Clinical Algorithm and Educational Presentation for the Management of Metabolic Side Effects Associated with Second Generation Antipsychotics

The aim of this project is to develop and provide an educational opportunity in regards to treating the metabolic side effects of second-generation antipsychotic (SGA) medications. Evidence-based resources, as well as VA specific resources, provided content for the development of a clinical algorithm, as well as an educational presentation for prescribers at a local Veterans Affair outpatient mental health clinic. Problem Statement: The metabolic side effects of SGAs can contribute to the development of chronic health problems such as diabetes, cardiovascular disease and stroke. Mental health providers are familiar with the metabolic side effects of SGAs; however, they are generally unprepared to treat these side effects. At a local Veteran Affairs outpatient mental health clinic, mental health prescribers are beginning to take on a greater role in the treatment of metabolic side effects. As such, this project will provide mental health prescribers with an educational opportunity and tools related to the various resources available for treating metabolic side effects. Objectives: The objectives of this project were 1. Reduced incidence of metabolic side effects to promote the health of veterans and reduce costs to the VA. 2. Develop a treatment algorithm utilizing evidence-based literature and VA specific resources. 3. Present treatment algorithm at a monthly prescriber meeting. 4. Obtain feedback on the utility of the treatment algorithm and educational opportunity, as well as concerns for future consideration. Literature Review: SGAs are a highly efficacious medication class, used in the treatment of various mental health disorders such as bipolar spectrum disorders and schizophrenia spectrum disorders. Mental health prescribers commonly use SGAs, but they carry significant risk for metabolic side effects such as weight gain, dyslipidemia and hyperglycemia. Several treatment interventions have demonstrated efficacy in mitigating the development of metabolic side effects. Treatment interventions include dietary modifications, exercise programs, switching agents and medications to treat metabolic disturbances. Implementation & Evaluation: In order to guide mental health providers in treating metabolic side effects of SGAs, the clinical algorithm included information consolidated from the literature review and from VA specific resources and programs available to veterans. Content experts provided recommendations for modifications. At a monthly prescriber meeting, a presentation of the algorithm included in-depth education regarding the treatment of metabolic side effects. A pre- and post-questionnaire provided during the presentation gathered feedback regarding the benefit of the educational presentation and treatment algorithm. Results: Nine mental health APRNs participated (n=9). Following an educational opportunity for treating metabolic side effects of SGAs, the APRNs comfort level and intention to treat increased (17% and 14% respectively); however, the pre- and post- comparison are not statistically significant at a 5% significance level. Summary: There is a need for mental health providers to provide a treatment intervention to their clients who are taking SGAs. This project provides foundational knowledge and resources, allowing mental health providers to mitigate the risk for the development of metabolic side effects.

Running head: CLINICAL ALGORITHM 1 Clinical Algorithm and Educational Presentation for the Management of Metabolic Side Effects Associated with Second Generation Antipsychotics Bradley Keep, BSN University of Utah in partial fulfillment of the requirements for the Doctor of Nursing Practice CLINICAL ALGORITHM 2 Executive Summary Description: The aim of this project is to develop and provide an educational opportunity in regards to treating the metabolic side effects of second-generation antipsychotic (SGA) medications. Evidence-based resources, as well as VA specific resources, provided content for the development of a clinical algorithm, as well as an educational presentation for prescribers at a local Veterans Affair outpatient mental health clinic. Problem Statement: The metabolic side effects of SGAs can contribute to the development of chronic health problems such as diabetes, cardiovascular disease and stroke. Mental health providers are familiar with the metabolic side effects of SGAs; however, they are generally unprepared to treat these side effects. At a local Veteran Affairs outpatient mental health clinic, mental health prescribers are beginning to take on a greater role in the treatment of metabolic side effects. As such, this project will provide mental health prescribers with an educational opportunity and tools related to the various resources available for treating metabolic side effects. Objectives: The objectives of this project were 1. Reduced incidence of metabolic side effects to promote the health of veterans and reduce costs to the VA. 2. Develop a treatment algorithm utilizing evidence-based literature and VA specific resources. 3. Present treatment algorithm at a monthly prescriber meeting. 4. Obtain feedback on the utility of the treatment algorithm and educational opportunity, as well as concerns for future consideration. Literature Review: SGAs are a highly efficacious medication class, used in the treatment of various mental health disorders such as bipolar spectrum disorders and schizophrenia spectrum disorders. Mental health prescribers commonly use SGAs, but they carry significant risk for metabolic side effects such as weight gain, dyslipidemia and hyperglycemia. Several treatment interventions have demonstrated efficacy in mitigating the development of metabolic side effects. Treatment interventions include dietary modifications, exercise programs, switching agents and medications to treat metabolic disturbances. Implementation & Evaluation: In order to guide mental health providers in treating metabolic side effects of SGAs, the clinical algorithm included information consolidated from the literature review and from VA specific resources and programs available to veterans. Content experts provided recommendations for modifications. At a monthly prescriber meeting, a presentation of the algorithm included in-depth education regarding the treatment of metabolic side effects. A pre- and post-questionnaire provided during the presentation gathered feedback regarding the benefit of