Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies

Practices of falls risk assessment and prevention in acute hospital settings: a realist investigation

Background

Falls are the most common safety incident reported by acute hospitals. The National Institute of Health and Care Excellence recommends multifactorial falls risk assessment and tailored interventions, but implementation is variable.

Aim

To determine how and in what contexts multifactorial falls risk assessment and tailored interventions are used in acute National Health Service hospitals in England.

Design

Realist review and multisite case study. (1) Systematic searches to identify stakeholders' theories, tested using empirical data from primary studies. Review of falls prevention policies of acute Trusts. (2) Theory testing and refinement through observation, staff interviews (n = 50), patient and carer interviews (n = 31) and record review (n = 60).

Setting

Three Trusts, one orthopaedic and one older person ward in each.

Results

Seventy-eight studies were used for theory construction and 50 for theory testing. Four theories were explored. (1) Leadership: wards had falls link practitioners but authority to allocate resources for falls prevention resided with senior nurses. (2) Shared responsibility: a key falls prevention strategy was patient supervision. This fell to nursing staff, constraining the extent to which responsibility for falls prevention could be shared. (3) Facilitation: assessments were consistently documented but workload pressures could reduce this to a tick-box exercise. Assessment items varied. While individual patient risk factors were identified, patients were categorised as high or low risk to determine who should receive supervision. (4) Patient participation: nursing staff lacked time to explain to patients their falls risks or how to prevent themselves from falling, although other staff could do so. Sensitive communication could prevent patients taking actions that increase their risk of falling.

Limitations

Within the realist review, we completed synthesis for only two theories. We could not access patient records before observations, preventing assessment of whether care plans were enacted.

Conclusions

(1) Leadership: There should be a clear distinction between senior nurses' roles and falls link practitioners in relation to falls prevention; (2) shared responsibility: Trusts should consider how processes and systems, including the electronic health record, can be revised to better support a multidisciplinary approach, and alternatives to patient supervision should be considered; (3) facilitation: Trusts should consider how to reduce documentation burden and avoid tick-box responses, and ensure items included in the falls risk assessment tools align with guidance. Falls risk assessment tools and falls care plans should be presented as tools to support practice, rather than something to be audited; (4) patient participation: Trusts should consider how they can ensure patients receive individualised information about risks and preventing falls and provide staff with guidance on brief but sensitive ways to talk with patients to reduce the likelihood of actions that increase their risk of falling.

Future work

(1) Development and evaluation of interventions to support multidisciplinary teams to undertake, and involve patients in, multifactorial falls risk assessment and selection and delivery of tailored interventions; (2) mixed method and economic evaluations of patient supervision; (3) evaluation of engagement support workers, volunteers and/or carers to support falls prevention. Research should include those with cognitive impairment and patients who do not speak English.

Study registration

This study is registered as PROSPERO CRD42020184458.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR129488) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 5. See the NIHR Funding and Awards website for further award information.

Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study

Purpose

We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it.

Patients and Methods

This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data.

Results

Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758).

Conclusion

SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.

Prediction of early falls using adherence and balance assessments in a convalescent rehabilitation ward

Objectives

To predict falls by adding an adherence assessment to a static balance ability assessment, and to evaluate fall prediction accuracy.

Methods

This study included 416 patients who were admitted to a 45-bed convalescent rehabilitation ward over a 2-year period. The patients were assessed at the time of admission using the Standing Test for Imbalance and Disequilibrium (SIDE) and three additional, newly developed adherence items. Patients were divided into two groups: a group that experienced falls (fall group) and a group that did not experience falls (non-fall group) within 14 days of admission. The sensitivity and specificity of the assessment items for predicting falls were calculated.

Results

Sensitivity was 0.86 and specificity was 0.42 when the cutoff was between SIDE levels 0-2a and 2b-4. Combining balance assessment using the SIDE with the memory and instruction adherence items improved fall prediction accuracy such that the sensitivity was 0.75 and the specificity was 0.64.

Conclusions

Our analysis suggested that adherence assessment can improve fall risk prediction accuracy.

Fall risk prediction ability in rehabilitation professionals: structural equation modeling using time pressure test data for Kiken-Yochi Training

Background

Falls occur frequently during rehabilitation for people with disabilities. Fall risk prediction ability (FRPA) is necessary to prevent falls and provide safe, high-quality programs. In Japan, Kiken Yochi Training (KYT) has been introduced to provide training to improve this ability. Time Pressure-KYT (TP-KYT) is an FRPA measurement specific to fall risks faced by rehabilitation professionals. However, it is unclear which FRPA factors are measured by the TP-KYT; as this score reflects clinical experience, a model can be hypothesized where differences between rehabilitation professionals (licensed) and students (not licensed) can be measured by this tool.

Aims

To identify the FRPA factors included in the TP-KYT and verify the FRPA factor model based the participants' license status.

Methods

A total of 402 participants, with 184 rehabilitation professionals (physical and occupational therapists) working in 12 medical facilities and three nursing homes, and 218 rehabilitation students (physical and occupational therapy students) from two schools participated in this study. Participant characteristics (age, gender, job role, and years of experience and education) and TP-KYT scores were collected. The 24 TP-KYT items were qualitatively analyzed using an inductive approach based on content, and FRPA factors were extracted. Next, the correction score (acquisition score/full score: 0-1) was calculated for each extracted factor, and an observation variable for the job role (rehabilitation professional = 1, rehabilitation student = 0) was set. To verify the FRPA factors associated with having or not having a rehabilitation professional license, FRPA as a latent variable and the correction score of factors as an observed variable were set, and structural equation modeling was performed by drawing a path from the job role to FRPA.

Results

The results of the qualitative analysis aggregated patient ability (PA), physical environment (PE), and human environment (HE) as factors. The standardized coefficients of the model for participants with or without a rehabilitation professional license and FRPA were 0.85 (p < 0.001) for FRPA from job role, 0.58 for PA, 0.64 for PE, and 0.46 for HE from FRPA to each factor (p < 0.001). The model showed a good fit, with root mean square error of approximation < 0.001, goodness of fit index (GFI) = 0.998, and adjusted GFI = 0.990.

Conclusion

Of the three factors, PA and PE were common components of clinical practice guidelines for fall risk assessment, while HE was a distinctive component. The model's goodness of fit, which comprised three FRPA factors based on whether participants did or did not have rehabilitation professional licenses, was good. The system suggested that rehabilitation professionals had a higher FRPA than students, comprising three factors. To provide safe and high-quality rehabilitation for patients, professional training to increase FRPA should incorporate the three factors into program content.

Blind Spots in Hospital Fall Prevention: Falls in Stroke Patients Occurred Not Only in Those at a High Risk of Falling

OBJECTIVES

Although the standard falls prevention strategy is to identify and respond to patients with high-risk conditions, it remains unclear whether falls in patients with high fall risk account for most observed falls. In this study, fall risk and number of falls were calculated based on patients' motor and cognitive abilities, and the relationship between the two was examined.

DESIGN

We conducted a retrospective cohort study.

SETTING AND PARTICIPANTS

We included 2518 consecutive patients with stroke who were admitted to a rehabilitation hospital.

METHODS

Data on falls during hospitalization and biweekly assessed Functional Independence Measure scores were retrieved from the medical records. The average Functional Independence Measure scores for the motor and cognitive items were obtained and categorized as complete dependence, modified dependence, and independence. The fall rate (falls/1000 person-days) and number of observed falls in each combined condition were investigated.

RESULTS

Modified dependence on motor ability and complete dependence on cognitive ability had the highest risk of falls, with a fall rate of 10.8/1000 person-days and 51 fall observations, which accounted for 4.3% of all falls. Independent motor and cognitive ability had the lowest risk of falls, a fall rate of 2.6/1000 person-days and 146 observed falls, accounting for 12.4% of all falls, which was 2.8 times higher than the number of falls observed in the highest risk of falls condition.

CONCLUSIONS AND IMPLICATIONS

The combined motor-cognitive ability with the highest risk of falls in stroke inpatients did not have the highest number of observed falls. Rather, the combined motor-cognitive ability with the lowest risk of falls tended to have a high number of observed falls. A different strategy is needed to reduce the total number of falls.

Validation of the stratify scale for the prediction of falls among hospitalized adults in a tertiary hospital in Colombia: a retrospective cohort study

The STRATIFY scale has been implemented as a preventive strategy for predicting the risk of accidental falls among hospitalized adults. However, there is still uncertainty about its accuracy. This study aimed to perform an external validation of the STRATIFY fall prediction scale in hospitalized adults in one tertiary care hospital in Bogotá, Colombia. The study was a retrospective cohort of adult hospitalized patients in a high-level complexity care hospital. The sample selected included admitted patients (age ≥ 18), consecutively by the institution between 2018 and 2020, with an evaluation of the fall risk measured by the STRATIFY score given to each at the time of hospital admission. For assessing the scale's feasibility, its discriminative capability was obtained by calculating sensitivity, specificity, likelihood ratios, predictive values, and area under the ROC curve. The evaluation included 93,347 patient hospital records (mean 56.9 years, 50.2% women). The overall sensitivity score was 0.672 [IC 95% 0.612-0.723], the specificity score was 0.612 [IC 95% 0.605-0.615], and the positive likelihood ratio was 1.73 [IC 95% 1.589-1.891]. The area under the ROC curve was 0.69 [IC 95% 0.66-0.72]. Subgroups of age obtained similar results. Applying the STRATIFY scale at hospital admission resulted in a lower performance of the tool-predict falls in hospitalized patients. It is necessary to implement an individual evaluation of the risk factors for falls in order to structure appropriate care plans to prevent and improve hospital safety.

