Frailty assessment in critically ill older adults: a narrative review.

Frailty, a condition that was first defined 20 years ago, is now assessed via multiple different tools. The Frailty Phenotype was initially used to identify a population of "pre-frail" and "frail" older adults, so as to prevent falls, loss of mobility, and hospitalizations. A different definition of frailty, via the Clinical Frailty Scale, is now actively used in critical care situations to evaluate over 65 year-old patients, whether it be for Intensive Care Unit (ICU) admissions, limitation of life-sustaining treatments or prognostication. Confusion remains when mentioning "frailty" in older adults, as to which tools are used, and what the impact or the bias of using these tools might be. In addition, it is essential to clarify which tools are appropriate in medical emergencies. In this review, we clarify various concepts and differences between frailty, functional autonomy and comorbidities; then focus on the current use of frailty scales in critically ill older adults. Finally, we discuss the benefits and risks of using standardized scales to describe patients, and suggest ways to maintain a complex, three-dimensional, patient evaluation, despite time constraints. Frailty in the ICU is common, involving around 40% of patients over 75. The most commonly used scale is the Clinical Frailty Scale (CFS), a rapid substitute for Comprehensive Geriatric Assessment (CGA). Significant associations exist between the CFS-scale and both short and long-term mortality, as well as long-term outcomes, such as loss of functional ability and being discharged home. The CFS became a mainstream tool newly used for triage during the Covid-19 pandemic, in response to the pressure on healthcare systems. It was found to be significantly associated with in-hospital mortality. The improper use of scales may lead to hastened decision-making, especially when there are strains on healthcare resources or time-constraints. Being aware of theses biases is essential to facilitate older adults' access to equitable decision-making regarding critical care. The aim is to help counteract assessments which may be abridged by time and organisational constraints.

Development and validation of a physical frailty phenotype index-based model to estimate the frailty index.

BACKGROUND: The conventional count-based physical frailty phenotype (PFP) dichotomizes its criterion predictors-an approach that creates information loss and depends on the availability of population-derived cut-points. This study proposes an alternative approach to computing the PFP by developing and validating a model that uses PFP components to predict the frailty index (FI) in community-dwelling older adults, without the need for predictor dichotomization. METHODS: A sample of 998 community-dwelling older adults (mean [SD], 68 [7] years) participated in this prospective cohort study. Participants completed a multi-domain geriatric screen and a physical fitness assessment from which the count-based PFP and the 36-item FI were computed. One-year prospective falls and hospitalization rates were also measured. Bayesian beta regression analysis, allowing for nonlinear effects of the non-dichotomized PFP criterion predictors, was used to develop a model for FI ("model-based PFP"). Approximate leave-one-out (LOO) cross-validation was used to examine model overfitting. RESULTS: The model-based PFP showed good calibration with the FI, and it had better out-of-sample predictive performance than the count-based PFP (LOO-R2, 0.35 vs 0.22). In clinical terms, the improvement in prediction (i) translated to improved classification agreement with the FI (Cohen's kw, 0.47 vs 0.36) and (ii) resulted primarily in a 23% (95%CI, 18-28%) net increase in FI-defined "prefrail/frail" participants correctly classified. The model-based PFP showed stronger prognostic performance for predicting falls and hospitalization than did the count-based PFP. CONCLUSION: The developed model-based PFP predicted FI and clinical outcomes more strongly than did the count-based PFP in community-dwelling older adults. By not requiring predictor cut-points, the model-based PFP potentially facilitates usage and feasibility. Future validation studies should aim to obtain clear evidence on the benefits of this approach.

Long-term influence of frailty in elderly patients after surgical emergencies.

PURPOSE: Frailty is known to increase vulnerability to stressful factors, and motivate a higher morbidity and mortality in several health conditions. However, long-term impact of frailty after surgical procedures remains unclear. The purpose of this study was to evaluate the relationship between frailty and long-term clinical outcomes after emergency surgery. METHODS: Prospective cohort study in patients older than 70 years undergoing emergency procedures. A total of 82 patients (mean age 78.5 years, 53.3% women) were consecutively enrolled. Data on demographics, surgical procedures, complications after 30 postoperative days, and frailty according to the clinical frailty scale, Triage Risk Screening Tool (TRST), and FRAIL scale were recorded. Readmission, mortality, and transition to frailty rates were analyzed at 6 and 18 months postoperatively. RESULTS: The prevalence of frailty ranged between 14.6 and 29.6% depending on the scale used. The overall mortality rate at 18 months was 19.5% (16 patients), and the survival curves demonstrated a significant difference in mortality between frail and non-frail patients assessed using the FRAIL scale and TRST (p = 0.049 and p = 0.033, respectively), with a hazard ratio of 2.28 (95% confidence interval 1.24-6.44). Logistic regression analysis showed that diabetes (p = 0.013) was an independent risk factor for transition to frailty, and antidepressant drug use was close to statistical significance (p = 0.08). CONCLUSION: Frailty is a predictive marker of long-term mortality in patients undergoing emergency procedures. Diabetes and depression may represent independent risk factors for transition to frailty over time.

Impact of frailty in surgical emergencies. A comparison of four frailty scales.

