# Presentation: 833339

## 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