University of Groningen Somatic monitoring of patients with mood and anxiety disorders Simoons, Mirjam

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(1)University of Groningen. Somatic monitoring of patients with mood and anxiety disorders Simoons, Mirjam. IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.. Document Version Publisher's PDF, also known as Version of record. Publication date: 2018 Link to publication in University of Groningen/UMCG research database. Citation for published version (APA): Simoons, M. (2018). Somatic monitoring of patients with mood and anxiety disorders: Problem definition, implementation and further explorations. Rijksuniversiteit Groningen.. Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.. Download date: 17-07-2021.

(2) Somatic monitoring of patients with mood and anxiety disorders Problem definition, implementation and further explorations. Mirjam Simoons.

(3) ISBN: 978-94-034-1060-9 (printed version) ISBN: 978-94-034-1059-3 (electronic version) Author: Mirjam Simoons Cover-design: Koen Jans, Baue van Leyden (Bord&Stift), Mirjam Simoons Lay-out and print production: Off Page, Amsterdam The research presented in this thesis was supported by Mental Health Services Drenthe, Assen. Printing of this thesis was financially supported by the Health Research Institute Science in Healthy Ageing & healthcaRE (SHARE), the University of Groningen, the Faculty of Science and Engineering and the Nederlands Bijwerkingen Fonds and this is gratefully acknowledged. © Mirjam Simoons, 2018. All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without written permission of the author. The copyright of previously published chapters of this thesis remains with the publisher or journal..

(4) Somatic monitoring of patients with mood and anxiety disorders Problem definition, implementation and further explorations. Proefschrift. ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op vrijdag 30 november 2018 om 12.45 uur. door. Mirjam Simoons geboren op 20 november 1988 te Hoorn.

(5) PROMOTORES Prof.dr. E.N. van Roon Prof.dr. R.A. Schoevers. COPROMOTORES Dr. H. Mulder Dr. H.G. Ruhé. BEOORDELINGSCOMMISSIE Prof.dr. A.C.G. Egberts Prof.dr. M. de Hert Prof.dr. R.C. Oude Voshaar.

(6) TABLE OF CONTENTS Chapter 1. General introduction. 7. Part I. Problem definition. Chapter 2. Medication discrepancies at outpatient departments for mood and 21 anxiety disorders in the Netherlands: risks and clinical relevance Journal of Clinical Psychiatry 2016; 77(11):1511-1518. Chapter 3. Monitoring of somatic parameters at outpatient departments for mood and anxiety disorders PLoS One 2018; 13(8):e0200520. 37. Chapter 4. Availability of CYP2D6 genotyping results in general practitioner and community pharmacy medical records Pharmacogenomics 2017; 18(9):843-851. 55. Part II. Implementation of the MOPHAR monitoring program. Chapter 5. Design and methods of the MOPHAR monitoring program Submitted. Chapter 6. Metabolic syndrome at an outpatient clinic for bipolar disorders: a case for systematic somatic monitoring Psychiatric Services 2018; in press (abridged version). Part III. Somatic monitoring beyond the MOPHAR monitoring program. Chapter 7. The relative risk for QT(c)-interval prolongation across different classes of antidepressants: a systematic review and meta-analysis Submitted. 133. Chapter 8. Limited evidence for risk factors for proarrhythmia and sudden cardiac death in patients using antidepressants: Dutch consensus on ECG monitoring Drug Safety 2018; 41(7):655-664. 177. Chapter 9. Modification of the association between paroxetine serum concentration and SERT-occupancy by ABCB1 (P-glycoprotein) polymorphisms in major depressive disorder Submitted. 201. Chapter 10. General discussion. 229. &. Addendum. 75 115. Summary251 Nederlandse samenvatting 257 List of co-authors 265 Dankwoord269 Publications related to this thesis 277 Publications Research Institute SHARE 279.

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(8) 1. General introduction.

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(10) GENERAL INTRODUCTION | CHAPTER 1. MIND THE BODY The Greek philosopher and writer Plato (±427 B.C. – 347 B.C.) has probably been one of. 1. the first to have posed the question of how the human mind and body causally interact, i.e. the mind-body problem. Plato believed the non-physical mind and physical body were two distinct entities, but they should be studied as a whole – a mind-body complex.1 René Descartes (1596 – 1650), a French philosopher and mathematician, more extensively described the dualist theory.1 Descartes too was firmly convinced of the possibility and importance of reciprocal interaction between mental and physical (bodily) substances.2 Many philosophers since Descartes have thought about the mind-body problem. Today, many health care professionals, in mental health care in particular, are still confronted with the mind-body problem in clinical practice. Unlike for example infectious disease specialists, whose practice has a significant overlap with internal medicine, psychiatrists and their mental health team have traditionally focused mainly on their patients’ mental status, mental rehabilitation, home life and occupational situation.3 The attention for the physical health needs of their patients may vary in clinical practice and may sometimes be limited to some well-defined treatment-related adverse effects (e.g. white blood cell count with clozapine or thyroid and kidney function with lithium). In the last few decades, it has become more and more clear that the segregation of psychiatric care from other medical care may not be justified. It is well known that many physical diseases, including nutritional and metabolic diseases, cardiovascular diseases, viral diseases, respiratory diseases, musculoskeletal diseases, and sexual dysfunction, have a higher prevalence among drug-naïve people with severe mental illness (SMI) than among the general population.4 In addition to the increased risk of somatic comorbidities in drug-naïve psychiatric patients, which are sometimes not recognised by health care workers, the side effects of psychotropic drugs, such as weight gain and associated metabolic disturbances, movement disorders, and haematological abnormalities are important reasons for combined somatic-psychiatric care. In fact, physical illness provides a major contribution (about 60%) to the two to three times higher mortality in people with SMI compared to the general population.4,5 This translates to a 13-30 year shortened life expectancy – even in countries with generally acknowledged high quality health care such as The Netherlands and the Scandinavian countries.4,5 Furthermore, common health threats and individual unhealthy lifestyle choices, such as cigarette smoking, little physical exercise and an unhealthy diet are highly prevalent in this population.4 These aspects warrant integrated somatic care for psychiatric patients with the aim to prevent, monitor and treat somatic co-morbidities and side effects of psychotropic drugs. However, general consensus on how to design and structure the efforts to capture the physical aspects of psychiatric treatment is lacking and it has not always been properly implemented. In this thesis, we focused on monitoring, with the goal to prevent somatic complications or detect them at an early stage.. 9.

(11) 1. Different health care providers should ideally organize the care for psychiatric patients in a patient-centered fashion (see Figure 1). The reciprocal relationships between patients and health care providers may all play a role when an outpatient receives somatic monitoring care during their psychiatric treatment. In this introduction, we will follow the patient during their treatment for a mental illness at an outpatient department for psychiatry. With respect to somatic monitoring, available guidelines and the current standard of care will be discussed at the two stages of the psychiatric outpatient treatment: the baseline assessment (here referred to as ‘intake’) and subsequent monitoring during treatment. Furthermore, a monitoring program as a potential solution to the shortcomings of the current standard of care will be introduced.. SOMATIC SCREENING AT INTAKE OF PATIENTS AT OUTPATIENT DEPARTMENTS FOR PSYCHIATRY The referral of a new patient to an outpatient department for psychiatry is not only the start of psychiatric treatment, but may also be the start of somatic monitoring. A baseline screening may be useful in different ways. First, it may serve to detect potential (additional) causes of the mental illness (e.g. thyroid dysfunction for depression) in the diagnostic phase. Second, by screening for existing somatic co-morbidities and side effects of psychotropic drugs already in use at intake (e.g. metabolic disturbances), untreated or high-risk individuals may be identified and selected for adequate care and/or (intensified) monitoring. Also, it may serve to establish baseline measures of physical and mental health in order to monitor. General practitioner. Mental health care outpatient department. Other medical specialists. Community pharmacy. Figure 1. Different health care professionals providing care for psychiatric patients in a patientcentred fashion. 10.

