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This study aimed to evaluate the performance of machine learning and regression methods in the prediction of 3-level version of EQ-5D (EQ-5D-3L) index scores from a large diverse data set.A total of 30 studies from 3 countries were combined. Predictions were performed via eXtreme Gradient Boosting classification (XGBC), eXtreme Gradient Boosting regression (XGBR) and ordinary least squares (OLS) regression using 10-fold cross-validation and 80%/20% partition for training and testing. We evaluated 6 prediction scenarios using 3 samples (general population, patients, total) and 2 predictor sets: demographic and disease-related variables with/without patient-reported outcomes. Model performance was evaluated by mean absolute error and percent of predictions within clinically irrelevant error range and within correct health severity group (EQ-5D-3L index0.45, 0.45-0.926,0.926).The data set involved 26 318 individuals (clinical settings n = 6214, general population n = 20 104) and 26 predictor variables plus diagnoses. Using all predictors and the total sample, mean absolute error values were 0.153, 0.126, and 0.131, percent of predictions within clinically irrelevant error range were 47.6%, 39.5%, and 37.4%, and within the correct health severity group were 56.3%, 64.9%, and 63.3% by XGBC, XGBR, and OLS, respectively. The performance of models depended on the applied evaluation criteria, the target population, the included predictors, and the EQ-5D-3L index score range.Regression models (XGBR and OLS) outperformed XGBC, yet prediction errors were outside the clinically irrelevant error range for most respondents. Our results highlight the importance of systematic patient-reported outcome (EQ-5D) data collection. Dialogs between artificial intelligence and outcomes research experts are encouraged to enhance the value of accumulating data in health systems.

Objective The analysis aims to assess the cost-effectiveness of cefuroxime (Aprokam®) in the prophylaxis of postoperative endophthalmitis (POE) after cataract surgery compared with the absence of antibiotic prophylaxis from the National Health Fund perspective in Poland. Methods We performed a cost-effectiveness and cost-utility analysis using the decision tree and Markov model, respectively, for patients after cataract surgery. The efficacy of Aprokam was 0.21 (95% confidence interval [CI], 0.08-0.55) and is based on the results of the European Society of Cataract and Refractive Surgery study. According to the epidemiological data from Poland, the risk of POE is 0.377%. The costs associated with the Aprokam administration and POE treatment costs were included. We determined the utilities of the health states in the model depending on visual loss due to POE. To determine the uncertainty of estimates parameters, a one-way deterministic and probabilistic sensitivity analysis were performed. Results Using Aprokam allows avoiding 0.003 POEs per patient. The benefit from the intervention is 0.0007 quality-adjusted life years per patient in the lifetime horizon. The total costs of prophylaxis are higher at about €1.70. The cost of avoiding one POE (incremental cost-effectiveness ratio) is about €569.85. The estimated incremental cost-effectiveness utility ratio is equal to €2427.72/quality-adjusted life-years, and it is significantly lower than the cost-effectiveness threshold in Poland in 2019 (about 7.5% of the threshold). In all scenarios of performed one-way sensitivity analyses, Aprokam is cost-effective. Conclusions In Poland, the use of Aprokam is cost-effective, with the estimated incremental cost-utility ratio significantly lower than the cost-effectiveness threshold.

Background A five-level version of the EuroQol five-dimensional (EQ-5D) descriptive system (EQ-5D-5L) has been developed, but value sets based on preferences directly elicited from representative general population samples are not yet available. The objective of this study was to develop values sets for the EQ-5D-5L by means of a mapping ("crosswalk") approach to the currently available three-level version of the EQ-5D (EQ-5D-3L) values sets. Methods The EQ-5D-3L and EQ-5D-5L descriptive systems were coadministered to respondents with conditions of varying severity to ensure a broad range of levels of health across EQ-5D questionnaire dimensions. We explored four models to generate value sets for the EQ-5D-5L: linear regression, nonparametric statistics, ordered logistic regression, and item-response theory. Criteria for the preferred model included theoretical background, statistical fit, predictive power, and parsimony. Results A total of 3691 respondents were included. All models had similar fit statistics. Predictive power was slightly better for the nonparametric and ordered logistic regression models. In considering all criteria, the nonparametric model was selected as most suitable for generating values for the EQ-5D-5L. Conclusions The nonparametric model was preferred for its simplicity while performing similarly to the other models. Being independent of the value set that is used, it can be applied to transform any EQ-5D-3L value set into EQ-5D-5L index values. Strengths of this approach include compatibility with three-level value sets. A limitation of any crosswalk is that the range of index values is restricted to the range of the EQ-5D-3L value sets.