A Patient Reported Outcome Ontology: Conceptual Issues and Challenges Addressed by the Patient-Reported Outcomes Measurement Information System® (PROMIS®)

Comparison of the EQ-5D-5L and the patient-reported outcomes measurement information system preference score (PROPr) in the United States

BACKGROUND

In contrast to prior research, our study presents longitudinal comparisons of the EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS) preference (PROPr) scores. This fills a gap in the literature, providing a much-needed understanding of these preference-based measures and their applications in healthcare research. Furthermore, our study provides equations to estimate one measure from the other, a tool that can significantly facilitate comparisons across studies.

METHODS

We administered a health survey to 4,098 KnowledgePanel® members living in the United States. A subset of 1,256 (82% response rate) with back pain also completed the six-month follow-up survey. We then conducted thorough cross-sectional and longitudinal analyses of the two measures, including product-moment correlations between scores, associations with demographic variables, and health conditions. To estimate one measure from the other, we used ordinary least squares (OLS) regression with the baseline data from the general population.

RESULTS

The correlation between the EQ-5D-5L and PROPr scores was 0.69, but the intraclass correlation was only 0.34 because the PROPr had lower (less positive) mean scores on the 0 (dead) to 1 (perfect health) continuum than the EQ-5D-5L. The associations between the two preference measures and demographic variables were similar at baseline. The product-moment correlation between unstandardized beta coefficients for each preference measure regressed on 22 health conditions was 0.86, reflecting similar patterns of unique associations. Correlations of change from baseline to 6 months in the two measures with retrospective perceptions of change were similar. Adjusted variance explained in OLS regressions predicting one measure from the other was 48%. On average, the predicted values were within a half-standard deviation of the observed EQ-5D-5L and PROPr scores. The beta-binomial regression model slightly improved over the OLS model in predicting the EQ-5D-5L from the PROPr but was equivalent to the OLS model in predicting the PROPr.

CONCLUSION

Despite substantial mean differences, the EQ-5D-5L and PROPr have similar cross-sectional and longitudinal associations with other variables. We provide the OLS regression equations for use in cost-effectiveness research and meta-analyses. Future studies are needed to compare these measures with different conditions and interventions to provide more information on their relative validity.

How to select and understand guidelines for patient-reported outcomes: a scoping review of existing guidance

BACKGROUND

Over the past few decades, patient-reported outcomes (PROs) have been used to understand patient health conditions better. Therefore, numerous PRO measures (questionnaires) and guidelines or guidance have been developed. However, it is challenging to select target guidance from among the many available guidance and to understand the chosen guidance. This study comprehensively collected the existing PRO guidance for clinical trials or studies and practices to support novice PRO users in academia, industry, clinical practice, and regulatory and reimbursement decision-making.

METHODS

For the scoping review, we searched the MEDLINE, Embase, Google Books, WorldCat, and the National Library of Medicine (NLM) Bookshelf databases from 2009 to 2023. The eligibility criteria were PRO guidance for clinical trials, clinical practice, or application such as health technology assessment. Those guidance cover aspects such as quality of life (QOL), PRO, health-related QOL, health state utilities, psychometric requirements, implementation methods, analysis and interpretation, or clinical practice applications. After the systematic search, three researchers individually reviewed the collected data, and the reviewed articles and books were scrutinized using the same criteria.

RESULTS

We collected the PRO guidance published in articles and books between 2009 and 2023. From the database searches, 1,455 articles and 387 books were identified, of which one book and 33 articles were finally selected. The collected PRO guidance was categorized into the adoption of PRO measures, design and reporting of trials or studies using PROs, implementation of PRO evaluation in clinical trials or studies or clinical practice, analysis and interpretation of PROs, and application of PRO evaluation. Based on this categorization, we suggest the following for novices: When selecting guidance, novices should clarify the "place" and "purpose" where the guidance will be used. Additionally, they should know that the terminology related to PRO and the scope and expectations of PROs vary by "places" and "purposes".

CONCLUSIONS

From this scoping review of existing PRO guidance, we provided summaries and caveats to assist novices in selecting guidance that fits their purpose and understanding it.

