Geriatric Dermatologic Surgery Part II: Peri and intraoperative considerations in the geriatric dermatologic surgery patient.

Geriatric patients compose a growing proportion of the dermatologic surgical population. Dermatologists and dermatologic surgeons should be cognizant of the unique physiologic considerations that accompany this group to deliver highly effective care. The purpose of this article is to discuss the unique preoperative, intraoperative, and postoperative considerations geriatric patients present with to provide goal-concordant care. Preoperative considerations include medication optimization and anxiolysis. Intraoperative considerations such as fall-risk assessment and prevention, sundowning, familial support, and pharmacologic interactions will be discussed. Lastly, effective methods for optimizing post-operative wound care, home care, and follow up are reviewed.

Quality evaluation of the usefulness of an emergency department fall risk assessment tool.

INTRODUCTION: Falls that occur within a hospital setting are difficult to predict, however, are preventable adverse events with the potential to negatively impact patient care. Falls have the potential to cause serious or fatal injuries and may increase patient morbidity. Many hospitals utilize fall "predictor tools" to categorize a patient's fall risk, however, these tools are primarily studied within in-patient units. The emergency department (ED) presents a unique environment with a distinct patient population and demographic. The Memorial Emergency Department Fall Risk Assessment Tool (MEDFRAT) has shown to be effective with predicting a patient's fall risk in the ED. This IRB-approved study aims to assess the predictive validity of the MEDFRAT by evaluating the sensitivity and specificity for predicting a patient's fall risk in an emergency department at a level 1 trauma center. METHODS: A retrospective cohort analysis was conducted using an electronic medical record (EMR) for patients who met study inclusion criteria at a level 1 trauma center ED. Extracted data includes MEDFRAT components, demographic information, and data from the Moving Safely Risk Assessment (MSRA) Tool, our institution's current fall assessment tool. A receiver operating characteristic (ROC) curve was constructed to determine the best cutoff for identifying any fall risk. Sensitivity, specificity, accuracy, positive likelihood ratio (LR+) and negative LR (LR-), with 95% CIs were then calculated for the cutoff value determined from the ROC curve. To compare overall tool performance, the areas under the ROC curves (AUC) were determined and compared with a z-test. RESULTS: The MEDFRAT had a significantly higher sensitivity compared to the MSRA (83.1% vs. 66.1%, p = 0.002), while the MSRA had a significantly higher specificity (84.5% vs. 69.0%, p = 0.012). For identifying any level of fall risk, ROC curve analysis showed that the cutoff providing the best trade-off between sensitivity and specificity for the MEDFRAT was a score of ≥1. Additionally, area under the curve was determined for the MEDFRAT and MSRA (0.817 vs. 0.737). CONCLUSION: This study confirms the validity of the MEDFRAT as an acceptable tool to predict in-hospital falls in a level 1 trauma center ED. Accurate identification of patients at a high risk of falling is critical for decreasing healthcare costs and improving health outcomes and patient safety.

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.

Predictability of Fall Risk Assessments in Community-Dwelling Older Adults: A Scoping Review.

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method's validity needs to be confirmed by large prospective studies.

Falls risk screening tools intended to reduce fall risk among independent community-dwelling older adults: A systematic review.

AIMS: The aim of this study is to evaluate an evidence-based fall risk screening tool to predict the risk of falls suitable for independent community-dwelling older adults guided by the World Health Organization's International Classification of Functioning, Disability and Health (WHO-ICF) components, and to examine the reliability and validity of the fall risk screening tool to predict fall risks, and to examine the feasibility of tools among independent community-dwelling older adults. METHODS: A systematic literature search guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was performed using the EBSCOHost® platform, ScienceDirect, Scopus and Google Scholar between July and August 2021. Studies from January 2010 to January 2021 were eligible for review. Nine articles were eligible and included in this systematic review. The risk of bias assessment used the National Institutes of Health quality assessment tool for observational cohort and cross-sectional studies. The WHO-ICF helped to guide the categorization of fall risk factors. RESULTS: Seven screening tools adequately predicted fall risk among community-dwelling older adults. Six screening tools covered most of the components of the WHO-ICF, and three screening tools omitted the environmental factors. The modified 18-item Stay Independent Brochure demonstrated most of the predictive values in predicting fall risk. All tools are brief and easy to use in community or outpatient settings. CONCLUSION: The review explores the literature evaluating fall risk screening tools for nurses and other healthcare providers to assess fall risk among independent community-dwelling older adults. A fall risk screening tool consisting of risk factors alone might be able to predict fall risk. However, further refinements and validations of the tools before use are recommended.

