MétaCan
Menu
Retour à la cohorte
Enregistrement W2345912100 · doi:10.2106/jbjs.15.01437

Rethinking Orthopaedic Decision-Making for Frail Patients with Hip Fracture

2016· letter· en· W2345912100 sur OpenAlexaboutno aff
Robert L Kane, Julie A. Switzer, Mary Forte

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2016
Typeletter
Langueen
DomaineMedicine
ThématiqueHip and Femur Fractures
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDeliriumHip fractureMedicinePerioperativeCognitionGeriatricsCognitive impairmentPhysical therapyIntensive care medicineSurgeryOsteoporosisPsychiatry

Résumé

récupéré en direct d'OpenAlex

Commentary There are substantial research knowledge gaps regarding orthopaedic outcomes after surgical procedures to treat hip fractures in vulnerable elderly patients1. The article by Heng et al. serves to remind us that treating hip fractures must be done thoughtfully. Given the risks related to surgical hip fracture treatment, attention should be paid to treatment decisions for subgroups of high-risk patients. Not all older patients are equally likely to benefit from standard orthopaedic care. Some older adults are at high risk for inpatient hospital complications such as delirium, particularly those with prefracture cognitive impairment. Geriatric hip fractures are associated with high morbidity, mortality, and prolonged functional impairment. At least one-third of patients die within 1 year after a hip fracture2 and less than one-half ever regain their prefracture level of function3. Given this trajectory, efforts such as cognitive screening and other measures of frailty can provide useful insights in planning treatment. Cognitive screening can serve several important roles. It can help surgeons to understand a patient’s ability to understand his or her diagnosis and to be a partner in decision-making. It can also inform surgical decision-making as to the expected demands that each patient may place on a newly stabilized fracture, given the patient’s perceived ability to comply with postoperative functional recommendations. As shown in this article by Heng et al., cognitive status can predict complications, especially perioperative delirium, which, in turn, is a harbinger of increased morbidity and mortality. Patients at high risk should be closely screened preoperatively and postoperatively using programs such as the Hospital Elder Life Program (HELP) for Prevention of Delirium, a multicomponent intervention to prevent delirium in hospitalized older patients. Tools such as the Confusion Assessment Method (CAM) are available in short and long versions. The current study by Heng et al. was conducted in two hospitals that employed a joint practice team with active participation of geriatricians to assist in the care management. However, such resources are not widely available. Less well-served orthopaedic programs and sites must determine ways to routinely involve their medical colleagues in postoperative management and, potentially, preoperative decision-making, particularly in the management of frail patients with hip fracture. Impaired cognition should also signal a need to consider what kind of fracture and rehabilitative treatment is best. The Mini-Cog is a simple screening test. It should be followed up with a more complete cognitive evaluation to assess the level of cognitive impairment. Patients with hip fracture and dementia and/or those admitted from nursing homes have particularly poor medical outcomes and high mortality. More than 25% of elderly patients with hip fracture in the United States have dementia and 20% are admitted from nursing homes4; and these proportions are expected to increase as the population ages. Both patient groups were recently identified as hip fracture outcomes research priorities by a panel of U.S. and Canadian hip fracture experts1. Patients with hip fracture and dementia have high mortality and poor medical outcomes compared with cognitively intact patients, although the mechanisms that account for their worse medical outcomes remain largely unknown. Patients admitted with hip fractures from nursing homes have double the early mortality of non-nursing home patients, approaching 25% by the third month postoperatively4. Hip fracture is associated with excess mortality, even after taking into account prefracture health status, genetic factors, preexisting comorbidities, and lifestyle factors. The highest excess mortality risk seems to be within the first 6 months to 1 year post-fracture, with some variation by age and prior health status, although some excess mortality persists beyond that timeframe2. There is, of course, the potential for confounding relationships5. Preexisting factors such as sex, age, frailty, comorbidity, dementia, and osteoporosis may be associated with fractures themselves6, overall and post-fracture mortality7, and related problems such as falls. Given the high risk that patients from nursing homes and those with dementia may represent, more attention should be paid to what type of treatment is most beneficial. In what instances should surgical treatment be avoided? Several investigators have described outcomes of nonoperative treatment of hip fractures in the elderly. Given the physiologic stress of surgical fixation of a hip fracture and the increasing number of extremely frail individuals who will sustain these and other fragility fractures, questions regarding the wisdom of operative treatment for these patients remain. Patients with impacted femoral neck fractures, patients who are extremely frail, and perhaps even patients who are living in nursing homes when they sustain their fractures may benefit from nonoperative treatment. Focused work regarding goals of care in frail elderly patients and the ability of operative treatment to meet those goals is warranted.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,231
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,023
Score d'incertitude au seuil0,089

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0170,231
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,005
Communication savante0,0050,007
Science ouverte0,0060,003
Intégrité de la recherche0,0230,026
Charge utile insuffisante (le modèle a refusé de juger)0,0140,004

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 Opus0,020
Tête enseignante GPT0,251
Écart entre enseignants0,232 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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

En bref

Citations2
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Bone and Joint SurgeryMême sujetHip and Femur FracturesTravaux en français237 207