) experience high glucose variability and frequent hyperglycemia. Poor glucose control is known to contribute to accelerated cardiovascular disease (CVD), a leading cause of death in people with T1DM; however poor glucose control does not completely explain the increased risk. Good sleep also has been shown to play an important role in maintaining cardiovascular and metabolic health. Sleep quality is reduced in people with T1DM, but the reasons for poor sleep quality are not known. There has been minimal research into the relationship between glucose variability and sleep disruption in young adults with T1DM.

Hypotheses and Specific Aims: To test the hypotheses that glucose variations are causally related to sleep disruption and that sleep disruption mediates inflammation and CVD risk in individuals with T1DM, two aims were proposed: 1) to quantify sleep disturbances and to determine their relationship to glucose variability and 2) to define the relationship between sleep disruption and markers of CVD risk in young adults with T1DM.

A prospective, cross-sectional design was used. Young adults, age 18-30, who had diabetes for at least 5 years, wore an insulin pump and had a normal sleep schedule were enrolled. Subjects wore a continuous glucose monitor (CGM) and a sleep/activity monitor in home for three days and two nights and underwent a formal sleep study, polysomnography (PSG), while wearing the CGM in the laboratory at the University of Illinois at Chicago on the third night. The CGM is a validated tool that provides an updated glucose value every five minutes. Total time in bed was between 7-8 hours; blood was drawn just before lights out (pre-sleep); at lights on (awakening) and one hour after lights on(1-hr post awakening) to measure amounts of CVD risk markers, inflammatory cytokines, interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α). The amount of power in five electroencephalogram (EEG) Bands – Delta and Theta (characteristic of sleep); Alpha, Beta and Gamma (characteristic of wakefulness) – was tracked throughout the PSG study night. Wavelet coherence analysis was applied to determine time varying and frequency specific relationship between glucose levels and the five EEG bands. Granger causality analysis along with vector coefficient analysis was applied to the glucose and five EEG bands to determine if potential causal interactions between glucose and EEG during sleep. Levels of IL-6 and TNF-α were determined using Enzyme linked immunosorbent assays (ELISA) and one-way ANOVA (Scheffe’s test for multiple comparisons) was applied to compare between the pre-sleep, awakening and 1-hour post awakening time points.

27 subjects (11 males, average age 23.9±4.1 years) were included in the analysis. Wavelet Coherence Analysis revealed a strong time-varying and frequency specific coupling between glucose and EEG, rapid fluctuations were more strongly coupled and exhibited more instances of strong coupling through the night (p<0.0001). 96.2% of subjects exhibited at least one instance of significant Granger causality between Glucose and EEG bands. Increasing glucose was causally related to increasing Alpha and decreasing Theta and Delta power, changes in the EEG signal which are consistent with an arousal or sleep disturbance (p=0.01). Increases in Delta (increases during deep sleep) consistently caused increasing glucose levels while increasing Theta (increases during rapid eye movement sleep) caused decreasing glucose. TNF-α was higher on awakening (1.44±0.66 pg/ml [SD]) and 1-hour post awakening (1.6±0.62 pg/ml) compared to pre-sleep (0.96±0.48pg/ml) (p<0.0001 for each). Subjects with good glycemic control exhibited a normal pattern of decreased IL-6 upon awakening.

Findings from the present study support a potential bi-directional causal relationship between glucose and brain activity during sleep. Increasing glucose led to changes associated with a sleep disturbance (increasing arousals and awakenings). Further, normal changes in the sleep process, were causally related to changes in glucose. The current findings also support that the sleep period may play an important role in increasing inflammation, a key mechanism in development of cardiovascular disease in people with T1DM. Collectively, findings from this study highlight the importance of the sleep period for glycemic control and inflammation in people with T1DM.

Sleep is a potentially modifiable behavior is increasingly recognized as playing an important role in maintenance of health. The findings from this study highlight that glucose variability during sleep may play an important role in disturbance of sleep, but also that sleep may influence glycemic control. This foundational evidence provide motivation for future interventional studies aimed to determine the mechanisms underlying the relationship between glucose variability and sleep disruption as well as the role of this relationship in development of CVD. Understanding how glucose variability and sleep disruption accelerates CVD may allow for improved diabetes nursing management strategies. Nursing interventions aimed at improving glycemic variability or minimizing sleep disturbances may help to improve glycemic control, decrease CVD development and ultimately improve quality of life in people with T1DM.

 

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