QOL Disease-specific Impact Scale (QDIS®) enables more valid outcome measurements across chronic conditions

JWRG’s multi-year NIH-sponsored Disease Specific Impact CAT (DICAT) Project improved patient reported outcome (PRO) estimates for disease-specific health-related quality of life (QOL). Improvements included broadening QOL domain content and also standardizing both content and scoring across chronic disease conditions. The main differences, in comparison with generic QOL tools, are QDIS item attributions to a specific disease or condition. For example, a QDIS question for chronic kidney disease asked, “How much did your kidney disease limit your everyday activities or your quality of life?”, while the same question was also asked with attribution to specific diseases (osteoarthritis, rheumatoid arthritis, angina, myocardial infarction, congestive heart failure, , diabetes, asthma or COPD) in early DICAT project studies. This standardization of content and scoring of disease-specific impact across conditions combines the precision and discrimination of disease-specific with the content representation of generic QOL measures. Further, impact estimates for all diseases can be compared on the same metric and interpreted using norm-based scores computed from representative population samples of chronically ill adults (mean=50, SD=10). The methods are documented and results from tests of crucial assumptions underlying the QDIS approach were evaluated favorably in an article published in Health and Quality of Life Outcomes.

Briefly, a bank of 49 disease impact items was constructed to represent 10 frequently measured QOL content areas (e.g., physical, mental, and role/social) and by applying different operational definitions including limitations in behavioral functioning and feelings. Responses (N= 5,418) for adults were analyzed by disease group and for all groups combined to confirm hypothesized unidimensionality, item parameter and scale-level invariance, reliability and validity using multi-group confirmatory factor analysis and item response theory (IRT) methods. QDIS was normed in an independent representing chronically ill US population adults (N=4,120). In support of the validity of disease-specific attributions, QDIS discriminated significantly better than the generic SF-8™ Health Survey across groups differing in disease severity levels and was more responsive in detecting average differences in outcomes across groups who rated their disease conditions as better, same or worse in follow-up surveys.

By integrating the richness of broader QOL item content with disease-specific attributions and by standardizing scoring metrics, QDIS achieves some of the advantages of both disease-specific and generic measurement traditions. Because all disease-specific QDIS scores are on the same metric, clinicians can better compare the relative impact of a patient’s multiple conditions with more specific and actionable information than is possible with generic measures. In addition, for observational outcomes research, QDIS may provide a practical way of adjusting for case-mix differences for each disease condition or an aggregate estimate of total multiple chronic condition QOL impact. QDIS also may enable a practical short-cut to achieving disease-specific QOL impact estimates for conditions for which comprehensive measures are not available, such as some rare diseases, or in the evaluation of orphan drugs.

In summary, QDIS standardization of content and scoring based upon a single (1 factor model) disease-specific impact score for each disease condition has proven to be both psychometrically sound and has repeatedly been shown to improve validity over legacy generic tools for purposes of responding to clinically defined group differences across all conditions studied to date. Directly comparable single item (QDIS-1), static multi-item (QDIS-7) or CAT-based (QDIS-CAT) administration estimates offer flexibility in managing respondent burden for each disease and enabling the first norm-based scoring and interpretation of disease-specific QOL impact across conditions. The JWRG DICAT Project launch of disease-specific QOL measurement innovation appears to be on the right track toward filling the conceptual/methodological gaps between specific symptoms that fail to comprehensively capture disease-specific QOL impact and generic QOL measures that are not disease-specific.