Comparison of patient-reported outcomes measurement information system (PROMIS®)-29 and PROMIS global physical and mental health scores

PURPOSE

The Patient-Reported Outcomes Measurement and Information System (PROMIS®): includes the PROMIS-29 physical and mental health summary and the PROMIS global physical and mental health scores. It is unknown how these scores coincide with one another. This study examines whether the scores yield similar or different information.

METHODS

The PROMIS-29 and the PROMIS global health items were administered to 5804 adults from Amazon's Mechanical Turk (MTurk) in 2021-2022 and to 4060 adults in the Ipsos KnowledgePanel (KP) in 2022.

RESULTS

The median age of those in MTurk (KP) was 36 (54) and 53% (50%) were male. Mean T-scores on the PROMIS-29 and PROMIS global physical health scales were similar, but PROMIS global mental health was 3-4 points lower than the PROMIS-29 mental health summary score. Product-moment correlations ranged from 0.69 to 0.81 between the PROMIS-29 physical health and PROMIS global physical health scales and 0.56-0.69 between the mental health scales. Multi-trait multimethod analyses indicated that only a small proportion of the correlations between the two methods of measuring mental health were significantly more highly correlated with one another than correlations between physical and mental health.

CONCLUSIONS

PROMIS-29 and PROMIS global mental health scales provide different information and, therefore, study conclusions may vary depending on which measure is used. Interpretation of results needs to consider that the PROMIS-29 mental health scale is a weighted combination of specific domains while the PROMIS global mental health scale is based on general mental health perceptions. Further comparisons of methods of assessing mental health are needed.

Quality of life after acute kidney injury

PURPOSE OF REVIEW

Deciphering the effect of acute kidney injury (AKI) during critical illness on long-term quality of life versus the impact of conditions that brought on critical illness is difficult.

RECENT FINDINGS

Reports on patient-centred outcomes such as health-related quality of life (HRQOL) have provided insight into the long-lasting impact of critical illness complicated by AKI. However, these data stem from observational studies and randomized controlled trials, which have been heterogeneous in their patient population, timing, instruments used for assessment and reporting. Recent studies have corroborated these findings including lack of effect of renal replacement therapy compared to severe AKI on outcomes and worse physical compared to cognitive dysfunction.

SUMMARY

In adults, more deficits in physical than mental health domains are found in survivors of AKI in critical care, whereas memory deficits and learning impairments have been noted in children. Further study is needed to understand and develop interventions that preserve or enhance the quality of life for individual patients who survive AKI following critical illness, across all ages.

PROMIS Profile-29 is a valid instrument with distinct advantages over legacy instruments for measuring the quality of life in chronic liver disease

BACKGROUND AND AIMS

The Patient-Reported Outcomes Measurement Information System (PROMIS) is increasingly used to measure health-related quality of life, yet, it has not been well-studied in chronic liver disease (CLD). This study compares PROMIS Profile-29 to Short-Form Health Survey (SF-36) and Chronic Liver Disease Questionnaire (CLDQ) in patients with CLD.

APPROACH AND RESULTS

In all, 204 adult outpatients with CLD completed PROMIS-29, CLDQ, SF-36 and usability questionnaires. Mean scores were compared between groups, the correlation between domain scores was assessed, and floor/ceiling effects were calculated. Etiologies of CLD were NAFLD (44%), hepatitis C (16%), and alcohol (16%). Fifty-three percent had cirrhosis and 33% were Child-Pugh B/C with a mean model for end-stage liver disease score of 12.0. In all 3 tools, the poorest scores were in physical function and fatigue. The presence of cirrhosis or complications was associated with worse scores in most PROMIS Profile-29 domains, indicating known group validity. Strong correlations ( r ≥ 0.7) were present between Profile-29 and SF-36 or CLDQ domains measuring similar concepts, indicating strong convergent validity. Profile-29 was completed faster than SF-36 and CLDQ (5.4 ± 3.0, 6.7 ± 3.3, 6.5 ± 5.2 min, p = 0.003) and rated equally on usability. All CLDQ and SF-36 domains reached the floor or ceiling, while none were noted for Profile-29. These floor/ceiling effects were magnified when assessed in those with and without cirrhosis, indicating the improved depth of measurement by Profile-29.

