Selection of a Survey Instrument for a Heart Failure Disease Management Study

1. 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

2. 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?

3. 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

4. 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

5. 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

6. 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)

7. 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?

8. 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

9. 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

10. The Choices • Short Form – 36 or 12 • Minnesota Living with Heart Failure Questionnaire • Chronic Heart Failure Questionnaire • Kansas City Cardiomyopathy Questionnaire

11. 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

12. 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…..

13. 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

14. 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

15. 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

16. 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)

17. 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