(12) GENERAL INTRODUCTION | CHAPTER 1. overall treatment effects, side effects and safety. A baseline measurement may thus be of added value in order to determine whether any treatment that may be initiated after intake. 1. has a deteriorating or beneficial effect on the baseline status of any pre-existent condition at intake. Without a baseline measurement, the treating psychiatrist cannot determine whether deviating measurements during treatment are the result of (drug) treatment or was pre-existent at intake. Several guidelines address the need for a baseline screening including aspects of both mental and physical health. There are guidelines with baseline screening recommendations for patients with specific psychiatric diagnoses (e.g. bipolar disorder, schizophrenia) and for patients using specific (classes of) psychotropic drugs (e.g. antidepressants or lithium). In 2015, the Dutch guideline on somatic screening in SMI patients was published. This guideline suggests an elaborate baseline somatic screening in all SMI patients, irrespective of the specific psychiatric diagnosis.6 The available guidelines differ somewhat in their specific recommendations, but generally agree that the personal and familial medical history of the patient, smoking status, alcohol and illicit substance use, medication use, exercise and dietary habits, physical examination (e.g. weight (BMI), waist circumference and blood pressure) and laboratory tests (e.g. plasma glucose and blood lipids) should be included.6-11 Some guidelines also consider ECG parameters necessary, but recommendations vary from all patients to patients with specific characteristics.6-11 A baseline somatic screening at intake for a mental health treatment as described in this chapter is considered basic health care by health insurance companies, policy makers and the government, including the healthcare inspectorate.6,12 This may reflect the expected clinical relevance of somatic monitoring in patients with (severe) mental illness. However, it is unclear whether current standards of daily clinical psychiatric practice always meet these proposed and expected standards.. SOMATIC MONITORING DURING PSYCHIATRIC OUTPATIENT TREATMENT After the start of treatment, the values for each parameter from the baseline screening may vary with time, as a consequence of the mental illness or the treatment. Therefore, it is necessary to perform repeated screenings, i.e. somatic monitoring, to ensure that somatic complications and adverse drug effects are detected and treated in a timely manner. Several guidelines and consensus documents have suggested parameters for somatic monitoring as part of routine clinical practice after the baseline screening in among others patients with schizophrenia, bipolar disorder and major depressive disorder.8,10,11 Similarly, guidelines have been published for somatic monitoring during the use of specific classes of psychotropic drugs.8-11,13 The previously mentioned 2015 Dutch guideline on somatic screenings in SMI patients advises a yearly somatic screening in all SMI patients, irrespective of the specific psychiatric diagnosis, plus extra monitoring if the patient starts psychotropic drugs.6 Naturally, somatic monitoring consists of many of the parameters. 11.

(13) 1. that are in the baseline screening as well, such as physical examination (anthropometrics), laboratory tests and medication reconciliation. Describing monitoring standards in guidelines, has the advantage of increasing awareness of the need for somatic monitoring by mental health care providers and raising an expectation that somatic monitoring should be incorporated in routine practice. However, although many of the suggested somatic monitoring parameters such as weight and blood pressure are simple, easy to perform and inexpensive, monitoring practices may not meet the proposed standards from the guidelines yet. For example, monitoring of serum lithium level, renal function and thyroid function in patients with bipolar disorder using lithium in the United Kingdom showed only 30-55% compliance to the available guideline.14 A meta-analysis of 39 studies (n=218940) on metabolic screening in patients with predominantly schizophrenia or related disorders using antipsychotics, showed that routine baseline metabolic screening before start of pharmacotherapy was suboptimal and above 50% only for blood pressure and triglycerides.15 Given the high prevalence of somatic co-morbidities (e.g. metabolic syndrome, pooled prevalence of 32.6% in a large cohort of SMI patients (n=52,678)16), suboptimal monitoring might put patients at considerable risk for iatrogenic harm, regardless of the specific psychiatric diagnosis. Metabolic disturbances and other somatic complications are not limited to patients with schizophrenia or specific parameters in patients using lithium. Mood disorders are increasingly treated with combinations of lithium, antipsychotics, mood stabilizers and antidepressants. 17,18 Therefore, these patients are at risk for somatic complications as well. Another essential element for somatic monitoring is medication reconciliation. An incomplete or erroneous medication overview may lead to failure to detect cause and consequence of side effects and somatic complications, prescribing errors and iatrogenic harm. Most research on medication reconciliation quality has been conducted in nonpsychiatric patients. Previous studies at outpatient departments such as for hemodialysis or internal medicine, found on average 0.97 to 3.4 discrepancies per patient.4,10-14 A systematic review examining the clinical relevance of such errors after general hospital admission, showed that approximately 11%-59% of the medication discrepancies were clinically important.5 However, little is known about the clinical importance of medication discrepancies in psychiatric populations. A single study investigating psychiatric inpatients after admission to a geriatric psychiatric clinic showed discrepancies in 78% of 50 patients with a median of two discrepancies per patient.6 Of all discrepancies, 82% were considered clinically relevant.6 To our knowledge, there are no studies reporting medication discrepancies and their clinical relevance in psychiatric outpatients.. MOPHAR: SOMATIC MONITORING IN A STRUCTURED MONITORING PROGRAM Poorly controlled somatic conditions have been shown to lead to potentially preventable medical hospitalizations and excess morbidity and mortality.4,5,19 The poor monitoring 12.

(14) GENERAL INTRODUCTION | CHAPTER 1. rates for somatic parameters in psychiatry as described in the preceding paragraphs therefore must improve in order to prevent and treat somatic co-morbidities and side. 1. effects. However, the introduction of new guidelines, education materials, consensus statements, or (national) quality improvement programs alone is only minimally effective in improving monitoring rates.15,20-23 Factors contributing to the lack of physical health care for psychiatric patients might be the perception that physical health and lifestyle are matters for general practitioners or the belief that patients are uninterested or unwilling to change.24-30 However, a recent study showed that SMI patients’ actually want to have their somatic health screened and monitored, as their ability to survey their own physical health interest is reduced.31 In addition, there is increasing evidence that disparities not only in health care access and utilization, but also in health care provision contribute to the poor physical health outcomes.24-30 Therefore, to our opinion, the active implementation of a more structured monitoring program is warranted, which includes (at minimum): physical examination, laboratory tests and medication reconciliation. In the northern part of The Netherlands, we developed the innovative care path ‘Monitoring Outcomes of Psychiatric Pharmacotherapy (MOPHAR)’, which is currently actively implemented as a restructured routine practice for outpatient care at all outpatient departments from Mental Health Services (MHS) Drenthe.32 In this program, somatic monitoring of psychiatric outpatients is incorporated in routine clinical practice at the outpatient department. Primary objective of this program is to prevent, monitor and treat somatic co-morbidities and adverse effects of psychotropic drugs. A nurse conducts and coordinates general somatic screenings with each patient at intake and yearly thereafter. In addition, recommended monitoring (among others of therapeutic effect and somatic adverse effects of psychotropic drugs) is performed according to pre-specified protocols per drug used as determined by regular medication reconciliation. Mental health care providers have immediate access to this up-to-date information and minimal burden to pursue these protocols. In addition, patients are asked for their informed consent to use the MOPHAR data and an extra blood sample for research purposes. The MOPHAR monitoring protocol comprises a set of elements based on literature, expert consensus and clinical experience from MHS Drenthe and health care providers from the northern part of the Netherlands. This set is dynamic and should be submitted to a process of continuous scrutiny and adaptation based on, among others, altered criteria for monitoring or parameters that can be in- or excluded for reasons of (in)efficiency. In addition, the results of the monitoring measurements need to be communicated to other health care providers involved. It is currently unclear to which extent monitoring measurements are shared between for example the outpatient department for psychiatry and the general practitioner, which warrants investigation and - if necessary - adjustment of communication practices. Furthermore, in order to take full advantage of the information from somatic monitoring, the results from monitoring measurements should be translated to interventions if necessary with subsequent follow-up. These issues too are relevant 13.

