• Sandra Oeyen, Karel Vermeulen, Dominique Benoit, Lieven Annemans, and Johan Decruyenaere.
• Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Department of Intensive Care, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium. Electronic address: sandra.oeyen@ugent.be.
• J Crit Care. 2018 Feb 1; 43: 133-138.

PurposeWe 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.MethodsThe 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.Results1831 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.ConclusionAlthough only 40% of variability in long-term QOL could be explained, this prediction model can be helpful in decision-making.Copyright © 2017 Elsevier Inc. All rights reserved.

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