Bibliographic record
Abstract
Frailty and delirium. Re-assessing these two ‘old elephants’ inthe operating room The decision whether to operate in elderly patients is not always easy. Detecting the presence of frailty or the risk of delirium, as part of the preoperative assessment, has recently received renewed interest as predictors of poor outcomes after surgery. Detecting their presence early may provide time to put in place strategies to reduce their effects – prehabilitation. Frailty is decreased physiological reserve across multiple organ systems, increasing the risk of disability and death and significantly increasing postoperative complications. Partridge (Age Ageing 2012; 41: 142–7) in a meta-analysis described two models: the frailty phenotype, defined as a set of criteria or deficits including: unintentional weight loss, grip strength, self-reported exhaustion, gait speed and low physical activity, or more popular with surgeons, the frailty index, a deficit accumulation model which creates a score by giving a weight to each deficit, developed by Rockwood in Canada (J. Gerontol. A Biol. Sci. Med. Sci. 2007; 62: 722–7) and Fried et al. at Johns Hopkins Hospital (J. Gerontol. A Biol. Sci. Med. Sci. 2009; 64: 1049–57). In their preoperative assessments of elderly patients having gastrointestinal surgery, Chen et al. (J. Gastrointest. Surg. 2015; 19: 927–34) established ‘the 8 red flags’ of frailty being: age greater than 75 years, eating soft food, hypertension, weight loss >3 kg, fair-to-weak grip strength, sleeplessness, no better than peer-perceived health, and short-term inability to recall two or three common words. What is the evidence that frailty is an index of poor outcomes? McIsaac et al. (JAMA Surg. 2016; 151: 538–44) conducted a retrospective cohort study on over 200 000 patients having non-cardiac surgery using the Johns Hopkins score, identifying 3% of patients being frail, mean age 77 years, and within 1 year of surgery 14% of these patients have died, compared with 5% in the non-frail group. The greater mortality occurred in the early postoperative period, and in the younger patients having joint replacement surgery. In a smaller cohort study of 220 patients, over the age of 65, having emergency general surgery, Joseph et al. (J. Am. Coll. Surg. 2016; 222: 805–13), using the Rockwood score, found 37% were frail. Paradoxically, the frailty index did not correlate either with age or the ASA score, but 35% of patients had postoperative complications, of which 19% had major complications. Of the seven patients who died in the study, all were frail, indicating the power of the frailty index as an independent predictor of postoperative complications. Delirium can be defined as an acute confusional state, characterized by fluctuating symptoms including inattention, disturbances of consciousness or disorganized thinking, the hallmarks including disorientation, memory impairment, perceptual disturbances, altered psychomotor activity and disturbed sleep/wake cycles (Das Gupta, Dumbrell, J. Am. Geriat. Soc. 2006; 54: 1578–89). The importance of the preoperative assessment of delirium, is that it will become a component of a new standard in the revised National Standards of the National Safety & Quality Health Service used by the Australian Council of Health Care Standards to accredit hospitals, from 2017. The risk of delirium can be easily assessed at the bedside by nursing staff. The most popular tools being the 4AT and the Confusion Assessment Method, the former the simplest to understand, being four items assessing: level of alertness, the abbreviated mental test, attention testing using ‘the months backwards’, and acute change or fluctuating in mental status (http://www.safetyandquality.gov.au/media_releases/delirium-clinical-care-standard-to-improve-care-and-prevention/). Indeed, there is a significant association between the presence of frailty and postoperative delirium. Brown et al. (Anesth. Analg. 2016; 12: 1213–7), in a small study of 55 patients assessed before cardiac surgery, found the prevalence of frailty to be as high as 31% and frail patients had a higher incidence of delirium at 47% compared with the non-frail patients. Similarly Jung et al. (J. Thorac. Cardiovasc. Surg. 2015; 149: 869–75) who used the EuroSCORE II showed a similar high number of frail patients at 54%, with a significant risk of postoperative delirium. Having identified these patients, what can be done to improve their condition before surgery? In the Australian review: Patient Frailty – The Elephant in the Operating Room, Hubbard & Story (Anesthesia 2014; 69(Suppl.1): 26–34) emphasize the importance of prehabilitation in reversing the potential effects of frailty. Prehabilitation is a relatively new concept combining a tailored exercise programme, and nutritional supplementation to reduce anaemia, malnutrition and improve muscle strength, and this may be extended for strategies to reduce the risk of delirium. This needs to be conducted some weeks before the surgery, giving you time to discuss the risks with the patient, and giving them time to either reconsider having the surgery or improving their physical and nutritional wellbeing, ultimately helping you in the decision making process, in this challenging group of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".