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Background

In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.

Objective

Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls.

Methods

A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric.

Results

The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent).

Conclusions

These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices.

Targeted optimum care approach for osteoporotic fragility fractures: tailored strategy based on risk stratification to reduce incidents of falls-an initiative by the Egyptian Academy of bone health based on the FLS national register

Since falling is the third cause of chronic disability, a better understanding of the frequency, severity, and risk factors of falls across diagnostic groups is needed to design and implement customized, effective fall prevention, and management programs for these individuals, particularly those at risk of sustaining a fragility fracture.

OBJECTIVE

(1) To assess the incidence of falls among osteoporotic patients with fragility fractures. (2) To evaluate the potential for stratifying the people at risk of falling in bone health setting aiming to provide targeted optimum care for them.

METHODS

This was a multi-center, cross-sectional, observational study. Both men and postmenopausal women, admitted with an osteoporotic fracture (whether major osteoporosis or hip fracture), were consecutively recruited for this work and managed under Fracture Liaison Service. All the patients were assessed for their Fracture risk (FRAX), falls risk (FRAS), and sarcopenia risk (SARC-F) as well as functional disability (HAQ). Blood tests for bone profile as well as DXA scan were offered to all the patients.

RESULTS

Four hundred five patients (121 males, 284 females) were included in this work. Mean age was 70.1 (SD = 9.2) years. The incidence of falls was 64.9%. The prevalence of falls was high (64.8%) in the patients presenting with major osteoporosis fractures and in those with hip fractures (61.8%). The prevalence of fragility fractures was positively correlated with HAQ score and the SARC-F score (p = 0.01 and 0.021 respectively). Falls risk score was positively correlated with FRAX score of major osteoporotic fractures, HAQ score, and SARC-F score (p = 0.01, 0.013, and 0.003 respectively). Seventy percent of the osteopenia patients who sustained fragility fracture had high falls risk and/or SARC-F score.

CONCLUSION

This study highlighted the importance of falls risk stratification in osteoporotic patients presenting with fragility fractures. Identification of the patients at increased risk of falls should be a component of the standard practice.

In-hospital fall prediction using machine learning algorithms and the Morse fall scale in patients with acute stroke: a nested case-control study

BACKGROUND

Falls are one of the most common accidents in medical institutions, which can threaten the safety of inpatients and negatively affect their prognosis. Herein, we developed a machine learning (ML) model for fall prediction in patients with acute stroke and compared its accuracy with that of the existing fall risk prediction tool, the Morse Fall Scale (MFS).

METHODS

This is a retrospective nested case-control study. The initial sample size was 8462 admitted to a single cerebrovascular specialty hospital with acute stroke. A total of 156 fall events occurred, and each fall case was randomly matched with six control cases. Six ML algorithms were used, namely, regularized logistic regression, support vector machine, naïve Bayes (NB), k-nearest neighbors, random forest, and extreme-gradient boosting (XGB).

RESULTS

We included 156 in the fall group and 934 in the non-fall group. The mean ages of the fall and non-fall groups were 68.3 (± 12.2) and 65.3 (± 12.9) years old, respectively. The MFS total score was significantly higher in the fall group (54.3 ± 18.3) than in the non-fall group (37.7 ± 14.7). The area under the receiver operating curve (AUROC) of the MFS in predicting falls was 0.76 (0.73-0.79). XGB had the highest AUROC of 0.85 (0.78-0.92), and XGB and NB had the highest F1 score of 0.44.

CONCLUSIONS

The AUROC values of all of ML algorithms were similar to those of the MFS in predicting fall risk in patients with acute stroke, allowing for accurate and efficient fall screening.

A short multi-factor screening tool to assess falls-risk in older people presenting to an Australian emergency department: A feasibility study

OBJECTIVE

The purpose of this study was to evaluate use of a short multi-factor falls-risk screening tool for older people within the emergency department, to enable rapid identification of falls-risk and triggers for multidisciplinary referral for further falls-specific assessment.

METHODS

Older people, aged ≥70 years, presenting to the emergency department with a fall-related injury or disease (n = 137) were recruited by a research nurse following randomisation. A short multi-factor screening tool was completed, comprised of 14 falls-risk-related assessment components.

RESULTS

Only one participant did not generate any referrals. Participants generated most referrals for medications (85.4%), social and housing (84.6%), vision (67.2%), podiatry (66.9%), or function and mobility (54.7%). Based on our results, the screening tool could be reduced to eleven components. The median time-to-screen was 11 min (IQR 9-15), with 736 triggers generated for referral and further assessment of falls-risk.

CONCLUSION

Falls are a major cause of ED presentation for older people. A short multi-factor screening tool with eleven components could be adapted to local familiar falls-risk tools and be completed in less than 10 min. Further research to trial the feasibility of completing ED referrals based on screening results is required to confirm the usefulness of such screening and referral within the ED.

Development and evaluation of a simple predictive model for falls in acute care setting

AIMS AND OBJECTIVES

To develop a simple and reliable assessment tool for predicting falls in acute care settings.

BACKGROUND

Falling injures patients, lengthens hospital stay and leads to the wastage of financial and medical resources. Although there are many potential predictors for falls, a simple and reliable assessment tool is practically necessary in acute care settings.

DESIGN

A retrospective cohort study.

METHODS

The current study was conducted for participants who were admitted to a teaching hospital in Japan. Fall risk was assessed by the modified Japanese Nursing Association Fall Risk Assessment Tool consisting of 50 variables. To create a more convenient model, variables were first limited to 26 variables and then selected by stepwise logistic regression analysis. Models were derived and validated by dividing the whole dataset into a 7:3 ratio. Sensitivity, specificity, and area under the curve for the receiver-operating characteristic curve were evaluated. This study was conducted according to the STROBE guideline.

RESULTS

Six variables including age > 65 years, impaired extremities, muscle weakness, requiring mobility assistance, unstable gait and psychotropics were chosen in a stepwise selection. A model using these six variables with a cut-off point of 2 with one point for each item, was developed. Sensitivity and specificity >70% and area under the curve >.78 were observed in the validation dataset.

CONCLUSIONS

We developed a simple and reliable six-item model to predict patients at high risk of falling in acute care settings.

RELEVANCE TO CLINICAL PRACTICE

The model has also been verified to perform well with non-random partitioning by time and future research is expected to make it useful in acute care settings and clinical practice.

PATIENT OR PUBLIC CONTRIBUTION

Patients participated in the study on an opt-out basis, contributing to the development of a simple predictive model for fall prevention during hospitalisation that can be shared with medical staff and patients in the future.

Time-Varying Hazard of Patient Falls in Hospital: A Retrospective Case-Control Study

This study aims to evaluate the association between patient falls and relevant factors and to quantify their effect on fall risk. This is a retrospective case-control study using the secondary data collected from a tertiary general hospital. Study subjects were 450 patients who were admitted to the hospital between January 2016 and December 2020. The prevalence of falls was associated with the fall risk level by the Morse Fall Scale (MFS) and individual status at admission including history of admission, dizziness, sleep disorder, bowel dysfunction, and urinary incontinence. The odds ratios of patient falls were higher in the low-risk group by the MFS score (odds ratio (OR) = 2.61, p < 0.001) and the high-risk group (OR = 5.51, p < 0.001) compared to the no-risk group. The hazard ratio of patient falls was higher in the high-risk group by the MFS score (hazard ratio (HR) = 3.85, p < 0.001). The MFS had a significant explanatory power to predict fall risk. Sleep disorder and urinary incontinence were the significant factors influencing patient falls.

Intrinsic capacity in acutely hospitalized older adults

OBJECTIVES

We aimed to examine the association between intrinsic capacity (IC) and adverse outcomes of hospitalization.

DESIGN

A prospective observational cohort study.

SETTING AND PARTICIPANTS

We recruited patients aged 65 years or older who were admitted to the geriatric ward of an acute hospital between Oct 2019 and Sep 2022.

MEASUREMENTS

Each of the five IC domains (locomotion, cognition, vitality, sensory, and psychological capacity) was graded into three levels, and the composite IC score was calculated (0, lowest; 10, highest). Hospital-related outcomes were defined as in-hospital death, hospital-associated complications (HACs), length of hospital stay, and frequency of discharge to home.

