Pulmonary hemangiomatosis is a rare, usually fatal disorder characterized by diffuse proliferation of blood vessels within the thorax. We describe a 7-year-old boy with cavernous-type pulmonary hemangiomatosis successfully treated with interferon alfa-2a. He presented with respiratory distress and hemoptysis that were alleviated during a 2-year follow-up period.

To predict 3-Level version of European Quality of Life-5 Dimensions (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT), the study attempts to collect EQ-5D-3L and CAT data from COPD patients. Response mapping under a backward elimination procedure was used for EQ-5D score predictions from CAT. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates. Afterwards, the predicted scores were transformed into the utility. The developed formula was compared with ordinary least squares (OLS) regression models and models using Mean Rank Method (MRM). The MLR models performed as well as other models according to mean absolute error (MAE) and root mean squared error (RMSE) evaluations. Besides, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in the OLS method was improved through the means of the MLR model based on bubble chart analysis. In conclusion, response mapping with the MLR model led to performance comparable to the OLS and MRM models for predicting EQ-5D utility from CAT data. Additionally, the bubble charts analysis revealed that the model constructed in this study and MRM could be a better predictive model.

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Objectives: In order to predict the European Quality of Life-5 Dimensions three-Level (EQ-5D-3L) questionnaire utility from the chronic obstructive pulmonary disease (COPD) assessment test (CAT).Methods: The EQ-5D-3L and CAT data from 323 patients were collected. At first, response mapping under a backward elimination procedure was used for the EQ-5D score predictions from the CAT scores. A multinomial logistic regression (MLR) model was used to identify the association between the score and the covariates using a minimized quasi-information criterion (QIC). Afterwards, the predicted scores were transformed to the utility. Validation in the model selection depended on the mean absolute error (MAE) and the root mean squared error (RMSE) for the validation group. We also compared the developed formula with previous models based on an ordinary least squares (OLS) regression. Results: Using response mapping with the MLR model to predict EQ-5D utility from CAT in this study performed as well as OLS regression models in previous studies using MAE and RMSE evaluations (all MAEs ≤ 0.100). In addition, the overestimation for low utility patients (utility ≤ 0.6) and underestimation for near health (utility > 0.9) in previous developed OLS models was improved in this study using a bubble chart analysis. Conclusions: Response mapping with the MLR model led to performance comparable to that using the OLS model for predicting EQ-5D utility from CAT data. In addition, the bubble charts revealed that the model constructed in this study was a better predictive model than other alternatives.