the educational presentation and treatment algorithm. Results: Nine mental health APRNs participated (n=9). Following an educational opportunity for treating metabolic side effects of SGAs, the APRNs comfort level and intention to treat increased (17% and 14% respectively); however, the pre- and post- comparison are not statistically significant at a 5% significance level. Summary: There is a need for mental health providers to provide a treatment intervention to their clients who are taking SGAs. This project provides foundational knowledge and resources, allowing mental health providers to mitigate the risk for the development of metabolic side effects. Project Committee: Michael Johnson, APRN, PhD, ElLois W. Bailey, DNP, PMHNP-BC, Specialty Track Director, Pamela Hardin, PhD, RN, Assistant Dean for MS & DNP programs Content Expert: Nathan Askerlund, APRN CLINICAL ALGORITHM 3 Table of Contents EXECUTIVE SUMMARY ............................................................................................................ 2 ACKNOWLEDGMENTS .............................................................................................................. 4 1. INTRODUCTION Problem Statement .................................................................................................. 5 Clinical Significance ............................................................................................... 5 Purpose and Objectives ........................................................................................... 6 2. LITERATURE REVIEW Overview of SGAs .................................................................................................. 7 SGA Induce Metabolic Side Effects ....................................................................... 8 Ameliorating Factors for Metabolic Side Effects ................................................... 9 3. THEORETICAL FRAMEWORK .............................................................................. 11 4. IMPLEMENTATION & EVALUATION .................................................................. 12 5. RESULTS ....................................................................................................................16 6. FUTURE RECOMMENDATIONS ............................................................................18 7. DNP ESSENTIALS .....................................................................................................19 8. CONCLUSIONS..........................................................................................................20 9. REFERENCES ............................................................................................................22 10. PROPOSAL DEFENSE POWERPOINT ........................................................... App. A 11. IRB APPROVAL CONFIRMATION ................................................................ App. B 12. PROVIDER TRAINING PRESENTATION POWERPOINT ........................... App. C 13. PROVIDER PRE AND POST QUESTIONNAIRE........................................... App. D 14. CLINICAL ALGORITHM ................................................................................. App. E 15. POSTER.............................................................................................................. App. F CLINICAL ALGORITHM 4 Acknowledgements I would like to thank the many individuals who have supported me throughout the duration of my doctoral education. First, to my content expert, Dr. Nathan Askerlund, who has been an invaluable resource in developing and implementing this project, thank you so much for your patience and availability throughout this project. To my project chairs, Dr. ElLois Bailey and Dr. Michael Johnson, for patiently working with me throughout the duration of this project despite many bumps along the way. To all my peers who have been a major support throughout this project, I could not have done it without you. Lastly, to my daughter, who has tolerated several days and nights of a father doing schoolwork. CLINICAL ALGORITHM 5 Problem Statement Previous students have completed scholarly projects on the subject of monitoring protocols for metabolic side effects of second-generation antipsychotics (SGAs) (Cooper, 2016; Askerlund, 2015). One project aimed to delineate why mental health prescribers were not following through with metabolic monitoring protocols. Results from the study identified that clinicians were not clear about their role in treating metabolic side effects of SGAs (Cooper, 2016). This finding supports previous findings by Parameswaran et al. (2013) that suggests that mental health providers believe that they are responsible for monitoring of metabolic side effects of SGAs, but not for actively treating the metabolic side effects of SGAs. The findings from these aforementioned studies prompted recommendations that providers receive educational opportunities, as well as tools to help promote the treatment of metabolic side effects of SGAs (Cooper, 2016; Parameswaran et al., 2013). Early treatment of metabolic side effects of SGAs is associated with better health outcomes in regards to weight and dyslipidemia (Varuni et al., 2016). As such, mental health providers are in an ideal position to intervene early when metabolic side effects begin to manifest, because they may be seeing the patient more frequently to monitor for efficacy and safety of medications. Currently underway at a local Veterans Affairs mental health clinic is an initiative to have mental health providers take on a greater role in treating the metabolic side effects of SGAs. As such, educational opportunities, as well as tools, for treating the metabolic side effects of SGAs would be beneficial. Therefore, this project will attempt to improve clinical practice by facilitating and encouraging mental health providers to take on a greater role in the treatment of metabolic side effects. Clinical Significance CLINICAL ALGORITHM 6 The metabolic side effects associated with SGA use include increased waist circumference, weight gain, dyslipidemia, hyperglycemia and hypertension. This conglomerate of symptoms is termed metabolic syndrome, and is associated with an increased risk for the development of diabetes, stroke and heart disease (U.S. Department of Health and Human Services, 2016). Diabetes, stroke and heart disease comprise the 7th, 5th and 1st leading cause of death in the United States (Center for Disease Control and Prevention, 2014). Along with the significant risk for mortality associated with metabolic syndrome, the financial