RESULTS

In total, 296 individuals (mean age 84.7 ± 5.4 years, 42.7 % males) were analyzed. Mean composite IC score was 6.5 ± 1.8, and 95.6 % of participants had impairment in at least one IC domain. A higher composite IC score was independently associated with lower frequency of in-hospital death (odds ratio [OR] 0.59) and HACs (OR 0.71), higher frequency of discharge to home (OR 1.50), and shorter length of hospital stay (β = -0.24, p < 0.01). The locomotion, cognition, and psychological domains were independently associated with the occurrence of HACs, discharge destination, and length of hospital stay.

CONCLUSION

Evaluating IC was feasible in the hospital setting and was associated with outcomes of hospitalization. For older inpatients with decreased IC, integrated management may be required to achieve functional independence.

Association between fall-related serious injury and activity during fall in an acute care hospital

OBJECTIVES

Few studies have evaluated the mechanism of serious injury in acute hospitalization. Thus, the association between fall-related serious injury and activity during falls in acute care hospital remains unclear. Herein, we investigated the relationship between serious injury caused by fall and activity at the time of the fall in an acute care hospital.

METHODS

This retrospective cohort study was conducted at Asa Citizens Hospital. All inpatients aged 65 years and older were eligible for the study, which was conducted from April 1, 2021, through March 31, 2022. The magnitude of the association between injury severity and activity during the fall was quantified using odds ratio.

RESULTS

Among the 318 patients with reported falls, 268 (84.3%) had no related injury, 40 (12.6%) experienced minor injury, 3 (0.9%) experienced moderate injury, 7 (2.2%) experienced major injury. Moderate or major injuries caused by a fall was associated with the activity during the fall (odds ratio: 5.20; confidence intervals: 1.43-18.9, p = 0.013).

CONCLUSION

This study recognizes that falling during ambulation caused moderate or major injuries in an acute care hospital. Our study suggests that falls while ambulating in an acute care hospital were associated not only with fractures, but also with lacerations requiring sutures and brain injuries. Among the patients with moderate or major injuries, more falls occurred outside the patient's bedroom as compared with patients with minor or no injuries. Therefore, it is important to prevent moderate or major injuries related to falls that occur while the patient is walking outside their bedroom in an acute care hospital.

Digital assistance systems in the field of incontinence care for individuals in need of long-term care (EASY): study protocol of a stratified randomised controlled trial

BACKGROUND

In general, urinary and faecal incontinence is a multifaceted problem that is associated with increasing burdens for those affected, a massive impairment of quality of life and economic consequences. Incontinence is associated with a high level of shame, which in particular reduces the self-esteem of those being incontinent and leads to additional vulnerability. Those affected by incontinence often perceive incontinence and the care they receiveas humiliating, hence they can no longer control their own urination; nursing care and cleansing support then lead to additional dependency. People with incontinence in need of care not uncommonly experience a poor communication and many taboos surrounding the issue as well as the use of force when incontinence products are changed.

AIMS AND METHODS

This RCT aims to validate the benefits of using a digital assistance system to optimise incontinence care and to enable statements concerning the effect of the assistance technology on nursing and social structures and processes as well as on the quality of life from the perspective of the person in need of care. In a two-arm, stratified, randomised, controlled interventional study, primarily incontinence-affected residents of four inpatient nursing facilities will be examined (n = 80). One intervention group will be equipped with a sensor-based digital assistance system, which will transmit care-related information to the nursing staff (via smartphone). The collected data will be compared to the data of the control group. Primary endpoints are falls occurring; secondary endpoints are quality of life and sleep, sleep disturbances and material consumption. In addition, nursing staff (n = 15-20) will be interviewed regarding the effects, experience, acceptance and satisfaction.

DISCUSSION

The RCT aims at the applicability and effect of assistance technologies on nursing structures and processes. It is assumed that, amongst other things, this technology may lead to a reduction of unnecessary checks and material changes, an improvement of life quality, an avoidance of sleep disturbances and thus an improvement of sleep quality as well as to a reduced risk of falling for people with incontinence in need of care. The further development of incontinence care systems is of social interest as this offers the prospect of improving the quality of care for nursing home residents with incontinence.

TRAIL REGISTRATION

Approval of the RCT is granted by the Ethics Committee at the University of Applied Sciences Neubrandenburg (Reg.-Nr.: HSNB/190/22). This RCT is registered at the German Clinical Trials Register on July 8^th, 2022, under the identification number DRKS00029635.

Development and Internal Validation of a Prediction Model for Falls Using Electronic Health Records in a Hospital Setting

OBJECTIVE

Fall prevention is important in many hospitals. Current fall-risk-screening tools have limited predictive accuracy specifically for older inpatients. Their administration can be time-consuming. A reliable and easy-to-administer tool is desirable to identify older inpatients at higher fall risk. We aimed to develop and internally validate a prognostic prediction model for inpatient falls for older patients.

DESIGN

Retrospective analysis of a large cohort drawn from hospital electronic health record data.

SETTING AND PARTICIPANTS

Older patients (≥70 years) admitted to a university medical center (2016 until 2021).

METHODS

The outcome was an inpatient fall (≥24 hours of admission). Two prediction models were developed using regularized logistic regression in 5 imputed data sets: one model without predictors indicating missing values (Model-without) and one model with these additional predictors indicating missing values (Model-with). We internally validated our whole model development strategy using 10-fold stratified cross-validation. The models were evaluated using discrimination (area under the receiver operating characteristic curve) and calibration (plot assessment). We determined whether the areas under the receiver operating characteristic curves (AUCs) of the models were significantly different using DeLong test.

RESULTS

Our data set included 21,286 admissions. In total, 470 (2.2%) had a fall after 24 hours of admission. The Model-without had 12 predictors and Model-with 13, of which 4 were indicators of missing values. The AUCs of the Model-without and Model-with were 0.676 (95% CI 0.646-0.707) and 0.695 (95% CI 0.667-0.724). The AUCs between both models were significantly different (P = .013). Calibration was good for both models.

CONCLUSIONS AND IMPLICATIONS

Both the Model-with and Model-without indicators of missing values showed good calibration and fair discrimination, where the Model-with performed better. Our models showed competitive performance to well-established fall-risk-screening tools, and they have the advantage of being based on routinely collected data. This may substantially reduce the burden on nurses, compared with nonautomatic fall-risk-screening tools.

Clinical study of falls among inpatients with hematological diseases and exploration of risk prediction models

Background

Falls are serious health events that can cause life-threatening injuries, especially among specific populations. This study assessed the risk factors associated with falls among inpatients with hematological diseases and explored the predictive value of fall risk assessment models.

Methods

Clinical data from 275 eligible hematology disease patients who visited Mianyang Central Hospital with or without falls from September 2019 to August 2022 were retrospectively analyzed. Fall risk scores were determined in all included patients. Clinical characteristics were compared between patients with and without falls. Binary logistic regression models were used to screen for potential fall-specific risk factors among hospitalized patients with hematology diseases.

Results

Falls occurred in 79 cases. Patients in the fall group had a higher Charlson Comorbidity Index (CCI), a higher incidence of diabetes mellitus, visual impairment, hematological malignancies, and maintenance of stable disease stage, higher glucose levels, and a greater proportion of dizziness, nocturnal defecation, and receipt of intensive chemotherapy than those in the non-fall group (all P < 0.05). Fall patients were also more likely to have used diuretics, laxatives, sedative-sleeping drugs, analgesics, albumin, and calcium, and to have had catheters placed. The Barthel Index, grade of nursing care, support of chaperones, body temperature, nutrition score, and pain score also differed significantly between the two groups (all P < 0.05). Multivariable logistic regression analysis showed that the maintenance of stable disease stage (OR = 4.40, 95% CI 2.11-9.18, P < 0.001), use of sedative and sleeping drugs (OR = 4.84, 95% CI 1.09-21.49, P = 0.038), use of diuretics (OR = 5.23, 95% CI 2.40-11.41, P < 0.001), and intensive chemotherapy (OR = 10.41, 95% CI 3.11-34.87, P < 0.001) were independent risk factors for falls. A high Barthel Index (OR = 0.95, 95% CI 0.93-0.97, P < 0.001), a high level of nursing care (OR = 0.19, 95% CI 0.04-0.98, P = 0.047), and availability of family accompaniment (OR = 0.15, 95% CI 0.06-0.34, P < 0.001) were protective factors for falls. A ROC curve analysis was used to evaluate the predictive value of different fall-specific risk scales among inpatients with hematological diseases. The Johns Hopkins Fall Risk Rating Scale had high sensibility and specificity with an area under the curve of 0.73 (95% CI 0.66-0.80, P < 0.001).

Conclusion

The Johns Hopkins Fall Risk Scale had a strong predictive value for falls among hospitalized patients with hematology diseases and can be recommended as a valid tool for clinical use.