(15) 1. aspects of a monitoring program for psychiatric outpatients, in order to provide the patient with a complete somatic monitoring care path that can be added to the mental treatment.. OBJECTIVES OF THIS THESIS The primary objective of this thesis was to investigate the need for, the construction of and the effects after introduction of a structured somatic monitoring program (MOPHAR) for psychiatric patients visiting an outpatient department of MHS Drenthe. The secondary objectives were to evaluate the criteria for included parameters in MOPHAR and to explore potential new monitoring parameters.. OUTLINE OF THE THESIS This thesis consists of three parts. In Part I, the lack of structured medication and somatic monitoring is quantified and associated risks for patients are described. Chapter 2 describes a cross-sectional study in which discrepancies between the actual medication use by patients and the medication overviews of their outpatient departments for mood and anxiety disorders, general practitioners and community pharmacies are determined. In addition, the clinical relevance of these discrepancies for the patients’ medication safety was investigated. In chapter 3, monitoring practices at outpatient departments for mood and anxiety disorders are quantified – in general and with a specific focus on metabolic monitoring in patients using atypical antipsychotics, lithium monitoring in patients using lithium and ECG monitoring. In chapter 4, current standards of between-health-careprovider communication of monitoring measurements are discussed with the CYP2D6 genotyping as an example. We investigated the availability of the genotyping test results in the medical records of psychiatric patients that had undergone CYP2D6 genotyping in the course of their treatment. Part II concerns the monitoring program ‘Monitoring of Psychiatric Pharmacotherapy’ (MOPHAR) at outpatient departments for psychiatry at MHS Drenthe. In chapter 5, the design and methods of the MOPHAR monitoring program are presented. Chapter 6 describes the first implementation of the somatic screening at intake from the MOPHAR monitoring program, at the outpatient department for bipolar disorders of MHS Drenthe. The results of the physical examinations and laboratory tests after implementation of the MOPHAR monitoring program are compared to the monitoring practices at the original intake before MOPHAR in outpatients with bipolar disorders. In part III of this thesis, studies on monitoring parameters that in the future may become a standard measurement in MOPHAR and their implications for clinical practice are described. In addition, a consensus view on ECG monitoring recommendations is presented. Chapters 7 is a systematic review and meta-analysis of the relative risks of antidepressants for prolongation of the QTc-interval on the ECG. In chapter 8, literature on risk factors associated with proarrythmia and sudden (cardiac) death is reviewed and Dutch consensus recommendations on ECG monitoring of patients using antidepressants. 14.

(16) GENERAL INTRODUCTION | CHAPTER 1. are presented. In chapter 9, as an example of a potential study that could be performed with the infrastructure provided by MOPHAR, the potential modification of the association. 1. between paroxetine serum concentration and SERT-occupancy by four ABCB1 (P-glycoprotein) single nucleotide polymorphisms (SNPs) is investigated in patients with major depressive disorder. Finally, in the general discussion presented in chapter 10, the main findings are put in a broader perspective. Implications and considerations for both clinical practice and future studies will be discussed.. 15.

(17) 1. REFERENCES 1.. Lipowski ZJ. What does the word “psychosomatic” really mean? A historical and semantic inquiry. Psychosom Med 1984 Mar-Apr;46(2):153-171.. 2.. Van Oudenhove L, Cuypers S. The relevance of the philosophical ‘mind-body problem’ for the status of psychosomatic medicine: a conceptual analysis of the biopsychosocial model. Med Health Care Philos 2014 May;17(2):201-213.. 3.. 4.. 5.. 6.. 16. Jennex A, Gardner DM. Monitoring and management of metabolic risk factors in outpatients taking antipsychotic drugs: a controlled study. Can J Psychiatry 2008 Jan;53(1):34-42. De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry 2011 Feb;10(1):52-77. Hjorthoj C, Sturup AE, McGrath JJ, Nordentoft M. Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis. Lancet Psychiatry 2017 Apr;4(4):295-301. Meeuwissen JAC, Meijel Bv, Piere Mv, Bak M, Bakkenes M, Kellen Dvd, et al. Multidisciplinary guideline Somatic screening in patients with severe mental illness. 2015; Available at: http://www.venvn.nl/Portals/1/ Nieuws/2015%20documenten/20150401%20 1%20Multidisciplinaire%20richtlijn%20 Somatische%20screening%20EPA.pdf. Accessed September 15th, 2018.. 7.. De Hert M, Cohen D, Bobes J, CetkovichBakmas M, Leucht S, Ndetei DM, et . Physical illness in patients with severe mental disorders. II. Barriers to care, monitoring and treatment guidelines, plus recommendations at the system and individual level. World Psychiatry 2011 Jun;10(2):138-151.. 8.. De Hert M, Vancampfort D, Correll CU, Mercken V, Peuskens J, Sweers K, et al. Guidelines for screening and monitoring of cardiometabolic risk in schizophrenia: systematic evaluation. Br J Psychiatry 2011 Aug;199(2):99-105.. 9.. Dodd S, Malhi GS, Tiller J, Schweitzer I, Hickie I, Khoo JP, et al. A consensus statement for safety monitoring guidelines of treatments for major depressive disorder. Aust N Z J Psychiatry 2011 Sep;45(9):712-725.. 10. Ng F, Mammen OK, Wilting I, Sachs GS, Ferrier IN, Cassidy F, et al. The International Society for Bipolar Disorders (ISBD) consensus guidelines for the safety monitoring of bipolar disorder treatments. Bipolar Disord 2009 Sep;11(6):559-595. 11. National Institute for Clinical Excellence. Bipolar Disorder: the Management of Bipolar Disorder in Adults, Children and Adolescents, in Primary and Secondary Care (Clinical Guideline 185). 2014; Available at: http://www.nice.org.uk/guidance/cg185. Accessed August 17th, 2017. 12. Inspectie voor de Gezondheidszorg. Toetsingskader IGZ Somatische comorbiditeit. 2012; Available at: http:// www.veiligezorgiederszorg.nl/speerpuntcomorbiditeit/2012-brief-igz-bouwstenen. pdf. Accessed November 2nd, 2017. 13. American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, North American Association for the Study of Obesity. Consensus development conference on antipsychotic drugs and obesity and diabetes. J Clin Psychiatry 2004 Feb;65(2):267-272. 14. Collins N, Barnes TR, Shingleton-Smith A, Gerrett D, Paton C. Standards of lithium monitoring in mental health trusts in the UK. BMC Psychiatry 2010 Oct 12;10:80-244X-10-80. 15. Mitchell AJ, Delaffon V, Vancampfort D, Correll CU, De Hert M. Guideline concordant monitoring of metabolic risk in people treated with antipsychotic medication: systematic review and metaanalysis of screening practices. Psychol Med 2012 Jan;42(1):125-147. 16. Vancampfort D, Stubbs B, Mitchell AJ, De Hert M, Wampers M, Ward PB, et al. Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder: a systematic review and meta-analysis. World Psychiatry 2015 Oct;14(3):339-347..

