A PATIENT-CENTERED APPROACH TO IMPROVE SCREENING 

FOR THE METABOLIC SIDE EFFECTS OF SECONDGENERATION 

ANTIPSYCHOTIC (SGA) MEDICATIONS 

Julie Kreyenbuhl, Pharm.D., Ph.D.

Assistant Director, Research Core, VA Capitol Healthcare Network (VISN 5) 

Mental Illness Research, Education, and Clinical Center (MIRECC) 

Associate Professor, University of Maryland School of Medicine, Department of 

Psychiatry, Division of Services Research 
POLL QUESTION 

• What best describes your position at the VA? 

– Researcher 

– Clinician 

– Administrator/policymaker 

– Other 

2 
POLL QUESTION 

• How familiar are you with the recommended 

screenings for the metabolic side effects of SGA 

medications? 

– Very familiar 

– Moderately familiar 

– Somewhat familiar 

– A little bit familiar 

– Not familiar at all 

3 
BACKGROUND 

• Individuals with serious mental illness (SMI) have 

significantly reduced life expectancy (~25 years of potential 

life lost). 

• Cardiovascular disease (CVD) and Type 2 diabetes are 

major contributors to morbidity and premature 

mortality in this population. 

• In addition to lifestyle factors, certain medication side 

effects likely contribute to CVD risk. 

4 
BACKGROUND 

• Metabolic side effects of second-generation antipsychotic (SGA) 

medications may contribute to CVD risk 

– Weight gain 

– Hyperglycemia 

– Hyperlipidemia 

• Differential metabolic risk across SGAs 

– High risk: Clozapine, olanzapine 

– Intermediate risk: Quetiapine, risperidone/paliperidone 

– Low risk/neutral: Aripiprazole, ziprasidone, asenapine (?), 

iloperidone (?), lurasidone (?) 

5 Lieberman JA et al. N Engl J Med. 2005;353:1209-1223. 

ADA et al. Diabetes Care 2004; 27(2):596-601.
FDA WARNING ABOUT METABOLIC SIDE EFFECTS OF SGAS 

June 2004 

 FDA has asked manufacturers of all atypical antipsychotic drugs to add a new warning to the drugs' labels about 

the increased risk of hyperglycemia and diabetes. Atypical antipsychotics include: Clozaril ® (clozapine), 

Risperdal ® (risperidone), Zyprexa ® (olanzapine), Seroquel ® (quetiapine), Geodon ® (ziprasidone), and Abilify ® 

(aripiprazole). 

Epidemiologic studies suggest that the risk of hyperglycemia and diabetes is increased in patients taking 

Clozaril, Risperdal, Zyprexa and Seroquel, although the relationship isn't completely understood. In some cases, 

the hyperglycemia was extreme and associated with ketoacidosis or hyperosmolar coma or death. Geodon and 

Abilify weren't marketed at the time the study was conducted. 

For some patients, the hyperglycemia resolved when the drug was discontinued, but others required continuing 

treatment for their diabetes even after they stopped taking the drug. 

The warning recommends that patients with diabetes who are started on atypical antipsychotics be monitored 

regularly for worsening of glucose control. 

Patients starting on these drugs who have diabetes risk factors, such as obesity or a family history of diabetes, 

should have fasting blood glucose testing at the start of treatment and periodically thereafter. 

And all patients treated with atypical antipsychotics should be monitored for symptoms of hyperglycemia, such 

as excessive thirst, excessive appetite, frequent urination, or weakness. If they develop symptoms of 

hyperglycemia while on these drugs, they should have a fasting blood glucose test. 

6 http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/psn/transcript.cfm?show=28#4. 
RECOMMENDATIONS FOR MONITORING FOR METABOLIC SIDE 

EFFECTS OF SGAS 

Screening 

parameter 

Mt. Sinai Recommendations ADA et al., Recommendations 

Weight and 

height/BMI/waist 

circumference 

Measure BMI before medication initiation or change 

and at every visit for the first 6 months after 

medication initiation or change. When the patient’s 

weight stabilizes, monitor at least quarterly and more 

often if the patient is overweight. BMI monitoring 

should be supplemented by measurement and 

recording of the patient’s waist circumference. 