Exploring variation in implementation of multifactorial falls risk assessment and tailored interventions: a realist review

BACKGROUND

Falls are the most common safety incident reported by acute hospitals. In England national guidance recommends delivery of a multifactorial falls risk assessment (MFRA) and interventions tailored to address individual falls risk factors. However, there is variation in how these practices are implemented. This study aimed to explore the variation by examining what supports or constrains delivery of MFRAs and tailored interventions in acute hospitals.

METHODS

A realist review of literature was conducted with searches completed in three stages: (1) to construct hypotheses in the form of Context, Mechanism, Outcome configurations (CMOc) about how MFRAs and interventions are delivered, (2) to scope the breadth and depth of evidence available in Embase to test the CMOcs, and (3) following prioritisation of CMOcs, to refine search strategies for use in multiple databases. Citations were managed in EndNote; titles, abstracts, and full texts were screened, with 10% independently screened by two reviewers.

RESULTS

Two CMOcs were prioritised for testing labelled: Facilitation via MFRA tools, and Patient Participation in interventions. Analysis indicated that MFRA tools can prompt action, but the number and type of falls risk factors included in tools differ across organisations leading to variation in practice. Furthermore, the extent to which tools work as prompts is influenced by complex ward conditions such as changes in patient condition, bed swaps, and availability of falls prevention interventions. Patient participation in falls prevention interventions is more likely where patient directed messaging takes individual circumstances into account, e.g., not wanting to disturb nurses by using the call bell. However, interactions that elicit individual circumstances can be resource intensive and patients with cognitive impairment may not be able to participate despite appropriately directed messaging.

CONCLUSIONS

Organisations should consider how tools can be developed in ways that better support consistent and comprehensive identification of patients' individual falls risk factors and the complex ward conditions that can disrupt how tools work as facilitators. Ward staff should be supported to deliver patient directed messaging that is informed by their individual circumstances to encourage participation in falls prevention interventions, where appropriate.

TRIAL REGISTRATION

PROSPERO: CRD42020184458.

CT image-based biomarkers acquired by AI-based algorithms for the opportunistic prediction of falls

Objective

Evaluate whether biomarkers measured by automated artificial intelligence (AI)-based algorithms are suggestive of future fall risk.

Methods

In this retrospective age- and sex-matched case-control study, 9029 total patients underwent initial abdominal CT for a variety of indications over a 20-year interval at one institution. 3535 case patients (mean age at initial CT, 66.5 ± 9.6 years; 63.4% female) who went on to fall (mean interval to fall, 6.5 years) and 5494 controls (mean age at initial CT, 66.7 ± 9.8 years; 63.4% females; mean follow-up interval, 6.6 years) were included. Falls were identified by electronic health record review. Validated and fully automated quantitative CT algorithms for skeletal muscle, adipose tissue, and trabecular bone attenuation at the level of L1 were applied to all scans. Uni- and multivariate assessment included hazard ratios (HRs) and area under the receiver operating characteristic (AUROC) curve.

Results

Fall HRs (with 95% CI) for low muscle Hounsfield unit, high total adipose area, and low bone Hounsfield unit were 1.82 (1.65-2.00), 1.31 (1.19-1.44) and 1.91 (1.74-2.11), respectively, and the 10-year AUROC values for predicting falls were 0.619, 0.556, and 0.639, respectively. Combining all these CT biomarkers further improved the predictive value, including 10-year AUROC of 0.657.

Conclusion

Automated abdominal CT-based opportunistic measures of muscle, fat, and bone offer a novel approach to risk stratification for future falls, potentially by identifying patients with osteosarcopenic obesity.

Advances in knowledge

There are few well-established clinical tools to predict falls. We use novel AI-based body composition algorithms to leverage incidental CT data to help determine a patient's future fall risk.

Requests for Compensation in Cases Involving Patients' Falls in Healthcare Settings: A Retrospective Analysis

Falls are the most frequent adverse events recorded in healthcare facilities. By employing a multifaceted strategy to ensure prevention interventions that are specific to the patient type and environmental risk management, risk factor evaluation may help to reduce falls in the hospital setting. Patient falls are one of the main causes of lawsuits against hospitals, which has led to the development of validated instruments that are beneficial in treating the patient after the incident and effective in minimizing the frequency of falls. The aim of our study is to evaluate compensation claims asserting healthcare culpability in situations where a patient fell in a hospital setting. The collected data relate to judgments issued in Italy until December 2022 regarding 30 episodes of falls that occurred between 2003 and 2018. Our research revealed that approximately 50% of Italian healthcare organizations lose the case in court when a patient falls in a hospital setting and dies or is injured. In half of these cases, the failure of the medical staff to use protective equipment against falls is what led to the court's acceptance of the compensation claim. In order to improve the quality of healthcare services, fall prevention techniques must continue to be implemented.

Patient Perception of Fall Risk and Fall Risk Screening Scores

BACKGROUND

Falls are the most prevalent adverse event among hospitalized patients. Multilevel risk factors are associated with falls, yet falls continue.

PURPOSE

To evaluate the relationship between the Johns Hopkins Fall Risk instrument, patient characteristics, and perception of fall risk.

METHODS

The Johns Hopkins Fall Risk score, patient perception of fall risk, and patient characteristics were analyzed among inpatient adults (n = 201) from 5 acute care units in a large southern California medical center.

RESULTS

Bivariate analyses revealed that fall risk was inversely associated with participants' confidence in their ability to perform high fall risk behaviors without help and without falling ( P = .018).

CONCLUSIONS

Perception of fall risk is a promising new indicator in preventing falls. Patient perception of fall risk may elicit a behavior change to help prevent falls. Increased health care provider awareness of patient perception of fall risk may improve fall risk interventions and prevention programs.

Retrospective analysis of nurse-administered fall assessment scales in the Epilepsy Monitoring Unit

INTRODUCTION

Inpatient falls within the Epilepsy Monitoring Unit (EMU) are a common, and potentially preventable adverse event contributing to morbidity for patients living with epilepsy. Accurate fall risk screening is important to identify and efficiently allocate proper safety measures to high-risk patients, especially in EMUs with limited resources. We sought to compare existing screening tools for the ability to predict falls in the EMU.

METHODS

This is a retrospective, single-center, case-controlled, comparative analysis of 7 nurse-administered fall risk assessment tools (NAFRAT) of patients admitted to the Vanderbilt University Medical Center (VUMC) EMU. Analysis of categorical data was compared using chi-square analysis while quantitative distributions were compared using student's t-test.

RESULTS

A total of 56 patient records (28 falls and 28 controls) were included in the analysis. Epilepsy Monitoring Unit falls were most common within the first 3 days of admission (p = .0094). Pre-admission documentation of falls was a strong predictor of falls within the EMU (p < .0001). Epilepsy Monitoring Unit falls were associated with documented falls after EMU discharge (p = .011). The John Hopkins fall risk assessment tool (JHFRAT) accurately stratified fall risk in the fall group compared to the control (p = .008), however, none of the 7 NAFRATs demonstrated significant categorical differences among the epilepsy subgroups. There was a significant difference in the distribution of quantitative scores, higher in the fall group according to the Morse Fall Scale (MFS) (p = 0.012), JHFRAT (p = 0.003), Schmid Fall Risk Assessment Scale (p = 0.029) and Hester-Davis Scale (p = 0.049). The modified Conley (p = 0.03) and Morse scale (p = 0.025) demonstrated differences in the distribution of quantitative scores in the epilepsy subgroups.

CONCLUSION

The findings of this study demonstrate variable accuracy of NAFRATs in assessing fall risk among patients admitted to the EMU, particularly among patients with epilepsy. The findings underscore the need for a validated, EMU-specific, fall assessment tool that accurately stratifies fall risk and inform efficient use of patient-specific fall prevention resources and protocols.

Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness

In-hospital falls are a serious threat to patient security and fall risk assessment (FRA) is important to identify high-risk patients. Although sensor-based FRA (SFRA) can provide objective FRA, its clinical use is very limited and research to identify meaningful SFRA methods is required. This study aimed to investigate whether examples of SFRA methods might be relevant for FRA at an orthopedic clinic. Situations where SFRA might assist FRA were identified in a focus group interview with clinical staff. Thereafter, SFRA methods were identified in a literature review of SFRA methods developed for older adults. These were screened for potential relevance in the previously identified situations. Ten SFRA methods were considered potentially relevant in the identified FRA situations. The ten SFRA methods were presented to staff at the orthopedic clinic, and they provided their views on the SFRA methods by filling out a questionnaire. Clinical staff saw that several SFRA tasks could be clinically relevant and feasible, but also identified time constraints as a major barrier for clinical use of SFRA. The study indicates that SFRA methods developed for community-dwelling older adults may be relevant also for hospital inpatients and that effectiveness and efficiency are important for clinical use of SFRA.

Risk stratification for early and late falls in acute care settings

BACKGROUND AND AIMS

Falling generally injures patients, lengthens hospital stays and leads to the wastage of financial and medical resources. Although falls can occur at any stage after hospital admission, there are no studies that characterise falls with length of hospital stay in acute care settings. This study aims to clarify risk stratification of early and late falls in acute care settings.

