FP707FRAILTY INTERVENTION TRIAL IN END STAGE PATIENTS ON HAEMODIALYSIS (FITNESS): AN OVERVIEW OF STUDY COHORT AND OUTCOME MEASURES TO DATE
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".