Sandra Oeyen1, Karel Vermeulen2, Dominique Benoit3, Lieven Annemans4, Johan Decruyenaere5. 1. 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. 2. Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, 9000 Ghent, Belgium. Electronic address: karelb.vermeulen@ugent.be. 3. 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: dominique.benoit@ugent.be. 4. Faculty of Medicine and Health Sciences, Department of Public Health, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium. Electronic address: lieven.annemans@ugent.be. 5. 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: johan.decruyenaere@ugent.be.
Abstract
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.
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.
Authors: Louis Onghena; Frederik Berrevoet; Aude Vanlander; Hans Van Vlierberghe; Xavier Verhelst; Eric Hoste; Carine Poppe Journal: Qual Life Res Date: 2022-01-21 Impact factor: 4.147
Authors: Lise F E Beumeler; Anja van Wieren; Hanneke Buter; Tim van Zutphen; Nynke A Bruins; Corine M de Jager; Matty Koopmans; Gerjan J Navis; E Christiaan Boerma Journal: PLoS One Date: 2020-12-14 Impact factor: 3.240
Authors: Wytske W Geense; Mark van den Boogaard; Marco A A Peters; Koen S Simons; Esther Ewalds; Hester Vermeulen; Johannes G van der Hoeven; Marieke Zegers Journal: Crit Care Med Date: 2020-09 Impact factor: 9.296