CONCLUSIONS

Profile-29 is a valid, more efficient, well-received tool that provides an improved depth of measurement when compared to SF-36 and CLDQ and, therefore, an ideal tool to measure general health-related quality of life in CLD.

Patient-reported outcomes for people with diabetes: what and how to measure? A narrative review

Patient-reported outcomes (PROs) are valuable for shared decision making and research. Patient-reported outcome measures (PROMs) are questionnaires used to measure PROs, such as health-related quality of life (HRQL). Although core outcome sets for trials and clinical practice have been developed separately, they, as well as other initiatives, recommend different PROs and PROMs. In research and clinical practice, different PROMs are used (some generic, some disease-specific), which measure many different things. This is a threat to the validity of research and clinical findings in the field of diabetes. In this narrative review, we aim to provide recommendations for the selection of relevant PROs and psychometrically sound PROMs for people with diabetes for use in clinical practice and research. Based on a general conceptual framework of PROs, we suggest that relevant PROs to measure in people with diabetes are: disease-specific symptoms (e.g. worries about hypoglycaemia and diabetes distress), general symptoms (e.g. fatigue and depression), functional status, general health perceptions and overall quality of life. Generic PROMs such as the 36-Item Short Form Health Survey (SF-36), WHO Disability Assessment Schedule (WHODAS 2.0), or Patient-Reported Outcomes Measurement Information System (PROMIS) measures could be considered to measure commonly relevant PROs, supplemented with disease-specific PROMs where needed. However, none of the existing diabetes-specific PROM scales has been sufficiently validated, although the Diabetes Symptom Self-Care Inventory (DSSCI) for measuring diabetes-specific symptoms and the Diabetes Distress Scale (DDS) and Problem Areas in Diabetes (PAID) for measuring distress showed sufficient content validity. Standardisation and use of relevant PROs and psychometrically sound PROMs can help inform people with diabetes about the expected course of disease and treatment, for shared decision making, to monitor outcomes and to improve healthcare. We recommend further validation studies of diabetes-specific PROMs that have sufficient content validity for measuring disease-specific symptoms and consider generic item banks developed based on item response theory for measuring commonly relevant PROs.

Patient-reported outcome measures for emotional functioning in cancer patients: Content comparison of the EORTC CAT Core, FACT-G, HADS, SF-36, PRO-CTCAE, and PROMIS instruments

BACKGROUND

Cancer and its treatment can have substantial impact on patients' emotional functioning. Several patient-reported outcome measures (PROMs) assessing emotional functioning are available, but differences in content limit the comparability of results. To better understand conceptual (dis)similarities, we conducted a content comparison of commonly used PROMs.

METHODS

We included emotional functioning items, scales, and item banks from the EORTC CAT Core, EORTC QLQ-C30, FACT-G, Hospital anxiety and depression scale (HADS), SF-36, PRO-CTCAE, and PROMIS (item banks for anxiety, depression, and anger). Item content was linked to the International Classification of Functioning, Disability, and Health (ICF) and a hierarchical framework established for PROMIS. Single items could be coded with more than one ICF category but were solely assigned to one facet within the PROMIS framework.

RESULTS

The measures comprise 132 unique items covering the ICF components 'Body functions' (136/153 codings, 88.9%) and 'Activities and participation' (15/153, 9.8%). Most ICF codings (112/153, 73.2%) referred to the third-level category 'b1528 Emotional functions, other specified'. According to the PROMIS framework 48.5% of the items assessed depression (64/132 items), followed by anxiety (41/132, 31.1%) and anger (26/132, 19.7%). The EORTC measures covered depression, anxiety, and anger in a single measure, while the PROMIS inventory provides separate item banks for these concepts. The FACT-G, SF-36, PRO-CTCAE and HADS covered depression and anxiety, but not anger.

CONCLUSION

Our results provide an in-depth conceptual understanding of selected PROMs and important qualitative information going beyond psychometric evidence. Such information supports the identification of PROMs for which scores can be meaningfully linked with quantitative methods.