Integration Models into Primary Health Care: the Example of Late-life Depression

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Integration Models into Primary Health Care: the Example of Late-life Depression. Benoit H. Mulsant, MD, MS, FRCPC Professor and Vice-Chair Department of Psychiatry University of Toronto Physician in Chief Centre for Addiction and Mental Health. L earning Objectives. - PowerPoint PPT Presentation

Transcript of Integration Models into Primary Health Care: the Example of Late-life Depression

Benoit H. Mulsant, MD, MS, FRCPCProfessor and Vice-ChairDepartment of Psychiatry

University of TorontoPhysician in Chief

Centre for Addiction and Mental Health

Integration Models into Primary Health Care:

the Example of Late-life Depression

At the conclusion of this session, the participants should be able to:

1. Assess the evidence supporting the efficacy of antidepressant medications in the treatment of late-life depression.

2. Assess the risks of antidepressant medications used in the treatment of late-life depression.

3. Maximize the effectiveness of pharmacotherapy when treating a patient with late-life depression in the primary care sector.

Learning Objectives

Treating Late-Life DepressionFighting therapeutic nihilism

One of the few medical conditions in which treatment can make a rapid and dramatic difference in an elderly patient’s level of function

Pinquart, Duberstein, & Lyness

Treatments for later-life depressive conditions: a meta-analytic comparison of pharmacotherapy and psychotherapy

Am J Psych, 163(9):1493-501, 2006

Meta-analysis of 62 placebo-controlled studies (N = 3,921)

Favorable outcomes: Drugs: 66% vs. Placebo: 31%

“Available treatments for depression work,

with effect sizes that are moderate to large…”

Outcome of Usual Care for Depressed PatientsTreated by Well-Trained Psychiatrists

Meyers et al (2002) Archives Gen Psych

• Six psychiatric clinics in Westchester County (USA)

• 165 patients with major depression

• 65% received an antidepressant• 45% received an adequate dose for 4+ weeks

(academic vs. non-academic sites: 53% vs. 36%, p =0.04)

• Remission rate after 3 months: 30%

• Adequate treatment: 3 fold higher likelihood of remission (OR = 3.2; p = 0.04)

Treating Late-Life Depression Closing the Efficacy-Effectiveness Gap

1. Systematic vs. personalized approach

2. Selecting a class and a specific agent

3. Optimal dose

4. Optimal trial duration

5. Management of treatment resistance

1. Argument for a systematic approach (“algorithm”, “clinical pathways”, “stepped care”) vs. an individualized approach (“usual care”)

2. Defining one’s algorithm for late-life depression:

• What is your first-line intervention?• Your second-line intervention?• Your third-line intervention?• How long should each step lasts?• When do you switch? When do you augment?

Outline

A Tale of Two Approaches

Systematic Approach• Based on best evidence or

guidelines• Clinical experience based

on large number of patients• Keeping the course: the

clinician is protected against personal biases, pressures form the patient or family

• Focus is on the patient

Usual Care• Based on fad “du jour”• Little cumulative experience

due to small numbers of patients receiving many different medications

• Ill-advised or ill-timed changes in treatment

• Focus is on the treatment (making decisions is exhausting)

Two Examples of Randomized Comparisons for Stepped-Care for Late-Life Depression:

1. IMPACT (Unutzer et al, JAMA, 2002)

2. PROSPECT (Bruce et al, JAMA, 2004)

Systematic Approach (algorithm, stepped care) vs. Individualized Approach (usual care)

PROSPECT: A Case Study

DEPRESSIONDEPRESSIONSPECIALISTSPECIALIST

Physician Education

Patient & FamilyPsycho-Education

&

Identification of Diagnosis

TREATMENT TREATMENT ALGORITHMALGORITHM

PROSPECT: Treatment Algorithm

Main Features of Treatment Algorithm

• Based on evidence and practice guideline

• Modified for the primary care office

• Use of psychopharmacological and psychosocial interventions

• Psychiatric consultation is offered in complex cases

• Covers acute and continuation/maintenance treatment

• Covers a wide range of syndromes ranging from mild to severe depression

PROSPECT Algorithm (1)

PROSPECT Algorithm (2)

PROSPECT: Results

PROSPECT: Cumulative Probability of Remission

All comparisons: p < 0.001 Alexopoulos et al (2005) Am J Psych

PROSPECT: Probability of Being Treated

All comparisons: p < 0.001 Alexopoulos et al (2009) Am J Psych

Psychoeducation is Essential for Successful Antidepressant Treatment

• Address the patient’s personal illness model

• It takes 2-6 weeks to show beneficial effects

• Side effects occur right away

• Patients must be encouraged and supported to be take dose regularly as prescribed