cost to the healthcare system is substantial. In one large study among elderly patients, Curtis et al. (2007) determined that metabolic syndrome accounted for a 20% increase in costs. Similarly, Boudreau et al. (2009) found health care costs increase by 24% with each additional metabolic syndrome risk factor. Therefore, in an effort to promote quality of life among individuals taking an SGA, while simultaneously reducing the financial burden on the health care system, it is necessary to treat the metabolic side effects associated with SGA use. Purpose and Objectives The purpose of this project is to educate mental health providers, at an intermountain VA outpatient mental health clinic, on how to treat the metabolic side effects associated with SGAs, utilizing various resources at their disposal. The following are objectives for this project. ● Reduced incidence of metabolic side effects to promote the health of veterans and reduce costs to the VA. ● Develop a treatment algorithm utilizing evidence-based literature and VA specific resources. ● Present treatment algorithm at a monthly prescriber meeting. ● Obtain feedback on the utility of the treatment algorithm, as well as concerns for future CLINICAL ALGORITHM 7 consideration and disseminate the findings to mental health leadership at the local VA. Literature Search Strategy The literature searches utilized CINAHL, PubMed, Google Scholar and CDC databases. The search terms included some combination of the following: SGA, atypical antipsychotic, metabolic side effects, metabolic syndrome, metformin, topiramate, metabolic management, dyslipidemia, weight gain, and statins. Literature related to three general strategies for mitigating metabolic side effects of SGAs (lifestyle modification, switching agents and pharmacological interventions) were included. The most contemporary literature (within 10 years), with the strongest evidence-based backing, provided content for the literature review. Literature Review Overview of SGAs Second generation antipsychotic (SGAs) medications are widely used in the psychiatric field and have indications for a variety of psychiatric disorders such as psychotic disorders, bipolar disorders and depressive disorders. SGAs differ from their first generation counterparts by the dual serotonin-dopamine antagonist actions-5HT2A receptor antagonism and D2 antagonism (Stahl, 2013). Furthermore, SGAs exert secondary actions at various neurotransmitter pathways throughout the brain that reduce the prevalence of common side effects associated with first generation antipsychotics (FGAs), such as extrapyramidal symptoms, tardive dyskinesia and elevated prolactin (Stahl, 2013). The reduced risk for the development of dyskinesias has made SGAs a popular alternative to FGAs for the treatment of various psychiatric disorders. Unfortunately, SGAs come with an increased risk for metabolic side effects such as weight gain, dyslipidemia and hyperglycemia (Stahl, 2013). SGA Induced Metabolic Side Effects CLINICAL ALGORITHM 8 All SGAs have secondary characteristics that account for their heterogeneous efficacy and side effect profiles. Secondary receptor targets for the various SGAs include histaminic, muscarinic and alternative serotonergic receptor (Stahl, 2013; Nasrallah, 2008). The exact mechanism of SGA induced metabolic side effects is not well known. However, these receptors are responsible for the development of metabolic side effects, through different mechanisms such as increased appetite, decreased insulin secretion, and insulin resistance (Correll, Lencz & Malhotra, 2011; Nasrallah, 2008). SGAs exhibit heterogeneous characteristics that results in variability amongst the rate of metabolic side effects related to the various SGAs. Rummel-Kluge et al. (2010) reviewed 48 randomized control trials that compared different SGAs and the rate of metabolic disturbances. Outcome measures for the study included weight, serum cholesterol and serum glucose. The authors concluded that olanzapine and clozapine produce the greatest metabolic side effects, with quetiapine and risperidone causing intermediate elevations, and finally aripiprazole and ziprasidone causing the lowest elevations in outcome measures. The variability of metabolic side effects among SGAs, while poorly understood, correlates with their secondary characteristics (Stahl, 2013). The specific drug's relative affinity for various cellular receptors determines its secondary characteristics and unique side effect profile. For example, histamine antagonism is associated with SGA induced metabolic side effects (Lian, Huang, Pai & Deng, 2016; Correll, Lencz & Malhotra, 2011). Consequently, SGAs that caused the greatest metabolic disturbances--clozapine, olanzapine and quetiapine-demonstrate potent antihistamine activity that likely contributes to their high metabolic side effect profiles (Stahl, 2013). Ameliorating Factors for Metabolic Side Effects CLINICAL ALGORITHM 9 Weiden, Newcomer, Loebel, Yang and Lebovitz (2008) compare metabolic measures, such as weight changes, cholesterol and triglyceride levels, at baseline and 58 weeks following a switch from olanzapine to ziprasidone. The authors found a mean reduction of 10.3% of initial body weight, over the course of the 58-week study period, in participants who switched from olanzapine to ziprasidone. Moreover, the authors observed rapid and sustained decreases in both plasma cholesterol and triglyceride levels, following the switch from olanzapine to ziprasidone (Weiden et al., 2008). The authors also found that study participants realized an overall improvement in psychopathological symptoms, namely negative symptoms of schizophrenia. A second option for consideration, when switching atypical antipsychotic agents is aripiprazole. Stroup et al. (2011) examined the effects of switching from medications with a high metabolic side effect profile such as olanzapine, quetiapine, or risperidone, to aripiprazole, which has a low metabolic side effect profile. The authors enlisted 187 participants, 89 of which make up the treatment group that switched from olanzapine to aripiprazole. Participants assigned to switch to aripiprazole realized significant reductions in weight, as well as serum triglycerides and serum inflammatory markers. Of note, however, is the fact that participants assigned to switch agents discontinued treatment prior to the 24-week study period at a significantly higher rate than those assigned to stay with their original medication (43.9% vs. 24.5%). The authors attribute the higher discontinuation