OBJECTIVE: To use triangulation methodology to better understand clinically important differences (CIDs) in the health-related quality of life (HRQoL) of patients with heart disease. DATA SOURCES/STUDY SETTING: We used three information sources: a nine-member expert panel, 656 primary care outpatients with coronary artery disease (CAD) and/or congestive heart failure (CHF), and the 46 primary care physicians (PCPs) treating these outpatients. From them, we derived CIDs for the Modified Chronic Heart Failure Questionnaire (CHQ) and the Medical Outcomes Study Short Form 36-Item Health Status Survey, Version 2 (SF-36). STUDY DESIGN: The expert physician panel employed Delphi and consensus methods to obtain CIDs. The outpatients received bimonthly HRQoL interviews for 1 year that included the CHQ and SF-36, as well as retrospective assessments of HRQoL changes. Their PCPs assessed changes in the patient's condition at follow-up clinic visits that were linked to HRQoL assessments to determine change over time. DATA COLLECTION/EXTRACTION METHODS: Patient- and PCP-assessed changes were categorized as trivial (no change), small, moderate, or large improvements or declines. Moderate or large changes in HRQoL reflect the added risk or investment associated with some treatment modifications. Estimates for each categorization were calculated by finding the mean change scores within anchored change classifications. PRINCIPAL FINDINGS: The small CID for the CHQ domains was consistently one to two points using the patient-assessed change categorizations, but small CIDs varied greatly for the SF-36. PCP-assessed changes differed substantially from patient estimates for both the CHQ and SF-36, while the panel-derived estimates were generally larger than those derived from patients. CONCLUSIONS: Triangulation methodology provides a framework for securing a deeper understanding of each informant group's perspective on CIDs for these patient-reported outcome measures. These results demonstrate little consensus and suggest that the derived estimates depend on the rater and assessment methodology.
OBJECTIVE: To use triangulation methodology to better understand clinically important differences (CIDs) in the health-related quality of life (HRQoL) of patients with heart disease. DATA SOURCES/STUDY SETTING: We used three information sources: a nine-member expert panel, 656 primary care outpatients with coronary artery disease (CAD) and/or congestive heart failure (CHF), and the 46 primary care physicians (PCPs) treating these outpatients. From them, we derived CIDs for the Modified Chronic Heart Failure Questionnaire (CHQ) and the Medical Outcomes Study Short Form 36-Item Health Status Survey, Version 2 (SF-36). STUDY DESIGN: The expert physician panel employed Delphi and consensus methods to obtain CIDs. The outpatients received bimonthly HRQoL interviews for 1 year that included the CHQ and SF-36, as well as retrospective assessments of HRQoL changes. Their PCPs assessed changes in the patient's condition at follow-up clinic visits that were linked to HRQoL assessments to determine change over time. DATA COLLECTION/EXTRACTION METHODS:Patient- and PCP-assessed changes were categorized as trivial (no change), small, moderate, or large improvements or declines. Moderate or large changes in HRQoL reflect the added risk or investment associated with some treatment modifications. Estimates for each categorization were calculated by finding the mean change scores within anchored change classifications. PRINCIPAL FINDINGS: The small CID for the CHQ domains was consistently one to two points using the patient-assessed change categorizations, but small CIDs varied greatly for the SF-36. PCP-assessed changes differed substantially from patient estimates for both the CHQ and SF-36, while the panel-derived estimates were generally larger than those derived from patients. CONCLUSIONS: Triangulation methodology provides a framework for securing a deeper understanding of each informant group's perspective on CIDs for these patient-reported outcome measures. These results demonstrate little consensus and suggest that the derived estimates depend on the rater and assessment methodology.
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