Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study*. Issue 5 (20th February 2023) - Porter, Lucy L.; Simons, Koen S.; Ramjith, Jordache; Corsten, Stijn; Westerhof, Brigitte; Rettig, Thijs C. D.; Ewalds, Esther; Janssen, Inge; van der Hoeven, Johannes G.; van den Boogaard, Mark; Zegers, Marieke
Abstract : OBJECTIVES: To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU and managing their expectations. DESIGN: Data from a prospective multicenter cohort study (MONITOR-IC) were used. SETTING: Seven hospitals in the Netherlands. PATIENTS: ICU survivors greater than or equal to 16 years old. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1, 804 patients included in analysis, 1, 057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R 2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R 2 = 52.5%). External validation showed good predictive power ( R 2 = 53.2%). CONCLUSIONS: This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.