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Record W7056122202

Development of a Falls Risk Screening Tool in a Traumatic Brain Injury Rehabilitation Population: a two-phase project

2017· dissertation· en· W7056122202 on OpenAlexaboutno aff

Bibliographic record

VenueThe Sydney eScholarship Repository (The University of Sydney) · 2017
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsRehabilitationTraumatic brain injuryPopulationPoison controlInjury preventionAcute careRisk assessmentHuman factors and ergonomicsCohort studyRetrospective cohort study
DOInot available

Abstract

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Background With higher rates of falls reported in sub-acute than acute care inpatient populations, patients in rehabilitation settings are generally described as at an increased risk of falling. This is especially the case for patients with cognitive impairment. In mixed inpatient rehabilitation cohorts, traumatic brain injury (TBI) is one diagnosis-related group with cognitive impairment that has been identified as at an increased risk for falls. However, despite the number of falls studies involving many patient populations, falls in the inpatient TBI rehabilitation population is under-researched. There has also been no falls risk screening tool (FRST) developed for, or validated in, this patient population. Consequently, there is the real possibility that frontline clinicians are using FRSTs that have poor clinical utility. This is likely to have implications for falls prevention. In order to prevent falls, it is essential to accurately identify those individuals who are most likely to fall and why. There is a need for research into the nature of falls and factors that contribute to falls in the inpatient TBI rehabilitation population, and for a validated FRST sensitive to this patient population to be developed. Aim This project aimed to: A. develop a falls risk patient profile for the inpatient TBI rehabilitation population (phase 1); and B. develop a FRST sensitive to the inpatient TBI rehabilitation population (phase 2). Design A two-phase research design was used that consisted of four discrete studies undertaken sequentially. Methods The four studies comprised: a retrospective cohort study (Chapter Four) to describe the nature of falls in the inpatient TBI rehabilitation population; a retrospective nonequivalent case-control study (Chapter Five) to describe the characteristics of patients who fall; a modified Delphi study to gain consensus from a panel of experts on patient characteristics that contribute to falls (Chapter Six); and an 18-month prospective cohort study (Chapter Seven) to develop a FRST sensitive to the inpatient TBI rehabilitation population. With the exception of the modified Delphi study which involved experts from a wide range of settings, the studies were undertaken in short-stay inpatient rehabilitation units specialising in rehabilitating individuals following a TBI. These units service the state of New South Wales, Australia. P a g e | 2 Before the studies commenced, an integrative review of the research literature was conducted (Chapter Three). The aim of this review was to critically appraise the research literature on the nature of falls and characteristics of fallers in TBI rehabilitation settings (inpatient and community). This review, the cohort study, case-control study and the modified Delphi study were used to develop a falls risk profile which was tested in the prospective cohort study and formed the basis for the development of a FRST. Results In the retrospective cohort study the fall incident rate was 5.18 per 1000 patient bed days; as a proportion of admitted patients 22% fell. Over a 24-hour period falls occurred in a trimodal pattern represented by peak fall periods 0900–0959 hours, 1500–1559 hours and 1700–1759 hours. At these times in this setting, patients were undertaking their morning routine, engaging in morning and afternoon therapy sessions and having their evening meal (often requiring one-to-one nursing assistance). Forty-three percent of first falls occurred in the first week of inpatient rehabilitation and 35% occurred after one month. In contrast to several studies identified in the literature, the retrospective nonequivalent case-control study revealed that age, sex, medication class and total number of medications administered on admission to rehabilitation were not associated with falls in the inpatient TBI rehabilitation population. Impaired mobility and cognition, bladder and/or bowel dysfunction (incontinence) and Functional Independence MeasureTM total and subscale scores were associated with patients who fell. In the case-control study, fallers were over 10 times more likely than non-fallers to require assistance with activities of daily living, transfers and continence/toileting on admission. Neurobehaviours, including noncompliance and anosognosia, were associated with patients who fell. In the three-round modified Delphi study, the predictive efficacy of 38 falls risk factor items in the TBI rehabilitation population were considered by a panel of experts. In round three, five items were rejected (such as, male gender and certain medication classes), five were rated as undecided (such as, antecedent falls and polypharmacy) and expert consensus was reached for 28 items. The panel of experts identified that some risk factors for falls, such as outdoor mobility, are more relevant at particular times during a patient’s rehabilitation. From results of the integrative review, retrospective cohort study, case-control study and modified Delphi study, a 21 falls risk variable dataset was identified for inclusion in the prospective cohort study. Twenty of these variables were significantly associated with patients who fell. Through multiple logistic regression modeling, 11 variables were identified as predictors for falls. Using hierarchical regression, five of these were identified for inclusion in the resulting FRST: a prescribed mobility aid (such as, wheelchair or frame), a fall since admission to hospital, impulsive behaviour, impaired orientation and bladder and/or bowel incontinence. The resulting tool, the Sydney Falls Risk P a g e | 3 Screening Tool (SFRST), was found to have good clinical validity (sensitivity = 0.9; specificity = 0.64; area under the curve = 0.87; Youden index = 0.54). The SFRST was significantly more accurate (p = .037 on DeLong test) in discriminating fallers from non-fallers than the Ontario Modified STRATIFY FRST. Conclusion TBI rehabilitation patients with a severe brain injury characterised by multisystem impairments are at an increased risk of falling, however, some common falls risk factors such as age, sex, antecedent falls, medication class and medication quantity were not associated with falls in this population. Some falls risk factors are more prominent at different times over a 24-hour day and at particular times during a patient’s rehabilitation. Some situations where a patient’s risk of falling may increase include the commencement of high-level mobility activities, outdoor mobility or weekend leave and when a patient has improved mobility but is not yet independent. Consequently, rehabilitation clinicians need to be mindful that a patient’s risk of falling is not linear but may increase over time. Rehabilitation settings should therefore consider cohort-specific falls risk profiling and periodic falls risk screening. In the TBI rehabilitation setting, generic falls prevention measures are insufficient for preventing falls and falls prevention initiatives should target times of high patient activity and situations where there is decreased nursing capacity to observe all patients concurrently (such as, during a patient’s morning routine and their evening meal). The Ontario Modified STRATIFY FRST has limited clinical utility in this patient population. A FRST has been developed using a comprehensive methodological framework and evidence has been provided of this tool’s clinical validity. The developed tool, the SFRST, should be considered for use in inpatient brain injury rehabilitation populations

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.063
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.041
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0040.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.024
GPT teacher head0.302
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
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