METHODS

A retrospective cohort study was conducted for participants who were admitted to a teaching hospital in Japan. Patients' falls were divided into two groups based on the median of the fall date (day 10). Considering a 70/30 split, the logistic regression model was used to extract independent predictors for early and late falls for nine risk variables based on exploratory analysis among 26 items selected from the modified Japanese Nursing Association Fall Risk Assessment Tool, and risk models were validated. This study was conducted according to the STROBE guideline.

RESULTS

Of the 10,975 patients admitted, 87 and 90 with early and late falls, respectively, were identified. The five significant risk factors extracted for early falls were fall history, muscle weakness, impaired understanding, use of psychotropics and the personality trait of 'doing everything on one's own'; risk factors identified for late falls were being older than 65 years, impaired extremities and unstable gait, in addition to muscle weakness. Using these variables for early and late falls in the validation cohort, the concordance indices of the risk models were both over 0.80.

CONCLUSIONS

By separately extracting risk factors for early and late falls in an acute care hospital setting, this study shed light on the characteristics of the respective types of falls.

RELEVANT TO CLINICAL PRACTICE

As the risk factors of falls vary according to the length of hospitalisation, specific preventive care can be implemented to avoid fall incidents.

Biopsychosocial effects and experience of use of robotic and virtual reality devices in neuromotor rehabilitation: A study protocol

BACKGROUND

Robot-assisted therapy (RAT) and virtual reality (VR)-based neuromotor rehabilitation have shown promising evidence in terms of patient's neuromotor recovery, so far. However, still little is known on the perceived experience of use of robotic and VR devices and the related psychosocial impact. The present study outlines a study protocol aiming to investigate the biopsychosocial effects and the experience of use of robotic and non-immersive VR devices in patients undergoing neuromotor rehabilitation.

METHODS

Adopting a prospective, two-arm, non-randomized study design, patients with different neuromotor diseases (i.e., acquired brain injury, Parkinson's Disease, and total knee/hip arthroplasty) undergoing rehabilitation will be included. In a real-world clinical setting, short- (4 weeks) and long-term (6 months) changes in multiple patient's health domains will be investigated, including the functional status (i.e., motor functioning, ADLs, risk of falls), cognitive functioning (i.e., attention and executive functions), physical and mental health-related quality of life (HRQoL), and the psychological status (i.e., anxiety and depression, quality of life satisfaction). At post-intervention, the overall rehabilitation experience, the psychosocial impact of the robotic and VR devices will be assessed, and technology perceived usability and experience of use will be evaluated through a mixed-methods approach, including both patients' and physiotherapists' perspectives. Repeated measures within-between interaction effects will be estimated, and association analyses will be performed to explore the inter-relationships among the variables investigated. Data collection is currently ongoing.

IMPLICATIONS

The biopsychosocial framework adopted will contribute to expanding the perspective on patient's recovery within the technology-based rehabilitation field beyond motor improvement. Moreover, the investigation of devices experience of use and usability will provide further insight into technology deployment in neuromotor rehabilitation programs, thereby maximising therapy engagement and effectiveness.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05399043.

Agreement and predictive performance of fall risk assessment methods and factors associated with falls in hospitalized older adults: A longitudinal study

Falls in hospitalized older adults are of concern and, despite the availability of fall risk assessment methods and knowledge about factors associated with falls, their validity and agreement remain poorly investigated. In a prospective study, we enrolled 102 hospitalized older adults (median [P25-P75]) 67 (64-73) years, 52 [51%] men, length of stay 20 [8-41] days). Fall risk was assessed at hospital admission using the Functional Independence Measure; Morse Fall Scale; St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients; Johns Hopkins Fall Risk Assessment Tool; and polypharmacy. The St. Thomas's Risk Assessment Tool in Falling Elderly Inpatients method showed the highest predictive performance (accuracy 92%) for the identification of fallers during hospitalization. A slightly better-then-chance agreement was estimated between all methods (Light's κ = 0.120). Fall risk assessment methods and factors associated with falls should not be used interchangeably as their overall and pairwise agreement are fair at best.

Drug-induced fall risk in older patients: A pharmacovigilance study of FDA adverse event reporting system database

Objectives: As fall events and injuries have become a growing public health problem in older patients and the causes of falls are complex, there is an emerging need to identify the risk of drug-induced falls. Methods: To mine and analyze the risk signals of drug-induced falls in older patients to provide evidence for drug safety. The FDA Adverse Event Reporting System was used to collect drug-induced fall events among older patients. Disproportionality analyses of odds ratio (ROR) and proportional reported ratio were performed to detect the adverse effects signal. Results: A total of 208,849 reports (34,840 fall events and 1,898 drugs) were considered. The average age of the included patients was 76.95 ± 7.60 years, and there were more females (64.47%) than males. A total of 258 drugs with positive signals were detected to be associated with drug-induced fall incidence in older patients. The neurological drugs (104, 44.1%) with the largest number of positive detected signals mainly included antipsychotics, antidepressants, antiparkinsonian drugs, central nervous system drugs, anticonvulsants and hypnotic sedatives. Other systems mainly included the circulatory system (25, 10.6%), digestive system (15, 6.4%), and motor system (12, 5.1%). Conclusion: Many drugs were associated with a high risk of falls in older patients. The drug is one of the critical and preventable factors for fall control, and the risk level of drug-induced falls should be considered to optimize drug therapy in clinical practice.

Risk and Influence Factors of Fall in Immobilization Period after Arthroscopic Interventions

Knee injuries are one of the most common injuries. Falls during the immobilization period can deteriorate the postoperative outcome. The risk factors causing falls after initial injury and the question of whether a rigid orthosis serves as a protective factor remain unclear. The primary aim of the study was to record the fall rate in the first six weeks after arthroscopic intervention. The secondary aim was to assess the influences of risk factors and protective factors on these fall ratios. Different scores were examined and compared in the groups ‘fall event’ and ‘no fall’. Data from 51 patients (39 males, 12 females) with a mean age of 31.2 years (19–57 years) were collected. A total of 20 patients suffered at least one fall event within the observation period. A total of 18 of 23 fall events happened within the first three weeks postoperatively. The Extra Short Musculoskeletal Function Assessment Questionnaire (XSMFA) showed a significant difference between the groups (p = 0.02). People with multiple injuries to the knee joint were more likely to suffer fall events. Conclusively, patients with limited knee functions appeared to fall more frequently within the first three weeks postoperatively. Therefore, appropriate measures should be taken to protect the postoperative outcome. Physical therapy and patient behavioural training should be practiced perioperatively in patients at risk.

Development of a predictive inpatient falls risk model using machine learning

AIM

The aims of this study were to create a model that detects the population at risk of falls taking into account a fall prevention variable and to know the effect on the model's performance when not considering it.

BACKGROUND

Traditionally, instruments for detecting fall risk are based on risk factors, not mitigating factors. Machine learning, which allows working with a wider range of variables, could improve patient risk identification.

METHODS

The sample was composed of adult patients admitted to the Internal Medicine service (total, n = 22,515; training, n = 11,134; validation, n = 11,381). A retrospective cohort design was used and we applied machine learning technics. Variables were extracted from electronic medical records electronic medical records.

RESULTS

The Two-Class Bayes Point Machine algorithm was selected. Model-A (with a fall prevention variable) obtained better results than Model-B (without it) in sensitivity (0.74 vs. 0.71), specificity (0.82 vs. 0.74), and AUC (0.82 vs. 0.78).

CONCLUSIONS

Fall prevention was a key variable. The model that included it detected the risk of falls better than the model without it.

IMPLICATIONS FOR NURSING MANAGEMENT

We created a decision-making support tool that helps nurses to identify patients at risk of falling. When it is integrated in the electronic medical records, it decreases nurses' workloads by not having to collect information manually.

The Stoplight Mobility Alert System for Safety and Prevention of Falls in Children With Physical and Cognitive Impairments

OBJECTIVES

This study aimed to decrease the rate of falls in children with cognitive and physical impairments on a pediatric acute inpatient rehabilitation unit (IRU) using a novel tool, the Stoplight Mobility Alert System (SMAS).

METHODS

We conducted a pilot, prospective, quality improvement study in an 8-bed (increased to 12 beds; October 1, 2019) acute inpatient pediatric IRU at a level 1 trauma center. All patients admitted between October 1, 2012, and October 1, 2020, were included as participants. Interventions used were as follows: (1) SMAS, a colored alert system placed on door slides and in-room for visual cues (red, assistance/hands on; yellow, supervision/eyes on; green, independent/hands off), and (2) handouts and one-on-one education for staff and patients/families. Main outcome measures included fall rate on the IRU.