Measure weight/BMI before medication 

initiation or change, at 4, 8, and 12 weeks after 

medication initiation or change, and quarterly 

thereafter. Measure waist circumference 

before medication initiation or change and 

annually thereafter. 

Fasting blood 

glucose (FPG) or 

HbA1c 

Measure FPG or HBA1c before medication initiation 

or change. In the absence of symptoms of diabetes 

or significant weight gain, OR for patients with 

significant risk factors for diabetes, monitor 4 

months later and annually thereafter. In patients who 

are gaining weight, monitor every 4 months. 

Measure FPG before medication initiation or 

change, 12 weeks after medication initiation or 

change, and annually thereafter. Monitor more 

frequently in patients with a higher baseline 

risk for diabetes. 

Lipid panel In patients with normal LDL levels, obtain lipid profile 

at least once every 2 years. In patients with LDL 

levels > 130mg/dl, monitor lipids every 6 months. 

Obtain lipid profile before medication initiation 

or change, 12 weeks after medication initiation 

or change, and every 5 years thereafter (more 

frequently if clinically indicated). 

Blood pressure No recommendation. Measure blood pressure before medication 

initiation or change, 12 weeks after medication 

initiation or change, and annually thereafter. 

7 

Marder SR et al. Am J Psychiatry 2004; 161:1334-49

ADA et al. Diabetes Care 2004; 27(2):596-601. 
BACKGROUND 

Extent of Monitoring for Metabolic Side Effects of SGAs 

Type of 

monitoring 

Baseline studies Post-guideline 

studies 

Prepost 

Number change 

of 

studies 

Rate of testing, % 

(95% CI) 

Number 

of 

studies 

Rate of testing, 

% (95% CI) 

Weight 19 47.9 (32.4-63.7) 3 75.9 (37.3-98.7) 28.0

Blood pressure 14 69.8 (50.9-85.8) 3 75.2 (45.6-95.5) 5.4

Glucose 30 44.3 (36.3-52.4) 7 56.1 (43.4-68.3) 11.8 

Lipids 23 22.2 (16.4-28.7) 7 37.2 (23.7-51.9) 15.0

8 

Mitchell et al. Psychological Medicine 2012; 42:125-147.
BACKGROUND 

• Extent of monitoring for metabolic side effects of SGAs 

is inadequate and was largely unaffected by release of 

FDA warning and guidelines. 

• Certain system- and provider-level efforts to enhance 

metabolic screening have been implemented in the VA, 

but 

– Have not been empirically evaluated; and 

– May have limited effectiveness without engaging the patient. 

9 
CONCEPTUAL FRAMEWORK 

Patient-Centered Care 

• Key dimensions 

– Active participation by patients in their own care; 

– Quality of clinician-patient relationship or interaction (therapeutic 

alliance). 

• Linked to positive outcomes 

– Increased adherence to treatment; 

– Improved disease management; 

– Improved health status; 

– Greater patient satisfaction. 

10

Mead and Bower. Soc Sci Med. 2000; 51: 1087-1110. 

Roter et al. Arch Intern Med. 1995;155: 1877–84.

Stewart MA. CMAJ. 1995;152: 1423–1433. 
RATIONALE 

Consumer Health Informatics (CHI) 

• VA is a leader in patient-centered CHI 

– Electronic medical record since 1997

– MyHealtheVet 

– PTSD Coach App! 

11
BACKGROUND 

Cognitive Impairments in Individuals with SMI 

• Standard website design approaches are inappropriate for individuals with 

cognitive impairments, e.g., 

– Attention 

– Memory 

– Visual-spatial processing 

– Information processing 

– Psychomotor skills 

– Executive functioning 

• Necessary website modifications include 

– Use of shallow hierarchies; 

– Use of memory aids to support navigation; 

– Minimize need to think abstractly; 

– Avoid distracting decorative features; 

– Use of brief, but explicit labels. 