• Reassure that side effects usually wear off

• Need for continuation and maintenance treatment

Mulsant et al (2003) CNS Spectrum; 8: 27-34

Response Rates in 13 Studies of Treatment-Resistant Late-Life Depression

Cooper et al (2011) Am J Psych; 168: 681-688

1. Argument for a systematic approach (“algorithm”, “clinical pathways”, “stepped care”) vs. an individualized approach (“usual care”)

2. Defining one’s algorithm for late-life depression:

• What is your first-line intervention?• Your second-line intervention?• Your third-line intervention?• How long should each step lasts?• When do you switch? When do you augment?

Outline

Efficacy

Tolerability

Safety

Cost

Possible Criteria for Choosing an Antidepressants for an Older Adult

Response Rates (%) in Eight Published Randomized Placebo-Controlled Trials

0%

10%

20%

30%

40%

50%

60%

70%

80%

1: Fluoxetine 2: Sertraline 3: ParoxetineIR Paroxetine

CR

4: Citalopram 5: FluoxetineEscitalopram

6: FluoxetineVenlafaxine

IR

7:Escitalopram

8: Duloxetine

Placebo

1. Tollefson et al (1995) Int Psychogeriatrics; 7:89–104 – 2. Schneider et al (2003) Am J Psych; 160:1277-85 – 3. Rapaport et al (2003) J Clin Psych; 64:1065–74 – 4. Roose et al (2004) Am J Psych; 161:2050-9 – 5. Kasper et al (2005) Am J Geri Psych; 13:884-91 – 6. Schatzberg & Roose (2006) Am J Geri Psych; 14:361-70 – 7. Bose et al. (2008) Am J Geri Psych; 16:14-20 – 8. Raskin et al (2007) Am J Psychiatry; 164:900-9

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Fluoxetine in the treatment of late-life depression Marked site variability in remission rates

Small et al (1996) Int J Geri Psych; 11:1089-95

Citalopram in the treatment of depression in the very old Marked site variability in response and remission rates

Roose et al (2004) Am J Psych; 161:2050-9

Efficacy

Tolerability

Safety

Cost

Possible Criteria for Choosing an Antidepressants for an Older Adult

Discontinuation Rates (%) Attributed to Adverse Effects in Eight RpCTs

0%

5%

10%

15%

20%

25%

30%

1: Fluo. 2: Sertraline 3:Paroxetine

IRParoxetine

CR

4: Cit. 5: Fluo.Escit.

6: Fluo.Venlafaxine

IR

7: Escit. 8:Duloxetine

Placebo

3-DColumn5

1. Tollefson et al (1995) Int Psychogeriatr; 7:89–104 – 2. Schneider et al (2003) Am J Psych; 160:1277-85 – 3. Rapaport et al (2003) J Clin Psych; 64:1065–74 – 4. Roose et al (2004) Am J Psych; 161:2050-9 – 5. Kasper et al (2005) Am J Geri Psych;13:884-91 – 6. Schatzberg & Roose (2006). Am J Geriatr Psychiatry; 14:361370 - 7. Bose et al. (2008) Am J Geriatr Psychiatry; 16:14-20 –8. Raskin et al (2007) Am J Psychiatry; 164:900-9

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Overall Discontinuation Rates (%) in Eight RpCTs

0%

5%

10%

15%

20%

25%

30%

35%

40%

1: Fluo. 2: Sertraline 3:Paroxetine

IRParoxetine

CR

4: Cit. 5: Fluo.Escit.

6: Fluo.Venlafaxine

IR

7. Escit. 8.Duloxetine

Placebo

1. Tollefson et al (1995) Int Psychogeriatr; 7:89–104 – 2. Schneider et al (2003) Am J Psych; 160:1277-85 – 3. Rapaport et al (2003) J Clin Psych; 64:1065–74 – 4. Roose et al (2004) Am J Psych; 161:2050-9 – 5. Kasper et al (2005) Am J Geri Psych;13:884-91 – 6. Schatzberg & Roose (2006). Am J Geri Psych; 14:361-70 -- 7. Bose et al. (2008) Am J Geri Psych; 16:14-20 – 8. Raskin et al (2007) Am J Psychiatry; 164:900-9

*

Role of newer antidepressants?• Escitalopram • Desvenlafaxine • Duloxetine

Role of atypical antipsychotics?• Quetiapine XR •Aripiprazole

New Safety Concerns• Venlafaxine • Citalopram & Escitalopram• Atypical antipsychotics

What is new since 2001?