(18) GENERAL INTRODUCTION | CHAPTER 1. 17. Fjukstad KK, Engum A, Lydersen S, Dieset I, Steen NE, Andreassen OA, et al. Metabolic Abnormalities Related to Treatment With Selective Serotonin Reuptake Inhibitors in Patients With Schizophrenia or Bipolar Disorder. J Clin Psychopharmacol 2016 Dec;36(6):615-620. 18. de Wit LM, van Straten A, Lamers F, Cuijpers P, Penninx BW. Depressive and anxiety disorders: Associated with losing or gaining weight over 2 years? Psychiatry Res 2015 Jun 30;227(2-3):230-237. 19. McGinty EE, Sridhara S. Potentially preventable medical hospitalizations among Maryland residents with mental illness, 2005-2010. Psychiatr Serv 2014 Jul;65(7):951-953. 20. Dhamane AD, Martin BC, Brixner DI, Hudson TJ, Said Q. Metabolic monitoring of patients prescribed second-generation antipsychotics. J Psychiatr Pract 2013 Sep;19(5):360-374. 21. Morrato EH, Druss B, Hartung DM, Valuck RJ, Allen R, Campagna E, et al. Metabolic testing rates in 3 state Medicaid programs after FDA warnings and ADA/APA recommendations for second-generation antipsychotic drugs. Arch Gen Psychiatry 2010 Jan;67(1):17-24. 22. Paton C, Adroer R, Barnes TR. Monitoring lithium therapy: the impact of a quality improvement programme in the UK. Bipolar Disord 2013;15(8):865-75. 23. Brody RS, Liss CL, Wray H, Iovin R, Michaylira C, Muthutantri A, et al. Effectiveness of a riskminimization activity involving physician education on metabolic monitoring of patients receiving quetiapine: results from two postauthorization safety studies. Int Clin Psychopharmacol 2016 Jan;31(1):34-41. 24. Lawrence D, Kisely S. Inequalities in healthcare provision for people with severe mental illness. J Psychopharmacol 2010 Nov;24(4 Suppl):61-68.. 25. McIntyre RS, Soczynska JK, Beyer JL, Woldeyohannes HO, Law CW, Miranda A, et al. Medical comorbidity in bipolar disorder: re-prioritizing unmet needs. Curr Opin Psychiatry 2007 Jul;20(4):406-416.. 1. 26. Nasrallah HA, Meyer JM, Goff DC, McEvoy JP, Davis SM, Stroup TS, et al. Low rates of treatment for hypertension, dyslipidemia and diabetes in schizophrenia: data from the CATIE schizophrenia trial sample at baseline. Schizophr Res 2006 Sep;86(1-3):15-22. 27. Roberts L, Roalfe A, Wilson S, Lester H. Physical health care of patients with schizophrenia in primary care: a comparative study. Fam Pract 2007 Feb;24(1):34-40. 28. Osborn DP, King MB, Nazareth I. Participation in screening for cardiovascular risk by people with schizophrenia or similar mental illnesses: cross sectional study in general practice. BMJ 2003 May 24;326(7399):1122-1123. 29. Fagiolini A, Goracci A. The effects of undertreated chronic medical illnesses in patients with severe mental disorders. J Clin Psychiatry 2009;70 Suppl 3:22-29. 30. Mitchell AJ, Malone D, Doebbeling CC. Quality of medical care for people with and without comorbid mental illness and substance misuse: systematic review of comparative studies. Br J Psychiatry 2009 Jun;194(6):491-499. 31. van Hasselt FM, Oud MJ, Loonen AJ. Improvement of care for the physical health of patients with severe mental illness: a qualitative study assessing the view of patients and families. BMC Health Serv Res 2013 Oct 21;13:426-6963-13-426. 32. MOPHAR - Better care for mental health issues: https://www.youtube.com/ watch?v=aQVb4RBH68A&t=0s. 2016; Available at: https://www.youtube.com/ watch?v=aQVb4RBH68A&t=0s. Accessed September 27th, 2017.. 17.

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(20) I Problem definition.

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(22) 2. Medication discrepancies at outpatient departments for mood and anxiety disorders in the. Netherlands:. risks and clinical relevance. Mirjam Simoons Hans Mulder Arne J. Risselada Frederik W. Wilmink Robert A. Schoevers Henricus G. Ruhé* Eric N. van Roon*. * These authors share senior authorship Journal of Clinical Psychiatry 2016; 77(11):1511-1518.

(23) PART I | PROBLEM DEFINITION. ABSTRACT. 2. Objective To identify discrepancies between actual drug use by outpatients with mood and anxiety disorders and medication overviews from health care providers as well as to investigate the clinical relevance of those discrepancies.. Methods A cross-sectional study in adults visiting one of four participating outpatient departments for mood and anxiety disorders was conducted between March and November 2014. DSM-5 criteria were used to assign the psychiatric diagnosis. The primary outcome was the number of discrepancies between the actual medication use, as determined by medication reconciliation with the patient, and the medication overview from the outpatient department, general practitioner, and community pharmacy. Our secondary outcome was the clinical relevance of discrepancies, as assessed by an expert panel that reviewed all discrepancies for their potential to cause patient harm.. Results Of 367 patients included, 94.8% had at least one discrepancy in the medication overview from the outpatient department. A mean of 3.9 discrepancies existed per patient. Most discrepancies (74.5%) related to omitted drugs (drugs taken regularly by patients but absent from the medication overview). Of all discrepancies at the outpatient departments, 22.7% had the potential to cause moderate to severe discomfort or clinical deterioration, affecting 49.3% of the patients. Both total number and number of clinically relevant discrepancies were lower in medication overviews from general practitioners and pharmacies.. Conclusion Patients from outpatient departments for mood and anxiety disorders may be at substantial risk for medication discrepancies that are often clinically relevant. Medication reconciliation at mental health care outpatient departments is in need of improvement.. 22.

(24) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. INTRODUCTION Psychiatric patients commonly use combinations of psychiatric and general medical drugs for their mental illness and the frequently occurring somatic comorbidities or side effects of psychiatric medication.1,2 Various prescribers from different health care institutions,. 2. including general practitioners, prescribe these drugs. In addition, patients may use nonprescription drugs, mostly unadvised and unsupervised by health care professionals. To correctly evaluate a patient’s clinical status and allow adequate adjustment of pharmacotherapeutic treatment, clinicians need to have a complete medication overview. This overview is obtained by a process called medication reconciliation in which the actual medication use is determined. Despite reliable community pharmacy records in the Netherlands, previous research has shown that patient counselling is a crucial part of medication reconciliation to create a complete overview of the actual medication use by the patient.3,4 Medication reconciliation through combination of pharmacy records and patient counselling results in an up-to-date and complete medication overview including current medication use and all medication allergies or intolerances. Most research on quality of medication reconciliation has been conducted in hospital settings and reported discrepant medication overviews in 34%–95% of patients.5,6 An incomplete or erroneous medication overview may lead to failure to detect cause and consequence of side effects and somatic complications, prescribing errors, and iatrogenic harm. However, little is known about the clinical importance of medication discrepancies. One systematic review5 examining the clinical relevance of such errors after hospital admission showed that approximately 11%–59% of the medication discrepancies were clinically important. However, in patients admitted to a geriatric psychiatric clinic, 82% of all discrepancies were clinically relevant.6 To our knowledge, there are no studies reporting medication discrepancies and their clinical relevance in psychiatric outpatients. Therefore, we addressed two issues. First, we examined whether psychiatrists have an up-to-date medication overview available for treatment evaluation when their patients visit them. Second, we assessed whether incomplete and erroneous medication overviews at psychiatric outpatient clinics are clinically relevant. We investigated outpatients with mood and anxiety disorders in the northern part of the Netherlands, aiming to identify discrepancies between the medication overview available at psychiatry outpatient departments and the actual drug use as well as to investigate their clinical relevance. For comparison, we also assessed discrepancies in the medication overviews from the general practitioners and community pharmacies of the same patients.. METHODS Design and setting We used a cross-sectional design to assess discrepancies between the reconciled medication use and the medication overview from different health care providers.. 23.