rate of the switch group to reduced efficacy of aripiprazole based on the clinical judgement of study providers. When switching agents is not a beneficial option, due to efficacy of the medication for specific psychiatric symptoms, it becomes necessary to treat these side effects through other means. In these cases, an emphasis on lifestyle modifications, such as diet and exercise, cannot be overstated. Several experts emphasize the importance of lifestyle factors in the management CLINICAL ALGORITHM 10 of metabolic side effects associated with olanzapine use (Stahl, 2013; Gohlke et al., 2012). Moreover, several researchers have demonstrated the positive outcomes of lifestyle modifications, on symptoms of metabolic syndrome (Blackford et al., 2016; Walden et al., 2016; Green et al., 2015). Unfortunately, lifestyle factors alone may not be enough to mitigate the development of metabolic side effects. In these circumstances, pharmacological interventions become necessary. Metformin is a medication used to mitigate weight gain with SGAs. Several researchers have demonstrated the utility of metformin in mitigating the metabolic side effects of SGAs (de Silva et al., 2016; Wu et al., 2016; Hebrani et al., 2015; Mizuno et al., 2014). In a meta-analysis, de Silva et al. (2016) reviewed 12 randomized controlled trials to determine the effects of metformin on SGA induced metabolic disturbances. The authors concluded that metformin resulted in significantly greater weight loss than placebo in individuals with SGA induced weight gain. An interesting finding from the study is that patients treated for a first episode illness experienced a significantly greater weight loss than those who have undergone chronic treatment with an SGA. This latter finding promotes the early use of metformin in SGA naive individuals experiencing SGA induced weight gain. While metformin has demonstrated positive effects on SGA induced weight gain, the drug also has utility for the treatment of other metabolic side effects. Wu et al. (2016) studied the effects of metformin on 201 participants with first episode schizophrenia and SGA induce dyslipidemia. The authors discovered that over the 24-week treatment period, the treatment group experienced an overall decrease in serum lipids. Another medication that demonstrates efficacy in the treatment of SGA-induced metabolic disturbances is topiramate. In a meta-analysis of 16 randomized control trials, Zheng CLINICAL ALGORITHM 11 et al. (2016) found topiramate to be an effective agent for both mitigating SGA-induced weight gain, as well as for positive and negative symptoms of schizophrenia. The authors found that augmentation with topiramate was not associated with a higher prevalence of early discontinuation of treatment. Furthermore, similar to the previously mentioned metformin studies, the authors determined that early intervention with topiramate was associated with an increased amount of weight loss. Statins are a class of drugs used to treat individuals with dyslipidemia. However, studies specific to the treatment of SGA induced dyslipidemia with statins is lacking. In small studies done on statin therapy for SGA induced dyslipidemia, Hanssens et al. (2007) and De Hert et al. (2006) demonstrate positive effects, evidenced by a decrease in triglycerides, total cholesterol, LDL cholesterol and non-HDL cholesterol. Andrade (2013) points out that statins carry the side effect of hyperglycemia and risk for the development of diabetes, which is also a concern with SGAs. Concomitant use of a statin with an SGA can theoretically place individuals at an even greater risk for developing diabetes. Consequently, more studies to determine the risks of statins to the psychiatric population are required; however, current knowledge suggests that statins are a safe option for SGA induced dyslipidemia (Andrade, 2013). Ultimately, mental health practitioners have many options at their disposal for treating the metabolic side effects of SGAs. These options are relatively safe and can prevent the longterm consequences of metabolic syndrome. Therefore, mental health providers would benefit from educational opportunities and clinical tools to aid in promoting comfort level with the different array of options for treating the metabolic side effects of SGAs. Theoretical Framework The Star Model of Transformation serves as a theoretical framework for the CLINICAL ALGORITHM 12 implementation of this project. According to the Star Model of Transformation, knowledge in clinical practice evolves from a primary research study, to a more practical application within the clinical setting (Stevens, 2012). Several research studies exist, related to the management of metabolic side effects of SGAs. Synthesizing this information into a practical tool for clinicians in the clinical setting becomes necessary to the implementation of evidenced-based practice. Recent trends in healthcare have encouraged the utilization of evidenced-based practice, requiring clinicians to be responsible for a plethora of information. Treatment algorithms provide clinicians a resource to access this evidence-based information (Stevens, 2012). The Star Model of Transformation highlights five stages by which knowledge evolves for use in clinical practice. These stages include Discovery Research, Evidence Summary, Translation to Guidelines, Practice Integration, Process and Outcome Evaluation (Stevens, 2012). Stages two, three and four will be the focus of this project. Stage two of this model provides the underpinnings for objective one of this project, as research into the management of metabolic side effects of SGAs is retrieved and synthesized. In stage three, an algorithm that provides easy access to evidenced based information on the management of metabolic side effects of SGAs is developed. Stage four involves presenting the algorithm to a group of stakeholders--represented by objective three of this project--in an effort to promote utilization of the algorithm in the clinical setting. Given the time-limited scope of this project, the final stage, which involves evaluating outcomes of the algorithm, will not be included, but may serve as the basis for future scholarly inquiry. Implementation and Evaluation Plan Objectives Implementation Evaluation Objective #1: Reduced -Search databases such as -A comprehensive literature CLINICAL ALGORITHM 13 incidence of metabolic side PubMed and CINAHL, for search was completed. effects to promote the health information on interventions -Comprehensive literature of veterans