Discrepancies between Perceived and Physiological Fall Risks and Repeated Falls among Community-Dwelling Medicare Beneficiaries Aged 65 Years and Older.

OBJECTIVES: This study examined the associations of discrepancies between perceived and physiological fall risks with repeated falls. METHODS: We analyzed the 2016 Medicare Current Beneficiary Survey of 2,487 Medicare beneficiaries aged ≥ 65 years with ≥ 1 fall. The outcome variable was repeated falls (≥ 2 falls), the key independent variable was a categorical variable of discrepancies between perceived (fear of falling) and physiological fall risks (physiological limitations), assessed using multivariate logistic regression. RESULTS: Among Medicare beneficiaries with ≥ 1 fall, 25.1% had low fear of falling but high physiological fall risk (Low Fear-High Physiological), 9.4% had high fear of falling but low physiological fall risk (High Fear-Low Physiological), 23.5% had low fear of falling and low physiological fall risks (Low Fear-Low Physiological), and 42.0% had high fear of falling and high physiological fall risks (High Fear-High Physiological). Having High Fear-High Physiological was associated with repeated falls (OR = 2.14; p < .001) compared to Low Fear-Low Physiological. Having Low Fear-High Physiological and High Fear-LowPhysiological were not associated with repeated falls. CONCLUSIONS: Given that High Fear-High Physiological was associated with repeated falls and that many at-risk Medicare beneficiaries had High Fear-High Physiological, prevention efforts may consider targeting those most at-risk including Medicare beneficiaries with High Fear-High Physiological. CLINICAL IMPLICATIONS: Assessing both perceived and physiological fall risks is clinically relevant, given it may inform targeted interventions for different at-risk Medicare beneficiaries among clinicians and other stakeholders.

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, sensitivityEvent Rate(ER) and specificityER. 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% (sensitivityER 33.6%) and specificity of 88.8% (specificityER 82.0%). The follow-up PH-FRAT assessments increased sensitivityER values although the specificityER 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% (sensitivityER 74.4%) and specificity of 55.2% (specificityER 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.

Assessment of the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings.

AIMS: This study was to assess the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) in inpatient settings. DESIGN: A case-control study. METHODS: This study was conducted in a tertiary hospital based on 2019 data. With a case-control design in a 1:2 ratio, the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) was determined by ROC curve. The best cut point was identified based on sensitivity, specificity, positive predict value and negative predict value. Conditional logistical regression analysis was conducted to test the predictive ability of each indicator. RESULTS: The study included 309 patients, with 103 in the case group and 206 in the control groups. Generally, the predictive ability was acceptable with the area under ROC curve value at 0.73 (95% CI: 0.67-0.79). Positive predict value and negative predict value performed best at the cut point of 13. Sensitivity at cut point 6 was much higher than that at cut point 13, though specificity was lower. Except for age, all indicators in the Johns Hopkins Fall Risk Assessment Tool (Chinese Version) demonstrated significant predictive ability as to occurrence of fall. CONCLUSION: The Johns Hopkins Fall Risk Assessment Tool (Chinese Version) is a reliable assessment instrument in the inpatient settings. IMPACT: This is the first study that evaluated the predictive ability of the Johns Hopkins Fall Risk Assessment Tool (Chinese version) in the inpatient settings, and proved that the instrument is reliable for assessing inpatient fall risks. Further studies could be carried out to assess the predict ability of Johns Hopkins Fall Risk Assessment Tool (Chinese version) among specific populations.