RESULTS

Using the SMAS, the total fall rate decreased from 10.78 to 4.36 falls per 1000 patient-days. Longitudinally, the intrinsic fall rate decreased from 8.36 to 5.60 falls per 1000 patient-days, and the extrinsic fall rate decreased from 4.56 to 1.36 falls per 1000 patient-days.

CONCLUSIONS

The implementation of the SMAS is effective in decreasing total, intrinsic, and extrinsic fall rates in an acute pediatric inpatient rehabilitation program both acutely and longitudinally.

Evaluating the discriminatory power of the velocity field diagram and timed-up-and-go test in determining the fall status of community-dwelling older adults: a cross-sectional observational study

Background

Systematic reviews demonstrated that gait variables are the most reliable predictors of future falls, yet are rarely included in fall screening tools. Thus, most tools have higher specificity than sensitivity, hence may be misleading/detrimental to care. Therefore, this study aimed to determine the validity, and reliability of the velocity field diagram (VFD -a gait analytical tool), and the Timed-up-and-go test (TUG)-commonly used in Nigeria as fall screening tools, compared to a gold standard (known fallers) among community-dwelling older adults.

Method

This is a cross-sectional observational study of 500 older adults (280 fallers and 220 non-fallers), recruited by convenience sampling technique at community health fora on fall prevention. Participants completed a 7-m distance with the number of steps and time it took determined and used to compute the stride length, stride frequency, and velocity, which regression lines formed the VFD. TUG test was simultaneously conducted to discriminate fallers from non-fallers. The cut-off points for falls were: TUG times ≥ 13.5 s; VFD’s intersection point of the stride frequency, and velocity regression lines (E₁) ≥ 3.5velots. The receiver operating characteristic (ROC) area under the curves (AUC) was used to explore the ability of the E₁ ≥ 3.5velots to discriminate between fallers and non-fallers. The VFD’s and TUG’s sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined. Alpha was set at p < 0.05.

Results

The VFD versus TUG sensitivity, specificity, PPV and NPV were 71%, 27%, 55%, and 42%, versus 39%, 59%, 55%, and 43%, respectively. The ROC’s AUC were 0.74(95%CI:0.597,0.882, p = 0.001) for the VFD. The optimal categorizations for discrimination between fallers/non-fallers were ≥ 3.78 versus ≤ 3.78 for VFD (fallers versus non-fallers prevalence is 60.71% versus 95.45%, respectively), with a classification accuracy or prediction rate of 0.76 unlike TUG with AUC = 0.53 (95% CI:0.353,0.700, p = 0.762), and a classification accuracy of 0.68, and optimal characterization of ≥ 12.81 s versus ≤ 12.81 (fallers and non-fallers prevalence = 92.86% versus 36.36%, respectively).

Conclusion

The VFD demonstrated a fair discriminatory power and greater reliability in identifying fallers than the TUG, and therefore, could replace the TUG as a primary tool in screening those at risk of falls.

A model for predicting fall risks of hospitalized elderly in Taiwan-A machine learning approach based on both electronic health records and comprehensive geriatric assessment

Backgrounds

Falls are currently one of the important safety issues of elderly inpatients. Falls can lead to their injury, reduced mobility and comorbidity. In hospitals, it may cause medical disputes and staff guilty feelings and anxiety. We aimed to predict fall risks among hospitalized elderly patients using an approach of artificial intelligence.

Materials and methods

Our working hypothesis was that if hospitalized elderly patients have multiple risk factors, their incidence of falls is higher. Artificial intelligence was then used to predict the incidence of falls of these patients. We enrolled those elderly patients aged >65 years old and were admitted to the geriatric ward during 2018 and 2019, at a single medical center in central Taiwan. We collected 21 physiological and clinical data of these patients from their electronic health records (EHR) with their comprehensive geriatric assessment (CGA). Data included demographic information, vital signs, visual ability, hearing ability, previous medication, and activity of daily living. We separated data from a total of 1,101 patients into 3 datasets: (a) training dataset, (b) testing dataset and (c) validation dataset. To predict incidence of falls, we applied 6 models: (a) Deep neural network (DNN), (b) machine learning algorithm extreme Gradient Boosting (XGBoost), (c) Light Gradient Boosting Machine (LightGBM), (d) Random Forest, (e) Stochastic Gradient Descent (SGD) and (f) logistic regression.

Results

From modeling data of 1,101 elderly patients, we found that machine learning algorithm XGBoost, LightGBM, Random forest, SGD and logistic regression were successfully trained. Finally, machine learning algorithm XGBoost achieved 73.2% accuracy.

Conclusion

This is the first machine-learning based study using both EHR and CGA to predict fall risks of elderly. Multiple risk factors of falls in hospitalized elderly patients can be put into a machine learning model to predict future falls for early planned actions. Future studies should be focused on the model fitting and accuracy of data analysis.

Using Healthcare Resources Wisely: A Predictive Support System Regarding the Severity of Patient Falls

Background

An injurious fall is one of the main indicators of care quality in healthcare facilities. Despite several fall screen tools being widely used to evaluate a patient's fall risk, they are frequently unable to reveal the severity level of patient falls. The purpose of this study is to build a practical system useful to predict the severity level of in-hospital falls. This practice is done in order to better allocate limited healthcare resources and to improve overall patient safety.

Methods

Four hundred and forty-six patients who experienced fall events at a large Taiwanese hospital were referenced. Eight predictors were used to ascertain the severity of patient falls solely based on the above study population. Multinomial logistic regression, Naïve Bayes, random forest, support vector machine, eXtreme gradient boosting, deep learning, and ensemble learning were adopted to establish predictive models. Accuracy, F1 score, precision, and recall were utilized to assess the models' performance.

Results

Compared to other learners, random forest exhibited satisfying predictive performance in terms of all metrics (accuracy: 0.844, F1 score: 0.850, precision: 0.839, and recall: 0.875 for the test dataset), and it was adopted as the base learner for a severity-level predictive system which is web-based. Furthermore, age, ability of independent activity, patient sources, use of assistive devices, and fall history within the past 12 months were deemed the top five important risk factors for evaluating fall severity.

Conclusions

The application of machine learning techniques for predicting the severity level of patient falls may result in some benefits to monitor fall severity and to better allocate limited healthcare resources.

Physical Activity and Fall Prevention in Geriatric Inpatients in an Acute Care Unit (AGIR Study): Protocol for a Usability Study

Background

Falls are one of the world’s top 10 risks associated with disability in people older than 60 years. They also represent more than two-thirds of adverse events in hospitals, mainly affecting patients older than 65 years. Physical activity is a central intervention in fall prevention for older people. Whatever the details of the prevention strategy that is adopted (ie, how a mono- or multifactorial intervention is evaluated, the category of person the intervention targets, and where it is used), it is important to ensure that the proposed intervention is feasible and usable for the patient and the health care team.

Objective

The primary objective is to study the usability of carrying out a physical activity intervention, including 3 types of exercises, in older patients hospitalized in a geriatric acute care unit and categorized according to 3 fall risk levels: low, moderate, and high. The secondary objectives are to determine the difficulty of the physical exercise for patients with different fall risk levels, to study the health care team’s perceptions of the intervention’s feasibility, and to study the benefits for patients.

Methods

This is an open-label, unicenter, nonrandomized, usability prospective clinical trial. The intervention tested is a daily physical activity program. It consists of 3 types of physical exercise: staying out of bed for at least 3 hours, performing balance exercises while standing for 2 minutes, and the Five Times Sit to Stand transfer exercise. These exercises are carried out under the supervision of the health care team. Fall risk in the patients is classified with the Brief Geriatric Assessment tool. The exercise program starts on the second day of hospitalization after inclusion in the study. Patient assessment continues until the last day of hospitalization or the 20th day of hospitalization, whichever is earlier. For each fall-risk group and each type of exercise, the intervention will be defined as usable if at least 80% of the participants complete 75% or more of the exercises (ie, the ratio between the number of days when the patient completes a type of exercise and the total number of hospitalization days). The perceived feasibility by the health care team is measured with 2 scales, measuring perceived difficulty and time spent with the patient. The intervention benefit is evaluated using the performance of the Five Times Sit to Stand test before and after the intervention.

Results

The first patient was recruited on March 16, 2015. The study enrolled 266 patients, including 75 with low fall risk, 105 with moderate risk, and 85 with high risk.

Conclusions

We have not yet analyzed the results, but our observations suggest that the usability of each type of exercise for a given patient will depend on their fall risk level.

Trial Registration

ClinicalTrials.gov NCT02393014; https://clinicaltrials.gov/ct2/show/NCT02393014

International Registered Report Identifier (IRRID)

DERR1-10.2196/32288

[Review of the validity of fall risk assessment scales in hospitalised patients]

Falls in the hospital setting are a major health problem due to their high prevalence and their physical, functional, psychological or economic consequences. Since 1990s, different fall risk assessment scales have been developed to detect high-risk patients, which are also applied in the hospital setting. The aim of this review is to analyse the validity of different scales for assessing fall risk in adults in the hospital setting, especially in elderly patients. Following a literature search in April 2021, 36 primary studies were found that analysed the validity of the Downton, Morse, HendrichII, Stratify and Tinetti scales. Meta-analyses of sensitivity and specificity showed a high heterogeneity that does not allow recommending a specific tool that can be considered as standard in acute inpatients.

