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Enregistrement W4389083948 · doi:10.1111/ans.18793

Knowledge and practice regarding frailty and cognitive impairment in older patients – a survey of surgical unit staff

2023· article· en· W4389083948 sur OpenAlexaboutno aff
Cilla Haywood, Laurence Weinberg, Vijayaragavan Muralidharan, Kathleen Gray

Notice bibliographique

RevueANZ Journal of Surgery · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueFrailty in Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineReferralCognitive impairmentGeriatricsCognitionDementiaGerontologyFamily medicinePhysical therapyDiseasePsychiatryInternal medicine

Résumé

récupéré en direct d'OpenAlex

The proportion of surgical candidates aged over 75 is increasing more rapidly than the proportion of that demographic in the general community.1 The geriatric syndromes of frailty and cognitive impairment are significant risk factors for postoperative morbidity and mortality2 and hinder return to preoperative function.3, 4 However, preoperative screening for frailty and cognitive impairment is not routine.5 There is a paucity of scholarly literature elucidating the reasons for this. Austin Health is a quaternary health service in Melbourne, Australia, offering a comprehensive range of surgical specialties. In 2019, Austin Health established a preoperative shared decision-making clinic for older people considering elective surgery. Approximately 100 elderly (mean age 82), frail (median clinical frailty score 5) patients are seen per year. About 50% of these patients are awaiting lower limb arthroplasty, and the remainder are from all other specialties. An increasing number of referrals are for those considering cancer surgery. To date, over 50% of patients attending this clinic decide to decline the proposed procedure after a comprehensive geriatric assessment and discussion with the proceduralist responsible. However, the absence of a protocolized preoperative screening process for frailty and cognitive impairment means that not all patients who might benefit from this clinic are referred. In 2023, we commenced a digital health initiative aiming to implement screening for these syndromes. The first objective was to assess doctors' understanding and practice of frailty and cognitive impairment screening and identify practical barriers and facilitators to screening and referral for further assessment. We surveyed doctors (both junior and senior medical staff) affiliated with the directorate of surgery and procedural medicine at Austin Health. The survey was developed by experts in geriatrics and digital health and was conducted using Qualtrics (see Data S1). The survey was approved by the Austin Health Research Ethics Committee (reference number HREC/97396/Austin-2023). Information about the survey was disseminated electronically and via posters. Sixty-seven people responded to the survey, representing a comprehensive range of procedural specialties (Table 1 in Data S2) and 15% of the total medical staff. Consultants comprised 39 respondents (58.2% – Table 2 in Data S2). The initial questions addressed respondents' knowledge and attitudes concerning frailty and cognitive impairment and offered Likert-type options. Sixty-four respondents (95.5%) agreed or strongly agreed that preoperative frailty and cognitive impairment are risk factors for postoperative morbidity and mortality. A similar number agreed or strongly agreed that frailty and cognitive impairment were important factors to consider during preoperative counselling (Table 3 in Data S2). Subsequent questions addressed the level of respondents' familiarity with metrics that assess frailty and cognitive impairment. In total, 39 respondents (58.2%) were familiar with one or more assessments for frailty, while 50 respondents (74.6%) were familiar with one or more assessments for cognitive impairment. In terms of specific assessments, 59 respondents (88.1%) reported being familiar with the Mini-Mental State Examination (MMSE), but only 29 respondents (43.3%) had familiarity with the Clinical Frailty Scale. A smaller proportion of respondents had familiarity with the Montreal Cognitive Assessment (MOCA), the Edmonton Frailty Score, the Informant Questionnaire on Cognitive Decline in the Elderly or the Rowland Universal Dementia Assessment Scale (Table 4 in Data S2). The next set of questions addressed the respondents' practice concerning the evaluation of frailty and cognitive impairment. The respondents were given response options ranging from ‘Always’ to ‘Never’. Only 15 respondents (23.5%) reported that they always conduct assessments for frailty, while 11 respondents (17.2%) reported this for cognitive impairment. Ten respondents (15.6%) indicated that they consistently recommend individuals with frailty for further testing, while nine respondents (14.3%) reported this for individuals with cognitive impairment. The most common answer to these questions was ‘Occasionally’ (Table 5 in Data S2). Regarding the timing of assessment for frailty and cognitive impairment, all but one respondent expressed the view that assessment should take place prior to obtaining consent (Table 6 in Data S2). In relation to the party who