Mapping chronic liver disease questionnaire scores onto SF-6D utility values in patients with primary sclerosing cholangitis

Quality of life in primary sclerosing cholangitis: a systematic review

BACKGROUND

Primary sclerosing cholangitis (PSC) is a rare bile duct and liver disease which can considerably impact quality of life (QoL). As part of a project developing a measure of QoL for people with PSC, we conducted a systematic review with four review questions. The first of these questions overlaps with a recently published systematic review, so this paper reports on the last three of our initial four questions: (A) How does QoL in PSC compare with other groups?, (B) Which attributes/factors are associated with impaired QoL in PSC?, (C) Which interventions are effective in improving QoL in people with PSC?.

METHODS

We systematically searched five databases from inception to 1 November 2020 and assessed the methodological quality of included studies using standard checklists.

RESULTS

We identified 28 studies: 17 for (A), ten for (B), and nine for (C). Limited evidence was found for all review questions, with few studies included in each comparison, and small sample sizes. The limited evidence available indicated poorer QoL for people with PSC compared with healthy controls, but findings were mixed for comparisons with the general population. QoL outcomes in PSC were comparable to other chronic conditions. Itch, pain, jaundice, severity of inflammatory bowel disease, liver cirrhosis, and large-duct PSC were all associated with impaired QoL. No associations were found between QoL and PSC severity measured with surrogate markers of disease progression or one of three prognostic scoring systems. No interventions were found to improve QoL outcomes.

CONCLUSION

The limited findings from included studies suggest that markers of disease progression used in clinical trials may not reflect the experiences of people with PSC. This highlights the importance for clinical research studies to assess QoL alongside clinical and laboratory-based outcomes. A valid and responsive PSC-specific measure of QoL, to adequately capture all issues of importance to people with PSC, would therefore be helpful for clinical research studies.

'Mapping' Health State Utility Values from Non-preference-Based Measures: A Systematic Literature Review in Rare Diseases

BACKGROUND

The use of patient-reported outcome measures (PROMs) to monitor the effects of disease and treatment on patient symptomatology and daily life is increasing in rare diseases (RDs) (i.e. those affecting less than one in 2000 people); however, these instruments seldom yield health state utility values (HSUVs) for cost-utility analyses. In such a context, 'mapping' allows HSUVs to be obtained by establishing a statistical relationship between a 'source' (e.g. a disease-specific PROM) and a 'target' preference-based measure [e.g. the EuroQol-5 Dimension (EQ-5D) tool].

OBJECTIVE

This study aimed to systematically review all published studies using 'mapping' to derive HSUVs from non-preference-based measures in RDs, and identify any critical issues related to the main features of RDs, which are characterised by small, heterogeneous, and geographically dispersed patient populations.

METHODS

The following databases were searched during the first half of 2019 without time, study design, or language restrictions: MEDLINE (via PubMed), the School of Health and Related Research Health Utility Database (ScHARRHUD), and the Health Economics Research Centre (HERC) database of mapping studies (version 7.0). The keywords combined terms related to 'mapping' with Orphanet's list of RD indications (e.g. 'acromegaly') in addition to 'rare' and 'orphan'. 'Very rare' diseases (i.e. those with fewer than 1000 cases or families documented in the medical literature) were excluded from the searches. A predefined, pilot-tested extraction template (in Excel^®) was used to collect structured information from the studies.

RESULTS

Two groups of studies were identified in the review. The first group (n = 19) developed novel mapping algorithms in 13 different RDs. As a target measure, the majority used EQ-5D, and the others used the Short-Form Six-Dimension (SF-6D) and 15D; most studies adopted ordinary least squares (OLS) regression. The second group of studies (n = 9) applied previously published algorithms in non-RDs to comparable RDs, mainly in the field of cancer. The critical issues relating to 'mapping' in RDs included the availability of very few studies, the relatively high number of cancer studies, and the absence of research in paediatric RDs. Moreover, the reviewed studies recruited small samples, showed a limited overlap between RD-specific and generic PROMs, and highlighted the presence of cultural and linguistic factors influencing results in multi-country studies. Lastly, the application of existing algorithms developed in non-RDs tended to produce inaccuracies at the bottom of the EQ-5D scale, due to the greater severity of RDs.

CONCLUSIONS

More research is encouraged to develop algorithms for a broader spectrum of RDs (including those affecting young children), improve mapping study quality, test the generalisability of algorithms developed in non-RDs (e.g. HIV) to rare variants or evolutions of the same condition (e.g. AIDS wasting syndrome), and verify the robustness of results when mapped HSUVs are used in cost-utility models.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

BACKGROUND

Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values (HSUVs) from data obtained from another health-related quality of life (HRQoL) measure. There have been several methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

CONCLUSIONS

The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions.

Prediction of Health Utility Scores in Patients with Chronic Hepatitis C Using the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV)

BACKGROUND

Preference-based health utilities are used in economic analyses of disease burden and health care interventions. When specifically designed instruments cannot be applied, mapping algorithms for non-preference-based instruments can be used for prediction of health utility scores.

OBJECTIVES

To develop a mapping algorithm for the Chronic Liver Disease Questionnaire-Hepatitis C Version (CLDQ-HCV), the hepatitis C virus-specific quality-of-life instrument.

METHODS

We used a sample of patients with HCV who completed the short form 36 health survey and the CLDQ-HCV in clinical trials; six-dimensional health state short form (SF-6D) utilities were derived from the 36-item short form health survey. Regression models with components of the CLDQ-HCV being predictors and SF-6D being the outcome were developed and tested in an independent testing set and in clinically significant subpopulations.

RESULTS

The sample of 34,822 records was split (4:1) into training and testing set. Simple mixed models had a root mean square error up to 0.088; predicted and observed utilities were highly correlated (Pearson correlation 0.81-0.82) although predicted utilities were underestimated in the range closest to perfect scores. Generalized linear models had better average accuracy (root mean square error up to 0.0839; correlations up to 0.844) and significantly better accuracy in the highest values (median error up to 0.065). Accuracy in the independent testing set was nearly identical, and so was accuracy in patients with compensated and decompensated cirrhosis; the errors of group means were less than 0.015.

CONCLUSIONS

A number of linear models for mapping domains or items of CLDQ-HCV to SF-6D health utilities have been developed. The models have excellent accuracy at the group level. Predicted health utility scores can be used in further economic analyses involving patients with HCV.