Fall Risk Assessment Using Wearable Sensors: A Narrative Review.

Recently, fall risk assessment has been a main focus in fall-related research. Wearable sensors have been used to increase the objectivity of this assessment, building on the traditional use of oversimplified questionnaires. However, it is necessary to define standard procedures that will us enable to acknowledge the multifactorial causes behind fall events while tackling the heterogeneity of the currently developed systems. Thus, it is necessary to identify the different specifications and demands of each fall risk assessment method. Hence, this manuscript provides a narrative review on the fall risk assessment methods performed in the scientific literature using wearable sensors. For each identified method, a comprehensive analysis has been carried out in order to find trends regarding the most used sensors and its characteristics, activities performed in the experimental protocol, and algorithms used to classify the fall risk. We also verified how studies performed the validation process of the developed fall risk assessment systems. The identification of trends for each fall risk assessment method would help researchers in the design of standard innovative solutions and enhance the reliability of this assessment towards a homogeneous benchmark solution.

A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.

Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.

Validity of the Morse Fall Scale and the Johns Hopkins Fall Risk Assessment Tool for fall risk assessment in an acute care setting.

AIMS AND OBJECTIVES: To evaluate the measured fall risk score that more accurately reflects the changeable conditions in acute care settings, and to efficiently evaluate the association between falls and fall risk score. BACKGROUND: The Morse Fall Scale (MFS) is a well-known easy-to-use tool, while the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) consists of items with high specificity. Evaluating suitable fall-risk assessment tools to measure these changeable conditions may contribute to preventing falls in acute care settings. DESIGN: Retrospective case-control study using the STROBE checklist. METHODS: In an acute care setting (708-bedded university hospital with a regional emergency medical centre), the non-fall group was adjusted to fall group using propensity score matching. According to the fall rate of 3-5%, non-fall groups for each tool were selected (1386 and 1947) from the before adjusted data, and the fall groups included 42 and 59. The applied covariates were individual characteristics that ordinarily changed such as age, gender, diagnostic department and hospitalisation period. The adjusted data were analysed using generalised estimating equations and mixed effect model. RESULTS: After adjustment, the fall group measured using the JHFRAT had a significantly higher difference between the initial and re-measured total score than the non-fall group. The JHFRAT, especially with the re-measured score, had a higher AUC value for predicting falls than the MFS. MFS's sensitivity was 85.7%, and specificity was 58.8% at 50 points; for JHFRAT, these were 67.8% and 80.2% at 14 points, respectively. These cut-off points were used to evaluate validity during tool development and are commonly used as reference scores. CONCLUSIONS: JHFRAT more accurately reflects acute changeable conditions related to fall risk measurements after admission. RELEVANCE TO CLINICAL PRACTICE: JHFRAT may be useful for effective fall prevention activities in acute care settings.

Discriminative Ability of the Four Balance Measures for Previous Fall Experience in Turkish Community-Dwelling Older Adults.

PURPOSE: This study aimed to compare the discriminative properties (discriminative effect, sensitivity, specificity, and cutoff values) of four commonly used balance measures for nonfallers, fallers, and multiple fallers among Turkish community-dwelling older adults. METHODS: Three hundred fifty-one community-dwelling older adults (122 fallers and 229 nonfallers) were evaluated with the timed up and go test, functional reach test, one-leg stance test, and Berg Balance Scale (BBS). RESULTS: Timed up and go test and functional reach test were not sensitive in detecting group differences between fallers and nonfallers, and BBS and one-leg stance test had significant but limited discriminative power with cutoff values of 53.5 points and 7.50 s, respectively. In addition, timed up and go test, functional reach test, and one-leg stance test had significant but limited discriminative power, and BBS had acceptable discriminative power for older adults who fell multiple times. CONCLUSIONS: These findings suggest that BBS is the most suitable tool for assessing the fall risk of Turkish community-dwelling older adults.

