Development of a Falls Risk Screening Tool in a Traumatic Brain Injury Rehabilitation Population: a two-phase project
Notice bibliographique
Résumé
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
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 | 0,063 | 0,041 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».