FP707FRAILTY INTERVENTION TRIAL IN END STAGE PATIENTS ON HAEMODIALYSIS (FITNESS): AN OVERVIEW OF STUDY COHORT AND OUTCOME MEASURES TO DATE
Notice bibliographique
Résumé
INTRODUCTION: Frailty is increasingly recognised as an important concept in healthcare, representing a state of low physiological reserve and multi-systemic dysregulation that leaves the individual susceptible to external stressors. Frailty is prevalent among haemodialysis patients, ranging between 30% to 78% dependent upon diagnostic tool used, and is associated with significant adverse outcomes such as falls, loss of function independence, hospitalisation and mortality. However, these studies are predominantly from the US and may not be directly translatable to a UK cohort. In addition, with a variety of subjective and objective measurements available, it is unclear which frailty tool is superior as a diagnostic/prognostic tool. Therefore, there is an urgent need for clinical research into how frailty is defined for our haemodialysis patients and its impact within a UK cohort. METHODS: The first component of FITNESS is a cohort study of prevalent haemodialysis patients which aims to identify the prevalence of frailty among this population and outcomes associated with frailty such as mortality, hospitalisation, and quality of life. The inclusion criteria include: 1) aged 18 years and over; 2) receiving regular haemodialysis of at least 3-months duration; and 3) able to provide informed consent. The only exclusion criteria are a preceding inpatient admission (unless for vascular access) within 4-weeks. Study recruits will undergo a range of assessments including; timed walk test over 4-metres, assessment of grip strength, Montreal Cognitive Assessment (MoCA), quadriceps ultrasound, health-based questionnaires (EuroQol and Patient Health Questionnaire) and extensive frailty-based investigations (to allow calculation of multiple frailty assessments including Fried, Edmonton Frailty Scale, Frailty Index Score). This will be compared to the Clinical Frailty Scale, a subjective assessment by the nephrologist in charge. Data will be extracted from electronic patient records to provide additional data on comorbidities, dialysis parameters, previous transplantation, biochemical data, medication history, and social deprivation score. Outcomes data will include dialysis parameters, procedures, hospital admissions and deaths. RESULTS: To date, 395 haemodialysis patients have been recruited and we are on target to accrue 500 patients by April 2019. Baseline demographics so far are mean age (62.7 years), non-white ethnicity (35.9%) and male sex (57.7%). The five commonest causes of kidney failure are diabetes (20.8%), ischaemic nephropathy (7.8%), hypertension (7.6%), IgA nephropathy (7.6%) and polycystic kidney disease (6.3%). Regardless of tool utilised, frailty is prevalent (e.g. using Fried scale; non-frail (13.2%), pre-frail (49.6%) and frail (37.2%)) with statistically significant correlation between all frailty tools (all p<0.001). Within three-months of recruitment, the study cohort has generated the following outcomes; line insertions (n=21), vascular access assessment/intervention (n=78), hospital admissions (n=92), kidney transplants (n=5) and deaths (n=5). CONCLUSIONS: FITNESS has demonstrated the potential of a well-characterise clinical cohort to investigate the prevalence and impact of the frailty phenotype and the optimum diagnostic tool. This will lead into the second component of the FITNESS study exploring the feasibility of a multi-disciplinary intervention to target pre-frail individual using evidence-based behaviour change techniques.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».