and reduce costs to for metabolic s/e. review approved by the the VA. project chair. -Continually integrate new data as it becomes available. -Submit project for approval by both the VA and University IRB's. Objective 2: Develop a -Create a treatment algorithm -Treatment algorithm created. treatment algorithm utilizing that synthesizes the -Approval of treatment evidence-based literature and information obtained from algorithm by content expert. VA specific resources. objective #1. -Coordinate with VA specific services, such as exercise and nutrition groups, to determine available resources that promote lifestyle modifications. -Gain the support of a content expert to provide feedback and recommendations. Objective #3: Present -Create a PowerPoint -Presentation approved by CLINICAL ALGORITHM 14 treatment algorithm at a presentation that provides project chair. monthly prescriber meeting. comprehensive education -Presentation provided to regarding the treatment prescribers at the outpatient algorithm. clinic. -Present PowerPoint to prescribers at the outpatient clinic prior to April 2017. Objective #4: Obtain feedback -Create questionnaire -Questionnaire developed. on the utility of the treatment inquiring about prescriber's -Questionnaire distributed to algorithm, as well as concerns comfort level and intention for group of prescribers. for future consideration and managing metabolic side -Information from disseminate the findings to effects. questionnaire consolidated mental health leadership at the -Distribute questionnaire and provided to outpatient local VA. following the presentation in mental health leadership team objective #3. for future direction. -Present results to VA mental -Leadership presentation health leadership. complete. Implementation and Evaluation After completing and passing a project proposal defense (Appendix A), the University of Utah IRB reviewed the project. The University of Utah IRB subsequently made the determination of non-human subject research (Appendix B). In order to fulfill objective 1, a CLINICAL ALGORITHM 15 comprehensive literature search provided guidance for subsequent objectives in this project. Some medications are not available on the VA formulary and were not included in the treatment algorithm and presentation to providers. Ultimately, the strategies with the most evidence-based backing provided the content for the treatment algorithm and educational presentation. As part of objective 2, information obtained in the literature search provided the content for a treatment algorithm (Appendix E). Strategies, such as lifestyle modifications, switching agents and pharmacological interventions emerged from the literature and provided a guiding framework for the treatment algorithm. The content expert worked in close collaboration and provided feedback regarding the content and aesthetics of the treatment algorithm. Based on the literature search from objective 1, three general strategies emerged for mitigating the metabolic side effects of SGAs. The first strategy is the promotion of lifestyle factors such as dietary modifications, exercise and smoking cessation, which is generally recommended for all clients prescribed an SGA. The second strategy from the literature was switching agents to an SGA with a lower risk for metabolic side effects. The final strategy is pharmacological interventions to promote weight loss and reduction in serum lipid levels. As a result, the treatment algorithm followed these general strategies. Completion of a PowerPoint presentation (Appendix C) provided material for objective 3. The content expert and project chair provided feedback and approval regarding the content and clarity of the presentation. The PowerPoint presentation to providers generally followed the outline of the treatment algorithm. Prior to the presentation, the project chair, as well as the content expert, provided feedback and approval of the presentation. The presentation was thirty minutes in length and took place during a monthly APRN meeting on March 20, 2017. The APRN meetings are one hour long, however time was limited to allow for another presenter. CLINICAL ALGORITHM 16 Lastly, the development of a questionnaire (Appendix D) included questions to assess the benefit of an educational presentation on provider comfort level and intent related to treating the metabolic side effects of SGAs. Based on feedback received in previous projects at the clinical site (Cooper, 2016), the questionnaire was intended to be brief and to promote participation in the questionnaire. Therefore, each questionnaire included two questions inquiring about the provider's comfort level in treating metabolic side effects, as well as future intention in treating metabolic side effects were developed. The pre- and post-questionnaire utilized a Likert-type scale. Providers at the APRN meeting subsequently filled out the pre-questionnaire prior to the presentation, and the post-questionnaire following the presentation. Finally, the chief of nursing services received the results of the questionnaire. Results Project Findings Nine APRNs attended the meeting, along with four APRN trainees. All the APRNs participated in filling out the pre- and post-questionnaire (N=9). The results from the matched data in the pre- and post-questionnaire utilized a two-tailed paired t-test, with a significance level set at p-value ≤ 0.05. Although the majority of APRN participants (5/9) indicated an improvement in their comfort level following the presentation, representing a 17% increase, the results were not statistically significant. Similarly, the majority of APRN participants (5/9) indicated a greater intent in treating metabolic side effects, representing a 14% increase; however, the results were not statistically significant. Although the comparison of the results from the pre- and post-questionnaire was not statistically significant, there does appear to be a benefit from the educational presentation. The CLINICAL ALGORITHM 17 sample size was relatively small (N=9), which likely affected the results. Furthermore, the results utilized a two-tailed paired t-test, which assumes a bi-directional outcome potential. However, with an assumption that an educational opportunity would act to improve comfort level, analysis of the data based on a one-tailed paired t-test, the 17% increase from question 1 is statistically significant at the p-value ≤ 0.05 level; although the same does not hold true for question 2. A few participants (n=3) provided written feedback regarding their concerns