Influence of a clinical experiential activity on student confidence in conducting effective fall-risk assessment on older adults.

The ability to confidently perform fall-risk assessment on older adults is critical for Doctor of Physical Therapy (DPT) students prior to entering workforce. The complex nature of falls makes it challenging to teach it realistically in traditional classroom settings. This could lead to lack of confidence in performing effective assessments in real clinical situations. For this purpose, an evidence-based experiential fall-risk assessment activity was implemented in the curriculum. The purpose was to investigate if this activity improved students' confidence in performing fall-risk assessment. Twenty-eight students completed this activity on thirty-three older adults from a senior living community. A 13-item questionnaire was used to investigate confidence before and after the activity. Significant improvements in students' confidence were noted for administering client interview (p = .001, r = -0.43), 30-Second Chair Stand Test (p = .046, r = -0.34) and 10-Meter Walk Test (p = .011, r = -0.27). Additionally, students demonstrated excellent inter-rater reliability (ICC > 0.9) with the faculty experts for administering 5-Times Sit-to-Stand, 10-Meter Walk, Berg Balance Scale, 4-Stage Balance, Timed Up and Go and 30-Second Chair Stand tests, and good inter-rater reliability (ICC = 0.78) for Single-Limb Stance Time test. This activity had a positive impact on DPT students' confidence in conducting effective fall-risk assessment.

Development and validation of a continuous fall risk score in community-dwelling older people: an ecological approach.

BACKGROUND: Fall risk assessment in older people is of major importance for providing adequate preventive measures. Current predictive models are mainly focused on intrinsic risk factors and do not adjust for contextual exposure. The validity and utility of continuous risk scores have already been demonstrated in clinical practice in several diseases. In this study, we aimed to develop and validate an intrinsic-exposure continuous fall risk score (cFRs) for community-dwelling older people through standardized residuals. METHODS: Self-reported falls in the last year were recorded from 504 older persons (391 women: age 73.1 ± 6.5 years; 113 men: age 74.0 ± 6.1 years). Participants were categorized as occasional fallers (falls ≤1) or recurrent fallers (≥ 2 falls). The cFRs was derived for each participant by summing the standardized residuals (Z-scores) of the intrinsic fall risk factors and exposure factors. Receiver operating characteristic (ROC) analysis was used to determine the accuracy of the cFRs for identifying recurrent fallers. RESULTS: The cFRs varied according to the number of reported falls; it was lowest in the group with no falls (- 1.66 ± 2.59), higher in the group with one fall (0.05 ± 3.13, p < 0.001), and highest in the group with recurrent fallers (2.82 ± 3.94, p < 0.001). The cFRs cutoff level yielding the maximal sensitivity and specificity for identifying recurrent fallers was 1.14, with an area under the ROC curve of 0.790 (95% confidence interval: 0.746-0.833; p < 0.001). CONCLUSIONS: The cFRs was shown to be a valid dynamic multifactorial fall risk assessment tool for epidemiological analyses and clinical practice. Moreover, the potential for the cFRs to become a widely used approach regarding fall prevention in community-dwelling older people was demonstrated, since it involves a holistic intrinsic-exposure approach to the phenomena. Further investigation is required to validate the cFRs with other samples since it is a sample-specific tool.

Fully Automatic Fall Risk Assessment Based on a Fast Mobility Test.