12 Rotondi AJ et al. Psychological Services 2007; 4: 202-224.

Rotondi AJ et al. Rehabilitation Psychology 2005;50:325-336. 
SPECIFIC AIMS 

• To determine the effects of exposure to a patient-centered 

computerized tool, relative to enhanced treatment as 

usual (e-TAU), on 

– Rates of screening by providers for the metabolic side effects of 

SGAs; 

– Recognition by providers of metabolic abnormalities associated 

with SGAs. 

– Self-efficacy in communicating with prescriber and preferences for 

participating in decision-making around screening. 

– Patterns of patient-provider communication related to screening 

for metabolic side effects of SGAs. 

13
METHODS 

• Study design: Randomized, controlled trial. 

– Patient-centered computerized tool (n=120) 

– Enhanced treatment as usual (e-TAU) (n=120) 

• Study sites: Two outpatient mental health clinics in VAMHCS. 

• Patient inclusion criteria: 

– Diagnosis (Psychotic disorder , bipolar disorder, major depression, PTSD) 

– Prescribed any SGA by a psychiatrist or NP in the VAMHCS 

– At least 2 visits with prescribing MD/NP in past year 

– Ability to read at a 4th grade reading level 

– Agreement by prescribing MD/NP 

• Patient is clinically stable to participate in study; 

• To have one visit with participant audio taped. 

14
KEY ELEMENTS OF THE INTERVENTION 

15
KEY ELEMENTS OF THE INTERVENTION 

16
KEY ELEMENTS OF THE INTERVENTION 

17
KEY ELEMENTS OF THE INTERVENTION

18
KEY ELEMENTS OF THE INTERVENTION

19
KEY ELEMENTS OF THE INTERVENTION 

20
KEY ELEMENTS OF THE INTERVENTION

21
KEY ELEMENTS OF THE INTERVENTION 

22
KEY ELEMENTS OF THE INTERVENTION

23
KEY ELEMENTS OF THE INTERVENTION

24
KEY ELEMENTS OF THE INTERVENTION 

25
THE COMPARISON CONDITION (e-TAU) 

26
METHODS 

Exposure to the Intervention/Comparison Condition 

• Location: Private room adjacent to prescriber’s waiting 

room. 

• Research staff assistance: Minimal. 

• Timing: Immediately prior to outpatient mental health 

visits with prescribing MD/NP. 

• Frequency: Up to 3 times, at least 4 months apart, over 

the one-year study period. 

27
RESULTS 

Enrollment of SGA Prescribers 

• All eligible SGA prescribers (psychiatrists/nurse 

practitioners) agreed to participate 

– N= 15 at Baltimore VAMC 

– N= 6 at Perry Point VAMC 

• Prescribers consented to have first research visit with 

any enrolled patient audio taped 

28
RESULTS 

Enrollment of Veterans (Goal: N=240) 

29

Eligible and contacted (N=630) 

Responded (N=431; 68%) Refused (N=191; 44%) 

Consented and randomized (N=240; 56%) 

Computer 

N=119 

e-TAU 

N=121 

Withdrawn 

N=13 (11%) 

Withdrawn 

N=11 (9%) 

RV 1 (N=115; 95%) 

RV 2 (N=99; 82%) 

RV 3 (N=61; 50%) 

12 m FU (N=96; 79%) 

RV 1 (N=113; 95%) 

RV 2 (N=95; 80%) 

RV 3 (N=53; 45%) 

12 m FU (N=86; 72%) 
RESULTS 

Demographic and Clinical Characteristics (n=240) 

Characteristic Mean (+ S.D.)/ % 

Demographics

Age 

Male gender 

Non-white race 

Unsupervised housing 

Ever married 

> 12 years education 

Working for pay 

Military service-connected benefits 

Other disability benefits 

54 (+9) years 

89%

53%

85%

68%

56%

20%

56%

43%

Primary psychiatric diagnosis

Psychotic disorder 

Bipolar disorder 

Major depression 

PTSD (only) 

