Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study
- PMID: 36825895
- DOI: 10.1097/CCM.0000000000005800
Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study
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 ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 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.
Trial registration: ClinicalTrials.gov NCT03246334.
Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.
Conflict of interest statement
Dr. Porter disclosed that this research is funded by the Radboudumc and Jeroen Bosch Hospital through a grant called the Junior Researcher Project, and she received support for article research from Radboudumc and Jeroen Bosch Ziekenhuis. Dr. Rettig received funding from Roche Diagnostics and Cablon Medical. The remaining authors have disclosed that they do not have any potential conflicts of interest.
Comment in
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Quality of Life After Critical Illness.Crit Care Med. 2023 May 1;51(5):691-693. doi: 10.1097/CCM.0000000000005828. Epub 2023 Apr 13. Crit Care Med. 2023. PMID: 37052442 No abstract available.
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Measuring Quality of Life. What Are We Missing?Crit Care Med. 2023 Nov 1;51(11):e244-e245. doi: 10.1097/CCM.0000000000005957. Epub 2023 Oct 12. Crit Care Med. 2023. PMID: 37902354 No abstract available.
References
-
- Needham DM, Davidson J, Cohen H, et al.: Improving long-term outcomes after discharge from intensive care unit: Report from a stakeholders’ conference. Crit Care Med 2012; 40:502–509
-
- Weigl W, Adamski J, Goryński P, et al.: Mortality rate is higher in Polish intensive care units than in other European countries. Intensive Care Med 2017; 43:1430–1432
-
- van de Klundert N, Holman R, Dongelmans DA, et al.: Data resource profile: The Dutch National Intensive Care Evaluation (NICE) registry of admissions to adult intensive care units. Int J Epidemiol 2015; 44:1850–1850h
-
- Harvey MA, Davidson JE: Postintensive care syndrome: Right care, right now...and later. Crit Care Med 2016; 44:381–385
-
- Desai SV, Law TJ, Needham DM: Long-term complications of critical care. Crit Care Med 2011; 39:371–379
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