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

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.