BASIS-24 psychiatric symptoms*

Overall 

Psychosis 

Emotional lability 

33%

32%

23%

12%

1.3 (+ 0.7; range: 0-3.4)

1.0 (+1.0; range: 0-3.8)

1.8 (+1.1; range: 0-4.0)

30 * Items rated on a 5-point scale (0-4), with higher scale scores indicating greater symptom frequency/severity. 
RESULTS 

Second-Generation Antipsychotics Prescribed at Baseline (n=240) 

31
BASELINE PREFERENCES FOR OBTAINING SGA METABOLIC 

SCREENING (N=240) 

32
BASELINE PREFERENCES FOR OBTAINING SGA METABOLIC 

SCREENING (N=240) 

33
BASELINE PREFERENCES FOR OBTAINING SGA METABOLIC 

SCREENING (N=240) 

34
BASELINE SELF-EFFICACY IN COMMUNICATING WITH CLINICIANS ABOUT METABOLIC SIDE 

EFFECTS OF SGAS (N=240) 

35 Not at all confident Totally Confident 
BASELINE PREFERENCES FOR PARTICIPATING IN DECISION-MAKING AROUND 

METABOLIC SCREENING (N=240) 

Strongly Agree 36
37

PARTICIPANTS’ NEUROCOGNITIVE FUNCTIONING (N=240)

Neurocognitive 

domain1

SGA study 

(n=240) 

Comparison groups2

Schizophrenia 

(n=56) 

Bipolar 

disorder 

(n=60) 

Healthy 

control 

(n=312) 

Immediate Memory 84.4 + 17.0 72.8 + 14.5 82.6 + 15.5 92.6 + 13.2

Language 92.6 + 8.2 77.6 + 16.6 89.6 + 15.5 97.1 + 16.5

Attention 86.7 + 17.1 76.6 + 16.6 84.6 + 17.1 98.3 + 16.0

1 As assessed by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). 

2 From Dickerson et al., Journal of Nervous and Mental Disease 2007; 195:566-571.
SPECIFIC AIMS 

• To determine the effects of exposure to a patient-centered 

computerized tool, relative to enhanced treatment as 

usual (e-TAU), on 

– Rates of screening by providers for the metabolic side effects of 

SGAs. 

– Recognition by providers of metabolic abnormalities associated 

with SGAs. 

– Self-efficacy in communicating with prescriber and preferences for 

participating in decision-making around screening. 

– Patterns of patient-provider communication related to screening 

for metabolic side effects of SGAs. 

38
THE ROTER INTERACTION ANALYSIS SYSTEM (RIAS) 

• Communication coding system developed by D. Roter, M.P.H., 

Dr.PH. 

• Applied to audio- or video taped patient-clinician interactions in 

hundreds of studies worldwide. 

– Not widely used in psychiatric settings. 

• Assigns each complete thought (‘utterance’) to one of 41 

categories of communication representing, e.g., 

– Communication processes e.g., giving information, asking questions 

by patient vs. clinician; 

– Affective or tonal quality of interaction; 

– ‘Patient-centeredness’, ‘communication control’.

39
THE ROTER INTERACTION ANALYSIS SYSTEM (RIAS) 

• Coding specific to this study 

– Any mention of metabolic parameters by patient or clinician; 

– Who first initiated discussion of metabolic parameters; 

– Who first initiated discussion of metabolic screenings (past, future); 

– Extent of talk about metabolic parameters/screening during visit; 

– Any mention of report, list, pamphlet, etc. 

