Is This Intensive Care Unit Patient Frail? Unraveling the Complex Interplay between Frailty and Critical Illness

Methods of Assessing Frailty in the Critically Ill: A Systematic Review of the Current Literature

INTRODUCTION

As new treatments have become established, more frail pre-ICU patients are being admitted to intensive care units (ICUs); this is creating new challenges to provide adequate care and to ensure that resources are allocated in an ethical and economical manner. This systematic review evaluates the current standard for assessing frailty on the ICU, including methods of assessment, time point of measurements, and cut-offs.

METHODS

A systematic search was conducted on MEDLINE, Clinical Trials, Cochrane Library, and Embase. Randomized and non-randomized controlled studies were included that evaluated diagnostic tools and ICU outcomes for frailty. Exclusion criteria were the following: studies without baseline assessment of frailty on ICU admission, studies in paediatric patients or pregnant women, and studies that targeted very narrow populations of ICU patients. Eligible articles were included until January 31, 2021. Methodological quality was assessed using the Newcastle-Ottawa Scale. No meta-analysis was performed, due to heterogeneity.

RESULTS

N = 57 articles (253,376 patients) were included using 19 different methods to assess frailty or a surrogate. Frailty on ICU admission was most frequently detected using the Clinical Frailty Scale (CFS) (n = 35, 60.3%), the Frailty Index (n = 5, 8.6%), and Fried's frailty phenotype (n = 6, 10.3%). N = 22 (37.9%) studies assessed functional status. Cut-offs, time points, and manner of baseline assessment of frailty on ICU admission varied widely. Frailty on ICU admission was associated with short- and long-term mortality, functional and cognitive impairment, increased health care dependency, and impaired quality of life post-ICU discharge.

CONCLUSIONS

Frailty assessment on the ICU is heterogeneous with respect to methods, cut-offs, and time points. The CFS may best reflect frailty in the ICU. Frailty assessments should be harmonized and performed routinely in the critically ill.

External validation of the hospital frailty risk score among older adults receiving mechanical ventilation

To externally validate the Hospital Frailty Risk Score (HFRS) in critically ill patients. We selected older adult (≥ 75 years old) hospitalizations receiving mechanical ventilation, using the Nationwide Readmissions Database (January 1, 2016-November 30, 2018). Frailty risk was subcategorized into low-risk (HFRS score < 5), intermediate-risk (score 5-15), and high-risk (score > 15). We evaluated the HFRS to predict in-hospital mortality, prolonged hospitalization, and 30-day readmissions, using multivariable logistic regression, adjusting for patient and hospital characteristics. Model performance was assessed using the c-statistic, Brier score, and calibration plots. Among 649,330 weighted hospitalizations, 9.5%, 68.3%, and 22.2% were subcategorized as low-, intermediate-, and high-risk for frailty, respectively. After adjustment, high-risk patient hospitalizations were associated with increased risks of prolonged hospitalization (adjusted odds ratio [aOR] 5.59 [95% confidence interval [CI] 5.24-5.97], c-statistic 0.694, Brier 0.216) and 30-day readmissions (aOR 1.20 [95% CI 1.13-1.27], c-statistic 0.595, Brier 0.162), compared to low-risk hospitalizations. Conversely, high-risk hospitalizations were inversely associated with in-hospital mortality (aOR 0.46 [95% CI 0.45-0.48], c-statistic 0.712, Brier 0.214). The HFRS was not successfully validated to predict in-hospital mortality in critically ill older adults. While it may predict other outcomes, its use should be avoided in the critically ill.

Efficacy of power training to improve physical function in individuals diagnosed with frailty and chronic disease: A meta-analysis