about treating the metabolic side effects of SGAs. One participant expressed concern about "stepping on toes" of the primary care physician. Another participant relayed concern about prescribing medications to treat metabolic side effects and the liability that comes with it. Concerns about pharmacological interventions for treating metabolic side effects are consistent with findings from other studies (Parameswaran et al., 2013) and may explain the lack of statistical significance. Limitations and Barriers The project presentation took place during a monthly APRN meeting that did not include psychiatrists. Therefore, the results of this project are limited to a small cohort of APRN participants. Furthermore, the presentation was a half hour due to scheduling restrictions and a longer presentation may allow for more in-depth discussion among the presenter and participants. In regards to the questionnaires, only two questions were included. A brief questionnaire was intended to promote participation, however limited the amount of data. Furthermore, the questions on the questionnaire inquired about treating the metabolic side effects of SGAs, which CLINICAL ALGORITHM 18 may be ambiguous. In fact one participant rated their comfort level and intention to treat metabolic side effects as the highest rating ("Strongly agree" and "A great deal," respectively), but included a written disclaimer that their answer pertained to lifestyle modifications and not pharmacological interventions. Future Recommendations This project may have been premature in its roll out, as the local VA is in the middle of developing policies and standard operating procedures (SOPs) that may impact the objectives of this project. One such policy relates to transitions of care between mental health specialty providers and primary care physicians (PCPs). Furthermore, an SOP regarding treatment of metabolic side effects is currently in the development phase, which will feature the treatment algorithm from this project. Based on some written feedback from the questionnaires, it is apparent that some APRN mental health providers consider treatment of metabolic side effects outside of their scope of practice and solely the responsibility of the PCP. Consequently, having these policies and SOPs in place prior to an educational presentation may provide more clarity for mental health providers on their expected role in treating metabolic side effects. Due to the limitations of the questionnaire, a more detailed assessment of mental health providers concerns about treating the metabolic side effects of SGAs may benefit future efforts. Questions can be more specific, with special attention to detail and the complexities surrounding the issue of treating metabolic side effects. Questions that generally inquire about mental health providers, as well as PCPs perception of when it is appropriate for them to treat the metabolic side effects of SGAs may help provide valuable information for future policy efforts. Furthermore, questions that differentiate between interventions (lifestyle interventions, switching agents and pharmacological interventions) and the perceived scope of practice may also be CLINICAL ALGORITHM 19 beneficial. In this case, a longer questionnaire allows for more data points for analysis and interpretation. Input from several mental health providers and even PCPs can provide valuable information to help guide future policy and procedures. Following the results of a comprehensive questionnaire, policies and procedures that provide guidance regarding when it is appropriate for mental health providers to treat SGA-induced metabolic disturbances may find benefit from the information obtained in the questionnaire. The content of this project may benefit mental health providers, but also may contribute to complexities within a large healthcare organization. As an example, the VA has PCPs that are readily available for referral, which seems to be the intervention of choice related to SGAinduced metabolic disturbances. As such, mental health providers may feel like there is no need to intervene with primary care so readily available. Alternatively, mental health providers working within smaller healthcare systems or in more rural areas may find more benefit from the content of this project, as PCP may not be as readily available. Within larger organizations, several policies and procedures seemingly overlap with projects of this nature. Future projects on the topic may benefit from attempting to integrate various policies and procedures, which may contain subtleties that are outside of the scope of this project. DNP Essentials Essential III of the DNP essentials discusses the DNP's role in the utilization and synthesis of scientific inquiry/discovery (American Association of Colleges of Nursing, 2006). Evidence-based inquiry is a cornerstone of the healthcare system and the DNP's practice. However, oftentimes the amount of evidence-based information available can be vast and time consuming to sift through in an effort to obtain practical information for practice. CLINICAL ALGORITHM 20 This project aims to bridge the gap between this evidence-based material and the practical dissemination of the material. Essential III alludes to the role that the DNP has in consolidating evidence-based information, and subsequent dissemination, in the improvement of patient care (AACN, 2006). As such, this project aims to fulfill this DNP essential by consolidating evidence-based information on the management of metabolic side effects associated with second-generation antipsychotic use. Conclusion The metabolic side effects of SGAs represent a major challenge for mental health providers. SGAs are beneficial agents for a variety of mental health disorders, such as schizophrenia spectrum disorders and bipolar spectrum disorders. Unfortunately, SGA agents that prove to be efficacious for mental health clients may also accelerate the development of cardiovascular disease, placing them at a greater risk for premature death. Therefore, mental health providers should be prepared to not only monitor for these metabolic side effects, but also have knowledge of strategies to mitigate these metabolic side effects. Several efficacious strategies for mitigating metabolic side effects are available to mental providers. Promoting lifestyle modifications is an effective first-line strategy and should be a consideration for anyone prescribed an SGA. Due to the heterogeneous characteristics of SGAs, some agents have a lower risk for developing