(25) PART I | PROBLEM DEFINITION. The study was conducted at four outpatient departments for mood and anxiety disorders in the northern part of the Netherlands: three from two large secondary mental health care. 2. institutions and one from an academic hospital. In the Netherlands, it is mandatory to have a complete and up-to-date medication overview (including drugs prescribed by other physicians) available for clinical decision making whenever a patient contacts a prescriber. The prescriber is responsible for updating this information through reconciliation with the patient. This information is recorded in the electronic medical record (EMR) of the patient.. Study population We included patients 18 years or older who had visited the participating outpatient department at least once. The latter criterion ensured that the treating mental health care provider had had the opportunity to certify information regarding medication use after the first visit. We consecutively recruited patients when visiting the outpatient departments between March and November 2014. We obtained written informed consent after complete verbal and written description of the study. An independent medical ethics committee (rTPO Leeuwarden, the Netherlands) waived formal review and approval of the study protocol since participants were not subject to interventions nor were they required to follow rules of behaviour for this study. We used the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), criteria for psychiatric classification of participants.. Outcomes Primary outcomes were the percentage of patients with at least one discrepancy and the number and type of discrepancies between the reconciled medication use and the medication use according to the medication overview from the mental health care institution, general practitioner, and community pharmacy on the day of inclusion. Discrepancies provide information regarding the actual medication use by the patient but not necessarily regarding the correct medication use in a pharmacologic sense. Secondary outcomes were the clinical relevance of the discrepancies and the need for intervention as a consequence of the discrepancies.. Discrepancy assessment and classification We determined actual medication use on the inclusion date by medication reconciliation with the patient immediately after the consult with a mental health care provider. In concordance with other studies3,4, we considered medication reconciliation combining recorded (pharmacy records) and patient-reported information the gold standard for determining the actual medication use by the patient. If the patient-reported medication use differed from the pharmacy records, we used the patient-reported information to. 24.

(26) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. assess the actual drug use by the patient. This actual medication use might be discrepant from the drug use the psychiatrist expected. We defined a discrepancy as any difference between the reconciled medication use or allergies/intolerances and the medication overview from the EMR at the outpatient. 2. department, the general practitioner, or the community pharmacy. We considered all drugs approved by the Dutch Medicines Evaluation Board or European Medicines Evaluation Authority. Drugs with a unique active ingredient, strength, or route of administration were considered separate drugs. To prevent overestimation of discrepancies, we counted a maximum of one discrepancy per actual drug or allergy/ intolerance instead of all differences (e.g., total daily dose and route of administration). In addition, we used a margin of 28 days around the theoretical starting and end dates of a drug on the medication overviews from the health care providers in which period the drug was considered to be still in use.7 For example, when a patient’s lithium refill had theoretically ended fourteen days before the inclusion date, but the patient reported to still use lithium, we recorded no discrepancy if the daily dose and route of administration matched as well. However, we would have recorded a discrepancy if the refill had ended more than 28 days before the inclusion date. We subsequently classified discrepancies as one of five types: extra drug, omitted drug, difference in total daily dose, difference in route of administration, or difference in allergy or intolerance. Whenever we identified differences in both dose and route of administration for 1 drug, we classified the discrepancy as a difference in total daily dose. Discrepancies do not necessarily reflect clinically relevant issues. To overcome this limitation to our design, we evaluated the clinical relevance of discrepancies. An expert panel consisting of a hospital pharmacist and clinical pharmacologist (A.J.R.) and a psychiatrist (F.W.W.) independently classified each discrepancy in one of three classes for its potential to cause patient harm, as adopted from Cornish et al.8 Class 1 discrepancies are those unlikely to cause patient discomfort or clinical deterioration, while class 2 and 3 discrepancies could potentially result in respectively moderate and severe discomfort or clinical deterioration. In addition, only for discrepancies at the mental health care institutions, the expert panel evaluated the need for intervention in order to prevent or alleviate any possible harm as a consequence of the discrepancy. The expert panel classified suggested interventions in six categories: consider measuring a somatic parameter, prescribing an extra drug, stopping a current drug, changing a current drug (without altering the active ingredient [e.g., a change in dose or route of administration]), replacing a current drug (by another drug within the same therapeutic class [e.g., one antidepressant by another antidepressant or one β-blocking agent by another β-blocking agent]), or re-evaluating total medication use. The two experts resolved all disagreements in classifications by discussion.. 25.

(27) PART I | PROBLEM DEFINITION. Statistical analysis Descriptive and statistical analysis was completed using Excel 2013 (Microsoft, Redmond,. 2. Washington) and IBM SPSS (version 20 for Windows; IBM Corp, Armonk, New York). We investigated the number of discrepancies and the percentage of clinically relevant discrepancies (classes 2 and 3) for differences per setting (both the four outpatient departments separately and the academic/community departments) in univariate linear regression models, using a p<0.05 significance level. Interrater reliability of the expert panel members for judging the clinical relevance and the need for intervention was analysed using a weighted and a Cohen. κ score, respectively.. κ. score with squared weights9. RESULTS Participants We asked 495 consecutively eligible patients to participate in the study at the four locations. Of these patients, 370 gave written informed consent (104, 103, 102, and 61 out of 131, 142, 143, and 79 patients at the four locations, respectively). Reasons for not participating. Table 1. Characteristics of the study populationa Characteristic. Value. Female, n (%) Age, mean±SD, years Educational level, n (%). 228 (62.1%) 44.3±12.4. No education. 1 (0.3%). Primary school. 17 (4.6%). Preparatory vocational secondary education. 83 (22.6%). Secondary vocational education. 145 (39.5%). Senior general secondary education or pre-university education. 39 (10.6%). Higher professional education. 69 (18.8%). Academic higher education. 12 (3.3%). Unknown Length of outpatient treatment, mean±SD, years Primary psychiatric diagnosis (DSM-5 diagnostic criteria), n (%). 1 (0.3%) 1.5±2.3. Bipolar or related disorder. 49 (13.4%). Depressive disorder. 141 (38.4%). Anxiety disorder. 59 (16.1%). Other psychiatric disorder. 84 (22.9%). Not yet diagnosed. 31 (8.4%). Unknown Number of drugs (psychotropic and somatic drugs), mean±SD. 3 (0.8%) 4.6±3.0. a Since outcomes did not differ statistically per setting, patient characteristics are presented for the total population (n=367). DSM Diagnostic and Statistical Manual of Mental Disorders.. 26.

(28) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. included “no time” and “privacy.” We subsequently excluded two patients from analysis because they had withdrawn consent and one because medication reconciliation could not be achieved due to “no show” of the patient. Since outcomes did not statistically differ per setting, we present patient characteristics for the total population (n = 367) in Table 1.. 2. As expected for mood and anxiety disorders, female participants were overrepresented (62.1%). Participants mostly had a low level of education and used a mean of 4.6 drugs.. Number and type of discrepancies We found at least one discrepancy in the medication overview of the outpatient departments for mood and anxiety disorders in 348 patients (94.8%), with a mean±SD of 3.9±2.8 discrepancies per patient (Figure 1). Discrepancy numbers did not differ significantly between outpatient departments (p=0.362) or between academic and community settings (p=0.773). In the medication overviews of the general practitioners and pharmacies, we found at least one discrepancy in 90.2% and 85.8% of the patients, respectively, with corresponding mean±SD values of 2.9±2.1 and 2.2±1.7 discrepancies per patient. Figure 1 shows numbers and types of discrepancies per patient per health care provider. Most discrepancies for each health care provider were omitted drugs, i.e., drugs the patient was regularly taking but that were absent from the health care provider’s records (74.5%, 65.4%, and 63.4% for the outpatient departments, general practitioners, and.              .      . . . . .    .  .  .    Figure 1. Number and type of discrepancies per health care provider. Data are presented for the total population (n=367), as the number of discrepancies did not significantly differ between the four outpatient departments (p=0.362) nor between academic and community settings (p=0.773). No differences in route of administration were found.. 27.