Muscle power training with emphasis on high-velocity of concentric movement improves physical functionality in healthy older adults, and, maybe superior to traditional exercise programs. Power training may also be advantageous for patients with acute and chronic illnesses, as well as frail individuals. To determine the efficacy of power training compared with traditional resistance training on physical function outcomes in individuals diagnosed with frailty, acute illness or chronic disease. PubMed (MEDLINE), CINAHL, PEDro, Web of Science, and Google Scholar. (1) at least one study group receives muscle power training of randomized controlled trial (RCT) (2) study participants diagnosed as prefrail, frail or have an ongoing acute or chronic disease, condition or illness; (3) study participants over the age of 18; (4) publication in English language; (5) included physical function as the primary or secondary outcome measures. Two independent reviewers assessed articles for inclusion and graded the methodological quality using Cochrane Risk-of-Bias tool for RCTs. Fourteen RCTs met the inclusion criteria. In seven studies, muscle power training was more effective at improving physical function compared to control activities with a mean fixed effect size (ES) of 0.41 (p = 0.006; 95% CI 0.12 to 0.71). Power training and conventional resistance training had similar effectiveness in eight studies with a mean fixed ES of 0.10 (p = 0.061; 95% CI -0.01 to 0.40). Muscle power training is just as efficacious for improving physical function in individuals diagnosed with frailty and chronic disease when compared to traditional resistance training. The advantages of power training with reduced work per session may support power training as a preferential exercise modality for clinical populations. The findings should be interpreted with caution since generalizability is questioned due to the heterogeneity of patient populations enrolled and participants were relatively mobile at baseline.

Frailty predicts 30-day mortality in intensive care patients: A prospective prediction study

BACKGROUND

Frailty is a multidimensional syndrome characterised by a loss of reserve and an increased risk of adverse outcomes.

OBJECTIVE

To study the impact of frailty on mortality in unselected intensive care patients, and to compare its discriminatory ability to an established model for outcome prediction in intensive care.

DESIGN

A prospective study with a comparison of two prediction models.

SETTING

A tertiary mixed ICU, from January 2017 to June 2018.

PATIENTS AND MAIN OUTCOME MEASURES

Data on premorbid frailty (clinical frailty scale; CFS), severity of illness (the simplified acute physiology score, third version; SAPS3), therapeutic procedures, limitations of care and outcome were collected in 872 adult ICU patients. A cut-off level of CFS for predicting death within 30 days was identified and unadjusted and adjusted analyses were used to evaluate the association of frailty to outcome.

RESULTS

The receiver operating curve, area under the curve of CFS [0.74 (95% confidence interval, 0.69 to 0.79)] did not differ significantly from that of SAPS3 [0.79 (0.75 to 0.83), P = 0.53], whereas combining the two resulted in an improved discriminatory ability [area under the curve = 0.82 (0.79 to 0.86), CFS + SAPS3 vs. SAPS3 alone, P = 0.02]. The correlation of CFS to SAPS3 was moderate (r = 0.4). A cut-off level was identified at CFS at least 5, defining 43% (n=375) of the patients as frail. Frail patients were older with higher SAPS3 and more comorbidities. Treatment in the ICU was more often withheld or withdrawn in frail patients, and mortality was higher. After adjustment for SAPS3, comorbidities, limitations of treatment, age and sex, frailty remained a strong predictor of death within 30 days [hazard ratio 2.12 (95% confidence interval, 1.44 to 3.14), P < 0.001].

CONCLUSION

Premorbid frailty was common in general ICU patients and was an independent predictor of death. Our study suggests that frailty could be a valuable addition in outcome prediction in intensive care.

Risk Factors for Frailty in Critical Care Survivors: A secondary analysis

OBJECTIVES

The purpose of this study was to investigate the prevalence of frailty and its risk factors among critical care survivors who were discharged after receiving treatment in an intensive care unit.

METHODS

This was a secondary analysis using data from a methodological study conducted between June and August 2018. The sample included 494 adults who had been admitted to the intensive care unit for more than 48 hours within a year. Only post-intensive care frailty was evaluated using the Kihon Checklist. The sociodemographic and intensive care-related risk factors for frailty were analysed using multivariate logistic regression.

RESULTS

The prevalence of frailty in the sample was 65.8%. The risk factors for frailty were female sex (adjusted odds ratio [aOR] = 1.68, 95% CI: 1.02-2.78), aged 70 years or older (aOR = 4.16, 95% CI: 2.00-8.65), unemployment (aOR = 2.41, 95% CI: 1.39-4.17) and longer ICU days (aOR = 2.29, 95% CI: 1.35-3.91). Analysis of differences in risk factors according to sex revealed that risk factors for frailty were unemployment and longer ICU length of stay for male and older age for female.

CONCLUSION

Health care providers should be aware of frailty risk factors in female and male patients and provide patient-specific interventions for preventing frailty.