Association Between Fatigue and Falls Risk Among the Elderly Aged Over 75 Years in China: The Chain Mediating Role of Falls Efficacy and Lower Limb Function

Background

Although fatigue has been shown to be strongly associated with falls risk, very few studies have focused on its mechanism involved in community-dwelling older subjects. The purpose of this study was to explore the relationship between fatigue and falls risk and its internal mechanism by constructing a chain mediation model.

Methods

A cross-sectional study design was adopted. A convenience sample of 270 older adults was recruited from July to October 2021 in an urban community, in Beijing, China. The participants completed the 14-item Fatigue Scale (FS-14), Falls Efficacy Scale International (FES-I), the Short Physical Performance Battery (SPPB) and Fall-Risk Self-Assessment Questionnaire (FRQ) to measure fatigue, falls efficacy, lower limb function and falls risk. The theory of unpleasant symptoms was used as a conceptual framework. Structural equation modeling (SEM) was utilized to test the hypothetical model.

Results

The overall fit of final model was found to be satisfactory: χ2/df = 1.61, CFI = 0.971, TLI = 0.962, RMSEA = 0.049 (95% CI 0.030/0.066) and SRMR = 0.023. Fatigue had a direct effect on falls risk (β = 0.559, S.E. = 0.089, 95% CI 0.380/0.731), and it also had indirect effects on falls risk (β = 0.303, S.E. = 0.072, 95% CI 0.173/0.460) through mediating factors. Falls efficacy and lower limb function were the main mediating variables, and there was a chain mediating effect (β = 0.015, S.E. = 0.010, 95% CI 0.003/0.046).

Conclusions

Our study suggests that fatigue can influence falls risk among the elderly in China. There are many mediating paths between fatigue and falls risk. These results may help healthcare professionals to better understand the inherent relationship between fatigue and fall risk that may benefit older adults.

The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data

BACKGROUND

The Peninsula Health Falls Risk Assessment Tool (PH-FRAT) is a validated and widely applied tool in residential aged care facilities (RACFs) in Australia. However, research regarding its use and predictive performance is limited. This study aimed to determine the use and performance of PH-FRAT in predicting falls in RACF residents.

METHODS

A retrospective cohort study using routinely-collected data from 25 RACFs in metropolitan Sydney, Australia from Jul 2014-Dec 2019. A total of 5888 residents aged ≥65 years who were assessed at least once using the PH-FRAT were included in the study. The PH-FRAT risk score ranges from 5 to 20 with a score > 14 indicating fallers and ≤ 14 non-fallers. The predictive performance of PH-FRAT was determined using metrics including area under receiver operating characteristics curve (AUROC), sensitivity, specificity, sensitivity_{Event Rate(ER)} and specificity_ER.

RESULTS

A total of 27,696 falls were reported over 3,689,561 resident days (a crude incident rate of 7.5 falls /1000 resident days). A total of 38,931 PH-FRAT assessments were conducted with a median of 4 assessments per resident, a median of 43.8 days between assessments, and an overall median fall risk score of 14. Residents with multiple assessments had increased risk scores over time. The baseline PH-FRAT demonstrated a low AUROC of 0.57, sensitivity of 26.0% (sensitivity_ER 33.6%) and specificity of 88.8% (specificity_ER 82.0%). The follow-up PH-FRAT assessments increased sensitivity_ER values although the specificity_ER decreased. The performance of PH-FRAT improved using a lower risk score cut-off of 10 with AUROC of 0.61, sensitivity of 67.5% (sensitivity_ER 74.4%) and specificity of 55.2% (specificity_ER 45.6%).

CONCLUSIONS

Although PH-FRAT is frequently used in RACFs, it demonstrated poor predictive performance raising concerns about its value. Introducing a lower PH-FRAT cut-off score of 10 marginally enhanced its predictive performance. Future research should focus on understanding the feasibility and accuracy of dynamic fall risk predictive tools, which may serve to better identify residents at risk of falls.

Gap between risk factors and prevention strategies? A nationwide survey of fall prevention among medical and surgical patients

Aims

This study aimed to determine the extent to which nurses report assessing evidence‐based falls risk factors and implementing targeted prevention for medical and surgical patients in China.

Design

This study was a national online survey.

Methods

The respondents were registered nurses working in medical and surgical units in 662 Chinese hospitals. The data concerning the falls risk factor assessments and targeted interventions implemented by nurses were collected online by the Nursing Management Committee of the Chinese Nursing Association in China in 2019.

Results

In total, 68 527 valid questionnaires were returned (95.0%). In medical and surgical units, nurses were most likely to report assessing balance, mobility and strength (81.6%) and orthostatic hypotension (76.4%) in falls patients and least likely to report assessing continence (61.3%) and feet and footwear (55.8%). Ensuring the use of appropriate footwear (79.3%) and managing syncope, dizziness and vertigo (73.8%) were the most common multiple interventions, while managing postural hypotension (48.8%) and cognitive impairment (48.4%) was the least common. Nine falls risk factors with clearly matched multifactorial interventions were identified in medical and surgical units (68.2%–97.1%).

Conclusions

The implementation of multifactorial interventions in medical and surgical wards is inconsistent as reported by nurses in medical and surgical wards. Throughout China, nurses are generally concerned about falls risk factors and prevention for their patients; however, limited attention has been focused on continence, feet and footwear assessment and the management of cognitive impairment. Evidence‐based falls prevention should be further tailored to the specific risk factors of each patient.

Impact

Best practice guidelines for falls prevention in hospitals have been developed and published, and it is important for nurses to use these guidelines to guide practice. Our findings identify that in routine care, healthcare providers and hospitals can prevent falls.

The Fall Risk Screening Scale Is Suitable for Evaluating Adult Patient Fall

(1) Background: This study aimed to test the feasibility of utilizing the screening tool for fall risk assessment in adult inpatient and verify its accuracy in a medical center in Taiwan. (2) Methods: This study retrospectively collected all adult fall cases among inpatients occurring in the general wards of a medical center between 1 January 2013 and 31 December 2015. This inpatient fall risk screening scale was measured by the sensitivity, specificity, and accuracy. (3) Results: There were 1331 (0.4%) falls among a total of 357,395 inpatients during this period. Factors predictive of falling risk included: age, consciousness, body shift assistance, use of fall risk medications, fall history, dizziness or weakness, toileting, and impaired mobility. Using the eight-factor assessment, two was the best cutoff point for identifying the fall risk group, with area under Receiver Operating Characteristic (ROC) curve (AUC) = 0.817, sensitivity = 80.93%, specificity = 73.0%, accuracy = 73.03%, and likelihood ratio = 11.48. (4) Conclusions: The accuracy of the eight-item fall risk assessment tool created for this study was validated. These results can serve as a reference for institutions to develop more effective fall risk assessment scale for inpatients, enabling clinical nurses to identify and more comprehensively assess the groups at highest risk for falling during their hospital stay.

Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments

Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls. We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing. Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.

Association of orexin receptor antagonists with falls during hospitalization

WHAT IS KNOWN AND OBJECTIVE

The use of hypnotics, especially benzodiazepines (BZs), increases the risk of falls. Regarding the association of orexin receptor antagonists with fall risk, consistent results have not been obtained for suvorexant, and studies of lemborexant have not been reported. Therefore, this study investigated whether orexin receptor antagonists, including lemborexant, increase the risk of falls.

METHODS

Data were obtained from the medical records of patients hospitalized at Saga University Hospital in Japan between July 2020 and April 2021. Patients were retrospectively divided into the fall and non-fall groups, and the groups were compared for medication usage.

RESULTS AND DISCUSSION

The fall and non-fall groups included 132 and 6857 patients respectively. A significantly higher proportion of patients in the fall group used hypnotics (40.2% vs. 21.7%; p < 0.0001). Hypnotics remained significantly associated with a higher risk of falls after adjusting for confounders (adjusted odds ratio [OR] = 1.67, 95% confidence interval [CI] = 1.13-2.48, p = 0.01). In particular, the use of benzodiazepines was associated with a significantly higher risk of falls (adjusted OR = 2.08, 95% CI = 1.38-3.15, p = 0.0005). Meanwhile, suvorexant use was not linked to the risk of falls, and lemborexant use was associated with a significantly lower risk of falls (adjusted OR = 0.27, 95% CI = 0.09-0.84, p = 0.02).

WHAT IS NEW AND CONCLUSION

The use of hypnotics is a risk factor for falls, but orexin receptor antagonists may represent a safe option for patients requiring hypnotics. Our results provide evidence supporting the safety of these drugs.