This paper presents a fall risk assessment approach based on a fast mobility test, automatically evaluated using a low-cost, scalable system for the recording and analysis of body movement. This mobility test has never before been investigated as a sole source of data for fall risk assessment. It can be performed in a very limited space and needs only minimal additional equipment, yet provides large amounts of information, as the presented system can obtain much more data than traditional observation by capturing minute details regarding body movement. The readings are provided wirelessly by one to seven low-cost micro-electro-mechanical inertial measurement units attached to the subject's body segments. Combined with a body model, these allow segment rotations and translations to be computed and for body movements to be recreated in software. The subject can then be automatically classified by an artificial neural network based on selected values in the test, and those with an elevated risk of falls can be identified. Results obtained from a group of 40 subjects of various ages, both healthy volunteers and patients with vestibular system impairment, are presented to demonstrate the combined capabilities of the test and system. Labelling of subjects as fallers and non-fallers was performed using an objective and precise sensory organization test; it is an important novelty as this approach to subject labelling has never before been used in the design and evaluation of fall risk assessment systems. The findings show a true-positive ratio of 85% and true-negative ratio of 63% for classifying subjects as fallers or non-fallers using the introduced fast mobility test, which are noticeably better than those obtained for the long-established Timed Up and Go test.

Relationship between malnutrition and different fall risk assessment tools in a geriatric in-patient unit.

BACKGROUND: Despite decades of research evaluating different predictive strategies to identify persons at risk for falls, nutritional issues have received little attention. Malnutrition leads to weight loss associated with muscle weakness and consequently increases the risk of falls. AIMS: The current study assessed the association between nutritional state and fall risk scores in a geriatric in-patient unit in Ain Shams University Hospital, Cairo, Egypt. METHODS: A cross-sectional study was conducted to assess the nutritional state of 190 older inpatients using a short form of the Mini-Nutritional Assessment (MNA-SF), and the risk of falls was assessed using the Morse Fall Scale (MFS), Johns Hopkins fall risk assessment tool (JH-FRAT), Schmid Fall Risk Assessment Tool (Schmid-FRAT), Hendrich II Fall Risk Model (HII-FRM) and Functional Assessment Instrument (FAI). The generalised linear models (GLM) and odds ratio (OR) were calculated to test the nutritional status as a risk factor for falls. RESULTS: Malnutrition was significantly associated with high fall risk as assessed by MFS and HII-FRM (OR = 2.833, 95% CI 1.358-5.913, P = 0.006; OR = 3.477, 95% CI 1.822-6.636, P < 0.001), with the highest OR for JH-FRAT (OR = 5.455, 95% CI 1.548-19.214, P = 0.008). After adjusting for age, the adjusted Charlson Comorbidity Index (ACCI), number of fall risk-increasing drugs (FRIDs), risk of malnutrition or malnourished were significantly associated with high fall risk as assessed by MFS (OR = 2.761, 95% CI 1.306-5.836, P = 0.008), JH-FRAT (OR = 4.938, 95% CI 1.368-17.828, P = 0.015), and HII-FRM (OR = 3.486, 95% CI 1.783-6.815, P < 0.001). CONCLUSIONS: This study demonstrated a significant association between malnutrition and fall risk assessment scores, especially JH-FRAT, in hospitalised older patients.

Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.

Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predictive signs of underlying locomotion conditions and precursors of falls. The advent of wearable sensors and wrist-worn devices provides new opportunities for continuous and unobtrusive monitoring of gait during daily activities, including the identification of unexpected changes in gait. To this end, we present in this paper a novel method for determining gait abnormalities based on a wrist-worn device and a deep neural network. It integrates convolutional and bidirectional long short-term memory layers for successful learning of spatiotemporal features from multiple sensor signals. The proposed method was evaluated using data from 18 subjects, who recorded their normal gait and simulated abnormal gait while wearing impairment glasses. The data consist of inertial measurement unit (IMU) sensor signals obtained from smartwatches that the subjects wore on both wrists. Numerous experiments showed that the proposed method provides better results than the compared methods, achieving 88.9% accuracy, 90.6% sensitivity, and 86.2% specificity in the detection of abnormal walking patterns using data from an accelerometer, gyroscope, and rotation vector sensor. These results indicate that reliable fall risk assessment is possible based on the detection of walking abnormalities with the use of wearable sensors on a wrist.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Analysis of Falls within Paediatric Hospital and Community Healthcare Settings.