(29) PART I | PROBLEM DEFINITION. pharmacies, respectively). Discrepancies regarding medication (allergies or intolerances excluded) mostly concerned paracetamol (Anatomical Therapeutic Chemical [ATC] code. 2. N02BE; 15.1%), anxiolytic benzodiazepine derivatives (N05BA; 6.4%), and proton pump inhibitors (A02BC; 4.9%) at the outpatient departments; paracetamol (20.8%), anxiolytic benzodiazepine derivatives (7.3%), and propionic acid derivatives (M01AE, e.g., ibuprofen; 6.1%) at the general practitioners; and paracetamol (25.9%), propionic acid derivatives (7.5%), and anxiolytic benzodiazepine derivatives (6.5%) at the pharmacies.. Clinical relevance of discrepancies Figure 2 shows the classification of the discrepancies for their potential to cause patient harm, as assessed by the expert panel. The interrater reliability for judging the clinical relevance (classes 1–3) was moderate (weighted. κ=0.58;. 95%CI 0.53–0.63).. Of the discrepancies at the outpatient departments, 77.2% were unlikely to cause harm (class 1), while 19.9% and 2.8% of the discrepancies were found to potentially cause moderate (class 2) or severe (class 3) discomfort or clinical deterioration, respectively..      .     .           .         . . .    .  .  .    Figure 2. Clinical relevance of discrepancies per health care provider. Discrepancies were classified by the expert panel as unlikely to cause patient harm, having the potential to cause moderate patient harm, and having the potential to cause severe patient harm. Data are presented for the total population (n=367), as the percentage of clinically relevant discrepancies did not significantly differ between the four outpatient departments (p=0.440) or between academic and community settings (p=0.379). The numbers next to the braces indicate the percentage of all patients affected by at least one discrepancy with the potential to cause moderate to severe patient harm.. 28.

(30) Citalopram, mirtazapine. Medication usea. Depressive Venlafaxine, fluticasone disorder nasal spray, ciclosonide inhaler, formoterol inhaler, lithium, lynestrenol, hydrochlorothiazide, lisinopril, montelukast, salbutamol Depressive Ibuprofen, temazepam, disorder venlafaxine, simvastatin, hydrochlorothiazide, mirtazapine, salmeterol/ fluticasone propionate inhaler, desloratadine, lisinopril, dextran 70/ hypromellose eye drops, polyacrylic acid eye gel. Depressive Paroxetine, mirtazapine, disorder zopiclon, citalopram. Anxiety disorder. Primary DSM-5 diagnosis. Esomeprazole, 20 mg/day, was present on the medication overview of the mental health care institution, but the patient was not taking it. Patient was taking ibuprofen, 400 mg as needed about four times a month, which was absent from the medication overview of the mental health care department Patient reported taking paroxetine, 20 mg/day, while the medication overview of the mental health care department stated paroxetine 10 mg/day Patient was taking hydrochlorothiazide, 12.5 mg/ day, which was absent from the medication overview of the mental health care institution. Description of discrepancy. Clinical relevance classb. 1. Extra drug. 2. Omitted drug 2. Difference in dose. Omitted drug 1. Type of discrepancy. Table 2. Examples of classification of discrepancies at mental health care outpatient departments. Yes. Yes. No. No. Consider prescribing an extra drug (restart esomeprazole for prevention of gastric complications as a possible adverse effect of the co-use of ibuprofen and venlafaxine). Consider measuring a somatic parameter (sodium level for diagnosis of possible SIADH as an adverse effect of the co-use of hydrochlorothiazide and venlafaxine). N/A. N/A. Intervention necessary? Type of intervention (supplement). MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. 2. 29.

(31) 30 Patient was taking valproic acid, Omitted drug 3 2000 mg/day, which was absent from the medication overview of the mental health care institution. Description of discrepancy. Clinical relevance classb. Yes. Yes. Yes. Consider replacing a current drug (replace metoprolol with atenolol or another non-lipophilic β-blocking agent, as lipophilic β-blocking agents may induce or worsen depressive symptoms). Consider measuring a somatic parameter (valproic acid serum concentration, as this may be not monitored in this patient and out-of-range valproic acid serum concentrations should be avoided because of the risks of undertreatment (too low) and toxicity (too high)) Re-evaluate total medication use (reconsider the dosages and combinations of psychotropic drugs; evaluate whether patient is taking them as prescribed). Intervention necessary? Type of intervention (supplement). b. a. As assessed during medication reconciliation. Class 1 = discrepancies unlikely to cause patient discomfort or clinical deterioration, class 2 = discrepancies that could potentially cause moderate discomfort or clinical deterioration, class 3 = discrepancies that could potentially cause severe discomfort or clinical deterioration. DSM Diagnostic and Statistical Manual of Mental Disorders; N/A not applicable; SIADH syndrome of inappropriate antidiuretic hormone (ADH) secretion.. Patient reported taking Difference in 3 mirtazapine, 135 mg/day, dose while the medication overview of the mental health care department stated mirtazapine 45 mg/day Depressive Rosuvastatin, metoprolol, Patient was taking metoprolol, Omitted drug 3 disorder omeprazole, acetylic acid, 190 mg/day, which was absent metformin, long-acting from the medication overview of insulin, short-acting insulin the mental health care institution. Levothyroxine, valproic acid, zuclopenthixol. Depressive Duloxetine, mirtazapine, disorder topiramate. Bipolar or related disorder. Medication usea. Type of discrepancy. 2. Primary DSM-5 diagnosis. Table 2. (continued). PART I | PROBLEM DEFINITION.

(32) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. Table 2 shows a few examples of discrepancies and their classification. The clinically relevant discrepancies (class 2 or 3) affected 49.3% of all patients. The percentage of clinically relevant discrepancies did not significantly differ between outpatient departments (p=0.440) or between academic and community settings (p=0.379).. 2. In comparison, 80.5%, 15.6%, and 4.2% of the medication discrepancies from general practitioners and 86.9%, 9.9%, and 3.2% of those from pharmacies were categorized as class 1, 2, and 3 discrepancies, respectively. Class 2 and 3 discrepancies from general practitioners and pharmacies were present in 33.2% and 22.3% of patients, respectively. In 35.4% of all patients, the expert panel considered intervention clinically necessary as a result of a discrepancy at the outpatient department. Initial agreement for judging the intervention necessity was limited (Cohen. κ=0.13;. 95%CI 0.06–0.20), but this was. solved by consensus in all cases. The expert panel suggested a mean±SD of 0.5±0.7 interventions per patient, with “consider measuring a somatic parameter” (38.4%) and “re-evaluate total medication use” (37.8%) most frequently suggested.. DISCUSSION This study indicates that medication reconciliation processes at outpatient departments for mood and anxiety disorders are potentially harmful and in need of improvement. Patients had a mean of 3.9 discrepancies. Moreover, almost 23% of all discrepancies had the potential to cause moderate to severe discomfort or clinical deterioration, affecting almost half of all patients. These figures were lower for general practitioners and community pharmacies. To the best of our knowledge, this is the first study worldwide to evaluate whether mental health care providers are aware of the drugs used by their outpatients when they visit them. Previous studies4,10-14 in non-psychiatric outpatient departments, such as those for haemodialysis or internal medicine, found on average 0.97 to 3.4 discrepancies per patient. A single study6 investigating psychiatric inpatients after admission to a geriatric psychiatric clinic showed discrepancies in 78% of 50 patients, with a median of 2 discrepancies per patient. While these results are in line with the numbers reported for general hospital inpatients5, we found fairly higher discrepancy frequencies in psychiatric outpatients, which indicates that this issue may especially be unknown and problematic in psychiatric outpatient settings. In addition, we observed no differences in the discrepancy risk or associated patient harm between the participating outpatient departments or between academic and community settings. Moreover, we found high numbers and clinical relevance of discrepancies, despite the generally high quality of health care in the Netherlands and in particular a guideline demanding a complete and up-to-date medication overview with every contact between patient and prescriber. Therefore, although replication is warranted, we believe our results apply to psychiatry outpatient departments in general. 31.

