MétaCan
Menu
Retour à la cohorte
Enregistrement W4241206032 · doi:10.1111/ans.13732

Medicine in small doses

2016· article· en· W4241206032 sur OpenAlexaboutno aff
Bruce P. Waxman

Notice bibliographique

RevueANZ Journal of Surgery · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueCardiac, Anesthesia and Surgical Outcomes
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineDeliriumGrip strengthWeight lossFrailty IndexGerontologyPhysical therapyObesityInternal medicineIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,145
Score d'incertitude au seuil0,399

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,064
Tête enseignante GPT0,286
Écart entre enseignants0,222 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueANZ Journal of SurgeryMême sujetCardiac, Anesthesia and Surgical OutcomesTravaux en français237 207