Development of a prediction model for long-term quality of life in critically ill patients

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Purpose:We developed a prediction model for quality of life (QOL) 1 year after intensive care unit (ICU) discharge based upon data available at the first ICU day to improve decision-making.Methods:The database of a 1-year prospective study concerning long-term outcome and QOL (assessed by EuroQol-5D) in critically ill adult patients consecutively admitted to the ICU of a university hospital was used. Cases with missing data were excluded. Utility indices at baseline (UIb) and at 1 year (UI1y) were surrogates for QOL. For 1-year non-survivors UI1y was set at zero. The grouped lasso technique selected the most important variables in the prediction model. R2 and adjusted R2 were calculated.Results:1831 of 1953 cases (93.8%) were complete. UI1y depended significantly on: UIb (P < 0.001); solid tumor (P < 0.001); age (P < 0.001); activity of daily living (P < 0.001); imaging (P < 0.001); APACHE II-score (P = 0.001); ≥80 years (P = 0.001); mechanical ventilation (P = 0.006); hematological patient (P = 0.007); SOFA-score (P = 0.008); tracheotomy (P = 0.018); admission diagnosis surgical P < 0.001 (versus medical); and comorbidity (P = 0.049). Only baseline health status and surgical patients were positively associated with UI1y. R2 was 0.3875 and adjusted R2 0.3807.Conclusion:Although only 40% of variability in long-term QOL could be explained, this prediction model can be helpful in decision-making.Highlights:We developed an easy-to-use model to predict long-term QOL in critical care.The prediction model was based on 16 variables available at the first ICU day.The prediction model explained 40% of variability in long-term QOL.The prediction model could be a helpful tool in decision-making.Baseline QOL and functionality had the greatest impact on long-term QOL.

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