Selection of a Survey Instrument for a Heart Failure Disease

Management Study

Lee R. Goldberg, MD, MPH

Heart Failure/Transplant program

University of Pennsylvania

June 9, 2005

Start with defining the question• Exactly what question do

you want to answer? – know your hypothesis

• Is your question disease state specific or general quality of life?

• Is the survey measurement a primary or secondary end-point of your study?

• What changes do you expect?

Our Study

• A study comparing 3 different care models of outpatient heart failure care– Usual care– Electronic monitoring (scale, BP cuff,

questions, +/- glucometer) with nurse case management

– Electronic monitoring with self-management – interactive voice response system

Our Primary Hypotheses

• Both electronic disease management strategies will be superior to usual care in reducing hospitalizations

• The patient self-management electronic disease management arm will not be inferior to nurse case management disease management arm

Our Secondary Hypotheses

• “Quality of life” will be improved for the patients in the electronic disease management arms as compared to usual care

• “Quality of life” will not be different between the two electronic disease management arms

How should we measure “Quality of Life”

• Disease specific or generic instrument– Will our intervention be expected to change QOL in

general or will it influence HF specific QOL to a greater extent? (Increased sensitivity)

– Will we miss some important “dimensions” or unintended impacts if we select a specific instrument?

– What have other researchers collected? (Will we be able to compare our study to others?)

– What elements of QOL are important (meaningful) to patients, their families and their clinicians? (Relevance)

What do we mean exactly by “Quality of Life”?

• Patient perspective– Physical– Emotional– Social– Ability to self-care

• How do we expect our technology interventions to impact these domains?

Thinking about it

• We were interested in– Physical functioning – heart failure impacts

exercise capacity– Symptom stability over time– “Self-efficacy” – essentially self-care – can

they manage their disease? Do they learn?– Social functioning

Our Population

• Adult heart failure population (NYHA II-IV)– Urban, suburban and rural

– Diverse racial groups

– Mean age about 55 to 60 years old

– About 50% women

– English or Spanish speaking

• We need an instrument that has been validated in this population

The Choices

• Short Form – 36 or 12

• Minnesota Living with Heart Failure Questionnaire

• Chronic Heart Failure Questionnaire

• Kansas City Cardiomyopathy Questionnaire

How did we choose?

• Review of previous studies

• Review of our experience with instruments

• Review of studies of instruments in our expected population (validity, responsiveness, reliability)

• Ease of administration and scoring

• Patient factors – elderly, dyspneic, fatigued

Why not do them all?

• Too many surveys “fatigue” patients

• Know your hypothesis• Too many surveys

make study logistics a challenge

• If you ask enough questions and run enough tests you are likely to find something…..

Our Conclusions

• Disease specific (heart failure)• Changes in physical functioning are what

we believe are the most important in this population

• Our technology arms should improve self-efficacy – we need to specifically test this

• Symptom stability is also important• We need repeated measures

Kansas City Cardiomyopathy Questionnaire

• Heart failure disease specific questionnaire• 23 items evaluating

– Physical limitation

– Symptoms

– Symptom stability

– Self-efficacy

– Heart failure influence on general quality of life

– Social limitation

– KCCQ functional status score

– KCCQ clinical summary score

KCCQ - Validation• Reliability cohort

– Mean age 64 years– 69% men– NYHA class 2.0 ± 0.59

• Responsiveness– Mean age 68 years– 62% men– NYHA class 3.3 ± 0.46

• Validation– Mean age 64.3 years– 70% men– All NYHA classes represented

Green CP, Porter CB, Bresnahan DR, Spertus JA. JACC 2000;35:1245-55

Unit of Measure

• How frequently to measure?– Baseline and end of study

– Intermediate time points

• Avoid too frequent measurements but intermediate time points can add information about the impact of an intervention over time

• We chose every 3 months (same as the validation studies for the KCCQ)

Conclusions• Selecting an appropriate

survey instrument requires a clear understanding of the project and anticipated outcomes

• Be sure that the instrument is appropriate for the population you are studying and has been validated

• Incorporate the instrument into both the protocol and the planned statistical analysis