INTRODUCTION: Frailty is a geriatric syndrome, leading to declines in homeostatic reserve and physical resistance. It has been considered as a risk factor for falls, fractures, need of institutionalization, length of stay and mortality. Our aim was to evaluate the relationship between frailty, 30-day postoperative mortality and morbidity, for elderly patients undergoing surgical emergencies. MATERIAL AND METHODS: Prospective, observational cohort Study (September 2017-April 2019), using four different frailty scales (Clinical Frailty Scale, FRAIL scale, TRST and Share-FI) as a risk factor of 30-day postoperative outcomes, for patients older than 70 years undergoing emergency surgery. We analyzed diagnoses, clinical examination at admission, surgical procedures, and postoperative outcomes during the first 30 days or until discharge. RESULTS: 92 patients were included, with a mean age was 78.7 years (SD 6.3). Frailty prevalence varied since 14.1% obtained using FRAIL scale, to 25%, 29.2% and 30.4%, from Clinical Frailty Scale, TRST and Share-FI, respectively. All four frailty scales show statistical differences to predict major complication and mortality in our sample. FRAIL scale showed the highest sensitivity-specificity pair to predict mortality in our sample (AUC = 0.870). TRST and FRAIL scales showed the strongest measure of association (OR 7.69 and 5.92, respectively) for major complications. Regarding need for admission to the ICU, hospital stay or reoperation rate, only FRAIL scale showed a statistically significant association. CONCLUSION: Frailty represents a predictive marker of mortality and major complications, in surgical emergencies. FRAIL score, shows the strongest relationship with mortality and complications, compared to other frailty scales.

Frailty Trait Scale-Short Form: A Frailty Instrument for Clinical Practice.

OBJECTIVES: To develop short versions of the Frailty Trait Scale (FTS) for use in clinical settings. DESIGN: Prospective population-based cohort study. SETTING AND PARTICIPANTS: Data from 1634 participants from the Toledo Study for Healthy Aging. METHODS: The 12-item Frailty Trait Scale (FTS) reduction was performed based on an area under the curve (AUC) analysis adjusted by age, sex, and comorbidity. Items that maximized prognostic information for adverse events were selected. Each item score was done at the same time as the reduction, identifying the score that maximized the predictive ability for adverse events. For each short version of the FTS, cutoffs that optimized the prognostic information (sensitivity and specificity) were chosen, and their predictive value was later compared with a surrogate gold standard for frailty (the Fried Phenotype). RESULTS: Two short forms, the 5-item (FTS5) (range 0-50) and 3-item (FTS3) (range 0-30), were identified, both with AUCs for health adverse events similar to the 12-item FTS. The identified cutoffs were >25 for the FTS5 scale and >15 for the FTS3. The frailty prevalence with these cutoffs was 24% and 20% for the FTS5 and FTS3, respectively, whereas frailty according to Fried Phenotype (FP) reached 8% and prefrailty reached 41%. In general, the FTS5 showed better prognostic performance than the FP, especially with prefrail individuals, in whom the FTS5 form identified 65% of participants with an almost basal risk and 35% with a very high risk for mortality (OR: 4) and frailty (OR: 6.6-8.7), a high risk for hospitalization (OR: 1.9-2.1), and a moderate risk for disability (OR: 1.7) who could be considered frail. The FTS3 form had worse performance than the FTS5, showing 31% of false negatives between frail participants identified by FP with a high risk of adverse events. CONCLUSIONS AND IMPLICATIONS: The FTS5 is a short scale that is easy to administer and has a similar performance to the FTS, and it can be used in clinical settings for frailty diagnosis and evolution.

Cognitive Performance across 3 Frailty Phenotypes: Toledo Study for Healthy Aging.

INTRODUCTION: Frailty is a strong predictor of adverse health events, but its impact on cognitive function is poorly understood. AIM: To assess cognitive performance in frailty and to identify the frailty stage where cognitive impairment begins. METHODS: Data were taken from 2044 people aged ≥65 years without cognitive impairment selected from the Toledo Study for Healthy Aging, a population-based cohort of older adults. Frailty status was assessed by 3 different scales: Frailty Phenotype (FP), Frailty Trait Scale (FTS), and Frailty Index (FI). Neuropsychological assessments of different cognitive domains included the Mini-Mental State Examination, Short and Long-Term Memory Recalling Test, the Boston Naming Test, Verbal Fluency Test, Digit Span Forward, Go/No-go Test, Luria Orders Test, Clock Drawing Test, and Serial Word Learning Test. The relationships between the score of the scales and frailty status (robust, prefrail, and frail for FP and quartiles for FTS and FI) were analyzed using multivariate linear regression models including age, sex, and educative level as possible confounders. RESULTS: Participants classified as the worst degree of frailty (frail in FP and fourth quartile of FTS and FI) presented more cognitive domains affected and to a higher extent than moderate frail (prefrail and second quartile and third quartile of FTS and FI) and robust (and first quartile of FTS and FI) participants. CONCLUSIONS: Cognitive performance progressively declined across the frailty state, regardless of the instrument used to assess frailty. In prefrail participants, cognitive impairment may be an early marker of frailty-dependent cerebral involvement and could be already subject to interventions aimed at reducing the transition to frailty.

Frailty at the front door.

Significant numbers of older people attending hospital can be considered to be frail or living with frailty. This is a multi-component syndrome with many manifestations that leads to poorer outcomes in terms of mortality, morbidity and institutionalisation. Recognition and management of frailty can be challenging, and requires a true multidisciplinary approach, but appropriate assessment and subsequent intervention have been proven to be beneficial. This article discusses the background to frailty, and a number of validated frailty scores which can be applied by non-specialists in the acute environment. It highlights other resources which are available to help with the management of this complex group of patients, and discusses potential local and national service developments in this area.