should assume responsibility for screening, 45 respondents (67.4%) indicated a preference for the proceduralist. Other responses included the referring doctor, a nurse specifically appointed for the task, or a perioperative physician (Table 7 in Data S2). A subsequent series of questions addressed the obstacles and facilitators associated with screening for frailty and cognitive impairment (Table 8 in Data S2). A majority, 38 respondents (56.7%), indicated that time limitations were a significant but manageable obstacle. Other factors, including an understanding of the optimal timing for screening, the selection of suitable tests and the management of frail or cognitively impaired patients, were generally identified as a moderate barrier. Potential applications of the screening outcomes were deemed to include ‘facilitating shared decision-making’ (31 respondents, 46.3%) and ‘assisting in determining the appropriateness of the procedure’ (16 respondents, 23.9%). Factors thought to facilitate screening for frailty and cognitive impairment included the inclusion of a dedicated section in the template used for multidisciplinary meetings (34 respondents, 50.7%), the implementation of clinical decision support systems that flag cases of frailty and cognitive impairment (32 respondents, 47.8%) and referral of patients to a shared decision-making clinic (23 respondents, 34.3%). The findings of this survey indicate that the respondents possess an understanding of the significance of conducting screenings for frailty and cognitive impairment, believe this is an integral aspect of their clinical responsibilities and would prefer to perform this prior to consent. Most responses came from consultants. It is unclear how having more years of experience affects perception of the importance of frailty, and it is not possible from this survey to determine this, especially given that the attitude towards the need for frailty assessment was overwhelmingly positive. Positivity towards frailty screening was demonstrated in similar surveys of surgeons.6, 7 These survey findings demonstrate a general desire to assess patients in accordance with the perioperative care framework.8 Further research as to the optimal way of implementing frailty screening would be useful. Our survey revealed that time constraints, lack of knowledge of screening tools and unclear referral pathways were practical barriers to implementation of screening for geriatric syndromes at Austin Health. Hence, from an implementation science perspective,9 an acceptable intervention to improve screening would be one which was simple, brief, and linked to clinical decision support which embedded referral pathways. Given the desire for early detection of frailty and cognitive impairment, a patient or informant-related tool could be administered just prior to initial review with the surgeon, potentially via the health service's customer relationship management platform. Integrating this information with the electronic medical record (EMR), alerting the staff to abnormal results, and designing referral pathways would then be necessary. This process would ideally be harmonized across each surgical unit, as at present each unit has a different way of using the EMR. The workflows would also ideally be co-designed with members of the medical and nursing staff. This would be a large undertaking for a health service; a project of this size would take months to years, requiring funding, project management and staff education. It would therefore likely need to be endorsed by a government health department as part of a surgical reform strategy. The workflow would need to be designed such that it minimized interruptions and alert fatigue.10 Our results have limitations. This was a single centre study, and our findings may not be generalizable to other hospitals. The sample size of ~15% of relevant staff means that selection bias is a potential concern, and the true knowledge and attitudes towards geriatric syndromes may be less positive than presented in this survey. Nevertheless, the results of the survey give new and valuable insights as to how the implementation gap in embedding screening for geriatric syndromes might be bridged in a large, high-surgical volume hospital. Cilla Haywood: Conceptualization; formal analysis; methodology; writing – original draft; writing – review and editing. Laurence Weinberg: Writing – review and editing. Vijayaragavan Muralidharan: Writing – review and editing. Kathleen Gray: Conceptualization; methodology; writing – review and editing. Data S1. Supporting Information. Data S2. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0,003
score de la tête « metaresearch » (Gemma)0,005
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 enseignants0,016
Score d'incertitude au seuil0,574

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
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 Opus0,086
Tête enseignante GPT0,358
Écart entre enseignants0,272 · 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
Publié2023
Routes d'admission1
Résumé présentoui

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Même revueANZ Journal of SurgeryMême sujetFrailty in Older AdultsTravaux en français237 207