The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study

Abstract

Purpose: Premorbid conditions affect prognosis of acutely-ill aged patients. Several lines of evidence suggest geriatric syndromes need to be assessed but little is known on their relative effect on the 30-day survival after ICU admission. The primary aim of this study was to describe the prevalence of frailty, cognition decline and activity of daily life in addition to the presence of comorbidity and polypharmacy and to assess their influence on 30-day survival.

Methods: Prospective cohort study with 242 ICUs from 22 countries. Patients 80 years or above acutely admitted over a six months period to an ICU between May 2018 and May 2019 were included. In addition to common patients' characteristics and disease severity, we collected information on specific geriatric syndromes as potential predictive factors for 30-day survival, frailty (Clinical Frailty scale) with a CFS > 4 defining frail patients, cognitive impairment (informant questionnaire on cognitive decline in the elderly (IQCODE) with IQCODE ≥ 3.5 defining cognitive decline, and disability (measured the activity of daily life with the Katz index) with ADL ≤ 4 defining disability. A Principal Component Analysis to identify co-linearity between geriatric syndromes was performed and from this a multivariable model was built with all geriatric information or only one: CFS, IQCODE or ADL. Akaike's information criterion across imputations was used to evaluate the goodness of fit of our models.

Results: We included 3920 patients with a median age of 84 years (IQR: 81-87), 53.3% males). 80% received at least one organ support. The median ICU length of stay was 3.88 days (IQR: 1.83-8). The ICU and 30-day survival were 72.5% and 61.2% respectively. The geriatric conditions were median (IQR): CFS: 4 (3-6); IQCODE: 3.19 (3-3.69); ADL: 6 (4-6); Comorbidity and Polypharmacy score (CPS): 10 (7-14). CFS, ADL and IQCODE were closely correlated. The multivariable analysis identified predictors of 1-month mortality (HR; 95% CI): Age (per 1 year increase): 1.02 (1.-1.03, p = 0.01), ICU admission diagnosis, sequential organ failure assessment score (SOFA) (per point): 1.15 (1.14-1.17, p < 0.0001) and CFS (per point): 1.1 (1.05-1.15, p < 0.001). CFS remained an independent factor after inclusion of life-sustaining treatment limitation in the model.

Conclusion: We confirm that frailty assessment using the CFS is able to predict short-term mortality in elderly patients admitted to ICU. Other geriatric syndromes do not add improvement to the prediction model. Since CFS is easy to measure, it should be routinely collected for all elderly ICU patients in particular in connection to advance care plans, and should be used in decision making.

Keywords: Activities of daily living; Cognitive functioning; Comorbidity; Critical care; Elderly; Frailty; Outcome; Prediction.

Fig. 1

Principal component analysis (PCA). Two-dimensional projection of the sample was constructed having the axes (principal components, PC) as the factors. Each PC is a linear combination of the original variables and PCs are orthogonal to each other. The angles between the vectors tell us how variables correlate with one another: when two vectors are close, forming a small angle, the two variables they represent are positively correlated. If they meet each other at 90 °, they are not likely to be correlated and when they diverge and form a large angle (close to 180 °), they are negatively correlated. The length of the vector shows how much weight a specific variable has on each principal component

Fig. 2

Adjusted survival curves according to geriatric symptoms: Adjusted survival curves for geriatric variables were produced using an inverse probability-weighted Kaplan–Meier estimation [19]. Variables used to adjust the curves were age, gender, place of living, reason for ICU admission and SOFA score. Significance was tested using a Cox regression model weighted by the same weights (inverse probability-weighted Cox). a Survival curves according to CFS. b Survival curves according to Katz ADL. c Survival curves according to IQCODE. d Survival curves according to CPS