(33) PART I | PROBLEM DEFINITION. There are several potential explanations for the higher discrepancy frequencies in psychiatric outpatients. First, these patients often have more than one health care provider. 2. (e.g., psychiatrist and general medical physician). This makes it difficult to keep track of changes in drug regimens made by different prescribers. Indeed, the number of prescribing physicians has been shown to increase medication discrepancies in outpatients.15 We could not evaluate whether there was a difference in number of discrepancies in prescriptions from the outpatient department and outside doctors, as we did not assess prescribers. Second, higher discrepancy numbers may reflect the often-reported suboptimal treatment of somatic conditions in psychiatric outpatients compared to non-psychiatric individuals.16 In addition to receiving a lower quality of medical care, psychiatric patients receive fewer prescriptions for several common drugs for existing medical disorders than individuals without mental illness.16 In contrast to previous studies in non-psychiatric outpatients, we also determined a measure of potential patient harm due to discrepancies. Assessing clinical relevance is essential to determine the impact of discrepancies. Almost 23% of all discrepancies had the potential to cause moderate to severe patient harm, which, importantly, affected almost half of all patients. Since there is currently no valid and reliable method to preidentify patients at risk for discrepancies, attention should not be limited to specific subsets of patients when implementing medication reconciliation. Our results at general practitioners and pharmacies are in line with several studies15,17-21 in population-based samples visiting primary care physicians. Since general practitioners and community pharmacists at least in some countries have the role of gatekeepers with the responsibility of having an adequate overview of the medication information about their patients, the numbers of discrepancies are still surprisingly high. In our opinion, our results demonstrate the need for implementation of a structured medication reconciliation process in clinical practice at psychiatry outpatient departments in order to minimize iatrogenic harm to outpatients. In different hospital settings, implementation of medication reconciliation with patient counselling substantially diminished discrepancies upon both admission and discharge in various countries.5,22-24 In addition, medication reconciliation upon hospital discharge resulted in higher benefits than costs related to the net time investment.25 Furthermore, prescribing safely and conducting adequate somatic monitoring of psychiatric patients as recommended by guidelines are impossible without a complete and up-to-date medication overview.26,27 We therefore developed an innovative care path called Monitoring Outcomes of psychiatric Pharmacotherapy (MOPHAR), which is currently being implemented. In this care path, a nurse conducts medication reconciliation with each patient at every visit to a prescriber. In case of relevant medication discrepancies, MOPHAR will notify the treating psychiatrist. After reconciliation, recommended (somatic) monitoring is performed according to pre-specified protocols per drug used. This information is immediately available in the electronic medical record in summarized form, thus instantly providing mental health care providers with up-to-date information on medication use and monitoring parameters.. 32.

(34) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. We will investigate the impact of this integrated care model regarding the benefits for psychiatric patients.28,29 Strengths of this study are the large population and the conduct of assessments at four different locations. However, a few limitations need to be considered. First, our study. 2. might suffer from performance bias, as collaboration of outpatient departments may have been selective for well-organized settings. This may have resulted in an underestimation of discrepancies, meaning true practice is more alarming still. Second, medication reconciliation involving psychiatric patients may not be as reliable as in other patient populations. However, medication reconciliation through combination of pharmacy records and patient counselling is currently considered the gold standard for determining the actual medication use. It is important to remark that this patient-reported medication use may not reflect the intended or correct use in a pharmacotherapeutic sense. Third, some medication overviews contain theoretical starting and end dates for medication refills that may not correspond with actual use by patients. However, we assume we covered most unintentional discontinuation periods by the 28-day permissible gap for medication refills. Fourth, we did not distinguish between different sources or reasons for discrepancies, such as clinical misunderstandings,. clinical errors, or administrative errors. In clinical. practice, it is important to make this distinction to resolve the discrepancy accordingly. Fifth, the classification method used to assess clinical relevance is, strictly speaking, unvalidated. However, this procedure has been used in previous studies on medication discrepancies and errors.6,8,30 Finally, our measure of clinical relevance concerned potential harm. Because of the cross-sectional design of this study, we were not able to collect evidence for actual adverse effects as a result of the discrepancies. In conclusion, this study shows that outpatients with mood and anxiety disorders may be at substantial risk of medication discrepancies that may be clinically relevant in almost half of the patients. We consider this risk a potentially general problem in the treatment of psychiatric outpatients, for which we suggest that medication reconciliation processes be improved to increase medication safety in psychiatric outpatients.. ACKNOWLEDGEMENTS The authors thank Casper van der Hoeven, PharmD; Eline Hemelt, BSc; Rabab Tarasse, BSc; and Ikrame Mouch, BSc, all from the Department of Clinical Pharmacy, Wilhelmina Hospital Assen, the Netherlands, for their assistance with data collection.. 33.

(35) PART I | PROBLEM DEFINITION. REFERENCES 1.. 2 2.. 3.. 4.. De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry 2011 Feb;10(1):52-77. Karapinar-Carkit F, Borgsteede SD, Zoer J, Smit HJ, Egberts AC, van den Bemt PM. Effect of medication reconciliation with and without patient counseling on the number of pharmaceutical interventions among patients discharged from the hospital. Ann Pharmacother 2009 Jun;43(6):1001-1010. Bedell SE, Jabbour S, Goldberg R, Glaser H, Gobble S, Young-Xu Y, et al. Discrepancies in the use of medications: their extent and predictors in an outpatient practice. Arch Intern Med 2000 Jul 24;160(14):2129-2134.. 5.. Tam VC, Knowles SR, Cornish PL, Fine N, Marchesano R, Etchells EE. Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review. CMAJ 2005 Aug 30;173(5):510-515.. 6.. Prins MC, Drenth-van Maanen AC, Kok RM, Jansen PA. Use of a structured medication history to establish medication use at admission to an old age psychiatric clinic: a prospective observational study. CNS Drugs 2013 Nov;27(11):963-969.. 7.. 8.. 34. Abdullah-Koolmees H, Gardarsdottir H, Stoker LJ, Vuyk J, Egberts TC, Heerdink ER. Prevalence of medication use for somatic disease in institutionalized psychiatric patients. Pharmacopsychiatry 2013 Nov;46(7):274-280.. Caetano PA, Lam JM, Morgan SG. Toward a standard definition and measurement of persistence with drug therapy: Examples from research on statin and antihypertensive utilization. Clin Ther 2006 Sep;28(9):1411-24; discussion 1410. Cornish PL, Knowles SR, Marchesano R, Tam V, Shadowitz S, Juurlink DN, et al. Unintended medication discrepancies at the time of hospital admission. Arch Intern Med 2005 Feb 28;165(4):424-429.. 9.. Roberts C, McNamee R. Assessing the reliability of ordered categorical scales using kappa-type statistics. Stat Methods Med Res 2005 Oct;14(5):493-514.. 10. Manley HJ, Drayer DK, McClaran M, Bender W, Muther RS. Drug record discrepancies in an outpatient electronic medical record: frequency, type, and potential impact on patient care at a hemodialysis center. Pharmacotherapy 2003 Feb;23(2):231-239. 11. Wagner MM, Hogan WR. The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc 1996 May-Jun;3(3):234-244. 12. Peyton L, Ramser K, Hamann G, Patel D, Kuhl D, Sprabery L, et al. Evaluation of medication reconciliation in an ambulatory setting before and after pharmacist intervention. J Am Pharm Assoc (2003) 2010 Jul-Aug;50(4):490-495. 13. Ernst ME, Brown GL, Klepser TB, Kelly MW. Medication discrepancies in an outpatient electronic medical record. Am J Health Syst Pharm 2001 Nov 1;58(21):2072-2075. 14. Chan WW, Mahalingam G, Richardson RM, Fernandes OA, Battistella M. A formal medication reconciliation programme in a haemodialysis unit can identify medication discrepancies and potentially prevent adverse drug events. J Ren Care 2015 Jun;41(2):104-109. 15. Tulner LR, Kuper IM, Frankfort SV, van Campen JP, Koks CH, Brandjes DP, et al. Discrepancies in reported drug use in geriatric outpatients: relevance to adverse events and drug-drug interactions. Am J Geriatr Pharmacother 2009 Apr;7(2):93-104. 16. Mitchell AJ, Lord O, Malone D. Differences in the prescribing of medication for physical disorders in individuals with v. without mental illness: meta-analysis. Br J Psychiatry 2012 Dec;201(6):435-443. 17. Andrus MR. Student pharmacist initiated medication reconciliation in the outpatient setting. Pharm Pract (Granada) 2012 Apr;10(2):78-82..