Natural History of Cognitive Impairment in Critical Illness Survivors. A Systematic Review

Long-term cognitive impairment is common among ICU survivors, but its natural history remains unclear. In this systematic review, we report the frequency of cognitive impairment in ICU survivors across various time points after ICU discharge that were extracted from 46 of the 3,350 screened records. Prior studies used a range of cognitive instruments, including subjective assessments (10 studies), single or screening cognitive test such as Mini-Mental State Examination or Trail Making Tests A and B (23 studies), and comprehensive cognitive batteries (26 studies). The mean prevalence of cognitive impairment was higher with objective rather than subjective assessments (54% [95% confidence interval (CI), 51–57%] vs. 35% [95% CI, 29–41%] at 3 months after ICU discharge) and when comprehensive cognitive batteries rather than Mini-Mental State Examination were used (ICU discharge: 61% [95% CI, 38–100%] vs. 36% [95% CI, 15–63%]; 12 months after ICU discharge: 43% [95% CI, 10–78%] vs. 18% [95% CI, 10–20%]). Patients with acute respiratory distress syndrome had higher prevalence of cognitive impairment than mixed ICU patients at ICU discharge (82% [95% CI, 78–86%] vs. 48% [95% CI, 44–52%]). Although some studies repeated tests at more than one time point, the time intervals between tests were arbitrary and dictated by operational limitations of individual studies or chosen cognitive instruments. In summary, the prevalence and temporal trajectory of ICU-related cognitive impairment varies depending on the type of cognitive instrument used and the etiology of critical illness. Future studies should use modern comprehensive batteries to better delineate the natural history of cognitive recovery across ICU patient subgroups and determine which acute illness and treatment factors are associated with better recovery trajectories.

Frailty Is Associated With Decreased Time Spent at Home After Critical Illness: A Population-Based Study

BACKGROUND

Frailty is characterized by vulnerability to stressors due to an accumulation of multiple functional deficits. Frailty is increasingly recognized as a risk factor for accelerated functional decline, increasing dependency, and risk of mortality. The objective of this study was to examine the association of frailty, at the time of critical care admission, with days alive at home and health care costs post-discharge.

METHODS

This retrospective cohort study used linked administrative data (2010-2016) in Ontario, Canada. We identified all patients admitted at the intensive care unit (ICU), aged 19 years and above, assessed using the Resident Assessment Instrument for Home Care (RAI-HC), within 6 months prior to index hospitalization including an ICU stay. Patients were stratified as robust, pre-frail, or frail based on a validated Frailty Index. The primary outcome was days alive at home in the year after admission. Secondary outcomes included mortality, health care-associated costs, ICU interventions, long-term care admissions, and hospital readmissions.

RESULTS

Frail patients spent significantly fewer days at home within 1 year of index hospitalization (mean 159 days vs 223 days in robust cohort, P < .001). Mortality was higher among frail patients at 1 year (59.6% in the frail cohort vs 45.9% in robust patients; odds ratio for death 1.59 [1.49-1.69]). Frail patients also had higher rates of long-term care admission within 1 year (30.1% vs 10.6% in robust patients). Total health care-associated costs per person alive were $30 450 higher the year after admission in the frail cohort.

CONCLUSIONS

Frailty prior to ICU admission among patients who were eligible for RAI-HC assessment was associated with higher mortality and fewer days spent at home following admission. Frail patients had markedly higher rates of long-term care admission and increased costs per life saved following critical illness. These findings add to the discussion of risk-benefit trade-offs for ICU admission.

Changes in frailty among ICU survivors and associated factors: Results of a one-year prospective cohort study using the Dutch Clinical Frailty Scale

PURPOSE

Frailty is an important predictor for the prognosis of intensive care unit (ICU) patients. This study examined changes in frailty in the year after ICU admission, and its associated factors.

MATERIALS AND METHODS

Prospective cohort study including adult ICU patients admitted between July 2016-December 2017. Frailty was measured using the Clinical Frailty Scale (CFS), before ICU admission, at hospital discharge, and three and 12 months after ICU admission. Multivariable linear regression was used to explore factors associated with frailty changes.

RESULTS

Frailty levels changed among 1300 ICU survivors, with higher levels at hospital discharge and lower levels in the following months. After one year were 42% of the unplanned, and 27% of the planned patients more frail. For both groups were older age, longer hospital length of stay, and discharge location associated with being more frail. Male sex, higher education level and mechanical ventilation were associated with being less frail in the planned patients.

CONCLUSION

One year after ICU admission, 42% and 27% of the unplanned and planned ICU patients, respectively, were more frail. Insight in the associated factors will help to identify patients at risk, and may help in informing patients and their family members.

REGISTRATION

ClinicalTrials.gov database (NCT03246334).