40
RESULTS 

Audio Recording of Research Visit 1 (RV 1) 

41

Enrolled (N=240) 

Computer 

N=119 

e-TAU 

N=121 

RV 1 Recorded 

N=86 (72%)

RV 1 Recorded 

N=89 (74%) 

RV 1 Not Recorded (N=32; 26%)

Recording error (N=9) 

Patient refused (N=11) 

Prescriber refused (N=0) 

Family present (N=3) 

Other provider present (N=3) 

No RV 1 (N=6)

RV 1 Not Recorded (N=33 (28%)

Recording error (N=10) 

Patient refused (N=8) 

Prescriber refused (N=2) 

Family present (N=5) 

Other provider present (N=2) 

No RV 1 (N=6)
RESULTS (N=175) 

Mentions of Metabolic Parameters/Report 

Topic mentioned Computer 

(N=86) 

e-TAU 

(N=89)

P value 

%, Weight 77% 67% 0.169 

%, Blood pressure 50% 48% 0.824 

%, Blood sugar 66% 74% 0.254 

%, LDL cholesterol 34% 15% 0.003 

%, HDL cholesterol 37% 14% <0.001 

%, Triglycerides 33% 17% 0.016 

%, Report, list, pamphlet, etc. 49% 20% < 0.001 

42
RESULTS 

First Mention of Metabolic Parameters/Screening by Prescriber 

Topic mentioned Computer 

(N=86) 

e-TAU 

(N=89) 

P value 

Any parameter 17/25 (68%) 16/18 (89%) 0.862

Weight 40/66 (61%) 40/60 (67%) 1.00

Blood pressure 28/43 (65%) 26/43 (61%) 0.786

Blood sugar 44/57 (77%) 53/65 (82%) 0.361

LDL cholesterol 23/29 (79%) 11/13 (85%) 0.040

HDL cholesterol 20/31 (65%) 11/12 (92%) 0.106

Triglycerides 23/28 (82%) 13/15 (87%) 0.100

Past metabolic screenings 52/73 (71%) 51/63 (81%) 0.922

Future metabolic screenings 7/8 (88%) 4/9 (45%) 0.366

43
RESULTS 

First Mention of Metabolic Parameters/Screening by Patient 

Topic mentioned Computer 

(N=86) 

e-TAU 

(N=89) 

P value 

Any parameter 8/25 (32%) 2/18 (11%) 0.058

Weight 26/66 (39%) 20/60 (33%) 0.376

Blood pressure 15/43 (35%) 17/43 (40%) 0.724

Blood sugar 13/57 (23%) 12/65 (18%) 0.842

LDL cholesterol 6/29 (21%) 2/13 (15%) 0.157

HDL cholesterol 11/31 (35%) 1/12 (8%) 0.004

Triglycerides 5/28 (18%) 2/15 (13%) 0.257

Past metabolic screenings 21/73 (29%) 12/63 (19%) 0.117

Future metabolic screenings 1/8 (12%) 5/9 (55%) 0.103

44
CONCLUSIONS 

• At baseline, veterans with SMI prescribed SGAs expressed high 

levels of 

– Interest in getting screened for the metabolic side effects of SGAs; 

– Confidence in asking their prescriber about getting screened; 

– Interest in obtaining the results of screenings and discussing them with 

their prescriber. 

• Veterans who had viewed a computer program about the 

metabolic side effects of SGAs had conversations with their SGA 

prescribers 

– Consisting of more talk about LDL and HDL cholesterol and triglycerides; 

– Including more mentions of a report or pamphlet about metabolic 

screenings. 

45
CONCLUSIONS 

• Feedback from participants in computer condition has been 

positive, particularly around 

– Education about metabolic side effects. 

– Provision of personalized screening status/results. 

• Potential next steps 

– Examine responses of prescribers (and patients) to findings of metabolic 

screenings. 

– Consider more widespread implementation of the computerized 

intervention in VA. 

46
ACKNOWLEDGEMENTS 

Funding for the study

VA HSR&D Merit Award (IIR 07-256-1)

Co-investigators

Lisa B. Dixon, M.D., M.P.H. 

Armando J. Rotondi, Ph.D. 

Clayton H. Brown, Ph.D. 

Deborah R. Medoff, Ph.D. 

Debra L. Roter, M.P.H., Dr.PH. 

Research Staff

Stephanie Tapscott 

Mary Brighid Walsh 

Elana Schwartz 

Wendy Potts 
CONTACT INFORMATION 

Julie.Kreyenbuhl@va.gov

410-637-1864