Bed Falls, Delirium in Old Age, Delirium Superimposed on Dementia, Fall
Conditions
Keywords
bed-exit, bed-exit warning device, contact mat, 3-D-radar device
Brief summary
During delirium patients are at risk of severe harm due to unattended bed-exits resulting in falls. This research intends to explore how effective alarming contact mats (CareMat®) in comparison to contactless bed-exit alarming devices (Qumea®) are to reduce the risk of unattended bed-exits and falls.
Detailed description
Delirium is a neuropsychiatric disorder with a sudden and reversible decline in attention and cognition due to a medical condition.8 Delirium is associated with emotional distress for patients, their relatives and medical staff.3-5 During delirium, patients are at risk of severe harm due to unattended bed-exits and subsequent falls.6, 7 As worldwide strategy, sitters are used for the prevention of harm in patients with delirium. However, evidence of the effectiveness of sitters is scant.9 A newly designed specialised acute care unit for older patients with delirium, the FELIX PLATTER delirium unit (DelirUnit), strives to overcome the aforementioned shortcomings. On the DelirUnit there are no physical barriers such as bed rails to prevent patients from bed-exits. Floor beds minimize injuries when patients leave their beds unattended. Specialised nurses and nursing aides care for this vulnerable patient group. Sitters are banned. As an alternative to sitters, nurses are informed about patients' intended bed-exit by electronic alarming contact mats at the bedside or in front of beds (CareMat®) or by a novel contactless radar-based bed-exit monitoring system (Qumea®). Up until now, evidence for the effectiveness of technical devices for fall risk prevention is low. This research intends to explore how effective contact mats (CareMat®) or contactless bed-exit alarming devices (Qumea®) are in fall risk detection and fall prevention.
Interventions
DEVICEQumea
Contactless motion sensor (Qumea®) for bed-exit detection in combination with Qumea fall detection.
Sponsors
University Department of Geriatric Medicine FELIX PLATTER
University of Basel
Velux Fonden
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Admission at or transferal to DelirUnit. During Covid pandemic, patients will be enrolled into the study after the second negative Covid swab (PCR).
Exclusion criteria
Patients who have been sectioned and must be treated in a facility, whether they agree or not (under the mental health act) (Fürsorgliche Unterbringung). No proxy consent available due to language barriers; Missing legal proxy in case of lacking family network
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Falls | 6 months | Number of patient falls in patient room |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Bed-exit patterns | 6 months | Qumea® provides the following bed-exit pattern categories: 'STANDING', 'SITTING, 'LYING' oder 'OUT\_OF\_BED' . Unit is number/percentage of bed-exit patterns. Artificial intelligence algorithms provide automatized detected bed-exit patterns by a three dimensional radar (Qumea®) |
| Nurses' workload | 6 months | Workload is defined as the time in minutes per patient and per day (24-hour period) that a nurse is present in each patient's room. This is measured as the time between activation (nurse enters room) and deactivation (nurse leaves room) of the Hospicall (the hospitals' patient call system) presence button by the nurse in each patient's room. Hospicall automatically generates a time stamp in the logbook of the Hospicall operating system when the presence button is activated and deactivated. |
| Time to confirm a CareMat® / Qumea® warning by nurse presence in room | 6 months | Time difference in seconds between timestamps from onset of warning and confirmation of warning with presence button in patient room of bed-exit warning from bed-exit surveillence systems (Qumea® or CareMat®) to Hospicall (a decentralised, scalable patient call system). It is the time a nurse needs to confirm a bed-exit warning from the bed-exit surveillence systems. |
| Delirium severity | 6 months | Delirium severity is a score between 0 and 39 points on a 13-item 4-point (0-3) Lickert scale, as measured with the Delirium Rating Scale Revised 98 (DRS-R-98). A cut-off score of 15.5 points indicates delirium. The higher the score, the more severe is the delirium. For the prediction models, the DRS-R-98 scores (1) at admission, (2) the mean values of the course of delirium and (3) the difference between admission and discharge will be calculated. The measurements are conducted by the research team (PI or research assistants) during the stay on the delirium unit (on Mondays, Wednesdays and Fridays) and on the day before discharge. |
| Delirium duration | 6 months | To determine the duration of delirium, the presence and/or severity of delirium is first measured with the modified Confusion Assessment Method for the Emergency Department (mCAM-ED). The mCAM-ED provides the following categories: (0) no delirium (1) probable delirium (2) definite delirium. Duration of delirium is measured in days between the first delirium positive mCAM-ED assessments (category 1 or 2) and the last delirium positive (category 1 or 2) mCAM-ED assessments. The mCAM-ED assessments are conducted by the research team (PI or research assistants) Mondays, Wednesdays and Fridays and on the day before discharge. |
Countries
Switzerland
Contacts
PRINCIPAL_INVESTIGATORWolfgang Hasemann, PhD
University Department of Geriatric Medicine FELIX PLATTER
Outcome results
None listed