Anticholinergic Drug Use on Admission and the Risk of In-Hospital Falls in Older Hospitalized Patients

Purpose

In-hospital falls, especially among older patients, are a major and underestimated problem. Several studies have suggested a possible association between anticholinergic drug use and falls, but the results are inconclusive and studies focusing on in-hospital falls are scarce. The aim of the present study was to investigate whether anticholinergic drug exposure on admission is associated with in-hospital falls.

Patients and Methods

This retrospective chart review study was conducted in the Erasmus MC University Medical Center, Rotterdam, the Netherlands. Patients aged 65 years and older, who were acutely admitted to the geriatric ward between 2012 and 2015, were included. Anticholinergic drug exposure was determined with the Anticholinergic Risk Scale (ARS), the Anticholinergic Cognitive Burden scale (ACB) and the list of Chew. Logistic regression was used to investigate the possible association between anticholinergic drug exposure and in-hospital falls. Analyses were adjusted for age, sex, fall history, fall as reason for admission, number of drugs on admission, use of a mobility aid and delirium.

Results

A total of 905 patients were included, of which 94 patients experienced one or more in-hospital falls. Each additional anticholinergic drug in use, according to the ARS, was associated with an increased odd of experiencing a fall (OR = 1.49, 95% CI: 1.06–2.10). Other measures, ie anticholinergic drug use (yes/no) and different categories of anticholinergic drug burden, measured with the ARS, ACB and list of Chew, were all not associated with in-hospital falls.

Conclusion

Anticholinergic drug exposure on admission is possibly not a main risk factor for in-hospital falls among older patients.

Adaptación transcultural para Colombia y validez de contenido de la escala RAC de evaluación del riesgo de infección en el adulto hospitalizado

Introducción: Las Infecciones Asociadas a la Atención en Salud (IAAS) son un grave problema de salud pública, que puede ser prevenidas al identificar los factores de riesgo con el uso de escalas. Objetivo: Adaptar transculturalmente y realizar la validación de contenido y de face de la escala Rodríguez-Almeida-Cañon (RAC) de evaluación del riesgo de infección en adultos hospitalizados. Materiales y Métodos: Estudio metodológico de adaptación transcultural. La recolección de datos se realizó de junio a noviembre de 2020. La muestra estuvo compuesta por 11 especialistas. La escala RAC se evaluó en su conjunto, determinando su alcance, los ítems fueron evaluados individualmente, verificando su claridad, relevancia y pertinencia. Para evaluar cada ítem se utilizó una escala tipo Likert de cuatro niveles. La validez de contenido fue evaluada a través del índice de validez de contenido (IVC). Resultados: Por medio de la evaluación del comité de especialistas fue posible determinar que la escala RAC es apta para uso en el contexto cultural colombiano. Se realizaron ajustes para mejorar la interpretación de algunos ítems. El IVC de los ítems estuvo entre 0.90 a 1.0 y el IVC promedio de la escala fue de 0.98. Discusión: Esta escala permite medir el riesgo de IAAS a un bajo costo, con el fin de poder planear y ejecutar intervenciones por parte del equipo multidisciplinario que tiene a cargo la salud y el cuidado del paciente. Conclusiones: La escala RAC en su versión en español es un instrumento apropiado para la evaluación del riesgo de IAAS en el adulto hospitalizado en Colombia.

Validation of a Falls Risk Screening Tool Derived From InterRAI Acute Care Assessment

OBJECTIVES

This study aimed to develop and validate a falls risk screening tool derived from interRAI Acute Care (AC) Assessment.

METHODS

For derivation and validation, two prospective cohorts were recruited from AC hospitals in Australia. The derivation cohort comprised 1418 patients from 11 hospitals. In the validation cohort, 393 patients were recruited from four hospitals. The interRAI AC tool was used to collect comprehensive geriatric assessment data at admission. In-hospital falls were documented from medical records. A falls risk score was calculated using logistic regression. Predictive ability was compared with St. Thomas Risk Assessment Tool In Falling elderlY (STRATIFY), using area under curve (AUC). The validation cohort provided external validity.

RESULTS

Complete data in the derivation cohort were available for 1288 patients (91%), with 75 (5.8%) having an in-hospital fall. The derived interRAI AC falls risk score (range = 0-6) had significantly better predictive ability (AUC = 0.70, 95% confidence interval [CI] = 0.63-0.76) compared with St. Thomas Risk Assessment Tool In Falling elderlY (AUC = 0.64, 95% CI = 0.58-0.70) (P = 0.033). At a cut point of three, 54 of 75 falls were correctly predicted by the falls risk score derived from interRAI AC (sensitivity = 0.72 [95% CI = 0.60-0.82] and specificity = 0.60 [95% CI = 0.57-0.62]). The falls risk score performed similarly in the validation cohort.

CONCLUSIONS

The falls risk tool developed from interRAI AC is a valid measure to screen for in-hospital falls. Reduction in assessment burden without loss of fidelity can be achieved through integrating the risk screener within the interRAI hospital system, which automatically triggers protocols for falls prevention based on identified risk.

The Utility of the Modified Dionne's Egress Test as a Predictor of Falls in Adult Medical and Surgical Patients

OBJECTIVE

The aim of this study was to compare the efficacy of a modified Dionne's Egress Test (Egress) as a predictor of falls with the Morse Fall Scale (MFS) in adult medical and surgical patients in an acute care setting.

BACKGROUND

Nurses must identify fall risk while balancing fall prevention and early mobility in their care delivery. Fall risk screening tools alone are not enough to assist nurses in predicting patients at risk of falling.

METHODS

A retrospective observational study design was used to compare the Egress as a predictor of falls to the MFS. The sample included data abstracted from 197 electronic health records and internal falls data.

RESULTS

The Egress and the MFS are moderately and negatively correlated; however, only Egress was a significant predictor of falls. Passing the Egress, not being on benzodiazepines, and having a longer length of stay (LOS) results were associated with being less likely to fall.

CONCLUSION

Egress is a better predictor of falls than MFS when benzodiazepines and LOS are controlled in the model.

Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series

BACKGROUND

Patient falls are a common cause of harm in acute-care hospitals worldwide. They are a difficult, complex, and common problem requiring a great deal of nurses' time, attention, and effort in practice. The recent rapid expansion of health care predictive analytic applications and the growing availability of electronic health record (EHR) data have resulted in the development of machine learning models that predict adverse events. However, the clinical impact of these models in terms of patient outcomes and clinicians' responses is undetermined.

OBJECTIVE

The purpose of this study was to determine the impact of an electronic analytic tool for predicting fall risk on patient outcomes and nurses' responses.

METHODS

A controlled interrupted time series (ITS) experiment was conducted in 12 medical-surgical nursing units at a public hospital between May 2017 and April 2019. In six of the units, the patients' fall risk was assessed using the St. Thomas' Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) system (control units), while in the other six, a predictive model for inpatient fall risks was implemented using routinely obtained data from the hospital's EHR system (intervention units). The primary outcome was the rate of patient falls; secondary outcomes included the rate of falls with injury and analysis of process metrics (nursing interventions that are designed to mitigate the risk of fall).

RESULTS

During the study period, there were 42,476 admissions, of which 707 were for falls and 134 for fall injuries. Allowing for differences in the patients' characteristics and baseline process metrics, the number of patients with falls differed between the control (n=382) and intervention (n=325) units. The mean fall rate increased from 1.95 to 2.11 in control units and decreased from 1.92 to 1.79 in intervention units. A separate ITS analysis revealed that the immediate reduction was 29.73% in the intervention group (z=-2.06, P=.039) and 16.58% in the control group (z=-1.28, P=.20), but there was no ongoing effect. The injury rate did not differ significantly between the two groups (0.42 vs 0.31, z=1.50, P=.134). Among the process metrics, the risk-targeted interventions increased significantly over time in the intervention group.

CONCLUSIONS

This early-stage clinical evaluation revealed that implementation of an analytic tool for predicting fall risk may to contribute to an awareness of fall risk, leading to positive changes in nurses' interventions over time.

TRIAL REGISTRATION

Clinical Research Information Service (CRIS), Republic of Korea KCT0005286; https://cris.nih.go.kr/cris/search/detailSearch.do/16984.

Sensor-Based Fall Risk Assessment: A Survey

Fall is a major problem leading to serious injuries in geriatric populations. Sensor-based fall risk assessment is one of the emerging technologies to identify people with high fall risk by sensors, so as to implement fall prevention measures. Research on this domain has recently made great progress, attracting the growing attention of researchers from medicine and engineering. However, there is a lack of studies on this topic which elaborate the state of the art. This paper presents a comprehensive survey to discuss the development and current status of various aspects of sensor-based fall risk assessment. Firstly, we present the principles of fall risk assessment. Secondly, we show knowledge of fall risk monitoring techniques, including wearable sensor based and non-wearable sensor based. After that we discuss features which are extracted from sensors in fall risk assessment. Then we review the major methods of fall risk modeling and assessment. We also discuss some challenges and promising directions in this field at last.