metabolic side effects, and switching to one of these low risk agents can reverse metabolic disturbances from high-risk agents. As a final consideration, pharmacological interventions such as metformin, topiramate and statin medications are an effective intervention to ameliorate metabolic disturbances associated with SGA use. Unfortunately, many mental health providers may not feel comfortable treating metabolic CLINICAL ALGORITHM 21 side effects, due to a lack of understanding of how to treat metabolic side effects. Moreover, many mental health providers may not think treating metabolic side effects is within their scope of practice and may be less willing to take on an active role. As such, educational opportunities may promote a greater comfort level and willingness to treat metabolic side effects among mental health providers. Furthermore, institutional policies and procedures that respect the opinion of mental health providers and PCPs may reduce ambiguity about mental health provider's role in treating metabolic side effects of SGAs. CLINICAL ALGORITHM 22 References American Association of Colleges of Nursing (AACN). (2006). The essentials of doctoral education of advanced nursing practice. Retrieved from http://www.aacn.nche.edu/dnp/Essentials.pdf Andrade, C. (2013). Primary prevention of cardiovascular events in patients with major mental illness: a possible role for statins. Bipolar Disorder, 15(8), 813-823. doi:10.1111/bdi.12130 Askerlund, R. N. (2015). Improving adherence to evidence-based metabolic monitoring for patients taking second generation antipsychotics in primary care. (Unpublished doctoral project). University of Utah, Utah. Blackford, K., Jancey, J., Lee, A. H., James, A. P., Waddell, T., & Howat, P. (2016). Homebased lifestyle intervention for rural adults improves metabolic syndrome parameters and cardiovascular risk factors: A randomised controlled trial. Preventative Medicine, 89, 1522. doi:10.1016/j.ypmed.2016.05.012 Boudreau, D. M., Malone, D. C., Raebel, M. A., Fishman, P. A., Nichols, G. A., Feldstein, A. C., . . . Okamoto, L. J. (2009). Health care utilization and costs by metabolic syndrome risk factors. Metabolic Syndrome and Related Disorders, 7(4), 305-314. doi:10.1089/met.2008.0070 Center for Disease Control and Prevention. (2014). Leading causes of death. Retrieved from http://www.cdc.gov/nchs/fastats/leading-causes-of-death.htm Consensus Development Conference on Antipsychotic Drugs and Obesity and Diabetes. (2004). Diabetes Care, 27(2), 596-601. CLINICAL ALGORITHM 23 Cooper, T. A. (2016). Identifying and diminishing barriers to evidence-based metabolic monitoring for patients taking second generation antipsychotics. (Unpublished doctoral project). University of Utah, Utah. Correll, C. U., Lencz, T., & Malhotra, A. K. (2011). Antipsychotic drugs and obesity. Trends in Molecular Medicine, 17(2), 97-107. doi:10.1016/j.molmed.2010.10.010 Curtis, L. H., Hammill, B. G., Bethel, M. A., Anstrom, K. J., Gottdiener, J. S., & Schulman, K. A. (2007). Costs of the metabolic syndrome in elderly individuals: findings from the Cardiovascular Health Study. Diabetes Care, 30(10), 2553-2558. doi:10.2337/dc07-0460 De Hert, M., Kalnicka, D., van Winkel, R., Wampers, M., Hanssens, L., Van Eyck, D., . . . Peuskens, J. (2006). Treatment with rosuvastatin for severe dyslipidemia in patients with schizophrenia and schizoaffective disorder. Journal of Clinical Psychiatry, 67(12), 18891896. de Silva, V. A., Suraweera, C., Ratnatunga, S. S., Dayabandara, M., Wanniarachchi, N., & Hanwella, R. (2016). Metformin in prevention and treatment of antipsychotic induced weight gain: a systematic review and meta-analysis. Boston Medical Center Psychiatry, 16(1), 341. doi:10.1186/s12888-016-1049-5 Gohlke, J. M., Dhurandhar, E. J., Correll, C. U., Morrato, E. H., Newcomer, J. W., Remington, G., . . . Allison, D. B. (2012). Recent advances in understanding and mitigating adipogenic and metabolic effects of antipsychotic drugs. Frontiers in Psychiatry, 3, 62. doi:10.3389/fpsyt.2012.00062 Hanssens, L., De Hert, M., Kalnicka, D., van Winkel, R., Wampers, M., Van Eyck, D., . . . Peuskens, J. (2007). Pharmacological treatment of severe dyslipidaemia in patients with schizophrenia. International Clinical Psychopharmacology, 22(1), 43-49. CLINICAL ALGORITHM 24 doi:10.1097/YIC.0b013e3280113d3b Hebrani, P., Manteghi, A. A., Behdani, F., Hessami, E., Rezayat, K. A., Marvast, M. N., & Rezayat, A. A. (2015). Double-blind, randomized, clinical trial of metformin as add-on treatment with clozapine in treatment of schizophrenia disorder. Journal of Research in Medical Sciences, 20(4), 364-371. Mangurian, C., Newcomer, J. W., Modlin, C., & Schillinger, D. (2016). Diabetes and Cardiovascular Care Among People with Severe Mental Illness: A Literature Review. Journal of General Internal Medicine, 31(9), 1083-1091. doi:10.1007/s11606016-3712-4 Mizuno, Y., Suzuki, T., Nakagawa, A., Yoshida, K., Mimura, M., Fleischhacker, W. W., & Uchida, H. (2014). Pharmacological strategies to counteract antipsychotic-induced weight gain and metabolic adverse effects in schizophrenia: a systematic review and meta-analysis. Schizophrenia Bulletin, 40(6), 1385-1403. doi:10.1093/schbul/sbu030 Nasrallah, H. A. (2008). Atypical antipsychotic-induced metabolic side effects: insights from receptor-binding profiles. Molecular Psychiatry, 13(1), 27-35. doi:10.1038/sj.mp.4002066 Newcomer, J. W., Campos, J. A., Marcus, R. N., Breder, C., Berman, R. M., Kerselaers, W., . . . McQuade, R. D. (2008). A multicenter, randomized, double-blind study of the effects of aripiprazole in overweight subjects with schizophrenia or schizoaffective disorder switched from olanzapine. Journal of Clinical Psychiatry, 69(7), 1046-1056. Parameswaran, S. G., Chang, C., Swenson, A. K., Shumway, M., Olfson, M., & Mangurian, C. V. (2013). Roles in and barriers to metabolic screening for people taking antipsychotic medications: a survey of psychiatrists. Schizophrenia Research, 143(2-3), 395-396. CLINICAL ALGORITHM 25 doi:10.1016/j.schres.2012.08.031 Rummel-Kluge, C., Komossa, K., Schwarz, S., Hunger, H., Schmid, F., Lobos, C. A., . . . Leucht, S. (2010). Head-to-head comparisons of metabolic side effects of second generation antipsychotics in the treatment of schizophrenia: a systematic review and meta-analysis. Schizophrenia Research, 123(2-3), 225-233. doi:10.1016/j.schres.2010.07.012 Stahl, S. M. (2013). Stahl's essential psychopharmacology: Neuroscientific basis and practical applications. New York, NY: Cambridge University Press. Stevens, K. R. (2012). Star