PURPOSE: To identify characteristics of paediatric falls within a healthcare setting. DESIGN AND METHODS: A retrospective analysis of falls occurring within inpatient, outpatient, emergency and community healthcare settings of children aged 0-<18 years was conducted using data from the Children's Health Queensland Hospital and Health Service (CHQ-HHS) Clinical Incident Database and Electronic Medical Record between January 1st 2015 and December 31st 2017. One-sample and two-sample Chi-squared tests with post-hoc tests were performed to assess relationships between categorical variables. RESULTS: The final dataset contained 385 fall events. Children 0-2 years fell most frequently (46.75%) and falls were higher in males (55.58%). Falls from bed were the most common mechanism (30.65%). The incidence rate of inpatient falls was 0.53 falls per 1000 bed days in the tertiary hospital setting and 1.2% of presentations to inpatient community health facilities. Falls from bed were most common in the tertiary hospital inpatient setting (39.84%, p < .001) and the emergency department (72.13%, p < .001). Falls from furniture/equipment constituted 26.04% of outpatient falls. Most falls occurred in the presence of parents/caregivers (79.48%) and 4.66% of fallers sustained multiple falls. CONCLUSIONS: This study provides a comprehensive review of the characteristics of fall events in CHQ-HHS over a three-year period and summarises the existing literature in paediatric fall prevention. PRACTICE IMPLICATIONS: Risk assessment and management plans should focus on education, particularly surrounding bed safety. Our findings have informed the development of an integrated evidence-based paediatric-specific fall risk assessment tool and management plan to prevent falls in hospital and community healthcare settings.

Feasibility and predictive performance of the Hendrich Fall Risk Model II in a rehabilitation department: a prospective study.

BACKGROUND: Falls are a common adverse event in both elderly inpatients and patients admitted to rehabilitation units. The Hendrich Fall Risk Model II (HIIFRM) has been already tested in all hospital wards with high fall rates, with the exception of the rehabilitation setting. This study's aim is to address the feasibility and predictive performances of HIIFRM in a hospital rehabilitation department. METHODS: A 6 months prospective study in a Italian rehabilitation department with patients from orthopaedic, pulmonary, and neurological rehabilitation wards. All admitted patients were enrolled and assessed within 24 h of admission by means of the HIIFRM. The occurrence of falls was checked and recorded daily. HIIFRM feasibility was assessed as the percentage of successful administrations at admission. HIIFRM predictive performance was determined in terms of area under the Receiver Operating Characteristic (ROC) curve (AUC), best cutoff, sensitivity, specificity, positive and negative predictive values, along with their asymptotic 95% confidence intervals (95% CI). RESULTS: One hundred ninety-one patents were admitted. HIIFRM was feasible in 147 cases (77%), 11 of which suffered a fall (7.5%). Failures in administration were mainly due to bedridden patients (e.g. minimally conscious state, vegetative state). AUC was 0.779(0.685-0.873). The original HIIFRM cutoff of 5 led to a sensitivity of 100% with a mere specificity of 49%(40-57%), thus suggesting using higher cutoffs. Moreover, the median score for non-fallers at rehabilitation units was higher than that reported in literature for geriatric non fallers. The best trade-off between sensitivity and specificity was obtained by using a cutoff of 8. This lead to sensitivity = 73%(46-99%), specificity = 72%(65-80%), positive predictive value = 17% and negative predictive value = 97%. These results support the use of the HIIFRM as a predictive tool. CONCLUSIONS: The HIIFRM showed satisfactory feasibility and predictive performances in rehabilitation wards. Based on both available literature and these results, the prediction of falls among all hospital wards, with high risk of falling, could be achieved by means of a unique tool and two different cutoffs: a standard cutoff of 5 in geriatric wards and an adjusted higher cutoff in rehabilitation units, with predictive performances similar to those of the best-preforming pathology specific tools for fall-risk assessment.