Response Rates (%): Older vs. Newer Medications

0%

10%

20%

30%

40%

50%

60%

70%

80%

1: Fluoxetine 2: Sertraline 3: Paroxetine IRParoxetine CR

5: FluoxetineEscitalopram

7: Escitalopram 8: Duloxetine 9: QuetiapineXR

Placebo

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*

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1. Tollefson et al (1995) Int Psychogeriatrics; 7:89–104 – 2. Schneider et al (2003) Am J Psych; 160:1277-85 – 3. Rapaport et al (2003) J Clin Psych; 64:1065–74 – 5. Kasper et al (2005) Am J Geri Psych;13:884-91 – 7. Bose et al. (2008) Am J Geri Psych; 16:14-20 – 8. Raskin et al (2007) Am J Psych; 164:900-9 – 9. Katila et al (2012) Am J Geri Psych

Discontinuation Rates Attributed to Adverse Effects: Older vs. Newer Medications

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

1: Fluoxetine 2: Sertraline 3: ParoxetineIR Paroxetine

CR

5: Fluo. Escit. 7:Escitalopram

8: Duloxetine 9: QuetiapineXR

Placebo

1. Tollefson et al (1995) Int Psychogeriatrics; 7:89–104 – 2. Schneider et al (2003) Am J Psych; 160:1277-85 – 3. Rapaport et al (2003) J Clin Psych; 64:1065–74 – 5. Kasper et al (2005) Am J Geri Psych;13:884-91 – 7. Bose et al. (2008) Am J Geri Psych; 16:14-20 – 8. Raskin et al (2007) Am J Psych; 164:900-9 – 9. Katila et al (2012) Am J Geri Psych

Role of newer antidepressants?• Escitalopram • Desvenlafaxine • Duloxetine

Role of atypical antipsychotics?• Quetiapine •Aripiprazole

New Safety Concerns• Venlafaxine • Citalopram & Escitalopram• Atypical antipsychotics

What is new since 2001?

Ray WA et al (2009) New England Journal of Medicine; 360:225-35

Atypical Antipsychotics and Risk of Sudden Cardiac Death Among Patients of All Age

Role of newer antidepressants?• Escitalopram • Desvenlafaxine • Duloxetine

Role of atypical antipsychotics?• Quetiapine •Aripiprazole

New Safety Concerns• Venlafaxine • Citalopram & Escitalopram• Atypical antipsychotics

What is new since 2001?

Antidepressants for the Older AdultPotential Safety Concerns

• Drug-drug interactions1

• Hyponatremia2

• Falls3,4

• Hip fractures5,6

• GI bleeds7

• Cardiovascular effects,8,9 • Cognitive impairment10,11,12

• Suicide13

• Bone metabolism14, 15

1. Mulsant & Pollock, BG (2004). American Psychiatric Publishing Textbook of Geriatric Psychiatry, 3rd Edition – 2. Fabian et al (2004) Arch Int Med; 164:327-32 – 3. Joo et al (2002) J Clin Psych; 63:936-41 – 4. Thapa et al (1998) NEJM; 339:875-82 – 5. Liu et al (1998) Lancet;351:1303-7 – 6. Richards et al (2007) Arch Int Med; 167:188-95 – 7. Yuan et al (2006) Am J Med; 119:719-27 – 8. Johnson et al (2006) Am J Geri Psych; 14:796-802 – 9. Oslin et al (2003) J Clin Psych; 64:875–882 – 10. Furlan et al (2001) Am J Geri Psych; 9:429-38 – 11. Ridout et al (2003) Hum Psychopharm; 18:261 – 12. Wingen et al (2005) J Clin Psych; 66:436-43 – 13. Jurlink et al (2006) Am J Psych;163:813-21 – 14. Diem et al (2007) Arch Intern Med; 167:1240-5 – 15: Richards et al (2007) Arch Intern Med 167:188–94

Discontinuation due to Adverse Events:

51% augmentation v. 8% switching

Falls:

42% augmentation v. 24% switching

Whyte et al (2004) J. Clin Psych; 65: 1634-1641

Augmentation v. SwitchingTolerability and Safety

Conclusions: Late-Life Depression

• Can be effectively treated

• Success requires a systematic approach

• Success requires persistence

• DO NOT GIVE UP!

Questions and Discussion