The Clinical Frailty Scale for mortality prediction of old acutely admitted intensive care patients: a meta-analysis of individual patient-level data

Abstract

Background This large-scale analysis pools individual data about the Clinical Frailty Scale (CFS) to predict outcome in the intensive care unit (ICU). Methods A systematic search identified all clinical trials that used the CFS in the ICU (PubMed searched until 24th June 2020). All patients who were electively admitted were excluded. The primary outcome was ICU mortality. Regression models were estimated on the complete data set, and for missing data, multiple imputations were utilised. Cox models were adjusted for age, sex, and illness acuity score (SOFA, SAPS II or APACHE II). Results 12 studies from 30 countries with anonymised individualised patient data were included (n = 23,989 patients). In the univariate analysis for all patients, being frail (CFS ≥ 5) was associated with an increased risk of ICU mortality, but not after adjustment. In older patients (≥ 65 years) there was an independent association with ICU mortality both in the complete case analysis (HR 1.34 (95% CI 1.25–1.44), p < 0.0001) and in the multiple imputation analysis (HR 1.35 (95% CI 1.26–1.45), p < 0.0001, adjusted for SOFA). In older patients, being vulnerable (CFS 4) alone did not significantly differ from being frail. After adjustment, a CFS of 4–5, 6, and ≥ 7 was associated with a significantly worse outcome compared to CFS of 1–3. Conclusions Being frail is associated with a significantly increased risk for ICU mortality in older patients, while being vulnerable alone did not significantly differ. New Frailty categories might reflect its “continuum” better and predict ICU outcome more accurately. Trial registration: Open Science Framework (OSF: https://osf.io/8buwk/). Graphical Abstract

Impact of frailty on long-term survival in patients discharged alive from hospital after an ICU admission with COVID-19

“…Furthermore, we are the first study to use the new updated CFS tool, 23 which better reflects the continuous nature of functional status, and more accurately predicts ICU outcomes. 32 There are a few limitations to this study. First, this is a retrospective study and causal inferences cannot be drawn.…”

Recovery following discharge from intensive care: What do patients think is helpful and what services are missing?

“…The mean clinical frailty score of the participants indicated a vulnerable population who were in the early stage of hospital discharge. Patients with scores of four to five (vulnerable/mildly frail) have been reported in other studies [ 21 ] and these scores have been associated with increased risk for ICU mortality [ 22 ]. Thus, the participants included in this study are representative of ICU populations and the study findings can be used to support patients discharged home from ICU.…”

Ethics

“…Students were provided with the triage AI algorithm (see Figure 2) following prioritization criteria based upon a predicted prognosis (Cardona et al, 2021). Students were provided four prognostication tools: Clinical Frailty Scale (Bruno et al, 2023), Glasgow Coma Scale (Jain & Iverson, 2023), (the ratio of arterial (PaO 2 ) to inspired (FiO 2 ) partial pressure of oxygen (P/F ratio) (ARDS Definition Task Force, 2012), and Sequential Organ Failure Assessment (Vincent et al, 1996), to calculate prioritization criteria in the triage AI algorithm as they were familiar with these tools. The Criteria for Screening and Triaging to Appropriate alternative care (CrisTAL) (Cardona et al, 2018; Cardona-Morell & Hillman, 2015), Simple Clinical Score (Subbe et al, 2010), and AGILITIES (Wilkens & Klein, 2020) tools were excluded as students had never been exposed to these tools in the AG-ACNP curriculum or utilized these tools clinically.…”