Frailty for Perioperative Clinicians: A Narrative Review
Bibliographic record
Abstract
Frailty is a multidimensional syndrome characterized by decreased reserve and diminished resistance to stressors. People with frailty are vulnerable to stressors, and exposure to the stress of surgery is associated with increased risk of adverse outcomes and higher levels of resource use. As Western populations age rapidly, older people with frailty are presenting for surgery with increasing frequency. This means that anesthesiologists and other perioperative clinicians need to be familiar with frailty, its assessment, manifestations, and strategies for optimization. We present a narrative review of frailty aimed at perioperative clinicians. The review will familiarize readers with the concept of frailty, will discuss common and feasible approaches to frailty assessment before surgery, and will describe the relative and absolute associations of frailty with commonly measured adverse outcomes, including morbidity and mortality, as well as patient-centered and reported outcomes related to function, disability, and quality of life. A proposed approach to optimization before surgery is presented, which includes frailty assessment followed by recommendations for identification of underlying physical disability, malnutrition, cognitive dysfunction, and mental health diagnoses. Overall, 30%-50% of older patients presenting for major surgery will be living with frailty, which results in a more than 2-fold increase in risk of morbidity, mortality, and development of new patient-reported disability. The Clinical Frailty Scale appears to be the most feasible frailty instrument for use before surgery; however, evidence suggests that predictive accuracy does not differ significantly between frailty instruments such as the Fried Phenotype, Edmonton Frail Scale, and Frailty Index. Identification of physical dysfunction may allow for optimization via exercise prehabilitation, while nutritional supplementation could be considered with a positive screen for malnutrition. The Hospital Elder Life Program shows promise for delirium prevention, while individuals with mental health and or other psychosocial stressors may derive particular benefit from multidisciplinary care and preadmission discharge planning. Robust trials are still required to provide definitive evidence supporting these interventions and minimal data are available to guide management during the intra- and postoperative phases. Improving the care and outcomes of older people with frailty represents a key opportunity for anesthesiologists and perioperative scientists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".