Background The Minnesota Living with Heart Failure Questionnaire (MLHFQ) is one of the most widely used health-related quality of life questionnaires for patients with heart failure (HF). The objective of the present study was to explore the responsiveness of the MLHFQ by estimating the minimal detectable change (MDC) and the minimal clinically important difference (MCID) in Spain. Methods Patients hospitalized for HF in the participating hospitals completed the MLHFQ at baseline and 6 months, plus anchor questions at 6 months. To study responsiveness, patients were classified as having “improved”, remained “the same” or “worsened”, using anchor questions. We used the standardized effect size (SES), and standardized response mean (SRM) to measure the magnitude of the changes scores and calculate the MDC and MCID. Results Overall, 1211 patients completed the baseline and follow-up questionnaires 6 months after discharge. The mean changes in all MLHFQ domains followed a trend ( P < 0.0001) with larger gains in quality of life among patients classified as “improved”, smaller gains among those classified as “the same”, and losses among those classified as “worsened”. The SES and SRM responsiveness parameters in the “improved” group were ≥ 0.80 on nearly all scales. Among patients classified as “worsened”, effect sizes were < 0.40, while among patients classified as “the same”, the values ranged from 0.24 to 0.52. The MDC ranged from 7.27 to 16.96. The MCID based on patients whose response to the anchor question was “somewhat better”, ranged from 3.59 to 19.14 points. Conclusions All of these results suggest that all domains of the MLHFQ have a good sensitivity to change in the population studied.

Objectives: To map the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) onto the EQ-5D-5L in patients with hip or knee osteoarthritis (OA).Methods: A prospective observational study was conducted on 758 patients with hip or knee OA who completed the EQ-5D-5L and WOMAC questionnaires, of whom 644 completed them both again 6 months later. Baseline data were used to derive mapping functions. Generalized additive models were used to identify to which powers the WOMAC subscales should be raised to achieve a linear relationship with the response. For the modeling, general linear models (GLM), Tobit models, and beta regression models were used. Age, sex, and affected joints were also considered. Preferred models were selected based on Akaike and Bayesian information criteria, adjusted R 2 , mean absolute error (MAE), and root mean squared error (RMSE). The functions were validated with the follow-up data using MAE, RMSE, and the intraclass correlation coefficient. Results:The preferred models were a GLM with Pain 2 1Pain 3 1Function1Pain$Function as covariates and a beta model with Pain 3 1Function1Function 2 1Function 3 as covariates. The adjusted R 2 were similar (0.6190 and 0.6136, respectively). The predictive performance of these models in the validation sample was similar and both models showed an overprediction for health states worse than death. Conclusion:To our knowledge, these are the first functions mapping the WOMAC onto the EQ-5D-5L in patients with hip or knee OA. They showed an acceptable fit and precision and could be very useful for clinicians and researchers when costeffectiveness studies are needed and generic preference-based health-related quality of life instruments to derive utilities are not available.

BackgroundRheumatoid arthritis (RA) deeply affects the quality of life (QoL) of patients. The preferred approach to evaluate treatment efficiency is to value health as patient preferences known as utilities, and subsequently, calculate Quality-Adjusted Life Years gained. A new 5-level of severity EQ5D has recently released and a new tariff proposed for Spain (Ramos-Goñi,2016). Although QoL questionnaires are not of routine use in clinical practice, it is possible to estimate it using the Health Assessment Questionnaire Disability Index (HAQ-DI)ObjectivesTo develop a function that allows the estimation of EQ5D-5L utility values from HAQ-DI updated to the newest proposed tariff for SpainMethodsPatients with RA from two teaching hospitals, participating in a prospective observational study completed the HAQ-DI and EQ5D-5L at 0–6-12 month follow-up visits. Inclusion criteria: ACR RA diagnosed patients, on biologic treatment and whose disease activity remained stable at least for 3 months EQ5D-5L is a standardized, generic instrument for describing and valuing health and QoL, consisting in a five-dimensional descriptive system (mobility, self-care, usual activities, pain/discomfort and anxiety/depression) and a visual analogue scale. A country-specific tariff converts patient's answer to a 0–1 (full health) utility index. HAQ-DI is a self-completed questionnaire used to assess the functional ability using 20 items, distributed across 8 dimensions and resulting in a four-level disability scale (0–3). In addition, socio-demographic and clinical data where recorded.To estimate the EQ5D-5L utility index OLS models were built. As this index is bounded to the [-0.416, 1] interval, Tobit models were also considered. Hereafter, the index was transformed to the open interval (0,1) and estimated through beta regression with a logit link. To determine the relationship grade between the index and the HAQ-DI scale and obtain residuals without trend, GAM models were used. Best fitting models were determined by AIC, MAE and RMSE. All analyses were performed using R softwareResults217 questionnaires fulfilled by 77 patients. Mean (SD) age was 57.0 (12.9), 87% women, AR duration 13.7 (7.1), mean DAS28 2.72 (1.00) and HAQ-DI 0.77 (0.60). Baseline EQ5D index: 0.768 (0.182). All the OLS estimation models resulted in the interval limits defined by the index, so Tobit models were not considered. When considering the linear model we obtained the best results with the HAQ-DI term and its third power: EQ5D5L = 0.9232 − 0.1760×HAQ − 0.0172×HAQ3 (AIC=−221.62; MAE=0.0974; RMSE=0.1363); for beta regression, we obtained the best model with the HAQ-DI to the first power alone: logit (EQ_01) = 2.5821 − 1.1165×HAQ (AIC=−444.4; MAE=0.0691; RMSE=0.0958). Considering the AIC and the residuals together, we obtained the best fitting model with the beta regression approach, with neither age nor sexConclusionsSo far, only a utility function using HAQ-DI and an older EQ5D-3L version was available for Spain (Carreño,2011). This updated utility function can be ...

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