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:

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