(36) MEDICATION DISCREPANCIES IN PSYCHIATRIC OUTPATIENTS | CHAPTER 2. 18. Andrus MR, Anderson AD. A retrospective review of student pharmacist medication reconciliation activities in an outpatient family medicine center. Pharm Pract (Granada) 2015 Jan-Mar;13(1):518. 19. Balon J, Thomas SA. Comparison of hospital admission medication lists with primary care physician and outpatient pharmacy lists. J Nurs Scholarsh 2011 Sep;43(3):292-300. 20. Orrico KB. Sources and types of discrepancies between electronic medical records and actual outpatient medication use. J Manag Care Pharm 2008 Sep;14(7):626-631. 21. Varkey P, Cunningham J, Bisping DS. Improving medication reconciliation in the outpatient setting. Jt Comm J Qual Patient Saf 2007 May;33(5):286-292. 22. Vira T, Colquhoun M, Etchells E. Reconcilable differences: correcting medication errors at hospital admission and discharge. Qual Saf Health Care 2006 Apr;15(2):122-126. 23. Bond CA, Raehl CL, Franke T. Clinical pharmacy services, hospital pharmacy staffing, and medication errors in United States hospitals. Pharmacotherapy 2002 Feb;22(2):134-147. 24. Gleason KM, Groszek JM, Sullivan C, Rooney D, Barnard C, Noskin GA. Reconciliation of discrepancies in medication histories and admission orders. of newly hospitalized patients. Am J Health Syst Pharm 2004 Aug 15;61(16):1689-1695. 25. Karapinar-Carkit F, Borgsteede SD, Zoer J, Egberts TC, van den Bemt PM, van Tulder M. Effect of medication reconciliation on medication costs after hospital discharge in relation to hospital pharmacy labor costs. Ann Pharmacother 2012 Mar;46(3):329-338.. 2. 26. Keck PE,Jr. Monitoring pharmacotherapy response, safety, and tolerability to enhance adherence in bipolar disorder. J Clin Psychiatry 2014 May;75(5):e12. 27. Dodd S, Malhi GS, Tiller J, Schweitzer I, Hickie I, Khoo JP, et al. A consensus statement for safety monitoring guidelines of treatments for major depressive disorder. Aust N Z J Psychiatry 2011 Sep;45(9):712-725. 28. Bradford DW, Cunningham NT, Slubicki MN, McDuffie JR, Kilbourne AM, Nagi A, et al. An evidence synthesis of care models to improve general medical outcomes for individuals with serious mental illness: a systematic review. J Clin Psychiatry 2013 Aug;74(8):e754-64. 29. Culpepper L. Improving patient outcomes in depression through guidelineconcordant, measurement-based care. J Clin Psychiatry 2013 Apr;74(4):e07. 30. Kwan Y, Fernandes OA, Nagge JJ, Wong GG, Huh JH, Hurn DA, et al. Pharmacist medication assessments in a surgical preadmission clinic. Arch Intern Med 2007 May 28;167(10):1034-1040.. 35.

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(38) 3. Monitoring of somatic parameters at outpatient departments for mood and anxiety disorders. Mirjam Simoons Hans Mulder Bennard Doornbos Robert A. Schoevers Eric N. van Roon* Henricus G. Ruhé*. * These authors share senior authorship PLoS One 2018; 13(8):e0200520.

(39) PART I | PROBLEM DEFINITION. ABSTRACT Introduction Somatic complications account for the majority of the 13-30 years shortened life expectancy. 3. in psychiatric patients compared to the general population. The study aim was to assess to which extent patients visiting outpatient departments for mood and anxiety disorders were monitored for relevant somatic comorbidities and (adverse) effects of psychotropic drugs - more specifically a) metabolic parameters, b) lithium safety and c) ECGs - during their treatment.. Methods We performed a retrospective clinical records review and cross-sectional analysis to assess the extent of somatic monitoring at four outpatient departments for mood and anxiety disorders in The Netherlands. We consecutively recruited adult patients visiting a participating outpatient department between March and November 2014. The primary outcome was percentage of patients without monitoring measurements. Secondary outcomes were number of measurements per parameter per patient per year and time from start of treatment to first measurement.. Results We included 324 outpatients, of whom 60.2% were female. Most patients were treated for depressive disorders (39.8%), anxiety disorders (16.7%) or bipolar or related disorders (11.7%) and 198 patients (61.1%) used at least one psychotropic drug. For 186 patients (57.4%), no monitoring records were recorded (median treatment period 7.3 months, range 0-55.6). The median number of measurements per parameter per year since the start of outpatient treatment for patients with monitoring measurements was 0.31 (range 0.012.9). The median time to first monitoring measurement per parameter for patients with monitoring measurements was 3.8 months (range 0.0-50.7).. Discussion Somatic monitoring in outpatients with mood and anxiety disorders is not routine clinical practice. Monitoring practices need to be improved to prevent psychiatric outpatients from undetected somatic complications.. 38.

(40) SOMATIC MONITORING IN OUTPATIENTS WITH MOOD AND ANXIETY DISORDERS | CHAPTER 3. INTRODUCTION Patients with a severe mental illness (SMI), including patients with bipolar disorders and major depressive disorder, have a 13-30 year shorter life expectancy compared to the general population.1 The majority (about 60%) of this excess mortality can be explained by somatic co-morbidity like respiratory, cardiovascular, nutritional and/or metabolic diseases.1-4 Several factors may contribute to this increased risk of somatic morbidity. 3. and mortality, such as an unhealthy lifestyle and disparities in health care access that are associated with mental illness.1,5 In addition, the use of psychotropic drugs may cause and/or increase the vulnerability of psychiatric patients to somatic complications due to adverse effects.1,6 In order to detect somatic complications and psychotropic drug-induced adverse effects, several guidelines and consensus documents have suggested to monitor essential somatic parameters as part of routine clinical practice in among others patients with schizophrenia, bipolar disorder and major depressive disorder.7-10 Similarly, guidelines have been published for somatic monitoring during the use of specific classes of psychotropic drugs.7-11 Next to the recognition that serum lithium levels, renal function and thyroid function should be monitored during lithium therapy, more recently metabolic monitoring during antipsychotic therapy has been advocated.7,9,11 For patients treated for major depressive disorder only recently the first consensus document on somatic monitoring has been published in which a number of baseline and antidepressant-specific follow-up tests are recommended, in part depending on patient vulnerability characteristics.8,12 Indeed, for antidepressants debate exists, e.g. regarding the necessity and appropriate frequency of monitoring of the electrocardiogram (ECG), notwithstanding a recent FDA warning.13,14 In contrast to the expected clinical relevance of somatic monitoring of patients with SMI and the availability of the above-mentioned guidelines, several studies have shown poor adherence to these guidelines. For example, monitoring of serum lithium level, renal function and thyroid function in patients with bipolar disorder using lithium in the United Kingdom showed only 30-55% compliance to the available guideline.15 A meta-analysis of 39 studies on metabolic screening in patients with predominantly schizophrenia or related disorders using antipsychotics, showed that routine baseline metabolic screening before start of pharmacotherapy was suboptimal.16 Given the high prevalence of somatic co-morbidities (e.g. metabolic syndrome, pooled prevalence of 32.6% in a large cohort of SMI patients (n=52,678)17), suboptimal monitoring might put patients at considerable risk for iatrogenic harm (i.e. harm resulting from treatment by a health care professional), regardless of the specific psychiatric diagnosis. Metabolic disturbances and other somatic complications are not limited to patients with schizophrenia or patients using antipsychotics. Mood disorders are increasingly treated with combinations of lithium, antipsychotics, mood stabilizers and antidepressants. Therefore, these patients are at risk for somatic complications as well. In this study, we investigated to which extent patients visiting outpatient departments for mood and. 39.

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