Model of EBP: Knowledge Transformation. Academic Center for Evidence-based Practice. The University of Texas Health Science Center at San Antonio. Retrieved from http://nursing.uthscsa.edu/onrs/starmodel/star-model.asp Stroup, T. S., McEvoy, J. P., Ring, K. D., Hamer, R. H., LaVange, L. M., Swartz, M. S., . . . Lieberman, J. A. (2011). A randomized trial examining the effectiveness of switching from olanzapine, quetiapine, or risperidone to aripiprazole to reduce metabolic risk: comparison of antipsychotics for metabolic problems (CAMP). American Journal of Psychiatry, 168(9), 947-956. doi:10.1176/appi.ajp.2011.10111609 U. S. Department of Health and Human Services, National Institutes of Health, National Heart, Lung and Blood Institute. (2016). What is Metabolic Syndrome?. Retrieved from http://www.nhlbi.nih.gov/health/health-topics/topics/ms Walden, P., Jiang, Q., Jackson, E. A., Oral, E. A., Weintraub, M. S., & Rubenfire, M. (2016). Assessing the incremental benefit of an extended duration lifestyle intervention for the components of the metabolic syndrome. Diabetes Metabolic Syndrome and Obesity, 9, 177-184. doi:10.2147/dmso.s94772 CLINICAL ALGORITHM 26 Weiden, P. J., Newcomer, J. W., Loebel, A. D., Yang, R., & Lebovitz, H. E. (2008). Long-term changes in weight and plasma lipids during maintenance treatment with ziprasidone. Neuropsychopharmacology, 33(5), 985-994. doi:10.1038/sj.npp.1301482 Wu, R. R., Zhang, F. Y., Gao, K. M., Ou, J. J., Shao, P., Jin, H., . . . Zhao, J. P. (2016). Metformin treatment of antipsychotic-induced dyslipidemia: an analysis of two randomized, placebo-controlled trials. Molecular Psychiatry. doi:10.1038/mp.2015.221 Young, S. L., Taylor, M., & Lawrie, S. M. (2015). "First do no harm": A systematic review of the prevalence and management of antipsychotic adverse effects. Journal of Psychopharmacology, 29(4), 353-362. doi:10.1177/0269881114562090 Zheng, W., Xiang, Y. T., Xiang, Y. Q., Li, X. B., Ungvari, G. S., Chiu, H. F., & Correll, C. U. (2016). Efficacy and safety of adjunctive topiramate for schizophrenia: a meta-analysis of randomized controlled trials. Acta Psychiatrica Scandinavica, 134(5), 385-398. doi:10.1111/acps.12631 CLINICAL ALGORITHM 27 Appendix A Proposal Defense CLINICAL ALGORITHM 28 Appendix A Proposal Defense CLINICAL ALGORITHM 29 CLINICAL ALGORITHM 30 CLINICAL ALGORITHM 31 CLINICAL ALGORITHM 32 CLINICAL ALGORITHM 33 CLINICAL ALGORITHM 34 Appendix B IRB Approval Confirmation CLINICAL ALGORITHM 35 Appendix B IRB Approval Confirmation ERICA IRB New Study Approval [e-mail] Actions To: BRADLEY KEEP Cc: Robert Askerlund; Michael Johnson Wednesday, January 25, 2017 3:01 PM To help protect your privacy, some content in this message has been blocked. If you're sure this message is from a trusted sender and you want to re-enable the blocked features, click here. IRB: IRB_00097655 PI: Bradley Keep Title: Clinical Algorithm and Educational Presentation for the Management of Metabolic Side Effects Associated with Second Generation Antipsychotics Date: 1/25/2017 Thank you for submitting your request for approval of this project. The IRB has administratively reviewed your application and has determined on 1/25/2017 that your project does NOT meet the definitions of Human Subjects Research according to Federal regulations. Therefore, IRB oversight is not required or necessary for your project. DETERMINATION JUSTIFICATION: According to the guidance released in October 2011 by the VHA (see http://www1.va.gov/vhapublications/ViewPublication.asp?pub_ID=2456), this activity meets the description for a Non-Research Operation Activity and does not constitute human subjects research. The project is not designed to produce information that expands the knowledge base of a scientific discipline (or other scholarly field) and does not constitute research. It is a Quality Assessment/Improvement (QA/QI) activity designed for internal VA purposes including routine data collection and analysis for operational monitoring, evaluation and program improvement activities. The activity is not funded or otherwise supported as research by ORD or any other entity, and is not a clinical investigation as defined by FDA. This determination of non-human subjects research only applies to the project as submitted to the IRB. Since this determination is not an approval, it does not expire or need renewal. Remember that CLINICAL ALGORITHM 36 all research involving human subjects must be approved or exempted by the IRB before the research is conducted. If you have questions about this, please contact our office at 581-3655 and we will be happy to assist you. Thank you again for submitting your proposal. SUPPORTING DOCUMENTS Literature Cited/References Reference List Click IRB_00097655 to view the application. Please take a moment to complete our customer service survey. We appreciate your opinions and feedback. CLINICAL ALGORITHM 37 Appendix C Provider Training Presentation PowerPoint CLINICAL ALGORITHM 38 Appendix C Provider Training Presentation PowerPoint CLINICAL ALGORITHM 39 CLINICAL ALGORITHM 40 CLINICAL ALGORITHM 41 CLINICAL ALGORITHM 42 CLINICAL ALGORITHM 43 CLINICAL ALGORITHM 44 CLINICAL ALGORITHM 45 CLINICAL ALGORITHM 46 CLINICAL ALGORITHM 47 CLINICAL ALGORITHM 48 CLINICAL ALGORITHM 49 CLINICAL ALGORITHM 50 CLINICAL ALGORITHM 51 Appendix D Provider Pre and Post Questionnaire CLINICAL ALGORITHM 52 Appendix D Provider Pre and Post Questionnaire Pre-Questionnaire Check ( X ) the appropriate box: I feel comfortable treating 1-Strongly 23-Neither metabolic side effects of disagree Disagree agree or second generation disagree antipsychotic medications. I currently treat metabolic side effects of second generation antipsychotic medications. 1-Never 2-Rarely 4-Agree 5-Strongly agree 34-A Occasionally moderate amount 5-A great deal Post-Questionnaire Check ( X ) the appropriate box: I feel comfortable treating 1-Strongly metabolic side effects of disagree second generation antipsychotic medications. I intend to treat metabolic side effects of second generation antipsychotic medications in the future. Fill in the blank space: What concerns do you have regarding treating metabolic side effects of second generation antipsychotic medications? 1-Never 2Disagree 2-Rarely 3-Neither agree or disagree 4-Agree 5-Strongly agree 34-A Occasionally moderate amount 5-A great deal CLINICAL ALGORITHM 53 Appendix E Clinical Algorithm CLINICAL ALGORITHM 54 Appendix E Clinical Algorithm CLINICAL ALGORITHM 55 CLINICAL ALGORITHM 56 Appendix F Poster CLINICAL ALGORITHM 57 Appendix F Poster